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Science is increasingly done in large teams, making it more likely that papers will be written by several authors from different institutes, disciplines, and cultural backgrounds. A small number of “Ten simple rules” papers have been written on collaboration and on writing but not on combining the two. Collaborative writing with multiple authors has additional challenges, including varied levels of engagement of coauthors, provision of fair credit through authorship or acknowledgements, acceptance of a diversity of work styles, and the need for clear communication. Miscommunication, a lack of leadership, and inappropriate tools or writing approaches can lead to frustration, delay of publication, or even the termination of a project. To provide insight into collaborative writing, we use our experience from the Global Lake Ecological Observatory Network (GLEON) to frame 10 simple rules for collaboratively writing a multi-authored paper. We consider a collaborative multi-authored paper to have three or more people from at least two different institutions. A multi-authored paper can be a result of a single discrete research project or the outcome of a larger research program that includes other papers based on common data or methods. The writing of a multi-authored paper is embedded within a broader context of planning and collaboration among team members. Our recommended rules include elements of both the planning and writing of a paper, and they can be iterative, although we have listed them in numerical order. It will help to revisit the rules frequently throughout the writing process. With the 10 rules outlined below, we aim to provide a foundation for writing multi-authored papers and conducting exciting and influential science.

Papers describing software are an important part of computational fields of scientific research. These “software papers” are unique in a number of ways, and they require special consideration to improve their impact on the scientific community and their efficacy at conveying important information. Here, we discuss 10 specific rules for writing software papers, covering some of the different scenarios and publication types that might be encountered, and important questions from which all computational researchers would benefit by asking along the way., Author summary Computational researchers have a responsibility to ensure that the software they write stands up to the same scientific scrutiny as traditional research studies. These 10 simple rules make doing so easier by enhancing usability, reproducibility, transparency, and other crucial characteristics that aren’t taught in most computer science or research methods curricula.

Among the challenges of sustainable management of meat production, the key issue is to improve the energy efficiency of production processes, which will consequently affect the reduction of greenhouse gas emissions. Such effects are achieved by combining various chilling systems with electrical stimulation that determines the quality of meat at the slaughter stage. The novelties of the research undertaken included determining the impact of various variants of meat production (chilling method: slow, fast, accelerated + HVES/NES) on changes in the basic (industrial) quality indicators (pH and temperature) of beef produced from Polish Holstein-Friesian breed cattle, and then indicating the optimal variant for energy-efficient (sustainable) beef production. The HVES and the fast chilling method yielded positive economic (meat weight loss), technological (high quality, hot-boning), energetic (lower electricity consumption), and organizational effects (reduced chilling and storage surfaces and expenditures for staff wages) compared to the slow and accelerated methods. Reaching the desired final temperature with an increased amount of chilled meat enables obtaining a few-fold decrease in the specific energy consumption and a higher energy efficiency of the process. This allows recommending the above actions to be undertaken by entrepreneurs in the pursuit of sustainable meat production.

Faecal Sludge Management (FSM) has become a prominent environmental concern in the today’s world. Dewatering of sludge and the treatment of wastewater (WW) are the prime spiny issue because of the deleterious essence of faecal sludge (FS) and WW in the environment. The main focus of this study was on FSM by ‘Deebag’ and ‘Jute Bag’ through dewatering and filtering. Deebag is a dewatering as well as filtering media which is made with geotextile and polypropylene. Contrariwise, three types of jute bags were made of jute fiber for using as the same purposes of Deebag. A polyacrylamide polymer was used in this study and both filtering and dewatering were done in two ways—with and without the presence of polymer. Biochemical Oxygen Demand at 5 days (BOD5), Chemical Oxygen Demand (COD), pH, Electrical Conductivity (EC), Chloride (Cl-), Phosphate (PO43-), Nitrate (NO3-), Total Suspended Solid (TSS) and Total Dissolved Solid (TDS) parameters of raw and filtering samples were analyzed to assess the performance of WW treatment by Deebag and jute bags. Only using polymer was observed as one kind of treatment of WW. Deebag has been found to show the maximum dewatering capacity as well as treatment efficiency comparing with the jute bags. However, among three types of jute bags, double jute layered bag has shown the best performance. Maximum dewatering for Deebag and jute bags were found 88% and 83% respectively while using the polymer.

Fundamental ecological principles of ecosystem-level respiration are extensively applied in greenhouse gas and elemental cycle studies. A laboratory system termed CEMS (Carbon Dioxide Evolution Measurement System), developed to explore microbial biofilm growth and metabolic responses, was evaluated as an early-warning system for microbial disturbances in industrial settings: in (a) potable water system contamination, and (b) bioreactor inhibition. Respiration was detected as CO2 production, rather than O2 consumption, including aerobic and anaerobic metabolism. Design, thresholds, and benefits of the remote CO2 monitoring technology were described. Headspace CO2 correlated with contamination levels, as well as chemical (R2 > 0.83–0.96) and microbiological water quality indicators (R2 > 0.78–0.88). Detection thresholds were limiting factors in monitoring drinking water to national and international standards (0 CFU/100 mL fecal coliforms) in both open- (>1500 CFU/mL) and closed-loop CO2 measuring regimes (>100 CFU/100 mL). However, closed-loop detection thresholds allow for the detection of significant contamination events, and monitoring less stringent systems such as irrigation water (2 fluctuations from chemical CO2 dynamics, to optimize this real-time, sustainable, low-waste technology, facilitating timeous responses to biological disturbances in bioreactors.

Due to the inefficient reproduction of microorganisms in oxygen-deprived environments of the reservoir, the applications of microbial enhanced oil recovery (MEOR) are restricted. To overcome this problem, a new type of air-assisted MEOR process was investigated. Three compounding oil degradation strains were screened using biochemical experiments. Their performances in bacterial suspensions with different amounts of dissolved oxygen were evaluated. Water flooding, microbial flooding and air-assisted microbial flooding core flow experiments were carried out. Carbon distribution curve of biodegraded oil with different oxygen concentration was determined by chromatographic analysis. The long-chain alkanes are degraded by microorganisms. A simulation model was established to take into account the change in oxygen concentration in the reservoir. The results showed that the optimal dissolved oxygen concentration for microbial growth was 4.5~5.5mg/L. The main oxygen consumption in the reservoir happened in the stationary and declining phases of the microbial growth systems. In order to reduce the oxygen concentration to a safe level, the minimum radius of oxygen consumption was found to be about 145m. These results demonstrate that the air-assisted MEOR process can overcome the shortcomings of traditional microbial flooding techniques. The findings of this study can help for better understanding of microbial enhanced oil recovery and improving the efficiency of microbial oil displacement.

In recent years, spread of invasive alien plant species (IAPS) has been a major concern in Nepal. One such IAPS is Ageratum houstonianum, an Asteraceae, that is a prolific seed producer and difficult-to-control in farmland and various ecological regions causing crop yield and biodiversity losses. However, very little information is available on the germination biology and ecology of this species. Therefore, experiments were conducted to assess the effect of water stress, pH level, and light requirement on seed germination, and the effect of seed burial depth on seedling emergence. Water stress was simulated by polyethylene glycol solutions ranging from 0-5.56 MPa and pH solutions ranging from 4 to 9 were prepared using hydrochloric acid and sodium hydroxide. Germination tests were conducted in petri dishes lined with filter paper and placed in a controlled environment chamber set at 20° C. Light requirement comparisons were made by having petri dishes wrapped with aluminum foil or left unwrapped. Seedling emergence was evaluated by placing seeds at depths ranging from 0 to 20 mm in the soil. Results indicated that this species was moderately drought-tolerant because germination ceased beyond 0.51 MPa. Greater germination occurred at neutral to acidic than at alkaline pH levels. The seeds were positively photoblastic because no germination occurred under dark condition. No seedlings emerged from seeds placed more than 2 mm deep in the soil, indicating that this is a primarily surface germinating species. These findings will help predict future invasions and in development of management strategies for this IAPS.

Honey bees (genusApis)are well known for the impressive suite of nest defenses they have evolved to protect their abundant stockpiles of food and the large colonies they sustain. In Asia, honey bees have evolved under tremendous predatory pressure from social wasps in the genusVespa, the most formidable of which are the giant hornets that attack colonies in groups, kill adult defenders, and prey on brood. We document for the first time an extraordinary collective defense used byApis ceranaagainst the giant hornetVespa soror. In response to attack byV.soror,A.ceranaworkers foraged for and applied spots of animal feces around their nest entrances. Fecal spotting increased after colonies were exposed either to naturally occurring attacks or to chemicals that scout hornets use to target colonies for mass attack. Spotting continued for days after attacks ceased and occurred in response toV.soror, which frequently landed at and chewed on entrances to breach nests, but notVespa velutina, a smaller hornet that rarely landed at entrances. Moderate to heavy fecal spotting suppressed attempts byV.sororto penetrate nests by lowering the incidence of multiple-hornet attacks and substantially reducing the likelihood of them approaching and chewing on entrances. We argue thatA.ceranaforages for animal feces because it has properties that repel this deadly predator from nest entrances, providing the first report of tool use by honey bees and the first evidence that they forage for solids that are not derived from plants. Our study describes a remarkable weapon in the already sophisticated portfolio of defenses that honey bees have evolved in response to the predatory threats they face. It also highlights the strong selective pressure honey bees will encounter if giant hornets, recently detected in western North America, become established.

The safety of highly aggregated tourist crowds is a challenging and important issue. This paper not only provided a comprehensive analysis of the accidents of highly aggregated tourist crowds but also determined the occurrence mechanism and coping paths. Based on the analysis of multiple cases, we found that the variable status of highly aggregated tourist crowds was the result of the interaction of three main elements: multisource pressure, state mutations and management responses. A series of factors interact and result in accidents, and the lack of a management response or a low-quality management response is the root cause of such accidents. A high-quality management response is a basic safety precaution for highly aggregated tourist crowds. Therefore, forming a virtuous circle of multisource pressure, state mutations and management responses is an effective path for coping with accidents.

When clothes are worn and washed, they emit fibres into the ecosystem via discharges of sewage that have been linked to the global dispersion of clothing fibres. Facilities that treat sewage divert some fibres from sewage effluent to sludge, but no current methods of filtration eliminate their environmental release. While filters for washing-machines are sold to consumers with the argument they will reduce the emissions of fibres from clothes to the environment, there is insufficient scientific peer-reviewed evidence assessing their ability to retain fibres from washed clothes and reduce environmental contamination. To improve our understanding and develop more realistic methods to assess the efficiency of filters, we washed replicate cotton and polyester garments in replicate domestic front-loaded washing-machines with and without replicate filters (micro- and milli-meter-sized pores), and then quantified the masses of the fibres retained by the filters and those released in the effluent. Here we show micrometer-sized filters significantly reduced the mass of cotton by 67% (F-2,F-6= 11.69, P

Water conservation and economics dictate that fresh produce processors reuse/recirculate the process water. However, the ensuing accumulation of organic matter in water depletes the chlorine sanitizer required for food safety. In this study, we comprehensively investigated chemical compounds that are responsible for water quality in relation to chemical oxygen demand (COD) and chlorine demand (CLD), the two most critical factors associated with water treatment and chlorine replenishment. Simulating commercial fresh-cut wash operations, multiple batches of diced cabbage (0.3 x 0.3 cm2) were washed in the same tank of water. The major components were isolated from the wash water and analyzed by HPLC. Sugars were the predominant compounds (82.7% dry weight) and the major contributor to COD (81.6%), followed by proteins/peptides (7.3% dry weight, 5.3% COD), organic acids (6.2% dry weight, 3.6% COD), and phenolics (0.5% dry weight, 0.5% COD). By repeated time course measures, the effect of these chemicals on CLD are dependent on the chemical structure, concentration in the wash water, and their rate of reaction. Proteins/peptides accounted for about 50% of the total CLD over a 120-min period and phenolics was 21% at 5 min, but diminished with time. The contribution by organic acids and sugars increased continuously, reaching 22% and 16% of total CLD at 120 min of chlorination, respectively. Collectively, these compounds represented 86% of the CLD in cabbage wash water at 5 min and greater than 94% CLD afterwards. This is the first systematic report on the source of COD and CLD during fresh produce washing. It provides essential information for the produce processors to develop safe, effective, and economical wash water treatment/reuse and chlorine replenishment strategies.

The self-cleaning screen filter is one of the most common types used in drip irrigation systems. Backwashing pressure difference and backwashing time for two screen filters with one geometry and two different screens (178 μm and 124 μm) using two water qualities (tap water and sand-water mixture) were studied in a total of 88 runs (42 runs for tap water, 22 and 24 filtration cycles for sand-water mixture and backwashing, respectively). The backwashing pressure difference and backwashing time were calculated using the experimental data, and the results were largely in the range of measured values. Three constraint conditions (flowrate, sand condition and filtration time) of backwashing pressure difference were analysed, and the optimal values of backwashing pressure difference were given as 60.0 and 70.0 kPa for 178 μm and 124 μm filters, respectively. The backwashing time of the screen filter should be an optimal value that ensures that the pressure difference between the internal and external surfaces of the screen decreased to the initial value, and the sand concentration of the backwashed outlet decreased to a small, stable value. Based on the results of the backwashing experiment and prototype observation, the optimal backwashing time was given as 30 to 45 s for both screen filters.

Assimilable organic carbon (AOC) is known to correlate with microbial growth, which can consequently degrade drinking water quality. Despite this, there is no standardised AOC test that can be applied to drinking water distribution systems (DWDS). Herein we report the development of a quick, robust AOC that incorporates known strains Pseudomonas fluorescens strain P-17 and Spirillum strain NOX, a higher inoculum volume and enumeration using flow cytometry to generate a quicker (total test time reduced from 14 to 8 days), robust method. We apply the developed AOC test to twenty drinking water treatment works (WTW) to validate the method reproducibility and resolution across a wide range of AOC concentrations. Subsequently, AOC was quantified at 32 sample points, over four DWDS, for a year in order to identify sinks and sources of AOC in operative networks. Application of the developed AOC protocol provided a previously unavailable insight and novel evidence of pipes and service reservoirs exhibiting different AOC and regrowth behaviour. Observed correlations between AOC and microbial growth highlight the importance of monitoring AOC as an integral part of managing drinking water quality at the consumers tap.

The current technologies of algal biofuels production and wastewater treatment (e.g., aerobic) process are still in question, due to the significant amount of fresh water and nutrients requirements for microalgae cultivation, and negative energy balance in both processes, especially when considered in the context of developing counties around the world. In this research, a simplistic sustainable approach of algal biofuels production from wastewater was proposed using a Hybrid Anaerobic Baffled Reactor (HABR) and Photobioreactor (PBR) system. The study suggests that the HABR was capable of removing most of the organic and solid (>90% COD and TSS removal) from wastewater, and produced a healthy feedstock (high N: P = 3:1) for microalgae cultivation in PBRs for biofuels production. A co-culture of Chlorella vulgaris, Chlorella sorokiniana, and Scenedesmus simris002 showed high lipid content up to 44.1%; and the dominant FAMEs composition (C16-C18) of 87.9% in produced biofuels. Perhaps, this proposed low-cost technological approach (e.g., HABR-PBR system) would connect the currently broken link of sustainable bioenergy generation and wastewater treatment pathway for developing countries.

The Multi-Criteria Decision Analysis (MCDA) procedure was used to compare waste management options for kraft pulp mill sludge following its anaerobic digestion. Anaerobic digestion of sludge is advantageous because it produces biogas that may be used to generate electricity, heat and biofuels. However, adequate management of the digested sludge is essential. Landfill disposal is a non-sustainable waste management alternative. Kraft pulp mill digested sludge applied to land may pose risks to the environment and public health if the sludge has not been properly treated. This study is aimed to compare several recycling alternatives for anaerobically digested sludge from kraft pulp mills: land application, landfill disposal, composting, incineration, pyrolysis/gasification, and biofuel production by algae. The MCDA procedure considered nine criteria into three domains to compare digested sludge recycling alternatives in a kraft pulp mill: environmental (CO2 emission, exposure to pathogens, risk of pollution, material and energy recovery), economic (overall costs, value of products) and technical (maintenance and operation, feasibility of implementation). The most suitable management options for digested sludge from kraft pulp mills were found to be composting and incineration (when the latter was coupled with recycling ash to the cement industry). Landfill disposal was the worst option, presenting low performance in feasibility of implementation, risk of pollution, material and energy recovery.

Organic materials in fresh-cut produce wash water deplete free chlorine that is required to prevent pathogen survival and cross-contamination. This research evaluated water quality parameters frequently used to describe organic load for their fitness to predict chlorine demand (CLD) and chemical oxygen demand (COD), which are major needs identified by the industry-led produce food safety taskforce. Batches of romaine lettuce, iceberg lettuce, or carrot of different cut sizes and shapes were washed in 40 liters of water. Physicochemical properties of wash water including CLD, COD, total organic carbon (TOC), total suspended solids (TSS), total dissolved solids (TDS), turbidity, total sugar content, and pH, were monitored. Results indicate that pH is primarily commodity dependent, while organic load is additionally impacted by cutting and washing conditions. Significant linear increases in COD, TOC, CLD, TDS, and turbidity resulted from increasing product-to-water ratio, and decreasing cut size. Physicochemical parameters, excluding pH, showed significant positive correlation across different cut sizes within a commodity. High correlations were obtained between CLD and COD and between COD and TOC for pooled products. The convenient measurement of TDS, along with its strong correlation with COD and CLD, suggests the potential of TDS for predicting organic load and chlorine reactivity. Finally, the potential application and limitation of the proposed models in practical produce processing procedures are discussed extensively.

Stormwater biofilters are used to attenuate the flow and volume of runoff and reduce pollutant loading to aquatic systems. However, the capacity of biofilters to remove microbial contaminants remains inadequate. While biochar has demonstrated promise as an amendment to improve microbial removal in laboratory-scale biofilters, it is uncertain if the results are generalizable to the field. To assess biochar performance in a simulated field setting, sand and biochar-amended sand biofilters were periodically dosed with natural stormwater over a 61-week conditioning phase. Impact of media saturation was assessed by maintaining biofilters with and without a saturated zone. Biochar-amended biofilters demonstrated improved Escherichia coli removal over sand biofilters during the first 31 weeks of conditioning though media type did not impact E. coli removal during the last 30 weeks of conditioning. Presence of a saturated zone was not a significant factor influencing E. coli removal across the entire conditioning phase. Following conditioning, biofilters underwent challenge tests using stormwater spiked with wastewater to assess their capacity to remove wastewater-derived E. coli, enterococci, and male-specific (F+) coliphage. In challenge tests, biochar-amended biofilters demonstrated enhanced removal of all fecal indicators relative to sand biofilters. Additionally, saturated biofilters demonstrated greater removal of fecal indicators than unsaturated biofilters for both media types. Discrepant conclusions from the conditioning phase and challenge tests may be due to variable influent chemistry, dissimilar transport of E. coli indigenous to stormwater and those indigenous to wastewater, and differences in E. coli removal mechanisms between tests. Mobilization tests conducted following challenge tests showed minimal (

The sustainable recovery of resources from wastewater streams can provide many social and environmental benefits. A common strategy to recover valuable resources from wastewater is to harness the products of fermentation by complex microbial communities. In these fermentation bioreactors high microbial community diversity within the inoculum source is commonly assumed as sufficient for the selection of a functional microbial community. However, variability of the product profile obtained from these bioreactors is a persistent challenge in this field. In an attempt to address this variability, the impact of inoculum on the microbial community structure and function within the bioreactor was evaluated using controlled laboratory experiments. In the course of this work, sequential batch reactors were inoculated with three complex microbial inocula and the chemical and microbial compositions were monitored by HPLC and 16S rRNA amplicon analysis, respectively. Microbial community dynamics and chemical profiles were found to be distinct to initial inoculate and highly reproducible. Additionally we found that the generation of a complex volatile fatty acid profile was not specific to the diversity of the initial microbial inoculum. Our results suggest that the composition of the original inoculum predictably contributes to bioreactor community structure and function., United States. National Institutes of Health (P30-ES002109)

Five full-scale wastewater treatment plants (WWTPs) in China using typical biodegradation processes (SBR, oxidation ditch, A2/O) were selected to assess the removal of four popular artificial sweeteners (ASs). All four ASs (acesulfame (ACE), sucralose (SUC), cyclamate (CYC) and saccharin (SAC)) were detected, ranging from 0.43 to 27.34μg/L in the influent. Higher concentrations of ASs were measured in winter. ACE could be partly removed by 7.11–50.76% through biodegradation and especially through the denitrifying process. The A2/O process was the most efficient at biodegrading ASs. Adsorption (by granular activated carbon (GAC) and magnetic resin) and ultraviolet radiation-based advanced oxidation processes (UV/AOPs) were evaluated to remove ASs in laboratory-scale tests. The amounts of resin adsorbed were 3.33–18.51 times more than those of GAC except for SUC. The adsorption ability of resin decreased in the order of SAC > ACE > CYC > SUC in accordance with the pKa. Degradation of ASs followed pseudo-first-order kinetics in UV/H2O2 and UV/PDS. When applied to the secondary effluent, ASs could be degraded from 30.87 to 99.93% using UV/PDS in 30 minutes and UV/PDS was more efficient and economic.

Sugarcane bagasse was refined into cellulose, hemicellulose, and lignin using an ethanol-based organosolv technique. The hydrothermal carbonization (HTC) reactions were applied for bagasse and its two components cellulose and lignin. Based on GC-MS analysis, 32 (13+19) organic byproducts were derived from cellulose and lignin, more than the 22 byproducts from bagasse. Particularly, more valuable catechol products were obtained from lignin with 56.8% share in the total GC-MS integral area, much higher than the 2.263% share in the GC-MS integral areas of bagasse. The organic byproducts from lignin make up more than half of the total mass of lignin, indicating that lignin is a chemical treasure storage. In general, bio-refinery and HTC are two effective techniques for the valorization of bagasse and other biomass materials from agriculture and forest industry. HTC could convert the inferior biomass to superior biofuel with higher energy quantity of combustion, at the same time many valuable organic byproducts are produced. Bio-refinery could promote the HTC reaction of biomass more effective. With the help of bio-refinery and HTC, bagasse and other biomass materials are not only the sustainable energy resource, but also the renewable and environment friendly chemical materials, the best alternatives for petroleum, coal and natural gas.

An ultrasound-assisted extraction (UAE) method using ethanol was applied for extracting arabinogalactan (AG) and dihydroquercetin (DHQ) simultaneously from larch wood, as a pretreatment for pulping and papermaking. The extraction parameters were optimized by a Box-Behnken experimental design with the yields of AG and DHQ as the response values. Under optimum conditions (three extractions, each using 40% ethanol, for 50 min, 200 W ultrasound power and 1∶18 solid-liquid ratio), the yields of AG and DHQ were 183.4 and 36.76 mg/g, respectively. After UAE pretreated, the wood chips were used for Kraft pulping (KP) and high boiling solvent pulping (HBSP). The pulping yield after pretreatment was higher than that of untreated (the pulping yields of untreated HBSP and KP were 42.37% and 39.60%, and the pulping yields of HBSP and KP after UAE-pretreated were 44.23% and 41.50% respectively), as indicated by a lower kappa number (77.91 and 27.30 for untreated HBSP and KP; 77.01 and 26.83 for UAE-pretreated HBSP and KP). Furthermore, the characteristics of paper produced from pretreated wood chips were superior to those from the untreated chips: the basis weight was lower (85.67 and 82.48 g·cm(-2) for paper from untreated KP and HBSP; 79.94 and 80.25 g·cm(-2) for paper from UAE-pretreated KP and HBSP), and the tensile strengths, tearing strengths, bursting strengths, and folding strengths were higher than these of paper after UAE-pretreated, respectively.

Endocrine disrupting chemicals (EDCs) are environmental contaminants causing increasing concerns due to their toxicity, persistence and ubiquity. In the present study, degradative capabilities of Trametes versicolor, Pleurotus ostreatus and Phanerochaete chrysosporium to act on five EDCs, which represent different classes of chemicals (phenols, parabens and phthalate) and were first applied as single compounds, were assessed. T. versicolor was selected due to its efficiency against target EDCs and its potentialities were exploited against a mixture of EDCs in a cost-effective bioremediation process. A fed-batch approach as well as a starvation strategy were applied in order to reduce the need for input of 'fresh' biomass, and avoid the requirement for external nutrients. The fungus was successfully operated in two different bioreactors over one week. Semi-batch cultures were carried out by daily adding a mixture of EDCs to the bioreactors in a total of five consecutive degradation cycles. T. versicolor was able to efficiently remove all compounds during each cycle converting up to 21 mg L-1 day-1 of the tested EDCs. The maintained ability of T. versicolor to remove EDCs without any additional nutrients represents the main outcome of this study, which enables to forecast its application in a water treatment process.

Gradual depletion of the world petroleum reserves and the impact of environmental pollution highlight the importance of developing alternative energy resources such as plant biomass. To address these issues, intensive research has focused on the plant Jatropha curcas, which serves as a rich source of biodiesel because of its high seed oil content. However, producing biodiesel from Jatropha generates large amounts of biomass waste that are difficult to use. Therefore, the objective of our research was to analyze the effects of different conditions of torrefaction on Jatropha biomass. Six different types of Jatropha tissues (seed coat, kernel, stem, xylem, bark, and leaf) were torrefied at four different temperature conditions (200°C, 250°C, 300°C, and 350°C), and changes in the metabolite composition of the torrefied products were determined by Fourier transform-infrared spectroscopy and nuclear magnetic resonance analyses. Cellulose was gradually converted to oligosaccharides in the temperature range of 200°C-300°C and completely degraded at 350°C. Hemicellulose residues showed different degradation patterns depending on the tissue, whereas glucuronoxylan efficiently decomposed between 300°C and 350°C. Heat-induced depolymerization of starch to maltodextrin started between 200°C and 250°C, and oligomer sugar structure degradation occurred at higher temperatures. Lignin degraded at each temperature, e.g., syringyl (S) degraded at lower temperatures than guaiacyl (G). Finally, the toxic compound phorbol ester degraded gradually starting at 235°C and efficiently just below 300°C. These results suggest that torrefaction is a feasible treatment for further processing of residual biomass to biorefinery stock or fertilizer.

The humans of modern society are enjoying the luxuries and comforts today but future generations will be facing a more polluted environment and scarcity of natural resources. So the effects of global warming and climatic changes are a major policy concern nowadays around the world. The majority of the literature treats the Carbon Dioxide emissions as an indicator of environmental deterioration but this paper considers the environmental performance index as an indicator of environment. This paper addresses the role of institutional reforms for environmental performance that is hardly discussed in the earlier literature. It is argued that a novel approach of institutional reforms can provide some useful insights for environmental performance in developing countries. There is wide agreement that institutional quality is crucial for economic sustainability but rarely focused to explore the impacts of institutional reforms on environmental performance. The institutional reforms are generally divided into two categories; economic and political reforms. This paper investigated the impact of each category of institutional reforms for environmental performance by using panel data of 122 developing economies for a period of 1996–2020. Difference in differences technique is applied to determine the impact of each category of reforms on the environment. It is found that economic and political reforms significantly contribute to protecting the environment in developing countries, and it will be a good policy option to reform the economic and political institutions to preserve the environment in these countries along with sustainable development.

The accurate identification of urban functional areas is of great significance for optimizing urban spatial structure, rationally allocating spatial elements, and promoting the sustainable development of the city. This paper proposes a method to precisely identify urban functional areas by coupling Open Street Map (OSM) and Point of Interest (POI) data. It takes the central urban area of Hangzhou as a case study to analyze the spatial distribution characteristics of the functional areas. The results show that: (1) The central urban areas of Hangzhou are divided into 21 functional areas (6 single functional areas, 14 mixed functional areas and 1 comprehensive functional area). (2) The single functional areas and the mixed functional areas show the geographical distribution characteristics of the looping stratification, which means “Core-periphery” differentiation is obvious, and the comprehensive functional area is relatively scattered. (3) The mixed degree of regional function with ecological function and production function is low while comprehensive functional areas are usually associated with higher potential and vitality. (4) The identification results are in great agreement with the actual situation of Hangzhou central urban area, and the method is feasible. Therefore, this paper can provide a reference for urban development planning and management.

The world of science is growing at an unprecedented speed with more and more scholarly papers produced each year. The scientific landscape is constantly changing as research specialties evolve, merge or become obsolete. It is difficult for researchers, research managers and the public alike to keep abreast with these changes and maintain a true and fair overview of the world of science. Such an overview is necessary to stimulate scientific progress, to maintain flexible and responsive research organizations, and to secure collaboration and knowledge exchange between different research specialties and the wider community. Although science mapping is applied to a wide range of scientific areas, examples of their practical use are sparse. This paper demonstrates how to use a topical, scientific reference maps to understand and navigate in dynamic research landscapes and how to utilize science maps to facilitate strategic thinking. In this study, the research domain of biology at Aarhus University serves as an example. All scientific papers authored by the current, permanent staff were extracted (6,830 in total). These papers were used to create a semantic cognitive map of the research field using a co-word analysis based on keywords and keyword phrases. A workflow was written in Python for easy and fast retrieval of information for topic maps (including tokens from keywords section and title) to generate intelligible research maps, and to visualize the distribution of topics (keywords), papers, journal categories, individual researchers and research groups on any scale. The resulting projections revealed new insights into the structure of the research community and made it possible to compare researchers or research groups to describe differences and similarities, to find scientific overlaps or gaps, and to understand how they relate and connect. Science mapping can be used for intended (top-down) as well as emergent (bottom-up) strategy development. The paper concludes that science maps provide alternative views of the intricate structures of science to supplement traditional bibliometric information. These insights may help strengthen strategic thinking and boost creativity and thus contribute to the progress of science.

As in other countries, short-term rentals for tourism services are growing rapidly in China’s tourist cities, which are mainly operated through Airbnb. This paper explores whether the spatial distribution of Airbnb in China’s rapid urbanization process exhibits characteristics, paths, and drivers that are different from those of cities in other countries. Airbnb is a model for the global sharing economy, but it is increasingly influenced by other functions and facilities in cities as it grows. In this paper, the zero-expansion negative binomial regression was used to study the factors affecting the spatial distribution of Airbnb in Nanjing, China. The results showed that the spatial distribution of Airbnb listings was correlated with the distribution of cultural attractions, universities, public transport accessibility, shopping centers, and business apartments. By analyzing the driving forces of Airbnb’s development in Nanjing, this paper found that a large number of business apartments developed in cities were essential providers of Airbnb listings, and affected its spatial distribution. The gap between short-term and long-term rentals was also correlated with the distribution of Airbnb. In addition, similar to the previous literature findings, the increase in the proportion of professional hosts changes the original intention of Airbnb for sharing and communication. Our empirical results applies to the current situation of Airbnb in Chinese cities, which is conducive to the government’s more intelligent management and effective promotion of the Airbnb market. Our findings also provide positive references for urban renewal policies and public participation methods in China.

The scientific evaluation of tourism resources provides the basic conditions for the rational development and utilization of tourism resources, which is significant for the sustainable development of tourism. On the basis of obtaining a large sample of tourism resource data, this study constructed an evaluation index system of regional tourism resources from four aspects: quantity, type, grade, and a combination of regional tourism resources, taking the township scale as the spatial evaluation unit. The combination weighting method was used to evaluate the grade of tourism resources in each township, and the hierarchical clustering method and spatial autocorrelation method were used to analyze the spatial patterns of the tourism resource values. On the basis of the evaluation’s results and spatial pattern analysis, this paper analyzed the influencing factors of tourism resource patterns and puts forward a tourism development model suitable for Hainan tourism development. The results showed that the overall value of tourism resources on Hainan Island was low, and the spatial pattern was obviously different, showing that the northern piedmont plain area was higher than the southern hilly area, and the east coast area was higher than the west coast area. Based on the analysis of quantity density, type abundance, grade advantage, and spatial combination, the tourism resource values of Hainan Island was divided into four combination areas: the first area was diversity and good combination, the second area was large quantity and good combination, the third area was diversity and high-quality resources, and the fourth area was large quantity. In addition, from the analysis of the spatial agglomeration effect, the phenomena of high and low agglomerations of tourism resource values were obvious. Finally, this paper puts forward a tourism development model for Hainan Island by grading, classification, and zoning. The results of this study can determine the time sequence and mode of regional tourism resources development and provide spatial implications and suggestions for regional tourism planning and management.

In recent years, digital finance has become a crucial part of the financial system and reshaped the mode of green finance in China. Digital finance has brought certain impact on economic growth, industrial structure, and resident income, which may affect pollution. The nexus of digital finance and environment in China is thus worth exploring. By revising the traditional Environmental Kuznets Curve model with income inequality variable, this paper decomposes the environmental effects of economic activities into income growth effect, industrial structure effect and income inequality effect, and use panel data of China’s provinces to conduct an empirical analysis. The results reveal the following: (1) the Environmental Kuznets Curve is still valid in sample, and digital finance can reduce air and water pollution (as measured through SO2 and COD emission) directly; (2) in the influence mechanism, digital finance can alleviate income inequality and promote green industrial structure, thus reducing pollution indirectly, but the scale effect of income growth outweighs the technological effect, which increases pollution indirectly; and (3) digital finance has a threshold effect on improving the environment, then an acceleration effect appears after a certain threshold value. From the regional perspective, digital finance development in eastern regions is generally ahead of central and western regions, and the effects of environmental improvement in the eastern regions are greater. According to the study, this paper suggest that digital finance can be an effective way to promote social sustainability by alleviating income inequality and environmental sustainability by reducing pollution.

Gajewski offers a formal comment on Griffiths et al. (2017), a paper that explored how microclimates and their varying ice cover regimes on lakes and ponds in Arctic regions modified the diatom assemblage responses to recent warming. One of Gajewski’s main criticisms is that the microclimate classification scheme used in Griffiths et al. (2017) is merely anecdotal; a claim which ignores the value of observational evidence and misunderstands the frequency that each site was visited or surveyed. We clarify that the study sites were visited multiple times via recurrent aerial surveys and ground observations dating back to the 1970s, which supports our microclimate classification scheme. Many of Gajewski’s claims regarding climate, catchment characteristics, and ice melting properties from field locations he has not visited were refuted by veteran Arctic scientists with long-term field experience in these regions. In addition, Gajewski makes several criticisms concerning radioisotopic dating, core chronology, sediment mixing, diagenesis, and preservation of bioindicators that relate more to general paleolimnological assumptions than to conclusions reached by Griffiths et al. (2017). Research from the 1980s and 1990s, when scientific consensus on these issues was first reached, readily show that the methodologies and data interpretation of Griffiths et al. (2017) are sound. We appreciate the opportunity to expound on the finer details of the Griffiths et al. (2017) paper, work based on field research by the study’s co-authors spanning almost three decades, with additional observations from colleagues dating back to the 1970s. We address Gajewski’s criticisms with relevant literature, expert statements, and a few clarifying comments.

Cryptococcus neoformans is responsible for life-threatening infections that primarily affect immunocompromised individuals and has an estimated worldwide burden of 220,000 new cases each year—with 180,000 resulting deaths—mostly in sub-Saharan Africa. Surprisingly, little is known about the ecological niches occupied by C. neoformans in nature. To expand our understanding of the distribution and ecological associations of this pathogen we implement a Natural Language Processing approach to better describe the niche of C. neoformans. We use a Latent Dirichlet Allocation model to de novo topic model sets of metagenetic research articles written about varied subjects which either explicitly mention, inadvertently find, or fail to find C. neoformans. These articles are all linked to NCBI Sequence Read Archive datasets of 18S ribosomal RNA and/or Internal Transcribed Spacer gene-regions. The number of topics was determined based on the model coherence score, and articles were assigned to the created topics via a Machine Learning approach with a Random Forest algorithm. Our analysis provides support for a previously suggested linkage between C. neoformans and soils associated with decomposing wood. Our approach, using a search of single-locus metagenetic data, gathering papers connected to the datasets, de novo determination of topics, the number of topics, and assignment of articles to the topics, illustrates how such an analysis pipeline can harness large-scale datasets that are published/available but not necessarily fully analyzed, or whose metadata is not harmonized with other studies. Our approach can be applied to a variety of systems to assert potential evidence of environmental associations., Author summary We expand the utility of Natural Language Processing (NLP), backtracking through metabarcodes, utilizing papers that may not mention our subject of interest, C. neoformans, in a departure from usual text analysis methods. We confirm that C. neoformans is associated with decomposing wood which is reinforced by the inferred literature studied here on C. neoformans and its close congeneric relatives. This work demonstrates the potential utility of pairing NLP with single-locus metagenetic data for the study of Neglected Tropical Diseases. While the results of this article are largely confirmatory, we present a novel method to study the ecological niches of rare pathogens that leverages the immense amount of data available to researchers in the NCBI Sequence Read Archive (SRA) combined with a text-mining analysis based on Natural Language Processing. We demonstrate that text processing, noun identification, and verb identification can play an important role in analyzing a large corpus of documents together with metagenetic data. Forging this connection requires access to all of the available ecological 18S ribosomal RNA and Internal Transcribed Spacer NCBI SRA datasets. These datasets use metabarcoding to query taxonomic diversity in eukaryotic organisms, and in the case of the Internal Transcribed Spacer, they specifically target Fungi. The presence of specific species is inferred when diagnostic 18S or ITS gene region sequences are found in the SRA data. We searched for C. neoformans in all 18S and ITS datasets available and gathered all associated journal articles that either cite the SRA data accessions or are cited in the SRA data accessions. Published metagenetic data often have associated metadata including: latitude and longitude, temperature, and other physical characteristics describing the conditions in which the metagenetic sample was collected. These metadata are not always presented in consistent formats, so harmonizing study methods may be needed to appropriately compare metagenetic data as commonly required in metanalysis studies. We present an analysis which takes as input articles associated with SRA datasets that were found to contain evidence of C. neoformans. We apply NLP methods to this corpus of articles to describe the niche of C. neoformans. Our results reinforce the current understanding of C. neoformans’s niche, indicating the pertinence of employing an NLP analysis to identify the niche of an organism. This approach could further the description of virtually any other organism that routinely appears in metagenetic surveys, especially pathogens, whose ecological niches are unknown or poorly understood.

The paper assessed access to WASH service in the Jariban district of Somalia. One hundred and sixty-seven households were sampled to administer a questionnaire. Central tendency and logistical regression were used to analyse the data in SPSS 26. The findings show that access to safe drinking water sources is 57.5%. Of the 42.5% of respondents who did not access safe drinking water source, only 10.8% confirmed that they treat drinking water at the point of use. The main reason for household water treatment was the positive mindset (.272) of the household head towards water treatment. The majority (80.2%) of the respondents access approximately 13 litres per person per day. Woman-headed households were more likely to treat water before drinking than male-headed households. Only 26.9% of the respondents accessed basic sanitation. Of the respondents, 55.7% did not share latrines, while 44.3% share resulting in open defecation. WASH access in the study area remains low, resulting in health-related risks, including diarrhoeal disease. The limitation is that the paper only focused on access to WASH facilities in fragile contexts. A cross-sectional analysis of biological, physical and chemical properties of water at the source and point of use is recommended for further research.

Water affordability is central to water access but remains a challenge to measure. California enshrined the human right to safe and affordable water in 2012 but the question remains: how should water affordability be measured across the state? This paper contributes to this question in three steps. First, we identify key dimensions of water affordability measures (including scale, volume of water needed to meet ‘basic’ needs, and affordability criteria) and a cross-cutting theme (social equity). Second, using these dimensions, we develop three affordability ratios measured at the water system scale for households with median, poverty level, and deep poverty (i.e., half the poverty level) incomes and estimate the corresponding percentage of households at these income levels. Using multiple measures conveys a fuller picture of affordability given the known limitations of specific affordability measures. Third, we analyze our results disaggregated by a key characteristic of water system vulnerability–water system size. We find that water is relatively affordable for median income households. However, we identify high unaffordability for households in poverty in a large fraction of water systems. We identify several scenarios with different policy implications for the human right to water, such as very small systems with high water bills and low-income households within large water systems. We also characterize how data gaps complicate theoretical ideals and present barriers in human right to water monitoring efforts. This paper presents a systematic approach to measuring affordability and represents the first statewide assessment of water affordability within California’s community water systems.

Drawing on fine particulate matter (PM2.5) from satellite raster data and matching with the county-level socio-economic data from 2008 to 2015 in China, this paper investigates the impacts of the establishment of National Key Ecological Functional Zones (NKEFZ) on environmental quality by employing the difference-in-differences method, which was stablished in June 2011. The results indicate that the establishment of the NKEFZ significantly decreased the concentration values of PM2.5, a drop of about 20% during the study period, after the paper controls for other factors affecting air quality. The robustness tests using the maximum and medium concentration values of PM2.5 show similar results. Through further analysis, the paper finds that the establishment of NKEFZ can improve the ecological utilization efficiency of land.

Neglected Tropical Diseases (NTDs) remain endemic to many regions of sub-Saharan Africa (SSA) left behind by socioeconomic progress. As such, these diseases are markers of extreme poverty and inequity that are propagated by the political, economic, social, and cultural systems that affect health and wellbeing. As countries embrace and work towards achieving the Sustainable Development Goals (SDGs), the needs of such vulnerable populations need to be addressed in local and global arenas. The research uses primary qualitative data collected from five NTD endemic counties of Kenya: interviews key informants (n = 21) involved in NTD implementation programs and focus groups (n = 5) of affected individuals. Informed by theories of political ecology of health, the research focuses on post-devolution Kenya and identifies the political, economic, social, and cultural factors that propagate NTDs and their effects on health and wellbeing. Our findings indicate that structural factors such as competing political interests, health worker strikes, inadequate budgetary allocations, economic opportunity, marginalization, illiteracy, entrenched cultural norms and practices, poor access to water, sanitation and housing, all serve to propagate NTD transmission and subsequently affect the health and wellbeing of populations. As such, we recommend that post-devolution Kenya ensures local political, economic and socio-cultural structures are equitable, sensitive and responsive to the needs of all people. We also propose poverty alleviation through capacity building and empowerment as a means of tackling NTDs for sustained economic opportunity and productivity at the local and national level., Author summary Wellbeing is currently seen as an avenue that shapes, happiness, productivity, environmental awareness, social inclusion, and justice; however, most countries presently adopting wellbeing measures are in the global north. Neglected Tropical Diseases (NTDs) significantly compromise populations’ health and well-being in the global south, causing undesirable effects on the personal, social, and economic capabilities of communities living in endemic regions. As countries work towards achieving the Sustainable Development Goals (SDGs), it is paramount that countries in the global south adopt wellbeing measures and, in doing so, capture the lived experiences of individuals experiencing inequities, particularly as these are shaped by NTDs. In sub-Saharan Africa (SSA), political and economic power manifests across different scales, determining human-environmental interaction, distribution of resources, and the transmission of infectious agents. This paper uses a political ecology of health approach to identify the political, economic, social and cultural factors that contribute to NTDs, which are among the world’s greatest global health problems. The vast majority of people bearing the burden of NTDs reside in low-income countries. Surprisingly, as the economies of the low-income countries improve to middle-income status, NTDs continue to thrive among sub-populations of low socioeconomic status because of the unequal distribution of economic gains. As a result, NTDs are found in environments characterized by poverty and income inequality, and other inequalities in access to health services, housing, safe water, and sanitation. This paper uses political ecology of health in the primarily biomedical field of NTDs to demonstrate that inequities are embedded within the broad political, socio-cultural, and economic systems that exacerbate NTD infection. The study recommends that NTD endemic countries in SSA formulate policies that enhance equity through capacity building and empowerment enhance population wellbeing.

Citizen science (CS) currently refers to the participation of non-scientist volunteers in any discipline of conventional scientific research. Over the last two decades, nature-based CS has flourished due to innovative technology, novel devices, and widespread digital platforms used to collect and classify species occurrence data. For scientists, CS offers a low-cost approach of collecting species occurrence information at large spatial scales that otherwise would be prohibitively expensive. We examined the trends and gaps linked to the use of CS as a source of data for species distribution models (SDMs), in order to propose guidelines and highlight solutions. We conducted a quantitative literature review of 207 peer-reviewed articles to measure how the representation of different taxa, regions, and data types have changed in SDM publications since the 2010s. Our review shows that the number of papers using CS for SDMs has increased at approximately double the rate of the overall number of SDM papers. However, disparities in taxonomic and geographic coverage remain in studies using CS. Western Europe and North America were the regions with the most coverage (73%). Papers on birds (49%) and mammals (19.3%) outnumbered other taxa. Among invertebrates, flying insects including Lepidoptera, Odonata and Hymenoptera received the most attention. Discrepancies between research interest and availability of data were as especially important for amphibians, reptiles and fishes. Compared to studies on animal taxa, papers on plants using CS data remain rare. Although the aims and scope of papers are diverse, species conservation remained the central theme of SDM using CS data. We present examples of the use of CS and highlight recommendations to motivate further research, such as combining multiple data sources and promoting local and traditional knowledge. We hope our findings will strengthen citizen-researchers partnerships to better inform SDMs, especially for less-studied taxa and regions. Researchers stand to benefit from the large quantity of data available from CS sources to improve global predictions of species distributions.

This paper proposes a method for classifying the river state (a flood risk exists or not) from river surveillance camera images by combining patch-based processing and a convolutional neural network (CNN). Although CNN needs much training data, the number of river surveillance camera images is limited because flood does not frequently occur. Also, river surveillance camera images include objects that are irrelevant to the flood risk. Therefore, the direct use of CNN may not work well for the river state classification. To overcome this limitation, this paper develops patch-based processing for adjusting CNN to the river state classification. By increasing training data via the patch segmentation of an image and selecting patches that are relevant to the river state, the adjustment of general CNNs to the river state classification becomes feasible. The proposed patch-based processing and CNN are developed independently. This yields the practical merits that any CNN can be used according to each user’s purposes, and the maintenance and improvement of each component of the whole system can be easily performed. In the experiment, river state classification is defined as the following problems using two datasets, to verify the effectiveness of the proposed method. First, river images from the public dataset calledPlacesare classified to images withMuddylabels and images withClearlabels. Second, images from the river surveillance camera in Nagaoka City, Japan are classified to images captured when the government announced heavy rain or flood warning and the other images.

Three-dimensional landscape patterns are an effective means to study the relationship between landscape pattern evolution and eco-environmental effects. This paper selects six districts in Xi’an as the study area to examine the spatial distribution characteristics of the three-dimensional architectural landscape in the city’s main urban area using three-dimensional information on the buildings in 2020 with the support of GIS. In this study, two new architectural landscape indices—landscape height variable coefficient and building rugosity index—were employed in landscape pattern analysis, whilst a system of rigorous and comprehensive three-dimensional architectural landscape metrics was established using principal component analysis. A mathematical model of weighted change of landscape metrics based on the objective weighting method was applied to carry out scale analysis of the landscape patterns. Spatial statistical analysis and spatial autocorrelation analysis were conducted to comprehensively study the differentiation of three-dimensional architectural landscape spatial patterns. The results show that the characteristic scale of the three-dimensional landscape pattern in Xi’an’s main urban area is around 8 km. Moreover, the three-dimensional landscape of the buildings in this area is spatially positively correlated, exhibiting a high degree of spatial autocorrelation whilst only showing small spatial differences. The layout of the architectural landscape pattern is disorderly and chaotic within the second ring, whilst the clustering of patch types occurs near the third ring. Moreover, the building density in the Beilin, Lianhu, and Xincheng districts is large, the building height types are rich, and the roughness of the underlying surface is high, such that these are key areas to be improved through urban renewal. The height, volume, density, morphological heterogeneity, and vertical roughness of the architectural landscape vary amongst functional areas within the study area. This paper is the first to apply the study of spatial heterogeneity of three-dimensional landscape patterns to Xi’an. It does so in order to provide a quantitative basis for urban landscape ecological design for urban renewal and the rational planning of built-up areas, which will promote the sustainable development of the city’s urban environment.

Enterprises acquire heterogeneous knowledge through external knowledge search and adapt to the change of external environment, which is of great significance to enterprise breakthrough innovation. This paper takes the innovation ecosystem as the boundary of the research paradigm. Based on innovation ecosystem theory, knowledge management theory and enterprise innovation theory, this paper constructs a moderated mediation model of the enterprise knowledge search, knowledge integration and breakthrough innovation under the characteristics of innovation ecosystem network. This model is tested on the survey data of 344 technology enterprise and manufacturing industries including R&D departments in the Beijing-Tianjin-Hebei region. The research results show that: knowledge integration plays part of the intermediary role between knowledge search and enterprise breakthrough innovation; the larger the network scale, the stronger the network connection, the stronger the intermediary role of knowledge integration on the relationship between knowledge search and enterprise breakthrough innovation. The research results reveal the important role of the innovation ecosystem in enterprise breakthrough innovation. At the same time, the research on knowledge search and enterprise breakthrough innovation should consider the network characteristics of innovation ecosystem and the ability of enterprise knowledge integration.

Stochastic models of biomolecular reaction networks are commonly employed in systems and synthetic biology to study the effects of stochastic fluctuations emanating from reactions involving species with low copy-numbers. For such models, the Kolmogorov’s forward equation is called the chemical master equation (CME), and it is a fundamental system of linear ordinary differential equations (ODEs) that describes the evolution of the probability distribution of the random state-vector representing the copy-numbers of all the reacting species. The size of this system is given by the number of states that are accessible by the chemical system, and for most examples of interest this number is either very large or infinite. Moreover, approximations that reduce the size of the system by retaining only a finite number of important chemical states (e.g. those with non-negligible probability) result in high-dimensional ODE systems, even when the number of reacting species is small. Consequently, accurate numerical solution of the CME is very challenging, despite the linear nature of the underlying ODEs. One often resorts to estimating the solutions via computationally intensive stochastic simulations. The goal of the present paper is to develop a novel deep-learning approach for computing solution statistics of high-dimensional CMEs by reformulating the stochastic dynamics using Kolmogorov’s backward equation. The proposed method leverages superior approximation properties of Deep Neural Networks (DNNs) to reliably estimate expectations under the CME solution for several user-defined functions of the state-vector. This method is algorithmically based on reinforcement learning and it only requires a moderate number of stochastic simulations (in comparison to typical simulation-based approaches) to train the “policy function”. This allows not just the numerical approximation of various expectations for the CME solution but also of its sensitivities with respect to all the reaction network parameters (e.g. rate constants). We provide four examples to illustrate our methodology and provide several directions for future research., Author summary We develop a deep learning framework for estimating solutions of the chemical master equation (CME) that is fundamental to stochastic analysis of reaction networks. The CME is a system of ordinary differential equations that describes the time-evolution of the probability density of the random state-vector, and owing to an inherent curse of dimensionality, directly solving the CME is generally impractical with existing approaches. Moreover, the commonly employed simulation-based approaches for estimating CME solutions often require an exorbitant amount of computational time, even for moderately-sized networks. To counter these issues, we develop a deep reinforcement learning based method, called DeepCME, in this paper. DeepCME not only estimates function expectations based on the CME solution, but it also solves the more challenging problem of estimating their sensitivities with respect to all the model parameters. We illustrate our approach with four carefully chosen reaction network examples with varying sizes. Our results demonstrate that DeepCME reliably estimates the expectations of interest, along with all the parametric sensitivities, at a fraction of the computational cost of simulation-based estimators. We present many directions for future research and suggest further improvements to DeepCME that can greatly enhance its accuracy and applicability.

In the case of a fire, the choice of exit in the highway tunnel is strictly limited by fire location, which seriously affects the evacuation time. A spontaneous or disorderly exit choice might result in a decreased evacuation efficiency and utilization rate of exits. In this paper, we propose a strategy to obtain the optimal exit choice based on fire location during highway tunnel evacuations. In our strategy, first, the vehicle distributions and locations of evacuating occupants are determined in the traffic simulation program VISSIM. The evacuation simulation software BuildingEXODUS is employed to obtain the corresponding parameters of the evacuation process and analyze the impacts of different fire locations on the evacuation time. During the analysis, the optimal productivity statistics (OPS) is selected as the evaluation index. Then, the feature points of the crowding occupants are captured by the fuzzy c-means (FCM) cluster algorithm. Next, based on the feature points, the relationship between the location of the fire and boundary of the optimal exit choice under the optimal OPS is obtained through the polynomial regression model. It is found that the R-squared(R2) and sum of squares for error (SSE) of the polynomial regression model, reflecting the accuracy estimation, are 98.02% and 2.79×10−4, respectively. Moreover, different fire locations impact the evacuation time of tunnel entrance and evacuation passageway. This paper shows that the location of the fire and boundary of optimal exit choice have a negative linear correlation. Taking the fire 110 m away from the evacuation passageway as an example, the OPS of our strategy can be decreased by 35.6% when compared with no strategies. Our proposed strategy could be applied to determine the location of variable evacuation signs to help evacuating occupants make optimal exit choices.

Drawing on Jakarta, Metro Manila and Singapore as case studies, we explore the paradox of slow political action in addressing subsiding land, particularly along high-density urban coastlines with empirical insights from coastal geography, geodesy analysis, geology, and urban planning. In framing land subsidence as a classic ‘wicked’ policy problem, and also as a hybrid geological and anthropogenic phenomenon that is unevenly experienced across urban contexts, the paper uses a three-step analysis. First, satellite-derived InSAR maps are integrated with Sentinel-1A data in order to reveal the socio-temporal variability of subsidence rates which in turn pose challenges in uniformly applying regulatory action. Second, a multi-sectoral mapping of diverse policies and practices spanning urban water supply, groundwater extraction, land use zoning, building codes, tenurial security, and land reclamation reveal the extent to which the broader coastal governance landscape remains fragmented and incongruous, particularly in arresting a multi-dimensional phenomenon such as subsidence. Finally, in reference to distinct coastal identities of each city–the ‘Sinking Capital’ (Jakarta), ‘Fortress Singapore’, and the ‘Disaster Capital’ (Manila) the paper illustrates how land subsidence is portrayed across the three metropolises in markedly similar ways: as a reversible, quasi-natural, and/or a highly individualized problem.

Unmanned ground vehicles (UGVs) are an important research application of artificial intelligence. In particular, the deep learning-based object detection method is widely used in UGV-based environmental perception. Good experimental results are achieved by the deep learning-based object detection method Faster region-based convolutional neural network (Faster R-CNN). However, the exploration space of the region proposal network (RPN) is restricted by its expression. In our paper, a boosted RPN (BRPN) with three improvements is developed to solve this problem. First, a novel enhanced pooling network is designed in this paper. Therefore, the BRPN can adapt to objects with different shapes. Second, the expression of BRPN loss function is improved to learn the negative samples. Furthermore, the grey wolf optimizer (GWO) is used to optimize the parameters of the improved BRPN loss function. Thereafter, the performance of the BRPN loss function is promoted. Third, a novel GA-SVM classifier is applied to strengthen the classification capacity. The PASCAL VOC 2007, VOC 2012 and KITTI datasets are used to test the BRPN. Consequently, excellent experimental results are obtained by our deep learning-based object detection method.

This paper proposes and tests a multilayer framework for simulating the network dynamics of inter-organizational coordination among interdependent infrastructure systems (IISs) in resilience planning. Inter-organizational coordination among IISs (such as transportation, flood control, and emergency management) would greatly affect the effectiveness of resilience planning. Hence, it is important to examine and understand the dynamics of coordination in networks of organizations within and across various systems in resilience planning. To capture the dynamic nature of coordination frequency and the heterogeneity of organizations, this paper proposes a multilayer network simulation framework enabling the characterization of inter-organizational coordination dynamics within and across IISs. In the proposed framework, coordination probabilities are utilized to approximate the varying levels of collaboration among organizations. Based on these derived collaborations, the simulation process perturbs intra-layer or inter-layer links and unveils the level of inter-organizational coordination within and across IISs. To test the proposed framework, the study examined a multilayer collaboration network of 35 organizations from five infrastructure systems within Harris County, Texas, based on the data gathered from a survey in the aftermath of Hurricane Harvey. The results indicate that prior to Hurricane Harvey: (1) coordination among organizations across different infrastructure systems is less than the coordination within the individual systems; (2) organizations from the community development system had a low level of coordination for hazard mitigation with organizations in flood control and transportation systems; (3) achieving a greater level of coordination among organizations across infrastructure systems is more difficult and would require a greater frequency of interaction (compared to within-system coordination). The results show the capability of the proposed multilayer network simulation framework to examine inter-organizational coordination dynamics at the system level (e.g., within and across IISs). The assessment of inter-organizational coordination within and across IISs sheds light on important organizational interdependencies in IISs and leads to recommendations for improving the resilience planning process.

Background Cryptosporidiosis is an emerging infectious disease of public health significance worldwide. The burden of disease caused by Cryptosporidium varies between and within countries/areas. To have a comprehensive understanding of epidemiological status and characteristics of human Cryptosporidium infection in China since the first report in 1987, a retrospective epidemiological analysis was conducted by presenting differences in the prevalence of Cryptosporidium by province, year, population, living environment and season and possible transmission routes and risk factors as well as genetic characteristics of Cryptosporidium in humans. Methodology/Principal findings A systematic search was conducted to obtain epidemiological papers of human Cryptosporidium infection/cryptosporidiosis from PubMed and Chinese databases. Finally, 164 papers were included in our analysis. At least 200,054 people from 27 provinces were involved in investigational studies of Cryptosporidium, with an average prevalence of 2.97%. The prevalence changed slightly over time. Variable prevalences were observed: 0.65–11.15% by province, 1.89–47.79% by population, 1.77–12.87% and 0–3.70% in rural and urban areas, respectively. The prevalence peak occurred in summer or autumn. Indirect person-to-person transmission was documented in one outbreak of cryptosporidiosis in a pediatric hospital. 263 Cryptosporidium isolates were obtained, and seven Cryptosporidium species were identified: C. hominis (48.3%), C. andersoni (22.43%), C. parvum (16.7%), C. meleagridis (8.36%), C. felis (3.04%), C. canis (0.76%) and C. suis (0.38%). Conclusions/Significances This systematic review reflects current epidemiological status and characteristics of Cryptosporidium in humans in China. These data will be helpful to develop efficient control strategies to intervene with and prevent occurrence of human Cryptosporidium infection/cryptosporidiosis in China as well as have a reference effect to other countries. Further studies should focus on addressing a high frequency of C. andersoni in humans and a new challenge with respect to cryptosporidiosis with an increasing population of elderly people and patients with immunosuppressive diseases., Author summary Cryptosporidium is a major cause of diarrheal disease in humans globally. Due to the lack of effective drug treatment and vaccine prevention against cryptosporidiosis, it is particularly important to develop efficient control strategies to intervene with and prevent Cryptosporidium infection in humans. The present review presented and analyzed epidemiological status and characteristics of Cryptosporidium infection in humans in China since the first report in 1987. To date, epidemiological investigations of Cryptosporidium infecion have been carried out in different populations in 27 provinces, autonomous regions, and municipalities. Average prevalence of Cryptosporidium was 2.97% (5,933/200,054). Like other infectious disease, due to poor sanitation conditions in rural areas, people living in rural areas had a significantly higher prevalence of Cryptosporidium (1.77–12.87%) than those living in urban areas (0–3.70%). Seven Cryptosporidium species were identified, including C. hominis, C. andersoni, C. parvum, C. meleagridis, C. felis, C. canis and C. suis. This retrospective epidemiological analysis indicates wide geographical distribution of human Cryptosporidium infection/cryptosporidiosis in China.

We are in the midst of unprecedented change—climate shifts and sustained, widespread habitat degradation have led to dramatic declines in biodiversity rivaling historical extinction events. At the same time, new approaches to publishing and integrating previously disconnected data resources promise to help provide the evidence needed for more efficient and effective conservation and management. Stakeholders have invested considerable resources to contribute to online databases of species occurrences and genetic barcodes. However, estimates suggest that only 10% of biocollections are available in digital form. The biocollections community must therefore continue to promote digitization efforts, which in part requires demonstrating compelling applications of the data. Our overarching goal is therefore to determine trends in use of mobilized species occurrence data since 2010, as online systems have grown and now provide over one billion records. To do this, we characterized 501 papers that use openly accessible biodiversity databases. Our standardized tagging protocol was based on key topics of interest, including: database(s) used, taxa addressed, general uses of data, other data types linked to species occurrence data, and data quality issues addressed. We found that the most common uses of online biodiversity databases have been to estimate species distribution and richness, to outline data compilation and publication, and to assist in developing species checklists or describing new species. Only 69% of papers in our dataset addressed one or more aspects of data quality, which is low considering common errors and biases known to exist in opportunistic datasets. Globally, we find that biodiversity databases are still in the initial stages of data compilation. Novel and integrative applications are restricted to certain taxonomic groups and regions with higher numbers of quality records. Continued data digitization, publication, enhancement, and quality control efforts are necessary to make biodiversity science more efficient and relevant in our fast-changing world.

HIV can evolve remarkably quickly in response to antiretroviral therapies and the immune system. This evolution stymies treatment effectiveness and prevents the development of an HIV vaccine. Consequently, there has been a great interest in using population genetics to disentangle the forces that govern the HIV adaptive landscape (selection, drift, mutation, and recombination). Traditional population genetics approaches look at the current state of genetic variation and infer the processes that can generate it. However, because HIV evolves rapidly, we can also sample populations repeatedly over time and watch evolution in action. In this paper, we demonstrate how time series data can bound evolutionary parameters in a way that complements and informs traditional population genetic approaches. Specifically, we focus on our recent paper (Feder et al., 2016, eLife), in which we show that, as improved HIV drugs have led to fewer patients failing therapy due to resistance evolution, less genetic diversity has been maintained following the fixation of drug resistance mutations. Because soft sweeps of multiple drug resistance mutations spreading simultaneously have been previously documented in response to the less effective HIV therapies used early in the epidemic, we interpret the maintenance of post-sweep diversity in response to poor therapies as further evidence of soft sweeps and therefore a high population mutation rate (θ) in these intra-patient HIV populations. Because improved drugs resulted in rarer resistance evolution accompanied by lower post-sweep diversity, we suggest that both observations can be explained by decreased population mutation rates and a resultant transition to hard selective sweeps. A recent paper (Harris et al., 2018, PLOS Genetics) proposed an alternative interpretation: Diversity maintenance following drug resistance evolution in response to poor therapies may have been driven by recombination during slow, hard selective sweeps of single mutations. Then, if better drugs have led to faster hard selective sweeps of resistance, recombination will have less time to rescue diversity during the sweep, recapitulating the decrease in post-sweep diversity as drugs have improved. In this paper, we use time series data to show that drug resistance evolution during ineffective treatment is very fast, providing new evidence that soft sweeps drove early HIV treatment failure.

Tropical tuna support some of the largest and most valuable artisanal and industrial fisheries worldwide, conducted to a large degree with Fish Aggregating Devices (FADs). Yellowfin, bigeye and skipjack are the main tuna species found in mixed aggregations around FADs and they are simultaneously encircled by the purse seining operation. One of the key challenges that purse seine fleets fishing with drifting FADs face in all oceans is to be able to target species in healthy condition such as skipjack, while reducing impacts on bigeye and yellowfin in areas where there is a need to reduce fishing pressure on these species. The present paper explores a technical solution for selective fishing at FADs by means of acoustic equipment used by purse seiners. Acoustic frequency response of skipjack and bigeye tuna were determined at 38, 120 and 200 kHz. Skipjack showed stronger response at higher frequencies. On the contrary, bigeye showed stronger responses at lower frequencies. The robust pattern shown in frequency responses of the two species demonstrates the potential to predict abundance and species proportions based on purely acoustic measures. The paper also addresses the conditions that need to be met to successfully apply this technology for selective fishing as well as other uses of direct acoustic observations to support tuna conservation.

This paper studies whether demographic similarities between middle and top managers with different tasks (strategy formation and strategy implementation) impacts organizational performance. By drawing on relational demography theory, we investigate the effect of similarity in gender, organizational tenure, and in both these demographics on the overall costs of Dutch municipalities. The main findings of this paper show that the similarity effects are interrelated: when middle and top managers diverge on only one demographic, performance is increased. Also, when leaders are similar on both demographics, performance is impaired. We conclude by discussing the implications for the literature on middle management, relational demography, and strategy formation and implementation.

Eco-cities have witnessed rapid growth in these years worldwide. As the Eco-cities entering operation stage gradually, more and more researchers have found that users (who are living or working in the Eco-cities) satisfaction is one of the most important factors to determine the success or failure of Eco-cities. Therefore, it is very important to investigate the user demands to attract more citizens willing to live or work in the Eco-cities, which will make the development of Eco-cities more sustainable and solid. The recent researches on user demands investigation and analysis in the Eco-cities mainly focused on understanding the user need itself, yet lack of research on the relationship between the user demand and user satisfaction. This paper initially introduced the Kano model analysis method to the research field of user demands in Eco-city, to explore the relationship between the user demand and user satisfaction. After proposing user demands library in Eco-city (including Land use, Ecological environment, Green building, Energy utilization, etc.), the user demands classification and importance analysis methods of Eco-city were proposed based on Kano model. The questionnaire survey for users of two Eco-cities in China as case study was conducted, consisted of user demand items questionnaire based on the Kano model and a questionnaire on the importance of the user demand items. By utilizing the integration of quantitative analysis methods based on the Kano model and Analytic Hierarchy Process (AHP) method, the final ranking of user demands importance was obtained. Comparing with the existing literatures in terms of user demands research for Eco-city, the user demands analysis method based on Kano model of this paper, is able to reveal the influence degree of user satisfaction towards the facilities and services provided in the Eco-city. The user demands analysis method can be used for other researchers worldwide to investigate and quantitively analyze user demands according to their local development situation and preference of Eco-city. The user demands analysis results obtained through this method, can benefit different stages of Eco-city.