Exploring the interplay of hop variety, harvest time and yeast: Sensory and chemical dynamics in beer brewing

Palm oil mill effluent (POME) remains a major environmental challenge in the palm-oil industry due to its high organic load, nutrient content, recalcitrant compounds, and methane emissions associated with conventional treatment and disposal. This review synthesizes two decades of scientific, technological, and policy developments to assess pathways for cleaner, resource-efficient POME management. Conventional treatment systems mainly open ponding—offer low-cost stabilization but can generate high greenhouse-gas emissions. Engineered biological reactors and membrane-based polishing units can achieve high organic-matter removal (COD and BOD removal often >80% in pilot- to full-scale treatment trains), although performance depends on influent strength and operating conditions. Nature-based solutions (NBS), including microalgae, floating macrophytes, and constructed wetlands, provide low-energy alternatives with strong nutrient removal and biomass valorization potential, though performance remains sensitive to hydraulic and climatic variability. Resource-recovery routes such as biogas, struvite precipitation, biochar production, polyhydroxyalkanoate formation, and single-cell protein generation highlight opportunities for circular-bioeconomy integration at the mill scale. Comparative policy analysis across major producer regions indicates persistent disparities in discharge limits, enforcement capacity, and methane-capture requirements, which influence technology adoption and sustainability certification. By integrating treatment efficiency, resource-recovery potential, technology readiness, and governance context within a structured decision framework, this review advances a systems-level roadmap for selecting and upgrading POME treatment pathways. Key research needs include improving NBS resilience, integrating digital monitoring and AI-based optimization, expanding techno-economic and life-cycle assessments, and harmonizing regulatory frameworks. Overall, this review identifies technical, ecological, economic, and governance strategies that can transform POME from an environmental liability into a low-carbon, resource-positive stream aligned with cleaner production objectives. • POME treatment remains a major bottleneck in sustainable palm oil processing • Anaerobic digestion with biogas recovery is the most mature POME valorization route • Hybrid systems improve effluent quality, energy recovery and operational stability • Nature-based solutions enable low-energy polishing but require resilience optimization

Plant-based diets can improve health outcomes while reducing food-system environmental impacts. Healthcare organisations can influence dietary practices through food procurement, policy, and education, but the extent to which plant-based foods are embedded in healthcare sustainability policy remains unclear. To map evidence on the role of plant-based foods in healthcare sustainability policies aimed at reducing healthcare-related emissions. The review employed a scoping methodology guided by the PRISMA-ScR framework. A comprehensive search of peer-reviewed literature was conducted across major academic databases including Scopus, Web of Science, and PubMed. Studies were included if they addressed plant-based or sustainable foods, healthcare organisations, sustainability practices, and/or health policy frameworks. Inclusion criteria focused on articles published in English between January 2013 and February 2024. Data were charted and thematically analysed. The search identified 2640 records; 29 sources met inclusion. Evidence supports plant-based food strategies as a lever for health and sustainability, yet explicit integration into healthcare policy was limited and inconsistent. Where present, plant-based approaches were usually embedded within broader nutrition or sustainability initiatives rather than framed as a standalone policy lever. Policy leadership was most evident in the United Kingdom and Qatar; Australia and the United States were described as facing structural and political constraints, including fragmented mandates, industry influence, and limited monitoring and evaluation. Plant-based foods remain underrepresented in healthcare sustainability policy despite strong rationale for emissions reduction and health co-benefits. Clearer policy commitments, measurable targets, evaluation frameworks, and cross-sector collaboration are needed to translate evidence into scalable healthcare practice.

Mapping the discourse of environmental sustainability in intensive care nursing: a lexicometric exploration of professional meaning-making.

Understanding what makes sustainability education effective: Insights from a university certificate program

Melt-blended biodegradable mulching films with zeolite fillers from industrial by-products: A scalable, solvent-free route for controlled zinc delivery

Federated learning (FL), as a decentralized machine learning paradigm, emerges as a pivotal approach to addressing the ecological challenges posed by traditional IoT systems. While existing research extensively explores FL in smart cities and healthcare, its potential for fostering sustainable IoT practices remains underexplored. This review fills this gap by exploring how FL can help in lowering the amount of carbon footprint and energy use of centralized IoT infrastructures. In comprehensive analyses, this study highlights the integration of FL with green computing concepts, it’s usage in various fields, including environmental monitoring and smart grids, and how it can interact with blockchain technology. Across selected case studies, federated learning is reported to improve runtime- or compute-related efficiency and predictive performance in specific environmental sensing settings, and FL–blockchain designs in smart-city settings are reported to reduce latency under the studied simulation assumptions. Despite these advancements, challenges like data heterogeneity, resource limitations, and privacy concerns exist. Proposed solutions include lightweight FL models, secure aggregation protocols, and adaptive resource allocation strategies. This review underscores FL’s transformative role in achieving a sustainable IoT ecosystem and identifies future research directions for robust and scalable green IoT implementations. • Federated Learning reduces IoT carbon footprint and energy consumption significantly. • Blockchain integration enhances data-sharing security and reduces latency by 20–30 ms. • FL improves computational efficiency up to 7.3 times and accuracy by over 13.2%. • Data heterogeneity and limited resources hinder the integration of FL in green IoT. • Lightweight FL models and secure protocols enhance efficiency and scalability.

Assessing historical and future spatio-temporal dynamics of water footprints across climatically diverse zones of an agriculturally dominant river basin

The ladybird Eriopis connexa (Germar, 1824), a voracious aphid predator, faces challenges from insecticide applications, compromising biological control. As a result, the number of studies analysing the resistance and susceptibility of ladybirds has increased. Some studies have found that resistant populations differ in predation and foraging behaviour from susceptible ones. This study modelled the population dynamics of resistant and susceptible E. connexa preying on Aphis gossypii Glover, 1877 and Myzus persicae (Sulzer, 1776). A logistic model with density dependence and type II functional response was constructed to analyse predation dynamics, incorporating bifurcation analysis of predation parameters (attack rate and handling time) and the mortality rate of susceptible ladybirds. This model was used to simulate scenarios that included or excluded insecticide application and aphid resistance. To simulate the effects of insecticide applications, the parameters related to the aphids’ intrinsic growth rate ( r 1 and r 2 ) were changed to reflect the responses of susceptible and resistant populations. The same approach was used for the mortality rate of ladybirds ( d 2 and d 3 ). The results demonstrated that mortality, attack rate, and handling time were critical in shaping predator–prey interactions. Temporal simulations revealed fluctuating abundances, highlighting the fragility of these interactions under insecticide stress. This study contributed to understanding the ecological implications of insecticides, which disrupt natural predation dynamics, and showed how changes in the rates of behaviours can impact prey control. This research demonstrated the importance of integrated strategies that balance insecticide applications with preserving natural enemies and causing sustainable agricultural practices. • A pest-biocontrol model with insecticide-resistant and susceptible strains was constructed. • Coexistence of susceptible/resistant predators aids biocontrol under insecticide stress. • Insecticides disrupt aphid-ladybird dynamics by reducing the abundance of the biocontrol agent. • Abrupt changes might occur if parameters are near bifurcation points. • The results collectively show the importance of integrated pest management for aphids.

Clarifying the sustainable logistics practice and environmental performance nexus: A 3-way interaction model

Microplastic (MP) contamination in coastal sediments poses growing ecological and human health concerns, yet data for developing nations remain limited. This study provides a comprehensive assessment of MPs along the Cox's Bazar shoreline, the world's longest natural sea beach and a rapidly expanding tourism hub in Bangladesh. Tourism-dominated beaches showed significantly higher abundances (up to 111 items kg-1 dw) compared to rural low-use sites, with fibres and fragments representing the dominant morphotypes. Polymer analysis identified polyethylene (PE) and polypropylene (PP) as the most common constituents, reflecting consumer packaging waste and fishing-related debris as major sources. Risk evaluation demonstrated that abundance alone underestimates potential ecological hazard. Novel, hazard-weighted indices developed in this study, the Sediment Polymer Hazard Index (SPHI) and Microplastic Pollution Risk Index (MPRI), identified tourism hotspots as high-risk zones due to elevated contributions from toxic polymers (e.g., PS, PET) and ingestion-prone particle characteristics. Multivariate analyses further indicated that site-use category significantly constrain MP composition, confirming the influence of direct human pressure. Machine learning models, applied to classify MP contamination in coastal sediments, demonstrated that polymer-specific composition outperforms total abundance in predicting high-risk sites, with Random Forest achieving the highest classification accuracy. These results highlight the need for targeted coastal management prioritizing tourism-intensive zones, improved waste handling, and sustainable fishing practices. Integrating hazard-based indices and advanced predictive tools into long-term monitoring frameworks will be essential to protect the ecological and socioeconomic value of Cox's Bazar as coastal development accelerates.

Sustainability strategies and capital costs: A study of non-financial disclosure in the GCC agri-food industry

Environmental-driven restructuring of intestinal microbiota promotes host fitness and enhances food safety potential in shrimp pond-cultured Chlamys nobilis.

Cowpea (Vigna Unguiculata), a vital legume for suitable agriculture and food security in sub-Saharan Africa, plays a crucial role in improving soil health through intricate plant-microbe interactions in the rhizosphere. This review synthesizes current knowledge on the microbial interactions in the rhizosphere, focusing on soil health, microbial diversity, and their contributions to nutrient cycling and plant growth. Cowpea roots foster a diverse microbial consortium, including nitrogen-fixing rhizobia, phosphate-solubilizing bacteria and organic matter decomposers, which enhance soil fertility and structure. The microbial community in the cowpea rhizosphere is shaped by complex soil physiochemical properties, such as potential of hydrogen (pH), nutrient availability, and salinity, which significantly influence plant-microbe interactions. However, contradictions persist regarding pH's effect on microbial diversity, with unresolved questions about how specific environmental conditions regulate microbial taxa. Advanced techniques, including metagenomic analyses, have provided deeper insights into the taxonomic and functional composition of rhizosphere microbiomes, uncovering both abundant and rare microbial taxa involved in these processes. Despite these advancements, gaps remain in understanding the dynamic responses of microbial communities to environmental stresses. Bridging these gaps through integrative multi-omics approaches will enable the development of microbiome-informed strategies to improve cowpea productivity and promote sustainable agricultural practices, ensuring resilience in the face of climate variability.

Abalone is a highly valued marine gastropod with a declining wild population due to increased fishing pressure. To meet market demand, there is growing interest in the cage culture of abalone juveniles. However, the most commonly used substrate in cage culture, polyvinyl chloride (PVC), has been linked to environmental and health risks. This study examined the efficacy of bamboo as an alternative substrate for the bottom and suspended cage culture of tropical abalone Haliotis asinina Linnaeus, 1758. Two culture experiments were conducted using (1) sea bottom cages and (2) suspended sea cages in Binduyan, Puerto Princesa City, and Pamantolon, Taytay, Palawan, respectively. Abalone juveniles grown with bamboo substrate (BS) had significantly higher weight gain, specific growth rate, and shell length growth rate compared to those reared with PVC substrate (PS) (P < 0.05). In addition, after 90 days and 150 days of culture, abalone juveniles on BS exhibited positive allometric growth compared to those on PS, which showed isometric growth. Moreover, the survival rates of abalone juveniles with BS were not significantly different from those with PS at P < 0.05. These findings suggest that BS is a viable alternative to PS for abalone juvenile culture, as it is indigenous, inexpensive, and environmentally friendly. The study's results can promote sustainable aquaculture practices for abalone while raising awareness of the potential environmental and health risks associated with PVC for cultured abalone and humans consuming cultured abalone.

• Hybrid physics-AI approaches for modelling bio-based building envelopes are reviewed. • Challenges of coupled heat and moisture transfer in bio-based materials are discussed. • PINN-based methods are critically compared with UDE and graph-based formulations. • Model robustness, interpretability and data dependency are assessed. • Perspectives for advanced hygrothermal modelling in building applications are outlined. The hygrothermal behavior of bio-based building materials plays a central role in determining indoor comfort, energy performance, and durability. Conventional physical models, grounded in conservation laws, provide interpretability and robustness but often struggle with hysteresis effects, heterogeneity, and computational cost. Conversely, data-driven machine learning (ML) approaches offer flexibility and efficiency but lack physical consistency and interpretability. In response to these limitations, hybrid physics-AI models have recently emerged as transformative tools. This review critically examines three ML paradigms: Physics-Informed Neural Networks (PINNs), Physics-Informed Graph Neural Networks (PIGNNs), and Universal Differential Equations (UDEs), and evaluates their potential for simulating coupled heat and moisture transfer in porous bio-based envelopes. PINNs demonstrate high accuracy under sparse data conditions but remain limited by training cost and scalability. PIGNNs offer scalability and adaptability to irregular geometries, enabling large-scale or real-time simulations of building envelopes, but face challenges in representing hysteresis effect. UDEs provide balanced trade-off by embedding physics while correcting unmodeled nonlinearities such as sorption hysteresis and multiscale porosity. By offering a critical state-of-the-art analysis of recent advances, this review identifies current limitations, experimental requirements, and future directions for the deployment of hybrid AI–physics approaches in hygrothermal analysis. It concludes by positioning these methods as a roadmap for next-generation digital twins of bio-based materials, supporting predictive design, performance monitoring, and sustainable building practices.

Towards a lightweight YOLOv8n for aquaculture feeding detection: Architectural improvements for feature enhancement and computational efficiency

Do audit committee characteristics moderate the relationship between ESG and financial performance? Cross-country analysis

Management of backwash water at drinking water treatment plants (WTP)

In the face of the rapid evolution of artificial intelligence (AI) and the increasing cost of energy in residential buildings, accurately predicting thermal loads has become crucial for sustainable construction practices. We present the use of artificial neural network (ANN) and convolutional neural network (CNN) models to predict the energy efficiency of residential buildings. The dataset comprises eight input parameters, namely surface area, relative compactness, wall area, overall height, roof area, glazing area, orientation, and glazing area distribution, with two output thermal loads (the heating load (HL) and the cooling load (CL)). Additionally, we split the data, comprising 768 observations, into training (70%), testing (15%), and validation sets (15%). Results based on the Pearson correlation matrix indicated that all input variables exhibit a positive correlation with the thermal loads, except the surface and roof areas of the building. In addition, the feature importance and Shapley Additive exPlanation (SHAP) analysis demonstrated that building geometry parameters, such as relative compactness, wall, surface, and glazing areas, dominate thermal load predictions. Furthermore, the ANN models showed high performance, with R 2 values ranging from 0.9618 to 0.9783 for HL and CL. However, the CNN models significantly outperformed ANN models. When comparing training, testing, and validation, CNN models achieve exceptional R 2 values exceeding 0.99 for all dataset splits, even in the presence of outliers. K-fold cross-validation analysis demonstrated the outstanding reliability of the CNN models, with coefficient of variation (CV) values of 0.26% for HL and 0.65% for CL, suitable for engineering applications and real-world deployment. However, the ablation study results identified the non-regularized CNN configuration as optimal for production deployment, having low gap metric values between training and validation HL (−0.0001) and CL (0.0040) models. Beyond technical achievement, this research demonstrates that building energy prediction serves as a tool for advancing household energy consumption. Community engagement and five ethical considerations are proposed for citizen science programs and scientific education initiatives focused on sustainable energy consumption. • CNN models outperform ANN models for predicting heating and cooling loads in residential buildings. • SHAP analysis shows relative compactness, wall area, and glazing area are key thermal load predictors. • K-fold cross-validation confirms the CNN model's reliability, with coefficient of variation values below 1%. • Ablation study selects non-regularized CNN configuration as optimal for production deployment. • Ethical considerations guidelines are proposed for citizen science in building energy research.