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This study introduces an advanced artificial model, grey wolf optimization (GWO)-nested complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN)-convolutional neural network (CNN)-bidirectional long short-term memory (BiLSTM), for wind speed forecasting. Initially, CEEMDAN with two nested layers decomposes the time series into intrinsic mode functions (IMFs) to enhance forecasting capabilities. Subsequently, CNN extracts features from IMFs, and BiLSTM captures temporal dependencies for precise predictions. GWO further enhances the accurac by selecting optimal hyperparameters based on decomposition results. Test results on diverse wind speed datasets demonstrate the model’s superiority, with a mean absolute percentage error (MAPE) of approximately 3%.

The determination of unknown parameters of photovoltaic models has always been a difficult problem in the field of nonlinear optimization, which is inseparable from the energy conversion efficiency of photovoltaic power generation systems. Due to the high degree of nonlinearity and difficulty in parameter identification of photovoltaic models with multiple diode branches, this paper proposes a novel and efficient evolutionary algorithm based on success-history adaptation differential evolution with linear population size reduction (L-SHADE)—Dynamic L-SHADE with parameter decomposition method (DaL-SHADE) to identify unknown parameters of photovoltaic models. In DaL-SHADE, a dynamic crossover rate sorting technology is introduced, which establishes the relationship between the population and its crossover rate that is ignored in L-SHADE, and thereby the optimization ability of the algorithm to identify unknown parameters is improved. Secondly, the unknown parameters of different types of photovoltaic models are decomposed into linear parameters and nonlinear parameters, so that the dimensionality of the photovoltaic model is reduced. Therefore, DaL-SHADE not only has efficient optimization capabilities but also reduces the dimensionality of different types of photovoltaic models. Through the examination of four photovoltaic models with different degrees of nonlinearization, root mean square error obtained by DaL-SHADE is better than that of the comparison algorithms.

Accurate estimation of unknown parameters of complex photovoltaic models is crucial to whether photovoltaic generators can efficiently convert energy. When a photovoltaic model has multiple diode branches, its complexity increases geometrically. To address the problems of high complexity and difficulty in estimation, this paper proposes an effective improved algorithm based on success-history adaptation differential evolution with linear population size reduction (L-SHADE)—Bi-parameter coordinated updating L-SHADE with parameter decomposition method (CSpL-SHADED). First in CSpL-SHADED, dynamic crossover rate ranking technology is developed to bridge the relationship between individuals and crossover rates, thereby improving effective mutation capabilities. In addition, a dynamic sub-population mechanism is also proposed to divide the entire population into multiple sub-populations so that they are evenly distributed in the search space, so the search ability of individuals in local areas is improved. Secondly, the unknown parameters of solar photovoltaic models of different complexity are effectively decomposed into linear and nonlinear parameters by the decomposition method. The nonlinear parameters are accurately estimated by CSpL-SHADED, and the linear parameters are calculated based on the constructed matrix equation. Through experiments on four solar photovoltaic models of different complexity, CSpL-SHADED showed strong competitiveness to varying degrees compared to the comparative algorithms.

The problem of unmanaged waste amount and the still high dependence on synthetic fertilizers can be overcome with the conversion of organic waste into organic liquid fertilizer through enzymatic fermentation. The process is carried out with MASARO (Manajemen Sampah Zero) technology. Hence, this study aims to study the enzymatic fermentation phenomenon in MASARO technology with vegetable and fruit waste as feedstocks. Several physical influences such as aeration (with vs. without), stirring frequency (1 time/day vs. 3 times/day), and bioreactor color (dark blue vs. white) were investigated. This study found that the first stage of fermentation offers a lower pH due to the formation of organic acids whereas the second stage of fermentation enhances the pH due to the presence of methane and ammonium. The results also prove that the parameter that most influences pH is aeration. The presence of aeration promotes methanogenesis and deamination thereby signifying the fermentation pH. This is also evidenced by the change in product color, which becomes darker over time when aeration is applied. Likewise, the optimal stirring frequency is 1 time/day and the recommended bioreactor color is dark blue. Finally, the application of organic liquid fertilizer on Indramayu paddy fields supports that this fertilizer creates soil looseness, augments the number of decomposers, and enriches soil nutrition so it can lead to productivity and agricultural sustainability.

Plantation forestry on sandy, nutrient poor soils has been developed over the last few decades in seasonally dry tropical monsoon environments. Despite the known benefits of harvest residue conservation between rotations in temperate climes, there are very few studies around the world on Eucalyptus harvest residue management in tropical areas. In Northern Australia on Melville Island 30,000-hectares of Acacia mangium is planned to be converted to Eucalyptus pellita. E. pellita harvest residues comprise high loads of stringy bark, leaves and twigs, posing a fire risk throughout the annual 6-to-8-month dry season in Australia's monsoonal tropics. In this environment, quantifying and demonstrating the anticipated benefits of residue retention is important for determining appropriate responses to fire risk.A 4-hectare experimental trial of 12-year-old E. pellita was cut with trees moved intact to the coupe edge and processed for woodchips. The tree residues from the roadside chipper were pushed back over the site. A total of 35 Mg ha−1 of harvest residue was left on the site, mainly comprised of bark (24.5 Mg ha−1 or 60 % of the total residue mass). Although the leaf and branch residues contained higher concentrations of N, Ca, Cu, K, Mg, Mn, P, and Zn than bark, stem and woodchips, bark N (51.5 kg ha−1) was twice foliage N (26.9 kg ha−1), and contributed to more than 50 % of residue N.Harvest residue bark, leaves and twigs were sampled for a litterbag decomposition study of mass loss on the harvested site. After 10 months about 80 % of bark original weight remained on site (20 % mass loss), compared to about 55 % of the leaves/twigs remaining (45 % mass loss).The potential non-CO2 emissions from the burning of E. pellita harvest residues were estimated at 12.3 Mg CO2-eq ha−1 or 3.35 Mg C ha−1, with the majority of emission arising from the smoldering of bark residues. This study discusses the implications of either retaining or burning harvest residues – for plantation long-term sustainability on sandy tropical sites, and for greenhouse gas emissions.

Though the use of per-and polyfluoroalkyl substances (PFAS) is strictly restricted worldwide, PFAS have been increasingly detected in aqueous environment with high human exposure risk. Effective PFAS remediation requires the simultaneous concentration and decomposition of these compounds from dilute solutions, presenting a significant challenge. The present work evaluated the suitability of layered double hydroxide (LDH) materials, promising adsorbents for anionic pollutants, for the removal of long-chain and short-chain PFAS in micro-polluted water. Additionally, it explored their potential for PFAS degradation after modification. The results suggested that LDH adsorbents have limited ability to extract short-chain PFAS such as perfluorobutane sulfonate (PFBS) from water matrices, especially in dilute solutions. Although organic modification of LDHs could enhance their uptake efficacy on PFAS, it cannot improve the decomposition of PFAS. Innovatively, a composite featuring zero-valent iron (ZVI) particles coupled with LDH was developed, in which the nanoscale ZVI core is coated with LDH to adsorb and decompose perfluorooctanoic acid (PFOA) synergistically. Characterization of the composite showed that LDH coating not only hinders the aggregation of ZVI particles, but also reduces the passivation of ZVI. The PFOA removal efficacy of the composite can be further facilitated in acid environment. By-product analysis revealed that the composite decomposed PFOA mainly through decarboxylation and formation of unstable alcohol.

ABSTRACT: Peroxyacetic acid (PAA) is widely used as an antimicrobial in poultry processing. Recent salmonellosis outbreaks caused by Salmonella Infantis (SI) from chicken products and Salmonella Reading (SR) from turkey products have raised concerns about their enhanced resistance (compared to Salmonella Typhimurium [ST]) to commonly used antimicrobial interventions such as PAA. The objective of this research was to evaluate the efficacy of PAA against Salmonella serotypes (Typhimurium, Infantis and Reading), effect on product color and decomposition of PAA at different pH levels. Fresh chicken wings (0.45 kg) were inoculated with a cocktail (ca. 6 log CFU/mL) of nalidixic acid resistant ST, rifampicin resistant SI and kanamycin resistant SR. Inoculated chicken wings were immersed in PAA solutions (100 or 500 ppm; adjusted to either pH 8.5 or unadjusted natural pH) for either 10 s or 60 min to replicate treatments for chicken parts or whole carcasses, respectively. Treated chicken wings were rinsed in buffered peptone water (100 mL) containing sodium thiosulfate (0.1 %), serially diluted in peptone water supplemented with 200 ppm of nalidixic acid, rifampicin or kanamycin for enumeration of ST, SI, and SR respectively, and plated on APC Petrifilm. Immersion of chicken wings in 500 ppm PAA for 60 min resulted in greater microbial reductions (P ≤ 0.05) of ST, SI, SR of ca. Two log CFU/mL each, compared to 10 s treatment. Regardless of concentration and pH of PAA, increased exposure time (60 min vs. 10 s) resulted in greater reductions (P ≤ 0.05) of ST, SI, SR. ST was slightly more resistant to PAA solutions than S. Infantis and S. Reading (P ≤ 0.05) for all experimental conditions (PAA conc, pH, and exposure times). Faster decomposition of PAA (100 and 500 ppm) was observed at pH 8.5 compared to unadjusted, natural pH (P ≤ 0.05). Product color (lightness, L*) was not affected regardless of the PAA concentration, exposure time or the pH.

Within the fields of forensic anthropology and forensic medicine in general, the ability to provide accurate estimates of time since death that minimize error, maximize precision, and consider a multitude of variables is a critical component of all forensic and medicolegal investigations that focus on the identification of human remains and the circumstances surrounding death. In comparison with many forensic cases where date of death is typically unknown, we begin with a known postmortem interval and progressively assess, through external testing measures (temperature, skin elasticity and skin color changes) followed by photography and videography, decompositional changes over time. This work seeks to provide well-described cases with documented biological parameters to propose hypotheses for future research. Field team members observe and record any decompositional changes associated with the states of algor, rigor, and livor mortis, as well as progressive changes in skin color and tissue integrity in an environment where variability in climate and ecological factors may vary from case to case. The two cases were followed for 31 (postmortem interval [PMI] 38) and 19 (PMI 27) days in humid subtropical and tropical monsoon Köppen-Geiger classified climatic zones, respectively, where delayed decompositional and putrefactive changes observed are at odds with the anticipated rate and expected suite of biological changes occurring within an indoor permeable setting following clinical death. Within this taphonomic context, we also address the value of describing and documenting postmortem intervals in a closed subpopulation from which some individuals are recognized for exhibiting attenuated decompositional changes. This study emphasizes the importance of addressing understudied and underrepresented regions, climates, and cultural contexts for furthering research into the factors that may contribute to the understanding of the early stages of the PMI period and improve the precision of PMI estimates more broadly.

The spatially continuous mapping of vegetation structure parameters in forested areas serves as a crucial foundation for research in various fields, including ecosystem ecology, climate change, hydrology, and forest management and protection. However, there is a notable scarcity of regional scale data regarding vegetation structure parameters. Therefore, our objective was to fill this data gap by providing a methodology for mapping vegetation structure parameters in forested areas at regional-scale. Global Ecosystem Dynamics Investigation (GEDI) provides precise observational data on vegetation structure parameters across the world. Using GEDI vegetation structure parameter point data, we developed a vegetation state coefficient based on the ratio of plant area index (PAI) and vegetation coverage (COVER). Subsequently, we proposed a method for decomposing vegetation coverage to simulate layered vegetation coverage. Taking Jiangxi Province as a case study, we integrated machine learning models to generate a spatially continuous map of vegetation structure parameters in forested areas, including layered coverage and layered leaf area index, in 2020. The map has a spatial resolution of 30 m and a vertical resolution of 5 m. The model exhibits excellent performance. Among the 140 sets of models, approximately 63 % of them achieved an R2 surpassing 0.6, while 89 % of the models achieved a root mean square error (RMSE) below 0.3. This study can serve as a valuable reference for the decomposition of vegetation structure parameters at the regional scale and provide a more precise depiction of the spatial characteristics of vertical vegetation structure at the regional scale. It contributes by providing data support and methodological guidance for research related to forest structure analysis, resource management, and ecological process simulation at the regional scale.

Using postmortem computed tomography (CT) images, we achieved personal identification of a body using transposed teeth and dental treatment marks. Transposition of teeth is a rare malpositioning anomaly. CT images can clarify the malpositioning of the teeth's roots, which is difficult to discern from gross observation of the dentition. Because dental hygiene has reduced the incidence of caries in recent years, it might be difficult to use treatment marks for personal identification in the future. Transposed teeth, although rare, provide an important clue to personal identification.

The decomposition of the ionic liquid hydroxylammonium nitrate (HAN) produces gas phase products which have utility in spacecraft propulsion systems. Among the various gas phase species generated from HAN decomposition is the nitroxyl (HNO) radical, a highly reactive molecule with implications in both chemical and electric propulsion applications. The work described here used a laser-induced fluorescence platform to directly detect the relative density of the HNO radical formed by passing HAN vapor through heated porous disks of varying composition. The use of heated porous 316-stainless steel and aluminum disks showed significant HNO density production and is attributed to a surface hydrogen abstraction mechanism. There was also evidence of surface modification to the metal disks which resulted in a shift in the HNO density temperature profiles. The results reported demonstrate that use of a heated porous material can easily generate a molecular vapor at moderate temperatures for combustion and electric propulsion applications.

The leaf litter of woody plants is an important, but insufficiently studied, source of volatile organic compounds (VOCs) in forest air, as well as the main source of dissolved organic carbon (DOC) for forest soil. This work is devoted to the determination of the molecular composition of litter-derived VOCs and DOC of four species of deciduous trees typical of boreal and mid-latitude forests: silver birch (Betula pendula), European hornbeam (Carpinus betulus), black alder (Alnus glutinosa), and aspen (Populus tremula). The qualitative composition of the compounds released from the litter was determined by gas chromatography with mass spectrometric detection. In the composition of VOCs of all four types of litter, both fresh and exposed during a six-month field experiment, 213 C1-C16 compounds of various classes, including 83 photochemically active terpenes, were identified. Litter-derived VOCs are a mixture of secondary plant metabolites and compounds of microbiological origin. In the composition of DOC leached by rainwater during the field experiment, the presence of 231 organic components was registered, 154 of which were identified. Most of them were carbohydrates, aliphatic acids and alcohols, which can be easily assimilated by soil microbes and affect biological processes with their participation. Thus, the important role of deciduous tree litter as a source of Corg, which plays an important role both in atmospheric processes and in soils, has been demonstrated.

Leaf litter decomposition is a major component of nutrient cycling which depends on the quality and quantity of the leaf material. Ash trees (Fraxinus excelsior, decay time ∼ 0.4 years) are declining throughout Europe due to a fungal pathogen (Hymenoscyphus fraxineus), which is likely to alter biochemical cycling across the continent. The ecological impact of losing species with fast decomposing leaves is not well quantified. In this study we examine how decomposition of three leaf species with varying decomposition rates including ash, sycamore (Acer pseudoplatanus, decay time ∼ 1.4 years), and beech (Fagus sylvatica, decay time ∼ 6.8 years) differ in habitats with and without ash as the dominant overstorey species. Ten plots (40 m × 40 m) were set up in five locations representing ash dominated and non-ash dominated habitats. In each plot mesh bags (30 cm × 30 cm, 0.5 mm aperture) with a single leaf species (5 g) were used to include (large holes added) and exclude macrofauna invertebrates (with a focus on decomposer organisms such as earthworms, millipedes, and woodlice). The mesh bags were installed in October 2020 and retrieved without replacement at exponential intervals after 6, 12, 24 and 48 weeks. Total leaf mass loss was highest in the ash dominated habitat (ash dominated: 88.5%, non-ash dominated: 66.5%) where macrofauna were the main contributor (macrofauna: 96%, microorganisms/mesofauna: 4%). The difference between macrofauna vs microorganisms and mesofauna was less pronounced in the non-ash dominated habitat (macrofauna: 68%, microorganisms/mesofauna: 31%). Our results suggest that if ash dominated habitats are replaced by species such as sycamore, beech, and oak, the role of macrofauna decomposers will be reduced and leaf litter decomposition rates will decrease by 25%. These results provide important insights for future ash dieback management decisions.

Generally, the principle of linear superposition is not applicable in nonlinear systems. The results of our researches prove that for some certain types of decomposed solutions of (2+1)-dimensional Caudrey–Dodd–Gibbon–Kotera–Sawada(CDGKS) equation, the linear superposition principle holds. First, beginning with the potential (2+1)-dimensional CDGKS equation, six sets of decomposition equations are formulated by the formally variable separation approach. On the basis of these decompositions, the linear superposition solutions are then discussed. More specifically, the combination of two periodic wave solutions, soliton and periodic wave interaction solution and the multi-soliton solution are obtained.

Women make up more than 50% of the agricultural labor force but contribute less than 30% to agricultural productivity. Agriculture is the main source of livelihood in Ethiopia but by contributes more than 35% to GDP, 90% to forex earnings, and 70% to employment sources. So Improving economic well-being ensuring sustainable development, and reducing poverty is impossible by ignoring the role of women. Consequently, the main objective of the study is to investigate the gender gap in agricultural productivity using national Panel data of 3474 households which were collected in 2017 and 2019 by the Policy Studies Institute for AGP II baseline study and midline evaluation. Among these, 69% (2404) were male-headed and 31% (1070) were female-headed households. The panel data were combined with DID, Oaxaca Decomposition, and the Random effect Tobit model to investigate the gender gap. The result from the DID Method of Impact Evaluation shows that female-headed households were less productive by 3.7% and 2.05 quintals per hectare when measured in terms of value in birr and quintals per hectare compared to male-headed households. In contrast, male-headed households were more productive by 4% and 2.05 quintals compared to female-headed households. Additionally, the results from the pooled and random effect Tobit model showed that soil fertility, sex of the household head, slope of the land, total livestock holding in TLU, extension contact, use of inorganic fertilizer, credit use, machinery use, and plantation method are among the determinants of the gender gap in agricultural productivity. Furthermore, results from the Oaxaca decomposition show that a gender productivity difference between male and female-headed households was roughly 11.2% when measured by value and 5% when measured by an area-weighted formula. The main finding of the study is that endowment effects were less likely to have a significant impact on the productivity gap than structural effects did. Differences in the unexplained characteristics of men and women may also contribute to the considerable productivity gap between male-headed and female-headed households. Therefore, working on women's empowerment to improve their structural disadvantages through various training programs that favor women or gender-mainstreamed extension training programs for lowering gender productivity differentials is a possible policy option.

Background: The World Health Organization declared COVID-19 no longer a global health emergency on 5th May 2023; however, the impact of COVID-19 on life expectancy throughout the pandemic period is not clear. This study aimed to quantify and decompose the changes in life expectancy during 2019–2023 and corresponding age and gender disparities in 27 countries. Methods: Data were sourced from the Human Mortality Database, the World Population Prospects 2022 and the United Kingdom's Office for National Statistics. Life expectancy was estimated using the abridged life table method, while differentials of life expectancies were decomposed using the age-decomposition algorithm. Results: There was an overall reduction in life expectancy at age 5 among the 27 countries in 2020. Life expectancy rebounded in Western, Northern and Southern Europe in 2021 but further decreased in the United States, Chile and Eastern Europe in the same year. In 2022 and after, lost life expectancy years in the United States, Chile and Eastern Europe were slowly regained; however, as of 7th May 2023, life expectancy in 22 of the 27 countries had not fully recovered to its pre-pandemic level. The reduced life expectancy in 2020 was mainly driven by reduced life expectancy at age 65+, while that in subsequent years was mainly driven by reduced life expectancy at age 45–74. Women experienced a lower reduction in life expectancy at most ages but a greater reduction at age 85+. Conclusions: The pandemic has caused substantial short-term mortality variations during 2019–2023 in the 27 countries studied. Although most of the 27 countries experienced increased life expectancy after 2022, life expectancy in 22 countries still has not entirely regained its pre-pandemic level by May 2023. Threats of COVID-19 are more prominent for older adults and men, but special attention is needed for women aged 85+ years.

In recent years, many-objective optimization problems (MaOPs) have been challenging. Classically, algorithms obtain first the Pareto front (PF). Next, a decision maker (DM) can choose the best solutions according to their preferences in the region of interest (ROI). However, the DM effort increases with objectives in MaOPs. For this reason, this paper proposes a new C++ software based on simulated annealing, decomposition, differential evolution, and outranking relations. MOSA/D-O and MOSA/D-O-II algorithms from the software can pressure toward the ROI at run-time in MaOPs. Both algorithms were tested with DTLZ and WFG benchmarks, showing promised performance in an ad hoc indicator for 5 and 10 objectives with 10 DMs.

Evidence of climate change is widespread and severe across all parts of the world. This poses a threat to humanity, and the entire environment. Appropriate policies are therefore required to help reduce greenhouse gas emission levels, limit the rate of global warming and its impact on climate change while pursuing national growth targets. This study employs the Tapio decoupling method to analyse the decoupling relationship (DR) between economic growth and carbon dioxide (CO2) emissions from 1998 to 2018. We also apply the Logarithmic Mean Divisia Index (LMDI) decomposition method on an extended Kaya identity to analyse CO2 emissions drivers in 145 countries. Last, the study examined the relative impacts of trade intensity and trade efficiency on the DR between economic growth and CO2 emissions. The results revealed that regions with relatively many developing and emerging countries (i.e., SSA, EAP, LAC, MENA, and SA) generally performed Weak Decoupling (WD), Expansive Negative Decoupling (END) and Expansive Coupling (EC), and the decoupling process was largely unstable. The ECA and NA regions on the other hand, which are typically composed of developed economies performed stable WD and Strong Decoupling (SD) statuses throughout the study period. The evidence further revealed that while trade intensity, activity, population, output per carbon emission and Carbon Intensity (CI) effects promote CO2 emissions, trade efficiency and energy intensity (EI) hinder emissions. We recommend that developing countries should enforce laws and cooperate with the developed economies to gain access to green technology to promote environmental sustainability.

As one of the most promising alternative energy, natural gas hydrate has aroused much attention owing to its controllable exploitation with the aid of chemical injections. Although alcohols are commonly used as promoters for hydrate decomposition, few studies have been carried out to investigate the effect of different concentrations of methanol on methane hydrate decomposition at a molecular level. For the first time, we systematically combined the parameters of angular order parameter (AOP), radial distribution function (RDF), and mean square displacement (MSD) to reveal the role of methanol in the decomposition mechanism of methane hydrates through the molecular dynamics approach. The results demonstrated that methanol concentration had an optimal value for hydrate decomposition, which was 20% methanol solution for the best efficiency for the hydrate decomposition and had been examined by analyzing the parameter of AOP. This can be attributed to the promotion effect of methanol on the deconstruction of the hydrogen bond networks of water molecules and the inhibition effect of methanol on the mass transfer effect of methane diffusion, both of which determined the decomposition of methane hydrates. These effects were further quantitatively evaluated by the structural parameters of RDF and MSD. The obtained results reveal the important influence of methanol concentrations on methane hydrates decomposition on molecular scale, and provide useful guidelines for highly efficient exploitation of natural gas hydrate reservoir.

Coastal nutrient loading has been linked to a decline in the capacity of seagrass ecosystems to sequester carbon (‘blue carbon’); however, the mechanisms are unclear. Here we investigated how nutrient loading can affect the contribution that seagrass plant material makes to blue carbon stocks by investigating plant quality-decomposition dynamics. Specifically, we used a combination of laboratory and field experiments to account for various changes in biogeochemical cycling from seagrass meadows, ranging from changes in leaf quality to CO2 fluxes. It was found that nutrient loading increased the ‘labile’ content of seagrass (i.e. increased levels of leaf nitrogen, phosphorus and soluble organic carbon (amino acid and soluble sugar content), and at the same time decreased levels of ‘recalcitrant’ carbon (i.e. materials that are harder for microbes to break down, such hemicellulose, cellulose and lignin contents). Nutrient-enriched leaves decomposed ∼ 18 % faster than non-enriched leaves (i.e. greater biomass loss from nutrient-affected seagrass), resulting in ∼ 80 % more CO2 emissions from nutrient-enriched seagrass. We also found that seagrass that naturally contained high levels of labile carbon at the start of the experiment were affected to a greater degree (i.e. higher CO2 emissions) by nutrients addition than seagrass that had high proportions of recalcitrant carbon to begin with. Overall, these findings suggest that nutrient loading can weaken the capacity of seagrass ecosystems to act as blue carbon sinks through its effect on seagrass leaf decomposability.

Tropical peatlands serve as huge carbon (C) pools in the global C balance, and degradation due to agricultural use is a major concern. The objective of the present study was to examine the relationship between the C loss rate in agriculturally drained tropical peat soils in relation to groundwater level (GWL) and the chemical structure of the resulting soil organic matter (SOM). Soil mesocosms containing three peat soils collected from three forest types in Malaysia, Mixed peat swamp (MPS), Alan Batu (ABt), and Alan Bunga (ABg) forests, which are classified based on dominant plant species, species diversity, and forest structure, were buried in an oil palm plantation for a period of five years. Soil samples packed in the top layer (0–20 cm depth) and the bottom (60–80 cm depth) layer were then analyzed and the results were compared with the initial values for these soils. Weight loss, total C and N contents, ash content, and pyrophosphate solubility index (PSI; an index of the degree of humification) were measured. Changes in the chemical structure of the SOC were investigated using solid-state 13C nuclear magnetic resonance (NMR) spectroscopy with phase-adjusted spinning side bands and thermally assisted hydrolysis and methylation-gas chromatography/mass spectrometry (THM-GC/MS). The C loss in the MPS forest soil, 2–12%, was smaller than that in the ABt and ABg forest soils, 20–37%, especially in the bottom layer. The higher C loss in the ABt and ABg soils, without significant difference between the top and bottom layers, can be attributed to the greater accumulation of less decomposed plant components as evidenced by the larger yields of lignin-derived phenols and polysaccharide-related compounds in THM-GC/MS analysis. The C composition as estimated by 13C NMR did not vary during incubation of the ABt and ABg soils while the yields of lignin-derived and other phenols as well as polysaccharide-related compounds in THM-GC/MS were decreased, especially in the top layer. These findings suggest that plant components with a low degree of decomposition are susceptible to drainage regardless of their structure. The progression of the oxidative degradation of lignin in the top layer of all soils was also suggested by the increases in PSI and the vanillic acid/vanillin ratio. The % O-alkyl C and % alkyl C may control the rate of C loss positively and negatively, respectively. PSI also appears to be useful for roughly estimating the decomposability of tropical peat SOM in various GWLs.

Understanding biophysical and biochemical processes in detritusphere is critical to quantifying and modeling plant residue decomposition dynamics and subsequent soil organic carbon (C) accrual. The objectives of the study were to explore (i) micro-environmental conditions within the detritusphere formed around decomposing corn and soybean leaves, and (ii) the relationships between moisture distribution, enzyme activity, and C dynamics during decomposition. We assessed spatial and temporal dynamics of moisture distribution using X-ray and neutron computed tomography, and activities of β-glucosidase and chitinase, two enzymes involved in soil C and N processing, using zymography. We used 13C labeled residue to track residue contribution to atmospheric CO2 and soil organic C. Moisture redistribution pattern varied depending on the residue type. While the water was immediately absorbed by the corn leaves and maintained afterward, switchgrass leaves absorbed water more slowly and created water-deficient zones within ∼1mm from the residue. This initial moisture depletion led to lower chitinase activity and residue-derived CO2 emissions in switchgrass. In contrast to chitinase, β-glucosidase activity was influenced by a combination of vegetation history and residue type, and it was higher when the origin of the residue matched the vegetation history of the soil. Pore size had an opposite impact on the studied enzymes, supporting the notion that contrasting soil pore architecture can stimulate activities of different enzymes through a selection of dominant enzyme producers. We concluded that the decomposition dynamics of plant residues is not only a simple function of residue chemistry, but rather a combined effect of the vegetation history, in part through its effect on microbial community composition, the plant residue chemical and likely physical characteristics, and the soil pore structure in the detritusphere. Together, they create temporally dynamic micro-environmental conditions influencing decomposition. Specifically, our study demonstrated that the initial micro-environment formulated in detritusphere can play an important role in enzyme activities and consequent C dynamics.

Decomposition is a major contributor to ecosystem respiration, determining the carbon emission and nutrient cycling rates. Our current understanding of decomposition dynamics and their underlying drivers has mainly focused on surface habitats but largely ignored in subterranean environments. Here we studied abiotic and microbial drivers of early-stage litter decomposition inside and outside caves along an elevational gradient in Tenerife. We found comparable decomposition rates (k) and litter stabilizing factors (S), with contrasting drivers and elevational variation. At the surface, we observed a mid-elevational trend in k, which tended to correlate with water availability, cooler temperatures, nutrient availability, and surface-specific bacterial taxa. In sharp contrast, caves showed no elevational impact nor influence of abiotic parameters and bacterial communities on k. Despite this, we found higher levels of S in caves, which were associated mainly with reduced water availability, lower temperatures and cave-specific bacterial taxa, indicating that conditions in caves are strongly linked with carbon storage. Our findings imply that our current perception of terrestrial habitat-based carbon cycling are underestimating the net carbon budget in areas with caves. Disentangling the role of the environment on decomposition in caves is key to fully characterize their roles in nutrient cycling and to understand how increasing anthropogenic pressures will affect fundamental processes in subterranean ecosystems.

To model the data and functions in various computer science applications, the researcher uses a Data Flow Diagram (DFD). DFD has been constructed using [open-source software tools that provide users with different shapes and environments. However, the existing approaches require substantial human effort, the validity of the generated output is still a loophole, and they have never gained traction in practice. Our research objective is to develop a semi-automated tool for drawing complex Data Flow Diagrams in the shortest time according to the specified features of the intended system. We developed a Natural Language Interface (NLI) that allows the user to compose a query and identify the system functionality and constraints for the composition of DFD. Natural Language Processing (NLP) techniques are applied to scrapped data to extract the keywords and develop a data repository. Also, we developed rule-based algorithms to map user queries onto respective token shapes to draw the required functionality into appropriate levels of DFD. For verification, output DFDs were converted into conceptual digraphs using adjacency and permutation matrices to evaluate isomorphism. The empirical results reflect that the DFDs generated by the system are correct, complete, and significant.

This study was conducted at Mount Rungwe Forest Reserve, Mbeya, Tanzania, East Africa to investigate the influence of elevation gradients and vegetation composition on soil organic carbon. Elevation gradients were established through three elevation grids, the higher, mid and lower elevation. We hypothesized that soil organic carbon would be richer in high plant diversity than in low plant diversity gradients. Findings from this study observed that, low elevation had high moisture content (47.72 ± 1.49) and % soil organic carbon (4.02 ± 0.56) with low bulk density (1.03 ± 0.001) and soil pH (5.96 ± 0.06). However, only moisture content, bulk density, organic matter and sand content were statistically different across elevation gradients. It was also observed the proportional decreases in diversity as elevation increases with both Shannon and Simpson index of diversity indicating higher species diversity at lower elevations (3.62 and 0.03 respectively). Results from two multiple linear regression models indicated that moisture contents, plant abundance and species diversity explained the most variation in soil organic matter across an elevation gradient with R2 = 0.4063, F (3, 38) = 8.67, p = 0.0002 and R2 = 0.3510, F (2, 39) = 10.55, p

Since 2011, Sargassum events have increased in frequency along the Caribbean and Atlantic coasts. The accumulation and decomposition of large amounts of Sargassum seaweed on beaches pose socio-economic, ecological, and health risks due to the emission of hydrogen sulfide (H2S), methane, and ammonia. However, limited research exists on the emission processes and the health effects of subchronic and chronic exposure to low levels of H2S. Additionally, the absence of emission factor data for Sargassum decomposition on-site makes health risk assessments challenging. This study aimed to create a custom chamber to simulate real-world Sargassum decomposition, exposing experimental animals to the generated gases. Metal content was analyzed, and emission rates were estimated in a controlled environment. The decomposition-exposure system replicated reported environmental gas emissions from the Caribbean region, except for NH3. H2S bursts were observed during the decomposition process at intervals of 2–10 days, with higher frequency associated with larger masses of decomposing Sargassum. The decomposed gas was transferred to the exposure chamber, resulting in an 80–87% reduction in H2S concentration. The maximum H2S emission was 156 ppm, with a concentration ranging from 50.4 to 56.5 ppm. An estimated emission rate of 7–8 g/h for H2S was observed, and significant levels of lead, arsenic, and aluminum were found in beached Sargassum from the northeast coast of Brazil. This study's developed model provides an opportunity to investigate the effects and risks to human health associated with exposure to gases produced during the environmental decomposition of Sargassum seaweed.

The Tea Bag Index (TBI) approach is a standardized method for assessing litter decomposition in terrestrial ecosystems. This method allows determination of the stabilized portion of the hydrolysable fraction during the decomposition process, and derivation of a decomposition constant (k) using single measurements of the mass-loss ratios of green and rooibos teas. Although this method is being applied to aquatic systems, it has not been tested if the TBI-based decomposition curves accurately represent the changes in remaining mass of the teas in these environments, where initial leaching tends to be higher than in terrestrial ecosystems. Here, we first tested a critical assumption of the TBI method that green tea decomposition plateaus during the standard incubation period of 90 days, and then tested the accuracy of a TBI-based asymptote model, which was determined using another essential assumption of the TBI approach: the ratio of the decomposable hydrolysable fraction to the total hydrolysable fraction of rooibos tea is the same as that of green tea. Validation data were obtained by incubating tea bags in water samples taken from a stream, a pond, and the ocean in Kumamoto, Japan. We found that green tea decomposition did not plateau during the 90-day period, contradicting a key assumption of the TBI method. The other key assumption was also violated, because the TBI-based asymptote models disagreed with actual decomposition data. Subtracting the leachable fraction from the initial tea mass improved the TBI-based model, but discrepancies with the actual decomposition data remained. Thus, we conclude that the TBI approach, which was developed for a terrestrial environment, is not appropriate for aquatic ecosystems.

Estimating the post mortem interval is an important aspect of the work of forensic pathologists and forensic anthropologists. Whilst temperature is generally agreed as the most important variable affecting decomposition, some formulae also incorporate relative humidity for a more detailed estimate. Both these variables are impacted by anthropogenic climate change. This study aims to provide a first overview of the likely extent to which anthropogenic climate change will affect future rates of decomposition. The post mortem interval from death until skeletonization (PMIDS) was calculated using the formula by Vass [1], as well as temperature and humidity predictions from two different climate models, to predict changes in the speed of decomposition between the decades 2020–2029 and 2090–2099. The changes are calculated for different climate zones, and a global average, as well as different climate change scenarios, and for decomposition starting in January and July. The estimated PMIDS is significantly (p

The Battle of Kettle Creek was the only major victory for U.S. forces in Georgia during the American Revolutionary War that took place on February 14, 1779. Around 50 loyalist prisoners were subsequently, and controversially, convicted of treason and 5 hanged. The battlefield site was only identified in 2008 and the land was purchased by the American Battlefield Trust in 2018. This article aims to present results from a forensic archaeological search to answer two questions: (1) was it possible to identify the burial ground(s) for site preservation from this historic battlefield site; and (2), were the fallen soldiers from the battle buried in a mass grave or in individual interments? Search methods included a phased investigation approach of a desk study, then ground searches involving cadaver dogs, geophysics (GPR), and subsequent forensic archaeological excavation and soil analysis (specifically mtDNA and VOC extractions) to recover possible trace evidence of human burials. Results suggest the main battlefield burial area was able to be identified, with 24 cadaver dog alert locations, geophysical and soil anomalies suggesting individual burials where fallen soldiers were interred. Archaeological excavation found contemporary battlefield artifacts, and although human remains were not recovered, the soil analysis showed the presence of mtDNA in these locations. Despite the difficult ground search conditions due to the large search area, 200 + years age of burials, and limited remains, such a focused search strategy has been proven to be an effective approach to detect other historic battlefield burial sites.

The electrochemical oxidation process has been widely studied in the field of wastewater treatment for the decomposition of organic materials through oxidation using ⋅OH generated on the anode. Pt anode electrodes with high durability and long-term operability have a low oxygen evolution potential, making them unsuitable for electrochemical oxidation processes. Therefore, to apply Pt electrodes that are suitable for long-term operation and large-scale processes, it is necessary to develop a new method for improving the decomposition rate of organic materials. This study introduces a method to improve the decomposition rate of organic materials when using a Pt anode electrode in the electrochemical oxidation process for the treatment of organic decontamination liquid waste. Electrochemical decomposition tests were performed using sodium dodecylbenzenesulfonate (SDBS) as a representative organic material and a Pt mesh as the anode electrode. Y2O3 particles were introduced into the electrolytic cell to improve the decomposition rate. The decomposition rate significantly improved from 21% to 99%, and the current efficiency also improved. These results can be applied to the electrochemical oxidation process without additional system modification to enhance the decomposition rate and current efficiency.

Graph theory is a powerful and essential tool for applied scientists and engineers in analyzing and designing algorithms for several problems. Graph theory has a vital role in complex systems, especially in computer sciences. Applications of graph theory can be found in many scientific disciplines such as operational research, engineering, life sciences, management sciences, coding, and computer science. In the literature, there are two algorithms for constructing decompositions for the circulant graphs C2r,r with 2r vertices and r degree. For r,m⩾2, a circulant-balanced complete multipartite graph Cmr,(m-1)r is a generalization to C2r,r. Here, the major contributions are the generalization of the algorithms used for constructing the decompositions for the circulant graphs C2r,r to generalized algorithms that can be used for the decomposition of Cmr,(m-1)r that have mr vertices and (m-1)r degree. These generalized algorithms manage us to decompose the circulant graphs Cmr,(m-1)r by different graph classes. The paper's novelty is demonstrated by the fact that it is the first to suggest general approaches for decomposing circulant-balanced complete multipartite graphs. The constructed results in the present paper are suitable for generating different codes and also have great importance in the design of experiments.

In this paper, a novel hybrid model of decomposition and deep learning embedded with GA optimization was proposed to forecast 24-hour ahead wind speed. The historical wind speed time series was pre-processed and then decomposed into intrinsic mode functions (IMFs) using Ensemble Empirical Mode Decomposition. Each IMFs then was trained and tested through a models of CNN-Bidirectional LSTM model. The hyperparameters of the hybrid CNN-Bi-LSTM model was optimized using GA. CNN can extract the internal characteristics of the time series directly meanwhile Bi-LSTM network can utilize the information in both forward and backward directions completely. The forecasting results of each IMFs were reconstructed to obtain the final forecast. The proposed method was applied to real WS dataset in Hanoi compared with 6 other methods. The result shows that the proposed method has demonstrated much better performance than the other methods.

Many structural models in chemistry, biology, computer science, sociology, and operations research can be analyzed using graph theory. Some examples of these structure models are species movement between regions, molecular bonds, shortest spanning trees, development of computer algorithms. This paper introduces the edge decomposition of circulant graphs with 2n vertices by different graph classes. These circulant graphs are denoted as C2n,n, where n is the degree of the graph C2n,n. We propose two algorithmic approaches for constructing edge decomposition of C2n,n. With aid of the proposed algorithmic approaches, we construct paths decompositions, trees decompositions, disjoint unions of cycles and trees decompositions, and complete bipartite graphs decompositions of C2n,n. In the end, a recursive construction of an edge decomposition, based on orthogonal edge decompositions, is proposed. This helps in using the mutually orthogonal decompositions of the circulant graphs C2n,n in several applications, such as the design of experiments and binary codes generation.

Nanosize nickel ferrite (NiF) with an average crystalline size of 28.6 nm has been successfully synthesized via the co-precipitation method. NiF was found to efficiently reduce the thermal decomposition of ammonium perchlorate (AP) and Composite solid propellants (CSPs) along with an increment in the burning rate of CSPs by 1.6 times. The high-temperature DSC exothermic decomposition peak of AP was decreased by 59.04 °C in the presence of 1% by weight NiF additive. Differential Scanning Calorimeter (DSC) exothermic peak of CSPs based on AP + NiF was decreased by 37.72 °C as compared to that of CSPs based on pure AP. Further, the kinetics parameters of AP and AP + NiF mixture were evaluated using iso-conversional methods. The results indicated that the activation energy the of AP + NiF mixture was increased despite reduction in the thermal decomposition temperature.

This research concentrates on using neural networks in the modelling and prediction of macroeconomic variables in specific. Macroeconomic predictors are particularly interested in neural networks because of their capacity to predict any linear or non-linear connection with a decent degree of precision. Two macroeconomic variables have been used for projecting: gross domestic product (volume, NGDPD) and total investment (NID NGDP) over the time period of 2013–2023. Moving averages, exponential smoothing, Brown’s single-parameter linear, exponential smoothing, Brown’s second-order exponential smoothing, Holt’s two-parameter linear exponential smoothing, and decomposition techniques are used as analytical tools. The research focuses on the usefulness of the artificial neural network model for predicting economic determinants in the long run and compares the ANN’s findings with the Conventional Time - Series data sets (Smoothing & Decomposition Techniques). To emphasise the point, a scientific illustration is used to forecast Pakistan’s two crucial macroeconomic indicators. Based on the empirical results, ANN can play a vital role in forecasting the macroeconomic fundamentals of Pakistan if compare to Exponential smoothing techniques.

Uptake of clean cooking fuels (CCF), such as liquefied petroleum gas (LPG), in place of traditional cooking fuels such as wood, charcoal, and kerosene can improve public health by reducing household air pollution exposures. Though studies have cross-sectionally examined socioeconomic determinants of cooking fuel adoption, little is known about socioeconomic disparities in CCF use over time. Data from the third (2005–06) and fourth (2015–16) rounds of the National Family Health Survey covering 109,041 and 601,509 households, respectively, were used to examine inequities in CCF use in India. While CCF use in India increased nationally from 25% in 2005–06 to 44% in 2015–16, the adoption of CCF varied widely across states and socio-economic groups. Approximately 2% of households in the poorest wealth quintile gained access to LPG during the study period, compared with an increase of 10% or more among households in the middle or richer wealth quintiles; the LPG access gap between the low (0.2%) and middle class (19.2%) was 19% in 2005–06 and nearly doubled to 35% (2.5% vs. 37.4%, respectively) in 2015–16. At the state level, there was a four-fold difference in the uptake of CCF over the two survey periods. The use of CCF increased by less than 10% in Himachal Pradesh, Bihar, Assam, Manipur, Mizoram, and Meghalaya as compared to the increases of at least 30% in Tamil Nadu (42%), undivided Andhra Pradesh (34%), and Kerala (30%). Further, in wealthier states (Delhi, Goa, Punjab, Haryana, Tamil Nadu, Kerala, and undivided Andhra Pradesh), CCF use increased by more than 20% among the poorest individuals compared with less than 1% among the poorest families in lower income states (Tripura, Meghalaya, Madhya Pradesh, Jharkhand, Chhattisgarh, Bihar). To promote a more equitable clean energy transition, poorer and rural Indian households should be prioritized for CCF promotion programs.

In this study we compared the microstructure evolution of U-Mo fuel foils produced with and without heat treatment at low burn-up via advanced post-irradiation examination. The aim of this study is to observe after irradiation the ways in which the fabrication processes have influenced fuel behavior at early-stage irradiation, as for very low burn up microstructural studies are lacking. In this work it was observed that the larger grain size detected in the heat-treated samples before irradiation led to decreased grain refinement after irradiation. Grain refinement was associated with the presence of small nano-size bubbles and precipitates. This phenomenon is hypothesized to influence early fuel swelling during reactor irradiation. Grain refinement was also observed to increase in regions where γ-U decomposition was present. Thus, it was enhanced in the samples fabricated without heat treatment. The heat treatment also increased the thickness of the U-Mo/Zr interface, namely of the UZr2 layer. However, the influence of this layer on fuel performance needs further investigation. On one side, it may contribute to better mechanical bonding; on the other, it may influence swelling and blistering in the interaction layer as porosity increases when this layer is increased. This was observed especially in the presence of increased area containing low Mo concentration, and thus containing a higher fraction of the α-U phase, which is highly susceptible to irradiation induced swelling. Strong evidence of reverse transformation under irradiation (α-U + γ′-U2Mo → bcc γ-U) was observed in these samples. While the precipitates (carbides and oxide) seem to be unaffected by the irradiation at these low burnups. However, further analyses are necessary at higher burn-up to assess the exact impact different heat treatments have on fuel performance.

Obesity rates have increased across all segments of society since the late 1970s, but the reason behind population-level increases in body weight remains unclear. We used the 1971–2020 NHANES data to examine whether the observed trend in obesity prevalence is attributable to changing public health behaviors (i.e., intracohort change) or changing publics (i.e., cohort replacement). We partitioned total change in mean BMI, and rates of obesity and severe obesity, into its IC and CR components using linear and algebraic decomposition methods. We found that the IC mechanism (i.e., broad sectors of individuals changing) plays a dominant role in the overall increase in mean BMI, and obesity and severe obesity prevalence. Birth cohort membership (i.e., the CR mechanism) is also influencing mean BMI, and rates of obesity and severe obesity, but in differing ways. Specifically, the large positive IC and the small positive CR effects are amplifying one another, thus creating a steep increase in the observed rates of severe obesity. Conversely, the large positive IC effect is offset by a small negative CR effect, which created a more gradual rise in mean BMI and rates of obesity. Furthermore, we computed total change for models that entered separately sociodemographic, lifestyle, nutritional, and physical activity measures to estimate differences in mean BMI, and rates of obesity and severe obesity, among cohorts and time periods. Adjustment for all the compositional differences among the cohorts during the study period indicate that a combination of a more pronounced IC and a less pronounced CR drove the observed increase in mean BMI, and rates of obesity and severe obesity. Thus, “universal prevention” (i.e., entire community) strategies for healthy weight promotion may need to be combined with “selective prevention” (i.e., at-risk groups) and/or “targeted prevention” (i.e., at-risk individuals) approaches in order to reverse the obesity epidemic.

In our previous work, we organized a project mainly to design a lunar mushroom farm. In this work, we proceeded to study the features of the production and consumption of oyster mushrooms in that project. Oyster mushrooms were grown in cultivation vessels containing a sterilized substrate. The fruit yield and mass of the spent substrate in the cultivation vessels were measured. A three-factor experiment was carried out with the subsequent application of the steep ascent method and correlation analysis in the R program. These factors included the density of the substrate in the cultivation vessel, its volume, and the number of harvesting flushes. The data obtained was used to calculate the process parameters: productivity, speed and degree of substrate decomposition, and biological efficiency. The consumption and dietary features of oyster mushrooms were modeled in Excel using the Solver Add-in. In the three-factor experiment, the highest productivity amounting to 272 g of fresh fruiting bodies/(m3*day) was obtained with a substrate density of 500 g/L, a cultivation vessel volume of 3 L, and two harvest flushes. The application of the method of steep ascent showed that it is possible to increase the productivity by increasing the substrate density and reducing the volume of the cultivation vessel. In production, there is a need to tally the substrate decomposition speed with the substrate decomposition degree and the biological efficiency of growing oyster mushrooms, since these process parameters have a negative correlation. Most of the nitrogen and phosphorus passed from the substrate into the fruiting bodies. These biogenic elements could limit the yield of oyster mushrooms. It is safe to set the daily intake of oyster mushrooms at 100–200 g while maintaining the antioxidant capacity of the food set.

In this paper, stock price data has been predicted using several state-of-the-art methodologies such as stochastic models, machine learning techniqus, and deep learning algorithms. An efficient decomposition method resonating with these Machine Intelligence (MI) models has been embedded with boosting ensemble method. Finally a Model Confidence Set (MCS) based algorithm has been proposed for forecasting stock price data. Complete Ensemble Empirical Mode Decomposition with Adaptive Noise (CEEMDAN) decomposed orthogonal subseries have been predicted using Random Forests (RFs). Then Kernel Ridge Regression (KRR) model is used to combine those predictions to form a hybrid predictor. In addition, improvement in prediction performance has been observed using kernel functions. Adaptive Boosting (AdaBoost) has been found stimulating prediction accuracy of Long Short-Term Memory (LSTM) and Gated Recurrent Unit (GRU) models. CEEMDAN has also increased the performance of AdaBoost. Nevertheless, combination of forecasts observed from various methodologies is a good approach for improving the result. Despite optimizing the weights for the combination of all the models, a heuristic MCS-based snuffing of the least important models prior averaging is conceded as a potent approach. MCS rescinds insignificant models based on the out-of-sample forecasting or in-sample prediction performance prior to equally average the superior models. The proposed methodologies have been compared to the existing standalone techniques using several validation measures. However, CEEMDAN with Support Vector Regression (CCEMDAN_SVR) model has been found to be the best predictor in the current scenario.

Objectives: Health forecasting is an important aspect of ensuring that the health system can effectively respond to the changing epidemiological environment. Common models for forecasting Alzheimer's disease and related dementias (AD/ADRD) are based on simplifying methodological assumptions, applied to limited population subgroups, or do not allow analysis of medical interventions. This study uses 5 %-Medicare data (1991–2017) to identify, partition, and forecast age-adjusted prevalence and incidence-based mortality of AD as well as their causal components. Methods: The core underlying methodology is the partitioning analysis that calculates the relative impact each component has on the overall trend as well as intertemporal changes in the strength and direction of these impacts. B-spline functions estimated for all parameters of partitioning models represent the basis for projections of these parameters in future. Results: Prevalence of AD is predicted to be stable between 2017 and 2028 primarily due to a decline in the prevalence of pre-AD-diagnosis stroke. Mortality, on the other hand, is predicted to increase. In all cases the resulting patterns come from a trade-off of two disadvantageous processes: increased incidence and disimproved survival. Analysis of health interventions demonstrates that the projected burden of AD differs significantly and leads to alternative policy implications. Discussion: We developed a forecasting model of AD/ADRD risks that involves rigorous mathematical models and incorporation of the dynamics of important determinative risk factors for AD/ADRD risk. The applications of such models for analyses of interventions would allow for predicting future burden of AD/ADRD conditional on a specific treatment regime.

Scavenging constitutes an understudied energy pathway in terrestrial ecosystems, with important connections to disease ecology. A prevailing null hypothesis in scavenging ecology is that carcasses serve as a risk-free meal for whatever animal first encounters them on the landscape. We tested this hypothesis by focusing on a suspected risk that scavengers would face at carcasses: the risk of pathogen exposure. We conducted field trials with mouse carcasses in which we manipulated potential cues to pathogen risk and then monitored scavenger foraging decisions. Separately, we studied pathogen and commensal bacteria dynamics within mouse carcasses through time in the laboratory to better understand how carcass age might impact pathogen risk to scavengers. A visual cue to pathogen risk in the field (carcasses deployed in groups of 7 in 1 m2) caused facultative vertebrate scavengers to pass on the opportunity to feed at a rate six-times higher than for carcasses deployed singly (46.9% vs. 7.7%), suggesting an ability to perceive cues to pathogen risk at carcasses. The cues to carcass age, however, produced no effect on facultative scavenger behavior in our field trials. Laboratory trials demonstrated that both commensal enteric bacteria and a known pathogen (Listeria monocytogenes) increased at least through bloat and active decay stages in carcasses, suggesting that cues to carcass age may not help scavengers reduce pathogen risk early in decomposition. In providing evidence counter to the free meal hypothesis, our results support the continued formation of an alternative risk-based framework to understand scavenger behavior at carcasses.

Redox-active organic molecules that store positive charge in aqueous redox flow cells (catholyte redoxmers) frequently exhibit poor chemical stability for reasons that are not entirely understood. While for some catholyte molecules, deprotonation in their charged state is resposible for shortening the lifetime, for well designed molecules that avoid this common fate, it is seldom known what causes their eventual decomposition as it appears to be energetically prohibitive. Here, a highly soluble (1.6 M) phenoxazine molecule with a redox potential of 0.48 V vs. Ag/AgCl has been examined in flow cells. While this molecule has highly reversible redox chemistry, during cycling the capacity fades in a matter of hours. Our analyses suggest a cooperative decomposition pathway involving disproportionation of two charged molecules followed by anion substitution and deprotonation. This example suggests that cooperative reactions can be responsible for unexpectedly low chemical instability in the catholyte redoxmers and that researchers need to be keenly aware of such reactions and methods for their mitigation.

Carbon release during macrophyte decomposing is critical for the carbon cycle in the aquatic ecosystems. Previous mechanic researches have demonstrated the different release rates of nutrients in different stages, e.g., the fast-leaching stage (in initial 4–10 days), the microbe conditioning stage with slow decay rate (from winter to spring with low temperature). Nevertheless, models for predicting these processes are still not available and the conventional used model inherently assumes a constant relative release rate. Here, we conducted a one-year in-situ decomposition experiment using the conventional (net) litterbag technique. The credibility of experimental data is increased by using fresh debris to avoid alternation of material traits caused by the conventional drying pretreatment, which is originally used in experiments for terrestrial debris. Our results demonstrated that the carbon release process showed an obvious three-stage pattern, which can be universally depicted by a piecewise three-stage exponential decaying model (r2 > 0.9). More importantly, contributions of the three key factor (i.e., leaching, microbe and macroinvertebrate) can be explicitly reflected by the corresponding relative release rate (ki, i denote i th decomposition stage), which variate greatly between different stages, with k1/k2 > 11.8 and k3/k2 > 2.63. More importantly, k3 can be further conveniently decomposed into microbe-induced k3-1 and macroinvertebrate-induced k3-2, correspondingly. Also, k3-2 showed an exponential increase with the number of macroinvertebrate (r2 > 0.95). The contributions of the microbe and macroinvertebrate can then be depicted by these two parameters. In conclusion, our model greatly increases the model realism and generality in simulating the carbon release process; Also, the explicit physical meaning of model parameters make the model mechanism clearer. This not only created the chance to further develop a mechanism-based model, but also facilitate the mechanistic study of the impacts of environmental factors (e.g., pH, dissolved oxygen) on k1, k2, k3-1 and k3-2 concerning different kinds of macrophyte, in which the parameters k1, k2, k3-1 and k3-2 acts as important ecological indicators in depicting the relative release rates caused by specific driving forces. These ecological indicators facilitate the future mechanical researches for the impacts of environmental factors on the carbon release.

Problem: Vietnam is a lower-middle-income country with a very high prevalence of smoking among men. The poor are more likely to smoke than the better off. In this study, we examine socioeconomic inequality in smoking and factors associated with the wealth-related inequality of smoking in Vietnam. Methods: We use data from the 2010 and 2015 Global Adult Tobacco Surveys (GATS). We use a concentration index approach to assess differences in smoking behavior over the distribution of wealth levels. We also use the regression-based decomposition method developed by Wagstaff et al. (2003) and Doorslaer and Koolman (2004) to decompose wealth-related inequality in smoking into inequalities in wealth and other explanatory variables, such as men's age and education. Results: Poorer men are more likely to smoke and smoke more than those better off. In 2015, 47.9% in the poorest wealth quintile smoked every day compared to 29.1% in the richest quintile. In 2015, the concentration index of wealth-related inequality in daily smoking was estimated at −0.104 (CI: 0.135; −0.072). Education and occupation are important factors in wealth-related inequality in smoking, because the poor tend to have lower education levels and are employed in unskilled jobs but are more likely than the rich to be smokers. At 41.4%, unskilled workers make the largest contribution to wealth-related inequality in smoking. Conclusions: Our findings suggest that tobacco prevention efforts should be focused on poor, less educated people. Policies that boost the access of the poor to education and better employment can help them not only increase their wealth level but also reduce smoking, thereby narrowing the wealth-inequality gap in smoking.

Variables that quantify nutrient cycling in terrestrial ecosystems, including aboveground and belowground biomass, litter biomass, inorganic nitrogen (NH4+ and NO3−), and soil CO2 efflux were measured in situ. From measured variables, seasonal litter inputs and nitrogen mineralization were also estimated. Data were collected over the course of one to two growing seasons (2017 and 2018) across three different land-use types under variable human management: an agricultural field (cultivating millet for the duration of the first growing season of the study and left fallow for the duration of the second growing season), a restored native C4 tallgrass prairie, and an approximately 16-year-old successional field. The area of focus within each field was approximately 1 hectare. Five representative 5 m x 5 m plots were randomly chosen in each of the three fields. Within each 5 m x 5 m plot, three 1 m2 subplots were randomly chosen for replicate sampling. These raw data can be utilized to calculate the ecosystem functions of net nitrogen (N) mineralization, decomposition, soil respiration, aboveground primary productivity, and N leaching, which are foundational components of supporting ecosystem services in terrestrial soils and plants. These data can be used in conjunction with other datasets that describe a suite of ecosystem functions in different land-use types under variable management.

The stability of soil organic carbon (SOC) plays a vital role in C sequestration, and largely depends on the availability of soil nitrogen (N) and phosphorus (P). Understanding how different fractions of SOC respond to N and P availability and the underlying microbial mechanism is crucial for mitigating climate changes. Here, we assessed how soil N and P availability modifies different SOC fractions and the soil microbial communities in a tropical forest. We measured soil chemical properties, SOC fractions, microbial PLFA abundance, fungal rDNA and its predicted gene abundance, and extracellular enzyme activities within a field N and P addition experiment. P addition decreased the concentration of recalcitrant SOC and greatly increased the soil oxidative extracellular enzyme activities, while N addition increased active SOC, mainly light fractions, and decreased soil phenol oxidase activity. P addition also induced the greatest abundance of oxidoreductases. Additionally, the transferases, lyases, hydrolases, isomerases, and ligases were also expressed at higher levels after P addition. The results indicate that enhanced soil microbial activities after P addition accelerated recalcitrant SOC decomposition by higher oxidative enzyme activities. Given the increasing N deposition, tropical forests that characterized by a low P have a great potential to sequester more SOC which will mitigate climate change. However, the increase in SOC might be vulnerable to disturbance, because most of the increased C is the active SOC.

Solving large-scale Mixed Integer Linear Programs (MIP) can be difficult without advanced algorithms such as decomposition based techniques. Even if a decomposition technique might be appropriate, there are still many possible decompositions for any large MIP and it may not be obvious which will be the most effective. The quality of a decomposition depends on both the tightness of the dual bound, in our case generated via Lagrangian Relaxation, and the computational time required to produce that bound. Both of these factors are difficult to predict, motivating the use of a Machine Learning function to predict decomposition quality based a score that combines both bound quality and computational time. This paper presents a comprehensive analysis of the predictive capabilities of a ML function for predicting the quality of MIP decompositions created via constraint relaxation. In this analysis, the role of instance similarity and ML prediction quality is explored, as well as the benchmarking of a ML ranking function against existing heuristic functions. For this analysis, a new dataset consisting of over 40000 unique decompositions sampled from across 24 instances from the MIPLIB2017 library has been established. These decompostions have been created by both a greedy relaxation algorithm as well as a population based multi-objective algorithm, which has previously been shown to produce high quality decompositions. In this paper, we demonstrate that a ML ranking function is able to provide state-of-the-art predictions when benchmarked against existing heuristic ranking functions. Additionally, we demonstrate that by only considering a small set of features related to the relaxed constraints in each decomposition, a ML ranking function is still able to be competitive with heuristic techniques. Such a finding is promising for future constraint relaxation approaches, as these features can be used to guide decomposition creation. Finally, we highlight where a ML ranking function would be beneficial in a decomposition creation framework.

The paper presents the environmental sustainability of the Nigeria transport sector (NTPS) through the decomposition and decoupling analysis from 1988–2019. The study’s objective is to determine ways of saving energy in the NTPS and reduce carbon emission for a sustainable environment. The approach was based on the Logarithmic Mean Divisa Index (LMDI) and the Tapio approach, built on Kaya extended identity. Five decoupling indicators were considered based on the four energy carriers consumed in the NTPS. The indicators include economic activity, energy structure, economic structure, population and energy intensity. The results identified three decoupling states, weak negative decoupling, weak decoupling and strong decoupling. The energy intensity, economic activity, population and energy structure prevented decoupling during the study period, while the economic structure factor promoted decoupling. The overall impact of carbon emissions from NTPS was calculated at 44.45 million tonnes of CO2. The study suggests frameworks that will support policy makers to formulate broad base policies for environmental sustainability.

Temperature sensitivity (Q10) of soil organic matter (SOM) decomposition is an essential parameter that reflects the feedback relationship between climate warming and atmospheric CO2 concentration and plays a key role in accurately estimating changes in soil carbon pools and their feedback to climate change. Setting the incubation temperature range (ITR) scenario is essential to accurately estimate the temperature sensitivity of SOM decomposition; however, this has been widely ignored. To address this issue, we conducted a systematic incubation experiment using 54 soils covering the most typical ecosystems in China and nine ITR scenarios. The results showed that ITR scenarios had a significant effect on Q10 in different ecosystems and soil types. Combining the results of fitting mean annual temperature (MAT) to the Q10 of different ITR and the interpretation rate of factors (including climate, soil, and microbial community composition) to the Q10 of different ITR, the results indicate that the growing season average temperature range (GSA) scenario had better performance than the other ITR scenarios and should be the optimum ITR scenario. Furthermore, the variation of main influence factors of Q10 across different ecosystems should be accounted for to accurately predict the feedback between soil C cycle and climate change. Overall, our findings highlight the importance of the ITR for the estimates of Q10 using widespread experimental data, providing a reference for subsequent experiments accurately measure Q10, as well as compare and compile global data to better predict the feedback between the global carbon cycle and climate change.