Search Results (1,185)

Aquaculture has rapidly become a vital sector for ensuring global food security by meeting the growing demand for animal protein. Bangladesh, one of the world’s leading aquaculture producers, recorded a production of 4.91 million MT in 2022–2023, largely driven by inland farming systems. Despite this remarkable growth, the sector is highly vulnerable to disease outbreaks, which are aggravated by different factors. Pathogens such as bacteria, viruses, fungi, and parasites cause significant losses, while conventional disease diagnosis in Bangladesh still depends mainly on visual assessment and basic laboratory techniques, limiting early detection. This narrative review highlights recent advances in diagnostics as molecular tools, immunodiagnostics, nanodiagnostics, machine learning, and next-generation sequencing (NGS) that are widely applied globally but remain limited in Bangladesh due to infrastructure gaps, lack of skilled manpower, and resource constraints. Current treatment strategies largely rely on antibiotics and aquaculture medicinal products (AMPs), often misused without proper diagnosis, contributing to antimicrobial resistance (AMR). Promising alternatives, including probiotics, immunostimulants, vaccines, and enhanced biosecurity, require greater adoption and farmer awareness. The near-term priorities for Bangladesh include standardized disease and AMR surveillance, prudent antibiotic stewardship, phased adoption of validated rapid diagnostics, and investment in diagnostic and human capacity. Policy-level actions, including a national aquatic animal health strategy, stricter antimicrobial regulation, strengthening diagnostic infrastructure in institution, are crucial to achieve sustainable disease management and ensure long-term resilience of aquaculture in Bangladesh. Full article

Plant-derived toxins such as ricin and abrin represent some of the most potent biological agents known, posing significant threats to public health and security due to their high toxicity, relative ease of extraction, and widespread availability. These ribosome-inactivating proteins (RIPs) have been implicated in politically and criminally motivated events, underscoring their critical importance in the context of biodefense. Public safety agencies, including law enforcement, customs, and emergency response units, require rapid, sensitive, and portable detection methods to effectively counteract these threats. However, many existing screening technologies lack the capability to detect biotoxins unless specifically designed for this purpose, revealing a critical gap in current biodefense preparedness. Consequently, there is an urgent need for robust, field-deployable detection platforms that operate reliably under real-world conditions. End-users in the security and public health sectors demand analytical tools that combine high specificity and sensitivity with operational ease and adaptability. This review provides a comprehensive overview of the biochemical characteristics of ricin and abrin, their documented misuse, and the challenges associated with their detection. Furthermore, it critically assesses key detection platforms—including immunoassays, mass spectrometry, biosensors, and lateral flow assays—focusing on their applicability in operational environments. Advancing detection capabilities within frontline services is imperative for effective prevention, timely intervention, and the strengthening of biosecurity measures. Full article

Crude oil and grain, as two pivotal global commodities, exhibit significant price co-movement that profoundly affects national economic stability and food security. From the perspective of systems theory, the energy and grain markets do not exist in isolation but rather form a highly coupled complex system, characterized by nonlinear feedback, cross-market risk contagion, and cascading effects. This study systematically investigates the transmission mechanisms from international crude oil prices to the domestic prices of Chinese four major grains, employing the DY spillover index, Vector Error Correction Model (VECM), and a mediation effect framework. The empirical findings reveal three key insights. First, rising international crude oil prices significantly strengthen the pass-through of global grain prices to domestic markets, while simultaneously weakening the effectiveness of domestic price stabilization policies. Second, higher crude oil prices amplify international-to-domestic price spillovers by increasing maritime freight costs, a key channel in global grain trade logistics. Third, elevated oil prices stimulate demand for renewable biofuels, including biodiesel and ethanol, thereby boosting international demand for corn and soybeans and intensifying the transmission of price fluctuations in these commodities to the domestic market. These findings reveal the key pathways through which shocks in the energy market affect food security and highlight the necessity of studying the “energy–food” coupling mechanism within a systems framework, enabling a more comprehensive understanding of cross-market risk transmission. Full article

This study proposes methods of computer simulation to study and optimize the cybersecurity of Small Modular Nuclear Reactors (SMRs). SMRs hold the potential to help build a clean and sustainable power grid but will struggle to gain widespread adoption without public confidence in their security. SMRs are emerging technologies and potentially carry higher cyber threats due to remote operations, large numbers of cyber-physical systems, and cyber connections with other industrial concerns. A method of agent-based computer simulations to model the effects, or payoff, of collaboration between cyber defenders, power plants, and cybersecurity vendors is proposed to strengthen SMR cybersecurity as these new power generators enter into the market. The agent-based model presented in this research is intended to illustrate the potential of using simulation to model a payoff function for collaborative efforts between stakeholders. Employing simulation to heighten cybersecurity will help to safely leverage the potential of SMRs in a modern and low-emission energy grid. Full article

The value of personal data can only be unlocked through efficient circulation. This study explores a multi-party collaborative mechanism for personal-data trading, aiming to improve data quality and market vitality via incentive-compatible institutional design, thereby supporting the high-quality development of the digital economy. Symmetry enhancement refers to the use of strategies and mechanisms to narrow the information gap among data controllers, operators, and demanders, enabling all parties to facilitate personal-data transactions on relatively equal footing. Drawing on evolutionary-game theory, we construct a tripartite dynamic-game model that incorporates data controllers, data operators, and data demanders. We analyze how initial willingness, payoff structures, breach costs, and risk factors (e.g., data leakage) shape each party’s strategic choices (cooperate vs. defect) and their evolutionary trajectories, in search of stable equilibrium conditions and core incentive mechanisms for a healthy market. We find that (1) the initial willingness to cooperate among participants is the foundation of a virtuous cycle; (2) the net revenue of data products significantly influences operators’ and demanders’ propensity to cooperate; and (3) the severity of breach penalties and the potential losses from data leakage jointly affect the strategies of all three parties, serving as key levers for maintaining market trust and compliance. Accordingly, we recommend strengthening contract enforcement and trust-building; refining the legal and regulatory framework for data rights confirmation, circulation, trading, and security; and promoting stable supply–demand cooperation and market education to enhance awareness of data value and compliance, thereby stimulating individuals’ willingness to authorize the use of their data and maximizing its value. Full article

The purpose of this study is to examine the impact of integrity-related considerations, such as ethics, privacy, protection, security, and trust, on online consumer shopping intention within the interactive marketing environment. To achieve this, the research uses partial least squares structural equation modelling (PLS-SEM), analysing data from a sample of 260 respondents collected through an online survey. The findings reveal that protection is the most influential factor driving consumer buying intentions, followed by trust, ethics and security. Privacy, while significant, has a more moderate influence on consumer behaviour compared to other factors. The study makes a key theoretical contribution by advancing the understanding of how these constructs interact to shape consumer behaviour in the digital marketplace, particularly highlighting the importance of data protection and ethical practices. Practically, the research offers actionable recommendations for e-commerce businesses, based on building a digital integrity model, suggesting the focus on enhancing data security and ethical transparency to build consumer trust. Furthermore, the findings highlight the need for policymakers to strengthen data privacy regulations and harmonise international security standards in e-commerce. Future research should consider longitudinal studies and explore these dynamics in different regulatory environments. Full article

Greenwashing threatens both consumer trust and the integrity of planetary health initiatives. Transparency in sustainability claims is therefore critical for promoting ecological wellbeing, strengthening food security, and fostering equitable development in the Anthropocene. This paper investigates greenwashing by adapting the Gompers Governance Index methodology to the context of sustainability claims. The focus of our greenwashing index in this case is the sustainability claims made by canned tuna brands in Australia. The index is created from a comprehensive set of criteria for environmental claims, based on the Australian Competition and Consumer Commission (ACCC)’s principles for trustworthy claims. We show that the canned tuna brands form two clusters: one at a very high level of achievement and a second group with notable opportunities to improve on their sustainability communication and transparency. The results also highlight several key issues, most notably a lack of information regarding future sustainability transition plans across most brands. A deeper analysis of the scoring scheme shows that the brands with third-party sustainability certification generally achieved a better alignment with the ACCC principles than other brands. Future iterations of this analysis could incorporate online transparency and third-party verification to provide a more comprehensive assessment. Overall, this study underscores the need for clearer sustainability messaging, greater regulatory enforcement, and improved accountability among brands to ensure consumers can make informed choices. Full article

High-performance deep learning models require extensive computational resources and datasets, making their ownership protection a pressing concern. To address this challenge, we focus on advancing model security through robust watermarking mechanisms. In this work, we propose a novel deep neural network watermarking method that embeds ownership information directly within the image feature space. Unlike existing approaches that often suffer from low embedding success rates and significant performance degradation, our method leverages convolutional kernels with orthogonal preferences to extract multiperspective features, which are then linearly mapped at the output layer for watermark embedding. Furthermore, we introduce an orthogonal regularization constraint into the loss function to increase the watermark robustness. This constraint enforces orthogonality in both convolutional and fully connected layer weights, suppresses redundancy in hidden layer representations, and minimizes interference between the watermark and the model’s original feature space. Through these innovations, we significantly improve the embedding reliability and preserve model integrity. Experimental results obtained on ResNet-18 and ResNet-101 demonstrate a 100% watermark detection rate with less than 1% performance impact, underscoring the practical security value of our approach. Comparative analysis further validates that our method achieves superior harmlessness and effectiveness relative to state-of-the-art techniques. These contributions highlight the role of our work in strengthening intellectual property protection and the trustworthy deployment of deep learning models. Full article

The withdrawal of rural households from contracted farmland optimizes land resource allocation, aligning with urbanization and agricultural modernization goals, and supports agricultural transformation and urban–rural integration. Utilizing survey data from 1478 rural households in Shanghai and Wuhan suburbs, this study employs ordered Probit models and mediation effect tests to examine how urban social security influences farmland withdrawal and the role of pension income. Results indicate that within the context of new urbanization, 56.90% of rural households exhibit a social security participation rate exceeding 50%, with urban social security enrollment significantly facilitating the withdrawal of contracted farmland by suburban rural households. Specifically, a one-unit rise in the proportion of insured individuals escalates the likelihood of permanent farmland withdrawal by 25%. Among these, pension income plays a positive mediating role in the process of urban social security influencing farmers’ withdrawal from farmland. Participation in urban social security enhances farmers’ pension income levels, thereby strengthening their farmland withdrawal degree. Consequently, to effectively advance the mechanism for rural farmland withdrawal, social security emerges as a fundamental pillar. This study furnishes empirical backing for the “substituting land security with social security” proposition and offers crucial insights for enhancing rural land withdrawal policies. Full article

The Internet of Medical Things (IoMT) is the latest advancement in the Internet of Things (IoT). Researchers are increasingly drawn to its vast potential applications in secure healthcare systems. The growing use of internet-connected medical device sensors has significantly transformed healthcare, necessitating the development of robust methodologies to assess their integrity. As access to computer networks continues to expand, these sensors have become vulnerable to a wide range of security threats, thereby compromising their integrity. To prevent such lapses, it is essential to understand the complexities of the operational environment and to systematically identify technical vulnerabilities. This paper proposes a unified hesitant fuzzy-based healthcare system for assessing IoMT sensor integrity. The approach integrates the hesitant fuzzy Analytic Network Process (ANP) and the hesitant fuzzy Technique for Order Preference by Similarity to the Ideal Solution (TOPSIS). In this study, a hesitant fuzzy ANP is employed to construct a comprehensive network that illustrates the interrelationships among various integrity criteria. This network incorporates expert input and accounts for inherent uncertainties. The research also offers sensitivity analysis and comparative evaluations to show that the suggested method can analyse many medical device sensors. The unified hesitant fuzzy-based healthcare system presented here offers a systematic and valuable tool for informed decision-making in healthcare. It strengthens both the integrity and security of healthcare systems amid the rapidly evolving landscape of medical technology. Healthcare stakeholders and beyond can significantly benefit from adopting this integrated fuzzy-based approach as they navigate the challenges of modern healthcare. Full article

Evaluating and enhancing system resilience is essential to strengthen the regional ability to external shocks and promote the synergistic development of environment, economy and society. Taking the Upper Yellow River (UYR) as an example, this paper constructed a resilience evaluation index system for the environment—economy—society (EES) composite system. A three-dimensional space vector model was built to calculate the resilience development index (RDI) of three subsystems and the composite system from 2009 to 2022. Pathways supporting high resilience levels of the composite system were examined using the fuzzy-set qualitative comparative analysis (fsQCA) method from a configuration perspective. The results revealed that (1) the RDI of three subsystems and the composite system in the UYR showed an increasing trend; relatively, the environment and economy subsystems were lower, and their RDI fluctuated between 0.01 and 0.06 for most cities. (2) The emergence of high resilience is not absolutely dominated by a single factor, but rather the interaction of multiple factors. To achieve high resilience levels, all the cities must prioritize both environmental protection and economic structure as core strategic pillars. The difference is that eastern cities need to further consider social development and life quality, while western cities need to consider social development, life quality, and social security. Other cities including Lanzhou, Baiyin, Tianshui, and Ordos should focus on social construction and social security. Exploring the interactive relationship between various influencing factors of the resilience of the composite system from a configuration perspective has to some extent promoted the transformation from a single contingency perspective to a holistic and multi-dimensional perspective. These findings provide policy recommendations for achieving sustainable development in the UYR and other ecologically fragile areas around the world. Full article

The Okapi Wildlife Reserve (OWR) in northeastern Democratic Republic of the Congo represents both a biodiversity hotspot and the ancestral homeland of the Indigenous Mbuti and Efe peoples, whose livelihoods and knowledge systems are closely tied to forest resources. This study investigates how Indigenous knowledge and practices contribute to sustainable resource management under conditions of rapid socio-cultural transformation. A mixed-methods approach was applied, combining socio-demographic surveys (n = 80), focus group discussions, floristic inventories, and statistical analyses (ANOVA, logistic regressions, chi-square, MCA). Results show that hunting, fishing, gathering, and honey harvesting remain central livelihood activities, governed by customary taboos and restrictions that act as de facto ecological regulations. Agriculture, recently introduced through intercultural exchange with neighboring Bantu populations, complements rather than replaces traditional practices and demonstrates emerging agroecological hybridization. Nevertheless, evidence of biodiversity decline (including local disappearance of species such as Dioscorea spp.), erosion of intergenerational knowledge transmission, and increased reliance on monetary income indicate vulnerabilities. Multiple Correspondence Analysis revealed a highly structured socio-ecological gradient (98.5% variance explained; Cronbach’s α = 0.977), indicating that perceptions of environmental change are strongly coupled with demographic identity and livelihood strategies. Floristic inventories confirmed significant differences in species abundance across camps (ANOVA, p < 0.001), highlighting site-specific pressures and the protective effect of persistent customary norms. The findings underscore the resilience and adaptability of Indigenous Peoples but also their exposure to ecological and cultural disruptions. We conclude that formal recognition of Indigenous institutions and integration of their knowledge systems into co-management frameworks are essential to strengthen ecological resilience, secure Indigenous rights, and align conservation policies with global biodiversity and climate agendas. Full article

Background: The hepatitis A virus (HAV) is a major cause of acute viral hepatitis and a significant global health concern. This study provides a pre-vaccination baseline for Oman, enabling longitudinal comparison with post-hepatitis A vaccination cohorts. This study aimed to determine the pre-vaccination seroprevalence of HAV antibodies (anti-HAV) in Oman and explore the associated demographic factors. Methods: A cross-sectional study was conducted from April 2014 to August 2015 among patients attending the medical outpatient clinic of the Medical City Hospital for Military and Security Services. Demographic data were collected via a structured questionnaire, and serum samples were tested for anti-HAV immunoglobulin IgG and IgM using enzyme-linked immunosorbent assays. Multivariate analysis was performed to identify the predictors of anti-HAV seroprevalence. Results: Among 1975 participants, 88.1% were positive for anti-HAV IgG. The mean age was 37.4 ± 16.1 years; however, those negative for anti-HAV IgG were considerably younger (mean age: 24.8 ± 15.7 years). Anti-HAV IgG seroprevalence was 37% in individuals aged ≤18 years and 91% in those >18 years (p < 0.001). The factors associated with seropositivity included older age (p < 0.001), consuming food prepared outside the home (p < 0.001), occupation (p < 0.001), and education level (p = 0.003). In the multivariable analysis, only age showed a strong independent association with serostatus: per 10-year increase, the aOR for anti-HAV IgG seropositivity was 2.87 (95% CI 2.25–3.63; p < 0.001). Conclusions: Our study estimates show high anti-HAV IgG seroprevalence and serve as a pre-vaccination baseline for evaluating the hepatitis A vaccination program in Oman over time. Given the lower natural exposure among younger cohorts, continued routine vaccination, scheduled serosurveys, and strengthened surveillance are required to identify emerging immunity gaps and prevent future HAV outbreaks. Full article

Background: Transitions from hospital to long-term care (LTC) facilities are critical periods for older adults living with dementia, often involving complex medical, cognitive, and psychosocial needs. Virtual team-based care has emerged as a promising strategy to improve communication, coordination, and continuity of care during these transitions. However, there is limited evidence on how such approaches are implemented in practice, particularly with respect to inclusion, equity, and engagement of older adults and families. Objective: This study aimed to identify the enablers and barriers to delivering virtual team-based care to support older adults with dementia in transitioning from hospital to LTC. Methods: We conducted a qualitative study using semi-structured interviews, focus groups, and a policy review. Data were collected from 60 participants, including healthcare providers, older adults, and family care partners across hospital and LTC settings in British Columbia, Canada. Thematic analysis was conducted using a hybrid inductive and deductive approach. Eighteen institutional policies and guidelines on virtual care and dementia transitions were reviewed to contextualize findings. Results: Four themes were identified: (1) enhancing communication and collaboration, (2) engaging families in care planning, (3) digital access and literacy, and (4) organizational readiness and infrastructure. While virtual huddles and secure messaging platforms supported timely coordination, implementation was inconsistent due to infrastructure limitations, unclear protocols, and staffing pressures. Institutional policies emphasized privacy and security but lacked guidance for inclusive engagement of older adults and families. Many participants described limited access to reliable technology, a lack of training, and the absence of tools tailored for individuals with cognitive impairment. Conclusions: Virtual care has the potential to support more coordinated and inclusive transitions for people with dementia, but its success depends on more than technology. Structured protocols, inclusive policies, and leadership commitment are essential to ensure equitable access and meaningful engagement. The proposed VIRTUAL framework offers practical tips for strengthening virtual team-based care by embedding ethical, relational, and infrastructural readiness across settings. Full article

Accurate and efficient pest monitoring in complex rice field environments is vital for food security. Existing detection methods often struggle with small targets and high computational redundancy, limiting deployment on resource-constrained edge devices. To address these issues, we propose YOLO-RP, a lightweight and efficient rice pest detection method based on YOLO11n. YOLO-RP reduces model complexity while maintaining detection accuracy. The model removes the redundant P5 detection head and introduces a high-resolution P2 head to enhance small-object detection. A lightweight partial convolution detection head (LPCHead) decouples task branches and shares feature extraction, reducing redundancy and boosting performance. The re-parameterizable DBELCSP module strengthens feature representation and robustness while cutting parameters and computation. Wavelet pooling preserves essential edge and texture information during downsampling, improving accuracy under complex backgrounds. Experiments show that YOLO-RP achieves a precision of 90.62%, recall of 87.38%, mAP@0.5 of 90.99%, and mAP@0.5:0.95 of 63.84%, while reducing parameters, GFLOPs, and model size by 61.3%, 50.8%, and 49.1% to 1.00 M, 3.1, and 2.8 MB. Cross-dataset tests on Common Rice Pests (Philippines), IP102, and Pest24 confirm strong robustness and generalization. On NVIDIA Jetson Nano, YOLO-RP attains 20.8 FPS—66.4% faster than the baseline—validating its potential for edge deployment. These results indicate that YOLO-RP provides an effective solution for real-time rice pest detection in complex, resource-limited environments. Full article