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Environment changes, water scarcity, soil depletion, and urbanisation are making it harder to produce food using traditional methods in various regions and countries. Aquaponics is emerging as a sustainable food production system that produces fish and plants in a closed-loop system. Aquaponics is not dependent on soil or external environmental factors. It uses fish waste to fertilise plants and can save up to 90–95% water. Aquaponics is an innovative system for growing food and is expected to be very promising, but it has its challenges. It is a complex ecosystem that requires multidisciplinary knowledge, proper monitoring of all crucial parameters, and high maintenance and initial investment costs to build the system. Artificial intelligence (AI) and the Internet of Things (IoT) are key technologies that can overcome these challenges. Numerous recent studies focus on the use of AI and the IoT to automate the process, improve efficiency and reliability, provide better management, and reduce operating costs. However, these studies often focus on limited aspects of the system, each considering different domains and parameters of the aquaponics system. This paper aims to consolidate the existing work, identify the state-of-the-art use of the IoT and AI, explore the key parameters affecting growth, analyse the sensing and communication technologies employed, highlight the research gaps in this field, and suggest future research directions. Based on the reviewed research, energy efficiency and economic viability were found to be a major bottleneck of current systems. Moreover, inconsistencies in sensor selection, lack of publicly available data, and the reproducibility of existing work were common issues among the studies.

Summary: Genetics have nominated many schizophrenia risk genes and identified convergent signals between schizophrenia and neurodevelopmental disorders. However, functional interpretation of the nominated genes in the relevant brain cell types is often lacking. We executed interaction proteomics for six schizophrenia risk genes that have also been implicated in neurodevelopment in human induced cortical neurons. The resulting protein network is enriched for common variant risk of schizophrenia in Europeans and East Asians, is down-regulated in layer 5/6 cortical neurons of individuals affected by schizophrenia, and can complement fine-mapping and eQTL data to prioritize additional genes in GWAS loci. A sub-network centered on HCN1 is enriched for common variant risk and contains proteins (HCN4 and AKAP11) enriched for rare protein-truncating mutations in individuals with schizophrenia and bipolar disorder. Our findings showcase brain cell-type-specific interactomes as an organizing framework to facilitate interpretation of genetic and transcriptomic data in schizophrenia and its related disorders.

Microplastics are ubiquitous in our environment but their presence in air is less well understood. Homes are likely a key source of airborne microplastics and microfibres to the environment owing to the frequent use and storage of plastics and textiles within them. Studying their presence, concentration and distribution in these environments is difficult without the participation of citizens due to accessibility challenges. Few studies have examined the intricacies of the prevalence of indoor microplastics and microfibres or the link between indoor exposure and behavioural and regulatory approaches that could reduce their concentrations. The application of a quintuple innovation helix framework, within which a co-creative citizen science research methodology is applied, provides an opportunity for citizens to shape the scientific method, ensuring that methods are accessible and appropriate for widespread use and designed by the citizen, for the citizen. Exploring behaviours and motivations in plastic and textile use by citizens with industry may reduce the generation of these particles. Future studies should consider the importance of citizen inclusion when designing research strategies for measuring and reducing microplastic concentrations in homes, enabling a nuanced understanding of their generation and distribution and facilitating the development of appropriate behavioural, industrial and regulatory messaging and mitigative measures.

Rapid and precise methods to detect pathogens are paramount in ensuring food safety and selecting appropriate disinfection treatments. Raman spectrometry is a promising technology being investigated for detecting pathogens and achieving rapid, culture-free, and label-free methods. Nonetheless, previous Raman techniques require additional steps, including the preparation of slides that could introduce significant variability. In this study, we investigated the capability of a Raman handheld device for rapid identification of monocultures of Listeria monocytogenes, Salmonella Typhimurium, Escherichia coli O157:H7, and Staphylococcus aureus, and the combination of co-cultures in BHI broth suspension by utilising principal component analysis (PCA) and support vector machine (SVM) classification of Raman spectra. The detection method accurately identified monocultures (0.93 ± 0.20), achieving good discrimination after 24 h of bacterial growth. However, the PCA–SVM system was less accurate for classifying co-cultures (0.67 ± 0.35). These results show that this method requires an isolation step followed by biomass enrichment (>8 log10 CFU/mL) for accurate identification. The advantage of this technology is its simplicity and low-cost preparation, achieving high accuracy in monocultures in a shorter time than conventional culture-dependent methods.

Abstract Background The use of spectral imaging within the plant phenotyping and breeding community has been increasing due its utility as a non-invasive diagnostic tool. However, there is a lack of imaging systems targeted specifically at plant science duties, resulting in low precision for canopy-scale measurements. This study trials a prototype multispectral system designed specifically for plant studies and looks at its use as an early detection system for visually asymptomatic disease phases, in this case Pyrenopeziza brassicae in Brassica napus. The analysis takes advantage of machine learning in the form of feature selection and novelty detection to facilitate the classification. An initial study into recording the morphology of the samples is also included to allow for further improvement to the system performance. Results The proposed method was able to detect light leaf spot infection with 92% accuracy when imaging entire oilseed rape plants from above, 12 days after inoculation and 13 days before the appearance of visible symptoms. False colour mapping of spectral vegetation indices was used to quantify disease severity and its distribution within the plant canopy. In addition, the structure of the plant was recorded using photometric stereo, with the output influencing regions used for diagnosis. The shape of the plants was also recorded using photometric stereo, which allowed for reconstruction of the leaf angle and surface texture, although further work is needed to improve the fidelity due to uneven lighting distributions, to allow for reflectance compensation. Conclusions The ability of active multispectral imaging has been demonstrated along with the improvement in time taken to detect light leaf spot at a high accuracy. The importance of capturing structural information is outlined, with its effect on reflectance and thus classification illustrated. The system could be used in plant breeding to enhance the selection of resistant cultivars, with its early and quantitative capability.

Recent studies have characterized the structures of air transport network in different countries and regions using complex network metrics. These studies coincided with the trend of increasingly available large empirical flight datasets that enable researchers to investigate the dynamics of the system, such as the propagation of flight delay. However, linking network structure with network dynamics remains a challenging task. In this paper, we proposed a method to construct flight delay networks from operational data. We provided a detailed comparison of the key structural properties of the flight delay networks in the United States and China. The comparisons of betweenness centrality of delay networks and flight networks show the advantage of the proposed method. We further found that airports in similar geographical locations do exhibit similar delay patterns in both countries. To explore the underlying mechanisms, the Multifractal Detrended Fluctuation Analysis (MF-DFA) is applied to the flights’ delay time series at both the airport level and network level. Singularity spectra analyses reveal the fundamental characteristics of the airport systems and air transportation system. Our findings contribute to the understanding of structure and dynamics of air transportation systems.

As landscapes become increasingly fragmented, research into impacts from disturbance and how edges affect vegetation and community structure has become more important. Descriptive studies on how microclimate changes across sharp transition zones have long existed in the literature and recently more attention has been focused on understanding the dynamic patterns of microclimate associated with forest edges. Increasing concern about forest fragmentation has led to new technologies for modeling forest microclimates. However, forest boundaries pose important challenges to not only microclimate modeling but also sampling regimes in order to capture the diurnal and seasonal dynamic aspects of microclimate along forest edges. We measured microclimatic variables across a sharp boundary from a clearing into primary lowland tropical rainforest at La Selva Biological Station in Costa Rica. Dynamic changes in diurnal microclimate were measured along three replicated transects, approximately 30 m in length with data collected every 1 m continuously at 30 min intervals for 24 h with a mobile sensor platform supported by a cable infrastructure. We found that a first-order polynomial fit using piece-wise regression provided the most consistent estimation of the forest edge, relative to the visual edge, although we found no “best” sensing parameter as all measurements varied. Edge location estimates based on daytime net shortwave radiation had less difference from the visual edge than other shortwave measurements, but estimates made throughout the day with downward-facing or net infrared radiation sensors were more consistent and closer to the visual edge than any other measurement. This research contributes to the relatively small number of studies that have directly measured diurnal temporal and spatial patterns of microclimate variation across forest edges and demonstrates the use of a flexible mobile platform that enables repeated, high-resolution measurements of gradients of microclimate.

Genotyping arrays are a cost effective approach when typing previously-identified genetic polymorphisms in large numbers of samples. One limitation of genotyping arrays with rare variants (e.g., minor allele frequency [MAF]

SNP genotyping arrays have been useful for many applications that require a large number of molecular markers such as high-density genetic mapping, genome-wide association studies (GWAS), and genomic selection. We report the establishment of a large maize SNP array and its use for diversity analysis and high density linkage mapping. The markers, taken from more than 800,000 SNPs, were selected to be preferentially located in genes and evenly distributed across the genome. The array was tested with a set of maize germplasm including North American and European inbred lines, parent/F1 combinations, and distantly related teosinte material. A total of 49,585 markers, including 33,417 within 17,520 different genes and 16,168 outside genes, were of good quality for genotyping, with an average failure rate of 4% and rates up to 8% in specific germplasm. To demonstrate this array's use in genetic mapping and for the independent validation of the B73 sequence assembly, two intermated maize recombinant inbred line populations - IBM (B73×Mo17) and LHRF (F2×F252) - were genotyped to establish two high density linkage maps with 20,913 and 14,524 markers respectively. 172 mapped markers were absent in the current B73 assembly and their placement can be used for future improvements of the B73 reference sequence. Colinearity of the genetic and physical maps was mostly conserved with some exceptions that suggest errors in the B73 assembly. Five major regions containing non-colinearities were identified on chromosomes 2, 3, 6, 7 and 9, and are supported by both independent genetic maps. Four additional non-colinear regions were found on the LHRF map only; they may be due to a lower density of IBM markers in those regions or to true structural rearrangements between lines. Given the array's high quality, it will be a valuable resource for maize genetics and many aspects of maize breeding.

A wealth of genetic associations for cardiovascular and metabolic phenotypes in humans has been accumulating over the last decade, in particular a large number of loci derived from recent genome wide association studies (GWAS). True complex disease-associated loci often exert modest effects, so their delineation currently requires integration of diverse phenotypic data from large studies to ensure robust meta-analyses. We have designed a gene-centric 50 K single nucleotide polymorphism (SNP) array to assess potentially relevant loci across a range of cardiovascular, metabolic and inflammatory syndromes. The array utilizes a "cosmopolitan" tagging approach to capture the genetic diversity across approximately 2,000 loci in populations represented in the HapMap and SeattleSNPs projects. The array content is informed by GWAS of vascular and inflammatory disease, expression quantitative trait loci implicated in atherosclerosis, pathway based approaches and comprehensive literature searching. The custom flexibility of the array platform facilitated interrogation of loci at differing stringencies, according to a gene prioritization strategy that allows saturation of high priority loci with a greater density of markers than the existing GWAS tools, particularly in African HapMap samples. We also demonstrate that the IBC array can be used to complement GWAS, increasing coverage in high priority CVD-related loci across all major HapMap populations. DNA from over 200,000 extensively phenotyped individuals will be genotyped with this array with a significant portion of the generated data being released into the academic domain facilitating in silico replication attempts, analyses of rare variants and cross-cohort meta-analyses in diverse populations. These datasets will also facilitate more robust secondary analyses, such as explorations with alternative genetic models, epistasis and gene-environment interactions.

Airlines and Air Navigation Service Providers are united in their goal to reduce fuel consumption. While changes to flight operations and technology investments are the focus of a number of studies, our study is among the first to investigate an untapped source of aviation fuel consumption: excess contingency fuel loading. Given the downside risk of fuel exhaustion of diverting to an alternate airport, airline dispatchers may load excess fuel onto an aircraft. Such conservatism comes at a cost of consuming excess fuel, as fuel consumed is a function of, among other factors, aircraft weight. The aim of this paper is to quantify, on a per-flight basis, the fuel burned due to carrying fuel beyond what is needed for foreseeable contingencies, and thereby motivate research, federal guidance, and investments that allow airline dispatchers to reduce fuel uplift while maintaining near zero risks of fuel exhaustion. We merge large publicly available aviation and weather databases with a detailed dataset from a major US airline. Upon estimating factors that capture the quantity fuel consumed due to carrying a pound of weight for a range of aircraft types, we calculate the cost and greenhouse gas emissions from carrying unused fuel on arrival and additional contingency fuel above a conservative buffer for foreseeable contingencies. We establish that the major US carrier does indeed load fuel conservatively. We find that 4.48% of the fuel consumed by an average flight is due to carrying unused fuel and 1.04% of the fuel consumed by an average flight is due to carrying additional contingency fuel above a reasonable buffer. We find that simple changes in flight dispatching that maintain a statistically minimal risk of fuel exhaustion could result in yearly savings of 338 million lbs of CO _2 , the equivalent to the fuel consumed from 4760 flights on midsized commercial aircraft. Moreover, policy changes regarding maximum fuel loads or investments that reduce uncertainty or increase the ability to plan flights under uncertainty could yield far greater benefits.

Sensor networks generate a variety of data streams in different temporal and spatial resolutions. The data come as numbers text, images, and audio and are dynamically produced periodically and sporadically. How can we organize hundreds of thousands of such data streams? How can we make it easy for scientists and engineers to publish and share such data streams? In this paper, we present our solution, SensorBase.org. It is a web application that not only provides the user with the functionality of a traditional database management system, but also runs under the notion of a Web 2.0 data experience with a responsive user interface design and RSS data feed techniques. SensorBase.org also aims to be a data search engine to promote exploration. Like a web search engine, the user should be able to search for structures, or rather, “signals”, in the data using simple language queries. We provide a solution to a specific type of signal search problem and describe a search framework derived from the solution. A visualization component is in development to help people understand the data and to enhance the results provided by a data search engine. The development of SensorBase.org allows us, the statisticians, to influence “data practices”. People share data and add documentation within the guidelines of SensorBase.org which supports good experimental design and helps future modeling efforts. Last but not least, SensorBase.org provides a base for data integrity and data quality.

Distributed, high-density spatiotemporal observations are proposed for answering many river-related questions, including those pertaining to hydraulics and multi-dimensional river modeling, geomorphology, sediment transport and riparian habitat restoration. We present here a case study of an autonomous, high-resolution robotic spatial mapping of cross-sectional velocity and salt concentration in a river basin. Several experiments for analyzing the spatial and temporal trends at multiple cross-sections of the San Joaquin River were performed during the campaign from August 21-25, 2006. Preliminary analysis from these experiments illustrating the range of investigations is presented. Lessons learned during the campaign are discussed to provide useful insights for similar robotic investigations in aquatic environments.

The creation over the past decade of unanticipated applications of the Internet, such as web services, peer-to-peer file sharing, networked gaming, podcasting, and voice telephony, is resulting in a recent rethinking of the core Internet infrastructure and the original architecture choices. In this project however we propose to go beyond reacting to these applications that have already emerged, and proactively consider the network architecture implications of a new class of applications involving embedded sensing technology as it moves from scientific, engineering, defense, and industrial contexts to the wider personal, social and urban contexts. Today, applications are emerging which draw on sensed information about people, objects, and physical spaces. These applications enable new kinds of social exchange: By collecting, processing, sharing, and visualizing this information, they can offer us new and unexpected views of our communities. They require new algorithms and software mechanisms because unlike scientific applications of distributed sensing, a single system is widely distributed, intermittently connected, and privately administered; and unlike traditional Internet applications the physical inputs are critical to the behavior.

CENS programs are designed to create an educational pipeline to graduate school, provide support and development to our graduate student affiliates and increase the diversity of our Center and those pursuing education and careers in science and engineering.

Plant and animal phenology measurements provide an important means of understanding the relationship between periodic phenomena, such as leaf flushes or bird nesting dates, and microenvironmental and climatic conditions. The collection of phenological data, however, can be labor intensive, and thus basic phenological information about many plant and animal species is not known. Imaging technologies at the James Reserve are currently being used to collect high-frequency, multiple-location phenological images together with micrometeorological data. Using simple algorithms for color analysis of images, results from the MossCam project have indicated that rainfalls of even 1 mm can result in significant increases of photosynthesis by the moss Tortula priceps and that leaf area of Rhododendron is positively correlated with green: red pixel ratios. Current work on improving algorithms, extending studies to analyze annual carbon budgets, and correlating micrometeorological data with nesting behavior will allow us to increase our understanding animal population dynamics and the climatic effects on plant distributions and primary productivity.

Understanding the dynamics of a microclimate environment is difficult because of the number of factors that lead to changes in the environment. Minimizing the number of sensors needed to accurately characterize the environment results in low deployment and maintenance cost, while maximizing the utility of the data. Optimal sensor placement is difficult because it is dependent upon the properties of the environment, the types of obstacles in the environment, as well as the sensing phenomenon. We have deployed a sensor network in a botanical garden consisting of both static and portable nodes. Each node is equipped with temperature and humidity sensors, and readings were taken once per minute for over a month. Using the data from this deployment we present and evaluate two different approaches to sensor placement. The basis for our deployment approach is spatial-temporal statistical analysis that combines splinebased modeling, principal component analysis, and data partitioning. We prove that sensor network deployment is an NP-complete problem using a transformation from the dominating set problem. We also develop an integer linear programming (ILP) formulation that calculates the provably optimal solution to the network deployment problem.

A main thrust of the seismic application is the development of algorithms for performing high spatial resolution measurements and modeling of structures using wired and wireless arrays. The UCLA Factor building is a testbed for predictive numerical modeling, wireless network, and smart event-detection developments related to structural health monitoring. Waveform data from the 72-node array in the 17-story moment-resisting steel frame Factor building are used in comparison with finite element calculations for predictive behavior.

Following in the footsteps of the field Networked Infomechanical Systems (NIMS) currently deployed at the James San Jacinto Mountain Reserve in Idyllwild California, two smaller scale NIMS architectures have emerged. The Laboratory Systems (NIMS-LS) provides an experimental testbed for verification of actuated sensing of environmental phenomena. The NIMS-LS System also includes emulation of environmental phenomena (i.e. light distribution in ecosystems). The Rapidly Deployable (NIMS-RD) platform provides a flexible means of sampling environmental phenomena directly with minimal infrastructure, rapid deployability and minimal impact on the environment. NIMS-RD has been deployed at the James San Jacinto Mountain Reserve, Medea Creek in the Santa Monica Mountains, Merced River in Merced CA and White Mountains Research Station near Bishop CA.

Rapidly growing stress on the natural environment has created an urgent demand for new environmental exploration systems that operate remotely and autonomously. As sensor networks are applied to these critical environmental monitoring problems, a series of new requirements have emerged for extensive characterization of dynamic phenomena in three-dimensional environments. The unpredictable presence of obstacles to sensing and the inherent temporal evolution of events create limitations for optimal deployment of sensors in the environment. Networked Infomechanical Systems (NIMS) technology has been developed to introduce precise and sustainable actuation for distributed sensing. NIMS devices are suspended on cable infrastructure to provide a combination of broad range motion combined with accurate localization.

High Integrity in Sensor Networks studies the effect of compromised data on sensor network data analysis and data fusion algorithms. We are at the start of a study to better understand the compromised data itself so that we can model it in such a way to characterize this affect on analysis. The accelerated life experiment we describe here was instrumented to see how the accuracy of a temperature and humidity sensor changes across mote and sensor systems and across time as the battery dies. We found that the error in the measurements was stable over time, but the bias in the measurements varied greatly across the motes. The experiment gave us more information on the important variables to include in calibration function, so that we can estimate the true measured value from the reported value of the sensor.

Rapidly growing stress on the natural environment has created an urgent demand for new environmental exploration systems that operate remotely and autonomously. As sensor networks are applied to these critical environmental monitoring problems, a series of new requirements have emerged for extensive characterization of dynamic phenomena in three-dimensional environments. The unpredictable presence of obstacles to sensing and the inherent temporal evolution of events create limitations for optimal deployment of sensors in the environment.

Radio duty cycling has received significant attention in sensor networking literature, particularly in the form of protocols for medium access control and topology management. While many protocols have claimed to achieve significant duty-cycling benefit in theory and simulation, these benefits have often not translated to practice. The dominant factor that prevents the optimal usage of the radio in real deployment settings is time uncertainty between sensor nodes. This poster presents an uncertainty-driven approach to duty-cycling where a model of long-term clock drift is used to minimize duty-cycling overhead.

In this poster, two major strategies for sampling phenomena with high rate of spatiotemporal variation, namely, multiscale sampling and adaptive sampling, are presented. Multiscale sampling is based on the hypothesis that a combination of sparse sensing along with auxiliary multiscale/multimode sensing will yield accuracy approaching exhaustive sampling. Adaptive sampling is a feature based sampling technique. Algorithms and simulation results for both techniques are demonstrated.