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Abstract As extreme precipitation intensifies under climate change, traditional risk models based on the ‘100-year return period’ concept are becoming inadequate in assessing real-world risks. In response, this nationwide study explores shifting extremes under non-stationary warming using high-resolution data across the contiguous United States. Results reveal pronounced variability in 100-year return levels, with Coastal and Southern regions displaying the highest baseline projections, and future spikes are anticipated in the Northeast, Ohio Valley, Northwest, and California. Exposure analysis indicates approximately 53 million residents currently reside in high-risk zones, potentially almost doubling and tripling under 2 °C and 4 °C warming. Drought frequency also rises, with over 37% of major farmland vulnerable to multi-year droughts, raising agricultural risks. Record 2023 sea surface temperature anomalies suggest an impending extreme El Niño event, demonstrating the need to account for natural climate variability. The insights gained aim to inform decision-makers in shaping adaptation strategies and enhancing the resilience of communities in response to evolving extremes.

Abstract Background The causative agents of Acute Encephalitis Syndrome remain unknown in 68–75% of the cases. In Nepal, the cases are tested only for Japanese encephalitis, which constitutes only about 15% of the cases. However, there could be several organisms, including vaccine-preventable etiologies that cause acute encephalitis, when identified could direct public health efforts for prevention, including addressing gaps in vaccine coverage. Objectives This study employs metagenomic next-generation-sequencing in the investigation of underlying causative etiologies contributing to acute encephalitis syndrome in Nepal. Methods In this study, we investigated 90, Japanese-encephalitis-negative, banked cerebrospinal fluid samples that were collected as part of a national surveillance network in 2016 and 2017. Randomization was done to include three age groups (15-years). Only some metadata (age and gender) were available. The investigation was performed in two batches which included total nucleic-acid extraction, followed by individual library preparation (DNA and RNA) and sequencing on Illumina iSeq100. The genomic data were interpreted using Chan Zuckerberg-ID and confirmed with polymerase-chain-reaction. Results Human-alphaherpes-virus 2 and Enterovirus-B were seen in two samples. These hits were confirmed by qPCR and semi-nested PCR respectively. Most of the other samples were marred by low abundance of pathogen, possible freeze-thaw cycles, lack of process controls and associated clinical metadata. Conclusion From this study, two documented causative agents were revealed through metagenomic next-generation-sequencing. Insufficiency of clinical metadata, process controls, low pathogen abundance and absence of standard procedures to collect and store samples in nucleic-acid protectants could have impeded the study and incorporated ambiguity while correlating the identified hits to infection. Therefore, there is need of standardized procedures for sample collection, inclusion of process controls and clinical metadata. Despite challenging conditions, this study highlights the usefulness of mNGS to investigate diseases with unknown etiologies and guide development of adequate clinical-management-algorithms and outbreak investigations in Nepal.

Abstract Emotions are fundamental to human behaviour. How we feel, individually and collectively, determines how humanity evolves and advances into our shared future. The rapid digitalisation of our personal, social and professional lives means we are frequently using digital media to express, understand and respond to emotions. Although recent developments in Artificial Intelligence (AI) are able to analyse sentiment and detect emotions, they are not effective at comprehending the complexity and ambiguity of digital emotion expressions in knowledge-focused activities of customers, people, and organizations. In this paper, we address this challenge by proposing a novel AI framework for the adaptable, robust, and explainable detection of multi-granular assembles of emotions. This framework consolidates lexicon generation and finetuned Large Language Model (LLM) approaches to formulate multi-granular assembles of two, eight and fourteen emotions. The framework is robust to ambiguous emotion expressions that are implied in conversation, adaptable to domain-specific emotion semantics, and the assembles are explainable using constituent terms and intensity. We conducted nine empirical studies using datasets representing diverse human emotion behaviours. The results of these studies comprehensively demonstrate and evaluate the core capabilities of the framework, and consistently outperforms state-of-the-art approaches in adaptable, robust, and explainable multi-granular emotion detection.

Abstract Industrialization and urbanization contribute significantly to environmental pollution, particularly in water bodies. The consequences of this pollution are dire, impacting both the environment and public health. A serious hazard to life on Earth, contaminated water can cause food poisoning, diarrhea, respiratory diseases, and skin ailments, among other health problems. In countries like Bangladesh, where industrial activities, particularly in the garment sector, are prevalent, waste dumping into rivers and canals has reached alarming levels. This has resulted in a drastic decline in water quality, endangering aquatic life and ecosystems. To solve this important issue, we built an innovative real-time water quality monitoring system using the Internet of Things (IoT). Our system operates at three levels, collecting data from different layers of wastewater. Key parameters, including temperature, turbidity, total dissolved solids (TDS), and pH, are continuously monitored to assess water quality. The analysis revealed that wastewater temperature varied between 25 and 37 °C across different sites, with higher temperatures observed in industrial zones, indicating potential thermal pollution. Total dissolved solids (TDS) levels exhibited substantial variations, ranging from 170 to 360 ppm, surpassing standard thresholds and posing a threat to aquatic ecosystems. Turbidity readings ranged from 1.30 to 12.89 NTU, with higher turbidity observed in areas receiving industrial discharge, highlighting the impact of suspended solids on water clarity. pH values exceeded 7.0 across all sites, ranging from 9.5 to 10.32, indicating alkaline wastewater, particularly in regions with dyeing and textile industries. By integrating these specific metrics into our monitoring system, we provide valuable insights into industrial wastewater quality and its environmental implications. These findings underscore the urgent need for real-time monitoring and targeted interventions to address water pollution. Implementing sustainable water management strategies in industrial contexts is critical to protecting public health and preserving ecosystems for future generations.

The enzyme α-glucosidase in the small intestine regulates blood glucose levels and stimulates the hydrolysis of oligosaccharides and polysaccharides, increasing glucose levels in the body. Inhibiting this enzyme slows glucose digestion and absorption and as a result post-prandial blood glucose levels remain low, causing decreased insulin demand. Here, we investigated the ayurvedic antidiabetic plants and virtually screened an in-house library of 478 phytochemicals of these plants against the human α-glucosidase. We identified 11 secondary metabolites, including palmitic acid α-monoglyceride, (+)-(2 R)-6-propionyloxyethyl-4′,5,7-trihydroxyisoflavanone, Abruquinone E, and Aurantiamide Acetate, among others, showed stronger interactions with the receptor than the native ligand N-acetyl cysteine. Surprisingly, except one, all of these metabolites were from Abrus precatorius L. [Fabaceae] affirming its ethnopharmacological use against diabetes. The stability of the interactions between the ligands and receptor protein was evaluated through Molecular Dynamic (MD) simulation trajectories including root mean square deviation (RMSD), root mean square fluctuation (RMSF), radius of gyration (Rg), H bonds, β-factor analysis, and binding energy calculation through MM/GBSA method. The efficacy of top metabolites in inhibiting α-glucosidase is depicted in pharmacophore analysis. A comprehensive pharmacokinetics analysis confirmed the druggability, safety, and efficiency of top drug candidates. Additionally, we predicted the interactions of these top metabolites within the biological system. The medicinal properties described in this study will help develop active drug candidates for therapeutic purposes. Further experiments are recommended to prove the effectiveness of these metabolites in inhibiting the α-glucosidase enzyme for exploring their potential in the treatment of diabetes.

Mercury, emitted from various industries, is toxic and has devastating environmental consequences. Therefore, it becomes imperative to monitor the levels of mercury closely. This work mainly focuses on preparing inexpensive zeolite-coated kaolin membranes for separating Hg2+ from water. The membrane support was prepared using kaolin (50 wt%), quartz (25 wt%), and calcium carbonate (25 wt%). This mixture was blended with a 3 % hydroxypropyl methylcellulose (HPMC) solution and then passed through an extruder to obtain tubular support, which was further sintered at 950 °C. The zeolite-coated membrane (ZP membrane) was fabricated by subjecting the sintered support to a 48-h hydrothermal synthesis in 7 Na2O: 1 Al2O3: 10 SiO2: 205H2O gel at 90 °C. The isoelectric point of the ZP membrane was estimated to be 4.5. The zeolite-coated membrane displayed a pure-water permeability of 22.7 × 10−9 m3/m2 s kPa, a porosity of 31.72 ± 0.86 %, and a pore size of 90 nm. The performance of the ZP membrane in separating the Hg2+ ion from an aqueous solution was investigated by pressure variations (69–345 kPa) and feed concentration (0.5–10 ppm). The results clearly showed that in all cases, the zeolite-P membrane exhibited more than 99 % rejection of Hg2+ ions from aqueous solutions during the filtration experiments. Thus, the prepared ZP membrane can effectively be used for the separation of Hg2+ ions from wastewater.

Faced with declining donor funding for HIV, low- and middle-income countries must identify efficient and cost-effective ways to integrate HIV prevention programs into public health systems for long-term sustainability. In Zambia, donor support to the voluntary medical male circumcision (VMMC) program, which previously funded non-governmental organizations as implementing partners, is increasingly being directed through government structures instead. We developed a framework to understand how the behaviors of individual decision-makers within the government could be barriers to this transition. We interviewed key stakeholders from the national, provincial, and district levels of the Ministry of Health, and from donors and partners funding and implementing Zambia’s VMMC program, exploring the decisions required to attain a sustainable VMMC program and the behavioral dynamics involved at personal and institutional levels. Using pattern identification and theme matching to analyze the content of the responses, we derived three core decision-making phases in the transition to a sustainable VMMC program: 1) developing an alternative funding strategy, 2) developing a policy for early-infant (0–2 months) and early-adolescent (15–17 years) male circumcision, which is crucial to sustainable HIV prevention; and 3) identifying integrated and efficient implementation models. We formulated a framework showing how, in each phase, a range of behavioral dynamics can form barriers that hinder effective decision-making among stakeholders at the same level (e.g., national ministries and donors) or across levels (e.g., national, provincial and district). Our research methodology and the resulting framework offer a systematic approach for in-depth investigations into organizational decision-making in public health programs, as well as development programs beyond VMMC and HIV prevention. It provides the insights necessary to map organizational development and policy-making transition plans to sustainability, by explaining tangible factors such as organizational processes and systems, as well as intangibles such as the behaviors of policymakers and institutional actors.

Abstract Freshwater is unquestionably the most crucial resource essential for the sustenance and advancement of humankind. This invaluable entity surpasses all societal, economic, and environmental domains, consequently rendering it a ubiquitous good. Globally, it has been estimated that the industrial sector employs approximately 20% of the available freshwater. The principal aim within the industrial domain is to maximize production efficiency, rather than prioritizing the enhancement of water conservation and efficiency. Research suggests that a favorable association exists between the monetary investments made in technological improvements for industrial water treatment and reuse and a profitable return on investment that is sustained over a prolonged duration. This could plausibly explain the dearth of willingness exhibited by some corporations in dedicating resources to this vital issue. The objective of this study is to explicate the notion of sustainability concerning water management that can be operationalized in the context of Pakistan, while delving into the latest advancements in the sphere of sustainable management practices. This research endeavor shall serve as an instructive source for executives, entrepreneurs, and vested parties in various industrial domains to propel their endeavors toward sustainable practices while simultaneously achieving optimization and surpassing the benchmarks set by national regulations and international establishments. This investigation has illuminated the imperative of executing an all-encompassing water management strategy that incorporates the ecological, financial, and societal dimensions as the essential constituents of viability in industrial water utilization. This work ought to possess a worldwide scope, bearing in mind the ubiquitous character of industrial practices in the epoch of globalization.

Generative Artificial Intelligence (AI) is heralding a new era in AI for performing a spectrum of complex tasks that are indistinguishable from humans. Alongside language and text, Generative AI models have been built for all other modalities of digital data, image, video, audio, and code. The full extent of Generative AI and its opportunities, challenges, contributions, and risks are still being explored by academic researchers, industry practitioners, and government policymakers. While this deep understanding of Generative AI continues to evolve, the lack of fluency, literacy, and effective interaction with Generative and conventional AI technologies are common challenges across all domains. Tertiary education institutions are uniquely positioned to address this void. In this article, we present the human-centred design of a universal AI literacy module, followed by its four primary constructs that provide core competence in AI to coursework and research students and academic and professional staff in a tertiary education setting. In comparison to related work in AI literacy, our design is inclusive due to the collaborative approach between multiple stakeholder groups and is comprehensive given the descriptive formulation of the primary constructs of this module with exemplars of how they activate core operational competence across the four groups.