Search Results (11,193)

H5 subtype high-pathogenicity avian influenza (HPAI) viruses continue to devastate the poultry industry and threaten food security and public health. The first outbreak of H5 HPAI in the Philippines was reported in 2017. Since then, H5 HPAI outbreaks have been reported in 2020, 2022, and 2023. Here, we report the first publicly available complete whole-genome sequence of an H5N1 high-pathogenicity avian influenza virus from a case in Central Luzon. Samples were collected from a flock of layer chickens exhibiting signs of lethargy, droopy wings, and ecchymotic hemorrhages in trachea with excessive mucus exudates. A high mortality rate of 96–100% was observed within the week. Days prior to the high mortality event, migratory birds were observed around the chicken farm. Lungs, spleen, cloacal swabs, and oropharyngeal–tracheal swabs were taken from two chickens from this flock. These samples were positive in quantitative RT-PCR assays for influenza matrix and H5 hemagglutinin (HA) genes. To further characterize the virus, the same samples were subjected to whole-virus-genome amplification and sequencing using the Oxford Nanopore method with mean coverages of 19,190 and 2984, respectively. A phylogenetic analysis of the HA genes revealed that the H5N1 HPAI virus from Central Luzon belongs to the Goose/Guangdong lineage clade 2.3.4.4b viruses. Other segments also have high sequence identity and the same genetic lineages as other clade 2.3.4.4b viruses from Asia. Collectively, these data indicate that wild migratory birds are the likely source of H5N1 viruses from the 2022 outbreaks in the Philippines. Thus, biosecurity practices and surveillance for HPAI viruses in both domestic and wild birds should be increased to prevent and mitigate HPAI outbreaks. Full article

Greenhouses (GHs) are important elements of agricultural production and help to ensure food security aligning with United Nations Sustainable Development Goals (SDGs). However, there are still environmental concerns due to excessive use of plastics. Therefore, it is important to understand the past and future trends on spatial distribution of GH areas, whereby use of remote sensing data provides rapid and valuable information. The present study aimed to determine GH area changes in an agricultural hotspot, Serik, Türkiye, using 2008 and 2022 Landsat imageries and machine learning, and to predict future patterns (2036 and 2050) via the Markov–FLUS model. Performances of random forest (RF), k-nearest neighborhood (KNN), and k-dimensional trees k-nearest neighborhood (KD-KNN) algorithms were compared for GH discrimination. Accordingly, the RF algorithm gave the highest accuracies of over 90%. GH areas were found to increase by 73% between 2008 and 2022. The majority of new areas were converted from agricultural lands. Markov-based predictions showed that GHs are likely to increase by 43% and 54% before 2036 and 2050, respectively, whereby reliable simulations were generated with the FLUS model. This study is believed to serve as a baseline for future research by providing the first attempt at the visualization of future GH conditions in the Turkish Mediterranean region. Full article

As technology advances rapidly, a diverse array of Internet of Things (IoT) devices finds widespread application across numerous fields. The intelligent nature of these devices not only gives people more convenience, but also introduces new challenges especially in security when transmitting data in fog-based cloud environments. In fog computing environments, data need to be transmitted across multiple devices, increasing the risk of data being intercepted or tampered with during transmission. To securely share cloud ciphertexts, an alleged proxy re-encryption approach is a commonly adopted solution. Without decrypting the original ciphertext, such a mechanism permits a ciphertext intended for user A to be easily converted into the one intended for user B. However, to revoke the decryption privilege of data users usually relies on the system authority to maintain a user revocation list which inevitably increases the storage space. In this research, the authors come up with a fog-based proxy re-encryption system with revocable identity. Without maintaining the traditional user revocation list, the proposed scheme introduces a time-updated key mechanism. The time-update key could be viewed as a partial private key and should be renewed with different time periods. A revoked user is unable to obtain the renewed time-update key and hence cannot share or decrypt cloud ciphertexts. We formally demonstrate that the introduced scheme satisfies the security of indistinguishability against adaptively chosen identity and chosen plaintext attacks (IND-PrID-CPA) assuming the hardness of the Decisional Bilinear Diffie–Hellman (DBDH) problem in the random oracle model. Furthermore, compared with similar systems, the proposed one also has lower computational complexity as a whole. Full article

Abstract: The Gedeo zone agroforestry systems are the main source of Ethiopia’s coffee beans. However, land-use and suitability analyses are not well documented due to complex topography, heterogeneous agroforestry, and lack of information. This research aimed to map the coffee coverage and identify land suitability for coffee plantations using remote sensing, Geographic Information Systems (GIS), and the Analytical Hierarchy Process (AHP) in the Gedeo zone, Southern Ethiopia. Remote sensing classifiers often confuse agroforestry and plantations like coffee cover with forest cover because of their similar spectral signatures. Mapping shaded coffee in Gedeo agroforestry using optical or multispectral remote sensing is challenging. To address this, the study identified and mapped coffee coverage from Sentinel-1 data with a decibel (dB) value matched to actual coffee coverage. The actual field data were overlaid on Sentinel-1, which was used to extract the raster value. Pre-processing, classification, standardization, and reclassification of thematic layers were performed to find potential areas for coffee plantation. Hierarchy levels of the main criteria were formed based on climatological, edaphological, physiographic, and socioeconomic factors. These criteria were divided into 14 sub-criteria, reclassified based on their impact on coffee growing, with their relative weights derived using AHP. From the total study area of 1356.2 km², the mapped coffee coverage is 583 km². The outcome of the final computed factor weight indicated that average annual temperature and mean annual rainfall are the primary factors, followed by annual mean maximum temperature, elevation, annual mean minimum temperature, soil pH, Land Use/Land Cover (LULC), soil texture, Cation Exchange Capacity (CEC), slope, Soil Organic Matter (SOM), aspect, distance to roads, and distance to water, respectively. The identified coffee plantation potential land suitability reveals unsuitable (413 km²), sub-suitable (596.1 km²), and suitable (347.1 km²) areas. This study provides comprehensive spatial details for Ethiopian cultivators, government officials, and agricultural extension specialists to select optimal coffee farming locations, enhancing food security and economic prosperity. Full article

The transportation industry has been recognized as one of the industries that can benefit from investment in blockchain-based systems and services that enable distributed data management and improve the effectiveness and efficiency of the transportation sector. However, the literature needs a guiding framework for integrating blockchain in issuing and preserving public transportation transactions in a technical environment that is secure, efficient, and transparent. This study proposes a blockchain-based transportation wallet (BTW) framework that facilitates the main digital transactions across diverse public transportation services. BTW embodies leveraging blockchain technology, which provides a decentralized and immutable ledger that records and verifies transactions, ensuring trust and reducing the risk of fraud. The framework has been validated by developing a blockchain-based public transportation smart wallet named “RideChain”. This serves as a single decentralized point for making public transportation transactions and payments, as well as identity authorizations and management. RideChain enhances passengers’ and service providers’ experience through a secure and authentic platform for offering several reliable public transportation transactions efficiently. In this study, we implemented a smart contract to establish a protocol between passengers and journey services. The testing methodologies used in this study comprise unit testing, integration testing, performance testing, and user acceptance testing. The findings suggest that BTW has been successfully verified to demonstrate its capability for secure transactions, authenticity of monetary transactions, automated smart contracts, decentralized identity authentication, and effortless payments. Full article

Residents of Puerto Rico face a high burden of food insecurity (FI), which has been associated with insomnia symptoms (IS). However, this association remains understudied in Puerto Rican young adults, a vulnerable group experiencing an elevated prevalence of FI and poor sleep. We evaluated the association between FI and IS and the mediating role of psychological distress symptoms among young adults in Puerto Rico. Data are derived from the PR-OUTLOOK cohort (2020–2023) of adults aged 18–29 y. We assessed FI with the six-item USDA Household Food Security Scale and IS with the 5-item Women’s Health Initiative Insomnia Rating Scale. Psychological distress symptoms included depressive symptoms (CES-D-10), anxiety (STAI-10), and perceived stress (PSS-4). Poisson’s regression models estimated prevalence ratios (PRs) with 95% confidence intervals (CIs). The Karlson–Holm–Breen method estimated the mediation percentage of each psychological distress symptom on the association between FI and IS. Notably, 24.8% of participants experienced FI, and 30.4% reported elevated IS. FI was associated with IS (PR = 1.41, 95% CI = 1.24, 1.60), an association partially mediated by depressive (31.6%), perceived stress (17.6%), and anxiety symptoms (17.2%), accounting for 35.8% of the mediation percentage. Future research should confirm these findings using objective assessments of sleep and psychosocial stress. Full article

The increasing presence of young people on the Nigerian streets participating in child labour has continued to attract public policy attention. Available research on child labour reveals sparse scholarly information on the security implications for young people in South-West Nigeria, particularly Ondo State. The study aims to understand the argument that child labour poses major security threats to the overall well-being of child labourers. A total of 147 questionnaires were distributed, with 12 focus group discussions and 12 semi-structured interviews conducted with young people and guardians (mostly mothers). Frequency distributions were employed to analyse the quantitative data, and NVivo (v.14) qualitative software was used to identify themes and sub-themes. A content analytical tool was used to make sense of the themes. Child labour activities include street trading, hawking, domestic help and construction work. Causes of child labour activities include lack of access to basic education, cultural and societal beliefs, poverty, and family breakdown, among others. Security threats include occasional kidnapping for ransom, sexual molestation, slavery, exploitation, risk of injury, diseases, and death. The study suggests a more responsive Child’s Rights Act in Nigeria for the protection of the rights and dignity of every child. Full article

The current study examined university students’ insights into generative AI writing tools regarding their familiarity with, perceived concerns about, and perceived benefits of these tools in their academic work. The study used a cross-sectional descriptive research design, and data were collected using a questionnaire instrument. The participants were ninety-five undergraduate and graduate students from a College of Education at a university in Jordan. The results show that university students show moderate familiarity with generative AI writing tools (M = 3.14, SD = 0.81), especially in engagement but lacking technical knowledge. They also have moderate concerns (M = 3.35, SD = 0.85), particularly about misinformation and data security. Despite these concerns, students recognize the benefits (M = 3.62, SD = 0.81), especially regarding the capabilities of these tools in simulating creativity and fostering innovation. In addition, the results showed that gender and educational level appear to have little effect on familiarity, concerns, and perceived benefits regarding these tools. Based on the findings, the study recommends enhancing students’ familiarity with generative AI tools through providing technical training, hands-on opportunities, and ethical discussions. In addition, the study recommends addressing students’ concerns regarding generative AI writing tools by improving data security related to generative AI, providing ethical guidelines regarding the use of these tools, and boosting AI literacy. Finally, it is recommended to enhance students’ perceptions of the benefits of generative AI writing tools by highlighting the creative potential of these tools within the educational setting, using these tools to offer personalized learning experiences that adapt to individual learning styles, and promoting collaboration through generative AI writing tools. Full article

Agricultural pests can cause direct damage to crops, including chlorosis, loss of vigor, defoliation, and wilting. In addition, they can also indirectly damage plants, such as by transmitting pathogenic micro-organisms while feeding on plant tissues, affecting the productivity and quality of crops and interfering with agricultural production. Among the known arthropod pests, mites are highly prevalent in global agriculture, particularly those from the Tetranychidae family. The two-spotted spider mite, Tetranychus urticae, is especially notorious, infesting about 1600 plant species and causing significant agricultural losses. Despite its impact on agriculture, the virome of T. urticae is poorly characterized in the literature. This lack of knowledge is concerning, as these mites could potentially transmit plant-infecting viral pathogens, compromising food security and complicating integrated pest management efforts. Our study aimed to characterize the virome of the mite T. urticae by taking advantage of publicly available RNA deep sequencing libraries. A total of 30 libraries were selected, covering a wide range of geographic and sampling conditions. The library selection step included selecting 1 control library from each project in the NCBI SRA database (16 in total), in addition to the 14 unique libraries from a project containing field-collected mites. The analysis was conducted using an integrated de novo virus discovery bioinformatics pipeline developed by our group. This approach revealed 20 viral sequences, including 11 related to new viruses. Through phylogenetic analysis, eight of these were classified into the Nodaviridae, Kitaviridae, Phenuiviridae, Rhabdoviridae, Birnaviridae, and Qinviridae viral families, while three were characterized only at the order level within Picornavirales and Reovirales. The remaining nine viral sequences showed high similarity at the nucleotide level with known viral species, likely representing new strains of previously characterized viruses. Notably, these include the known Bean common mosaic virus (BCMV) and Phaseolus vulgaris alphaendornavirus 1, both of which have significant impacts on bean agriculture. Altogether, our results expand the virome associated with the ubiquitous mite pest T. urticae and highlight its potential role as a transmitter of important plant pathogens. Our data emphasize the importance of continuous virus surveillance for help in the preparedness of future emerging threats. Full article

This research paper aims to investigate the impact of ownership concentration, insider ownership, and board size on employee productivity for 136 Jordanian public shareholding firms listed on the Amman Stock Exchange (ASE) from 2012 to 2021. Ownership concentration has been measured by Herfindahl–Hirschman Index (HHI), whereas insider ownership and board size have been represented as the proportion of shares held by insiders and by the number of board members, respectively. Lastly, employee productivity has been measured using a data envelopment analysis (DEA) tool. We employed ordinary least squares regression (OLS) including firm-year-fixed effects. Our empirical results indicate a non-linear relation between ownership concentration and employee productivity, whereby the productivity of employees increases in firms with a proportion of ownership concentration less than 60%. In addition, we found a non-linear relation between insider ownership and employee productivity, whereby the productivity of employees increases in firms with proportion of insider ownership less than 50%. Moreover, we found a non-linear relation between board size and employee productivity, whereby the productivity of employees increases in firms that have less than 11 board members. Our outcome contributed to the knowledge found in the previous literature, as it is the first to highlight the productivity of employees in emerging economies, such as the economy in Jordan. Furthermore, our findings could be useful for the Jordan Securities Commission (JSC) and the ASE on their continuous process to improve and develop corporate governance instructions. Full article

Deep Generative Models (DGMs), as a state-of-the-art technology in the field of artificial intelligence, find extensive applications across various domains. However, their security concerns have increasingly gained prominence, particularly with regard to invisible backdoor attacks. Currently, most backdoor attack methods rely on visible backdoor triggers that are easily detectable and defendable against. Although some studies have explored invisible backdoor attacks, they often require parameter modifications and additions to the model generator, resulting in practical inconveniences. In this study, we aim to overcome these limitations by proposing a novel method for invisible backdoor attacks. We employ an encoder–decoder network to ‘poison’ the data during the preparation stage without modifying the model itself. Through meticulous design, the trigger remains visually undetectable, substantially enhancing attacker stealthiness and success rates. Consequently, this attack method poses a serious threat to the security of DGMs while presenting new challenges for security mechanisms. Therefore, we urge researchers to intensify their investigations into DGM security issues and collaboratively promote the healthy development of DGM security. Full article

Agricultural productivity is one of the critical factors towards ensuring food security across the globe. However, some of the main crops, such as potato, tomato, and mango, are usually infested by leaf diseases, which considerably lower yield and quality. The traditional practice of diagnosing disease through visual inspection is labor-intensive, time-consuming, and can lead to numerous errors. To address these challenges, this study evokes the AgirLeafNet model, a deep learning-based solution with a hybrid of NASNetMobile for feature extraction and Few-Shot Learning (FSL) for classification. The Excess Green Index (ExG) is a novel approach that is a specified vegetation index that can further the ability of the model to distinguish and detect vegetative properties even in scenarios with minimal labeled data, demonstrating the tremendous potential for this application. AgirLeafNet demonstrates outstanding accuracy, with 100% accuracy for potato detection, 92% for tomato, and 99.8% for mango leaves, producing incredibly accurate results compared to the models already in use, as described in the literature. By demonstrating the viability of a deep learning/IoT system architecture, this study goes beyond the current state of multi-crop disease detection. It provides practical, effective, and efficient deep-learning solutions for sustainable agricultural production systems. The innovation of the model emphasizes its multi-crop capability, precision in results, and the suggested use of ExG to generate additional robust disease detection methods for new findings. The AgirLeafNet model is setting an entirely new standard for future research endeavors. Full article

The increasing complexity of modern aircraft systems necessitates advanced monitoring solutions to ensure operational safety and efficiency. Traditional aircraft health monitoring systems (AHMS) often rely on reactive maintenance strategies, detecting only visible faults while leaving underlying issues unaddressed. This gap can lead to critical failures and unplanned downtime, resulting in significant operational costs. To address this issue, this paper proposes the integration of artificial intelligence (AI) and blockchain technologies within an enhanced AHMS, utilizing the iceberg model as a conceptual framework to illustrate both visible and hidden defects. The model highlights the importance of detecting and addressing issues at the earliest possible stages, ensuring that hidden defects are identified and mitigated before they evolve into significant failures. The rationale behind this approach lies in the need for a predictive maintenance system capable of identifying and mitigating hidden risks before they escalate. Key tasks completed in this study include: a comparative analysis of the proposed system with existing monitoring solutions, the selection of AI algorithms for fault prediction, and the development of a blockchain-based infrastructure for secure, transparent data sharing. The evolution of AHMS is discussed, emphasizing the shift from traditional monitoring to advanced, predictive, and prescriptive maintenance approaches. This integrated approach demonstrates the potential to significantly improve fault detection, optimize maintenance schedules, and enhance data security across the aviation industry. Full article

The Ebinur Lake Basin (ELB), which is a typical watershed in an arid region, has an extremely delicate natural ecosystem. Rapid urbanisation and economic growth have triggered substantial ecological and environmental transformations in this key economic hub of Xinjiang. However, a comprehensive and systematic knowledge of the evolving ecological conditions in the ELB remains limited. Therefore, this study modelled the landscape ecological risk index (LERI) using land use/land cover (LULC) data from 1985 to 2022 and assessed the drivers of landscape ecological risk (LER) using a geographical detector model (GDM). The findings revealed that (1) from 1985 to 2022, the construction land, cropland, and forestland areas in the ELB increased, whereas those of water bodies, grasslands, and barren land decreased. (2) Between 1985 and 2022, LER in the ELB showed a downward trend. Spatially, LER was predominantly characterised by lower and lowest risk levels. The higher and highest risk status has been around Ebinur lake and has continued to improve each year. (3) Climatic factors, particularly temperature and precipitation, were identified as the most significant drivers of the LER change from 1985 to 2022. The findings provide crucial scientific knowledge for advancing sustainable development and maintaining ecological security in the ELB. Full article

The wild relatives of crops play a critical role in enhancing agricultural resilience and sustainability by contributing valuable traits for crop improvement. Shifts in climatic conditions and human activities threaten plant genetic resources for food and agriculture (PGRFA), jeopardizing contributions to future food production and security. Studies and inventories of the extant agrobiodiversity, in terms of numbers and distribution patterns of species and their genetic diversity, are primordial for developing effective and comprehensive conservation strategies. We conducted an ecogeographic study on Ipomoea species and assessed their diversity, distribution, and ecological preferences across different topographic, altitudinal, geographical, and climatic gradients, at a total of 450 sites across Mauritius. Species distribution maps overlaid with climatic data highlighted specific ecological distribution. Principal Component Analysis (PCA) revealed species distribution was influenced by geographical factors. Regional richness analyses indicated varying densities, with some species exhibiting localized distributions and specific ecological preferences while the other species showed diverse distribution patterns. Field surveys identified 14 species and 2 subspecies out of 21 species and 2 subspecies of Ipomoea reported in Mauritius. A gap in ex situ germplasm collections was observed and several species were identified as threatened. Further investigations and a more long-term monitoring effort to better guide conservation decisions are proposed. Full article