Background Abiotic stressors impair crop yields and growth potential. Despite recent developments, no comprehensive literature review on crop abiotic stress assessment employing deep learning exists. ...Unlike conventional approaches, deep learning-based computer vision techniques can be employed in farming to offer a non-evasive and practical alternative. Methods We conducted a systematic review using the revised Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) statement to assemble the articles on the specified topic. We confined our scope to deep learning-related journal articles that focused on classifying crop abiotic stresses. To understand the current state, we evaluated articles published in the preceding ten years, beginning in 2012 and ending on December 18, 2022. Results After the screening, risk of bias, and certainty assessment using the PRISMA checklist, our systematic search yielded 14 publications. We presented the selected papers through in-depth discussion and analysis, highlighting current trends. Conclusion Even though research on the domain is scarce, we encountered 11 abiotic stressors across 7 crops. Pre-trained networks dominate the field, yet many architectures remain unexplored. We found several research gaps that future efforts may fill.
With planned expansion of oil sands facilities, there is interest in being able to characterize the magnitude and extent of deposition of metals and polycyclic aromatic hydrocarbons (PAH) in the ...Athabasca Oil Sands Region (AOSR) of Alberta. A study was undertaken using a bulk collection system to characterize wintertime atmospheric deposition of selected inorganic and organic contaminants in the AOSR. The study was carried out from January to March 2012 at two sampling sites near (within a 20 km circle of oil sands development) and two sampling sites distant (>45 km) to oil sands development. Triplicate bulk samplers were used to estimate precision of the method at one distant site. Monthly deposition samples were analyzed for 36 metals, ultra-low mercury, and 25 PAHs (including alkylated, and parent PAH). At the two sites located within 20 km of oil sands development, 3-month wintertime integrated deposition for some priority metals, alkylated and parent PAH were higher compared to distant sites. Deposition fluxes of metals and PAH were compared to other available bulk deposition studies worldwide. Median bulk measurement uncertainties of metals and both PAH classes were 26% and within ±15%, respectively suggesting that the bulk sampling method is a potential alternative for obtaining future direct measures of wintertime metals and PAH deposition at locations without access to power in the AOSR.
The efficiency with which conventional boilers perform, in terms of sustainability, is affected by a variety of factors. Unsustainable boiler operating practices are still surprisingly frequent in ...developing countries, resulting in environmental liabilities and catastrophic accidents. It is a serious problem in developing countries like Bangladesh, where boilers are utilized extensively in the apparel manufacturing sector. However, no research has yet examined the challenges or barriers associated with sustainable boiler operation in the apparel manufacturing sector. This study, thereby, utilizes an integrated MCDM approach, combining the fuzzy theory and the decision-making trial and evaluation laboratory (DEMATEL) method, to identify, prioritize, and explore the relations among the barriers to sustainable boiler operation in the apparel manufacturing industry, from an emerging economy perspective. The barriers were initially identified from the literature and a visual survey of 127 factories. After expert validation, thirteen barriers were finally selected to be analyzed utilizing the fuzzy DEMATEL method. The study findings revealed that 'Absence of water treatment facilities', 'Fossil fuel burning and GHG emissions', and 'Excessive consumption of groundwater' are the three most prominent barriers to sustainable boiler operation. The cause-effect relations among the barriers suggest that 'Inadequate compliance with safety and hazard regulations' is the most influential and 'Fossil fuel burning and GHG emissions' is the most influenced barrier. This study is expected to guide the managers and policymakers of the apparel manufacturing sector in successfully overcoming the barriers to sustainable boiler operation, thus mitigating the operational hazards and achieving the sustainable development goals (SDGs).
The emerging technologies of Industry 4.0 (I4.0) are crucial to incorporating agility, sustainability, smartness, and competitiveness in the business model, enabling long-term sustainability ...practices in the pharmaceutical supply chain (PSC). By leveraging the latest technologies of I4.0, pharmaceutical companies can gain real-time visibility into their supply chain (SC) operations, allowing them to make data-driven decisions that improve SC performance, efficiency, resilience, and sustainability. However, to date, no research has examined the critical success factors (CSFs) that enable the pharmaceutical industry to adopt I4.0 successfully to enhance overall SC sustainability. This study, therefore, analyzed the potential CSFs for adopting I4.0 to increase all facets of sustainability in the PSC, especially from the perspective of an emerging economy like Bangladesh. Initially, sixteen CSFs were identified through a comprehensive literature review and expert validation. Later, the finalized CSFs were clustered into three relevant groups and analyzed using a Bayesian best-worst method (BWM)-based multi-criteria decision-making (MCDM) framework. The study findings revealed that "sufficient investment for technological advancement", "digitalized product monitoring and traceability", and "dedicated and robust research and development (R&D) team" are the top three CSFs to adopt I4.0 in the PSC. The study's findings can aid industrial practitioners, managers, and policymakers in creating effective action plans for efficiently adopting I4.0 in PSC to avail of its competitive benefits and ensure a sustainable future for the pharmaceutical industry.
The advent of Social Behavioral Biometrics (SBB) in the realm of person identification has underscored the importance of understanding unique patterns of social interactions and communication. This ...paper introduces a novel multimodal SBB system that integrates human micro-expressions from text, an emerging biometric trait, with other established SBB traits in order to enhance online user identification performance. Including human micro-expression, the proposed method extracts five other original SBB traits for a comprehensive representation of the social behavioral characteristics of an individual. Upon finding the independent person identification score by every SBB trait, a rank-level fusion that leverages the weighted Borda count is employed to fuse the scores from all the traits, obtaining the final identification score. The proposed method is evaluated on a benchmark dataset of 250 Twitter users, and the results indicate that the incorporation of human micro-expression with existing SBB traits can substantially boost the overall online user identification performance, with an accuracy of 73.87% and a recall score of 74%. Furthermore, the proposed method outperforms the state-of-the-art SBB systems.
► Runoff was projected from 15 GCMs and three warming levels over south-western Australia. ► All GCMs project a drier and hotter future in south-western Australia by 2030. ► An ensemble of ...rainfall-runoff models projects a median decline in runoff of 25%. ► 90th percentile projected runoff declines 53% in the north and 40% in the south of the project area. ► Rainfall-runoff elasticity increases under projected climate scenarios.
This paper presents the results of computer simulations of runoff from 13 major fresh and brackish river basins in south-western Australia (SWA) under climate projections obtained from 15 GCMs with three future global warming scenarios equivalent to global temperature rises of 0.7°C, 1.0°C and 1.3°C by 2030. The objective was to apply an efficient methodology, consistent across a large region, to examine the implications of the best available projections in climate trends for future surface water resources. An ensemble of rainfall-runoff models was calibrated on stream flow data from 1975 to 2007 from 106 gauged catchments distributed throughout the basins of the study area. The sensitivity of runoff to projected changes in mean annual rainfall is examined using the climate ‘elasticity’ concept. Averaged across the study area, all 15 GCMs project declines in rainfall under all global warming scenarios with a median decline of 8% resulting in a median decline in runoff of 25%. Such uniformity in projections from GCMs is unusual. Over SWA the average annual runoff under the 5th wettest and 5th driest of the 45 projections of the 2030 climate declines by 10 and 42%, respectively. Under the 5th driest projection the runoff decline ranges from 53% in the northern region to 40% in the southern region. Strong regional variations in climate sensitivity are found with the proportional decline in runoff greatest in the northern region and the greatest volumetric declines in the wetter basins in the south. Since the mid 1970s stream flows into the major water supply reservoirs in SWA have declined by more than 50% following a 16% rainfall reduction. This has already had major implications for water resources planning and for the preservation of aquatic and riparian ecosystems in the region. Our results indicate that this reduction in runoff is likely to continue if future climate projections eventuate.
Over the past decade, gait recognition had gained a lot of attention in various research and industrial domains. These include remote surveillance, border control, medical rehabilitation, emotion ...detection from posture, fall detection, and sports training. The main advantages of identifying a person by their gait include unobtrusiveness, acceptance, and low costs. This paper proposes a convolutional neural network KinectGaitNet for Kinect-based gait recognition. The 3D coordinates of each of the body joints over the gait cycle are transformed to create a unique input representation. The proposed KinectGaitNet is trained directly using the 3D input representation without the necessity of the handcrafted features. The KinectGaitNet design allows avoiding gait cycle resampling, and the residual learning method ensures high accuracy without the degradation problem. The proposed deep learning architecture surpasses the recognition performance of all state-of-the-art methods for Kinect-based gait recognition by achieving 96.91% accuracy on UPCV and 99.33% accuracy on the KGB dataset. The method is the first, to the best of our knowledge, deep learning-based architecture that is based on a unique 3D input representation of joint coordinates. It achieves performance higher than previous traditional and deep learning methods, with fewer parameters and shorter inference time.
PurposeNatural calamities impair agricultural households' ability to invest in their farms. Facilitating access to agricultural credit may assist farmers in the face of negative revenue shocks. The ...aim of this study is to investigate the impact of agricultural credit on the agricultural input expenditure of disaster-affected farmers in Bangladesh.Design/methodology/approachThe study utilizes data on 2,519 disaster-affected farming households from Bangladesh's Household Income and Expenditure Study (HIES) 2016–2017, which employs a nationwide representative five-year interval survey. Further, propensity score matching (PSM) identification strategy is used to estimate the average treatment effect on the treated (ATET), and Mahalanobis distance matching (MDM) is used for the robustness test. In addition, heterogeneous analysis has been conducted to explore the impact of agricultural credit on different types of farming households.FindingsThe findings reveal that access to agricultural credit has a favorable and significant effect on farm input expenditure for disaster-affected farmers. Therefore, agricultural credit accessibility could be utilized as a policy tool to assist disaster-affected farmers in improving their investment capacity, and hence, agricultural output.Originality/valueThis study, using a quasi-experimental design of access to agricultural credit on agricultural input expenditures of the disaster-affected farming households in coastal areas of Bangladesh to estimate the causal effect.
ABSTRACT
The emergence of antibiotic resistant bacteria is a major health concern worldwide in recent years. The objective of this study is to establish the larvae of the silk moth (commonly known as ...silkworm), Bombyx mori as an infection model to study antibacterial effect of antibiotics against Klebsiella pneumoniae. In this study, the pathogenicity of a K. pneumoniae strain isolated from food to silkworm larvae was examined. Within 72 h of bacterial injection, all silkworm larvae were killed in a dose-dependent manner with their body color turning into black due to increased melanization. Bacterial numbers in the larval hemolymph (blood) significantly increased after 9 h of infection with a decrease in viable circulatory hemocytes in hemolymph. When presented with bacteria laden leaves, larvae did not eat but injection of bacteria directly into the midgut killed larvae within 12 h with a higher load required in comparison to that required for the killing by hemolymph injection. Administration of four different antibiotics into larval hemolymph showed therapeutic effect at different doses with varying efficacies against hemolymph-injected K. pneumoniae. These results indicate that the silkworm larvae can be used as an infection model not only to study the pathogenicity of K. pneumoniae but also to perform rapid screening for the identification of antibiotics effective against multidrug-resistant strains of K. pneumoniae.
Silk moth larvae can be used as an infection model for K. pneumoniae pathogenesis.
Gel-based materials have garnered significant interest in recent years, primarily due to their remarkable structural flexibility, ease of modulation, and cost-effective synthesis methodologies. ...Specifically, polymer-based conductive gels, characterized by their unique conjugated structures incorporating both localized sigma and pi bonds, have emerged as materials of choice for a wide range of applications. These gels demonstrate an exceptional integration of solid and liquid phases within a three-dimensional matrix, further enhanced by the incorporation of conductive nanofillers. This unique composition endows them with a versatility that finds application across a diverse array of fields, including wearable energy devices, health monitoring systems, robotics, and devices designed for interactive human-body integration. The multifunctional nature of gel materials is evidenced by their inherent stretchability, self-healing capabilities, and conductivity (both ionic and electrical), alongside their multidimensional properties. However, the integration of these multidimensional properties into a single gel material, tailored to meet specific mechanical and chemical requirements across various applications, presents a significant challenge. This review aims to shed light on the current advancements in gel materials, with a particular focus on their application in various devices. Additionally, it critically assesses the limitations inherent in current material design strategies and proposes potential avenues for future research, particularly in the realm of conductive gels for energy applications.
