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Reconceptualizing plastic pollution regulation in Nigeria, the U.S., and the U.K. from a corporate social responsibility (CSR) perspective
Full text linkPlastic pollution is an escalating crisis, yet Corporate Social Responsibility (CSR) remains largely voluntary in certain countries. In Nigeria, weak regulations and a lack of corporate accountability worsen the problem. While the United States and the United Kingdom have stronger sustainability initiatives, CSR in these countries is still not explicitly legally mandated, resulting in inconsistent corporate efforts. Despite growing advocacy for stricter environmental policies, businesses are not legally required to take responsibility for plastic waste. This article explores how CSR can go beyond voluntary commitments to become a structured, enforceable approach to addressing plastic pollution. By comparing CSR models in Nigeria, the United States, and the U.K., the article assesses best practices that could help Nigeria develop stronger corporate sustainability policies. Ultimately, it advocates for a clear CSR framework that holds businesses accountable and ensures they actively contribute to reducing plastic waste and safeguarding the environment
Omega-3 fatty acids in depression: A systematic review of human studies with supporting evidence from preclinical models
Full text linkBackground: Omega-3 polyunsaturated fatty acids (n-3 PUFAs) are essential for brain function and have been increasingly studied for their potential preventive and therapeutic roles in depression.Methods: A systematic review was conducted in accordance with PRISMA guidelines, focusing on recent human studies evaluating the effects of n-3 PUFA supplementation and Mediterranean dietary patterns on depression-related outcomes. Studies were identified through electronic databases and manual searches and critically appraised using the Joanna Briggs Institute Checklist for Randomized Controlled Trials. To contextualize human data, relevant preclinical animal studies were also reviewed.Results: The review identified mixed and context-dependent evidence for the efficacy of n-3 PUFAs in depression prevention among general populations. In contrast, more consistent therapeutic effects were observed in treatment studies, particularly when EPA-predominant formulations were used as adjunctive interventions. However, many studies lacked statistical power or did not achieve significance. Six preclinical studies demonstrated robust antidepressant and anxiolytic effects of EPA and DHA across models of nicotine withdrawal, chronic stress, aging, and neurotrophin deficiency. These effects were linked to anti-inflammatory, neuroprotective, and neurotrophic mechanisms.Conclusions: Omega-3 PUFAs—especially EPA—may offer modest yet clinically relevant benefits as adjunctive treatments for depression. While preventive efficacy remains unclear, preclinical data provide strong mechanistic support. Future large-scale, biomarker-informed human trials are warranted to clarify efficacy and optimize dosing strategies
Maternal diet-induced hypercholanemia alters gut microbiota and metabolome in adult female Western diet-fed offspring
Full text linkChildren of mothers with intrahepatic cholestasis of pregnancy (ICP) are more likely to develop metabolic disease later in life. Using a mouse model of gestational cholestasis, we previously found that 18-week-old offspring had metabolic alterations that were exacerbated in female offspring when challenged with a Western diet (WD). Microbiota changes are emerging as a potential mechanism for developmental programming, and the maternal gut microbiota is known to be altered in pregnancy and in ICP. We hypothesized that, in our model, the offspring gut microbiota is altered by maternal gestational disease, potentially impacting future offspring metabolic health. Female mice were fed a cholic acid (CA)-supplemented diet for 1 week preceding and throughout pregnancy to mimic gestational hypercholanemia. Female offspring were challenged with a WD from 12 to 18 weeks of age and cecal contents were collected for metataxonomics and metabolomic profiling. Maternal CA dietary supplementation was associated with markedly increased cecal sulfated bile acid species (up to 387-fold increase). Whilst WD-feeding of offspring was associated with a greater proportion of primary to secondary bile acids, and more tauro-conjugated bile acids than for offspring fed a normal diet, this adaptation to WD-feeding was not evident for those whose mothers were fed a CA-supplemented diet. Indeed, WD-fed offspring of CA-supplemented mothers had a >2-fold reduction in CA and dehydrocholic acid levels compared to those from NC-fed mothers. This corresponded with an altered profile of cecal microbiota, with clear separation of microbiotal profiles according to maternal diet in the WD-fed, but not NC-fed, offspring. This observational mouse study has shown that exposure to maternal hypercholanemia can significantly impact the effects of an obesogenic diet on offspring intestinal bile acid metabolism and gut microbiota, likely increasing their vulnerability to metabolic dysfunction when exposed to the “second hit” of an unhealthy postnatal environment
Downlink optimization for direct-to-satellite IoT with LEO satellites and LoRaWAN
Full text linkDirect-to-satellite communication systems for the Internet of Things, particularly those based on low-Earth-orbit satellite constellations, are emerging as a transformative solution to achieve global connectivity. However, ensuring efficient and reliable downlink communication from satellites to ground-based IoT devices remains a significant challenge due to intermittent satellite visibility, short contact durations, limited bandwidth, device energy constraints, and high network density. Unlike prior studies that primarily focus on uplink optimization, this work proposes a downlink-aware optimization framework that integrates satellite dynamics, LoRaWAN MAC constraints, and energy-aware scheduling. The framework accounts for physical-layer limitations, satellite visibility modeling, time-slot feasibility, and realistic system parameters consistent with LEO satellite operations. Simulations demonstrate that the proposed downlink-aware optimization framework improves the packet delivery ratio from 0.41 (achieved under random scheduling) to 0.96, while reducing the average energy consumption per successful transmission by approximately 55 %. These results highlight the efficiency of the proposed NSGA-II-based scheduling approach and provide an initial pattern that points toward potential scalability, compared to conventional non-optimized methods, demonstrating its promise for next-generation satellite-enabled IoT networks
Protocell computing on Aragonite substrates
Full text linkAragonite-proteinoid microstructures are an emerging type of biocomputing material. They mix inorganic calcium carbonate with self-assembled organic proteinoid networks. Scanning electron microscopy shows a range of structures. These include isolated microspheres and complex networks over 50 μm. They have dendritic shapes, with uneven nodes that create linear patterns resembling simple network topologies. Electrochemical testing shows a threshold response. This allows for all seven basic Boolean logic operations: AND, OR, NOT, NAND, NOR, XOR, and XNOR. It does this by classifying analog signals into binary states. This suggests a promising future for material-based computation. Frequency-dependent square wave voltammetry shows power-law scaling. It performs best in the 30–50 Hz range, which is important for biological use. This indicates adjustable electrochemical properties that are ideal for bioelectronic applications. The systems show autonomous oscillatory behavior for over 25 h. They maintain a steady ultralow frequency, like biological rhythms. This means they generate signals on their own, without any outside help. Impedance spectroscopy shows stable circuit features. There are strong links between resistive and capacitive parts. However, cyclic voltammetry shows that electrochemical degradation increases over time. These findings show that aragonite-proteinoid microstructures are well-suited for novel computing uses. They can help with things like autonomous sensing, neuromorphic devices, and biohybrid electronics. These microstructures use mineral-organic interfaces for processing information and generating signals. This approach connects synthetic materials to biological computing principles
The self-management support needs of people diagnosed with psoriatic arthritis: A realist review protocol
Full text linkIntroduction: Psoriatic arthritis (PsA) is a form of inflammatory arthritis linked to psoriasis. Previous research from the UK has found that many people feel unsupported when diagnosed with PsA and lack confidence in managing their condition. This realist review aims to understand what works and does not work for whom and in what circumstances, in relation to healthcare professionals engaging with people to support them in developing self-management skills. Methods and analysis: This protocol was developed by defining the scope of the review, using a brief directed literature review to support discussion by an expert group of researchers, healthcare professionals and a patient partner. A theoretical domains framework was generated, consisting of nine initial programme theories. These were further refined with input from Patient and Public Involvement and Engagement groups and used to develop a database search strategy. A systematic search of MEDLINE, CINAHL, Embase, Emcare and APA PsycINFO will be carried out, supplemented by citation tracking, exploration of grey literature and a mixed methods survey of rheumatology health professionals. Data selection will be performed by a minimum of two reviewers and data from included sources will be extracted using a template. Data will be synthesised narratively with respect to the identified initial programme theories, using these data to refine or refute these theories. This will generate refined programme theories to explain what works for whom and in what circumstances. Ethics and dissemination: Ethical approval for the health professionals survey was granted through the Research Ethics Committee, University of the West of England (Project ID: 10991848). Outputs will be disseminated to the research community through conference presentations and a peer-reviewed journal article. The strategy for sharing outputs with patients and health professionals will be discussed and agreed with knowledge user groups
Optimised waveband selection for low-cost multispectral estimation of tomato lycopene concentration using machine learning
No full textThis paper presents an investigation into a cost-effective method for non-destructive lycopene quantification in tomatoes using multispectral imaging, while aiming for high precision and practical applicability across various phases of tomato harvesting, processing, and storage. Lycopene, a carotenoid with antioxidant properties, is known for its health benefits, with its consumption being linked with reduced risk of cardiovascular disease, cancer, and neurodegenerative disorders. Tomatoes are the primary dietary source of lycopene due to their high concentration levels and widespread consumption. This study adopts a multispectral imaging approach, strategically selecting wavebands to enhance sensitivity and accuracy beyond conventional RGB systems. It does this while limiting the number of wavebands to the minimum required to reduce hardware complexity and operational costs. A primary contribution of this work lies in the streamlined approach to waveband selection in optimised capture conditions, which iteratively adds wavebands and evaluates their individual contributions to the model's performance using the coefficient of determination of predictors (R²). The method is validated through repeated cross-validation. The study evaluates four machine learning methods—SVR (R² = 0.940), k-NN (0.920), CNN (0.932), and SNN (0.959), to assess their performance on low-cost hardware. Notably, a simplified two-waveband configuration using a fast SNN achieved an R² of 0.951 and RMSEP of 6.317mg/kg, offering substantial reductions in hardware cost and processing time while maintaining high predictive accuracy, making it a promising and inexpensive solution
Hardware security modules for secure communications in the industrial Internet of Things
Full text linkThe Industrial Internet of Things (IIoT) offers transformative potential but introduces critical security risks, including unauthorized access, data breaches, and privacy compromise. Hardware Security Modules (HSMs) have emerged as robust solutions to protect IIoT ecosystems by enabling secure cryptographic operations, providing tamperresistant hardware and creating trusted execution environments. This work presents the first comprehensive review of HSMs tailored for secure IIoT communications, addressing their architectural foundations, operational mechanisms, and deployment scenarios. It first outlines the IIoT security landscape and HSM deployment architectures, including cloud-based, edge-integrated, and distributed models. Next, cutting-edge HSM implementations are analyzed, emphasizing their effectiveness in authentication, secure communication protocols, and physical tamper resistance. It then explores attack surfaces and vulnerabilities, such as firmware exploits, logical flaws, and network-based threats, along with mitigation strategies. Case studies from smart manufacturing, energy grids, and logistics demonstrate practical HSM applications, while a comparative evaluation assesses commercial and open-source solutions based on performance, compliance, and scalability. Emerging trends such as AI-driven threat detection, post-quantum cryptography, and decentralized HSMs are also discussed. Finally, key challenges are highlighted, including latency in real-time systems, supply chain risks, and regulatory hurdles, and future directions for research and industry adoption are proposed. This work serves as a roadmap for securing IIoT deployments, offering actionable insights for researchers, practitioners, and policymakers
Deep learning architectures for software fault prediction: The impact of error-type metrics and class imbalance
No full textSoftware Fault Prediction (SFP) plays a crucial role in modern software development by enabling early identification of fault‐prone modules and efficient allocation of testing resources. While deep learning approaches have shown promise in this domain, challenges persist regarding architectural choices, metric selection, and class imbalance issues. This study presents a comprehensive comparison between Deep Neural Networks (DNNs) and hybrid Graph Neural Network‐Long Short‐Term Memory (GNN+LSTM) models for SFP, investigating their effectiveness when combined with both conventional software metrics and Error‐type Metrics. We evaluate these approaches on four real‐world Java projects: ANTLR v4, JUnit, OrientDB, and Elastic Search. Our results demonstrate that GNN+LSTM models consistently outperform traditional DNN approaches, achieving improvements of up to 4% in accuracy and 4% in F1‐score. However, we identify challenges in combining different metric sets, with performance actually degrading compared to our previous study using Error‐type Metrics alone, suggesting potential multi‐collinearity issues. Additionally, we examine the effectiveness of the Synthetic Minority Oversampling Technique (SMOTE) in addressing the class imbalance issue, observing improvements of up to 6.6% in accuracy for GNN+LSTM models in severely imbalanced datasets. Our findings provide practical insights for selecting appropriate model architectures and metric combinations in SFP while highlighting the importance of carefully considering feature interactions and class imbalance mitigation strategies
Exploring science with children from under-represented groups through shared interests: Insights from a decade of practice
Full text linkThrough a series of projects dating back to 2015, the Science Hunters programme has delivered eight ‘Minecraft Clubs’ to engage children with Special Educational Needs, care-experienced children, and children in low socioeconomic status areas with science, technology, engineering, and maths. Science concepts are used as themes to build around, rather than the key focus of the activity, which is communal gameplay and having fun. Delivery has been developed through reflective practice, insights from which are drawn upon to extract key takeaways for engaging children with science outside of traditional settings through community-based activities and existing interests. These include drawing on the experiences of those with relevant backgrounds in design and delivery approaches, embedding STEM content rather than making it a primary feature of the activity, seeking and incorporating participants’ input, and having alternative approaches and resources available to facilitate accommodation of different needs and circumstances