Search Results (2,685)

This study examines the interrelated challenges of climate change and energy poverty across two distinct industrial regions: Borsod-Abaúj-Zemplén in Hungary and Zarqa in Jordan. Both areas face unemployment and low-income levels, as well as environmental legacies of industrial activity; however, they differ significantly in their energy policies and infrastructure development. Using 2025 survey data, we develop indices of energy poverty, financial poverty, and climate perceptions, aligned with OECD guidelines. Regression analysis indicates that the model accounts for approximately 40% of the variance in energy poverty. Notably, heightened perceptions of climate change are associated with increased reports of energy hardship, suggesting that economically deprived households possess greater climate risk awareness. Resilience capacities, including adaptive skills, income stability, and community support, are found to substantially mitigate energy poverty. Income and employment status also play protective roles, underscoring the importance of economic resources. The impact of financial poverty varies markedly, being negligible in Hungary but severe in Jordan due to structural and infrastructural constraints. Our findings underscore the need for tailored, inclusive policy interventions that emphasize energy efficiency and retrofitting in Hungary and promote financial support and the adoption of renewable energy in Jordan. Integrating principles of energy justice into climate resilience strategies is crucial for promoting equitable and sustainable energy transitions, mitigating local vulnerabilities, and enhancing overall household resilience. Full article

Additive manufacturing enables lightweight sandwich structures with complex cellular cores, but the selection of material and process settings typically involves trade-offs among mechanical performance, cost, and sustainability. This study proposes an integrated multi-criteria decision-making framework to identify the most suitable configuration for Fused Filament Fabrication (FFF) sandwich structures featuring a gyroid triply periodic minimal surface (TPMS) core. Eight alternatives are evaluated by combining two materials (PLA and PLA–Flax biocomposite) with two extrusion temperatures (200 °C and 220 °C) and two infill densities (20% and 30%). Mechanical performance is represented by flexural strength obtained from three-point bending tests reported in a previously published experimental campaign, while economic and environmental indicators are quantified through material cost and printing energy consumption, respectively. Criteria weights are derived using the Analytic Hierarchy Process (AHP) based on expert judgment and consistency-ratio verification, and the alternatives are ranked using the TOPSIS method. The results highlight a clear dominance of PLA-based configurations under the adopted weighting scheme, with PLA printed at 200 °C and 20% infill emerging as the best compromise solution. PLA–Flax options are penalized by higher material cost, higher printing-process energy demand, and lower flexural strength in the investigated conditions. The proposed AHP–TOPSIS workflow supports transparent, data-driven selection of AM process–material combinations for gyroid sandwich structures, and it can be readily extended by including additional sustainability metrics (e.g., CO₂-equivalent) and application-specific constraints. A sensitivity analysis under alternative weighting scenarios further confirms the robustness of the obtained ranking. Full article

As social media influencers increasingly shape sustainable consumption, understanding the psychological mechanisms underlying consumer responses is essential. Drawing on social influence theory and reactance theory, this study examines how influencer characteristics affect sustainable behavioral intentions through perceived pressure and consumer reactance, while considering the moderating role of green self-identity. Using survey data from 382 respondents, the proposed model was tested using partial least squares structural equation modeling (PLS-SEM). Given the cross-sectional research design and the reliance on self-reported data, the findings should be interpreted as associational rather than strictly causal. The results show that influencer expertise, homophily, and social influence significantly increase perceived pressure. Perceived pressure, in turn, positively influences consumer reactance, which negatively affects sustainable purchase intention and positively affects avoidance intention. In addition, green self-identity significantly moderates the relationship between perceived pressure and reactance, such that consumers with a stronger green self-identity exhibit heightened sensitivity to perceived pressure and experience stronger reactance responses, indicating heightened sensitivity among environmentally self-identified consumers. These findings extend existing sustainability and influencer marketing research by revealing the dual and potentially counterproductive effects of persuasive communication. The study highlights the importance of autonomy-supportive and identity-consistent messaging for promoting sustainable consumption and provides practical guidance for designing effective influencer-based sustainability strategies. Full article

The growing demand for resources for production in intensive livestock farming requires research to operate with an environmentally sustainable perspective and respect for animal welfare, promoting circularity in the livestock industry. In this context, animal monitoring plays a key role in livestock management, not only to ensure their well-being but also to preserve the balance of the territory. In particular, early detection of oestrus events is one of the crucial elements in livestock monitoring. This study presents the development and on-farm validation of a low-power oestrus detection system for dairy cows, based on stand-alone smart pedometers (SASPs) connected through a Low-Power Wide-Area Network (LPWAN). The system implements an upgradeable, threshold-based algorithm that analyzes cow motor activity using a 24 h moving-mean approach and three behavioral indicators related to oestrus expression. Data are processed on board and transmitted to a cloud platform for visualization through a farmer-oriented WebApp, without requiring any fixed installation in the barn. The system was tested on a commercial free-stall dairy farm over three experimental campaigns (2021–2023). Oestrus events were validated through farmer visual observation and milk progesterone analysis, used as the reference method. A total of 22 confirmed oestrus events were analyzed. The system achieved a detection rate of 72.7% for certain oestrus events and 86.4% when including probable detections, with a mean oestrus duration of 18.1 ± 2.5 h, consistent with values reported in the literature. The proposed solution demonstrates the feasibility of a transparent, low-computational-cost oestrus detection approach compatible with LPWAN constraints. Its plug-and-play design, reduced infrastructure requirements, and upgradable firmware, although not able to self-update, limiting its potential compared to the machine learning-based methods present in the literature, make it suitable for practical adoption, particularly in farms where conventional connectivity and high-cost commercial systems are limiting factors. Full article

The Amazon Forest harbors one of the largest fungal diversities on the planet, occupying a wide variety of ecological niches comprising terra firme (non-flooded forest), várzea (white-water floodplains), and igapó (black-water floodplains). In this review article, we examine Amazonian fungal diversity based on three complementary approaches—culture-based surveys, in situ inventories of macrofungi, and environmental DNA/metagenomic analyses—discussing advances, limitations, and contributions to regional mycological knowledge. Subsequently, we present a critical synthesis of the potential of Amazonian basidiomycetes regarding the production of metabolites with antimicrobial activity, highlighting the main genera reported in the literature, the chemical classes involved (e.g., terpenes, steroids, quinones, and bioactive peptides), and the metabolic pathways responsible for their biosynthesis. The integration between biodiversity and bioprospecting underscores the importance of Amazonian fungi both for understanding ecological processes and for the development of new solutions to the antimicrobial resistance challenge. This work seeks to fill current gaps in the academic literature and to contribute to future strategies for the conservation and sustainable use of regional mycobiota. Full article

Conducting polymers (CPs) have become cornerstone materials in electrochemical sensors and biosensors due to their mixed ionic–electronic conduction, mechanical softness, and intrinsic biointerface compatibility. This review provides a comprehensive and critical overview of the field, tracing the evolution of CP-based devices from classical poly(3,4-ethylenedioxythiophene):poly(styrene sulfonate) (PEDOT:PSS), polyaniline (PANI), and polypyrrole (PPy) electrodes to emerging nanostructured, hybrid, wearable, and transient systems. We discuss fundamental charge-transport mechanisms, doping strategies, structure–property relationships, and the role of morphology and biofunctionalization in dictating sensitivity, selectivity, and stability. Particular emphasis is placed on reliability challenges—including drift, dopant leaching, environmental degradation, and biofouling—and on the current lack of standardized metrology, which hampers cross-study comparability. We propose a framework for rigorous calibration, reference electrode design, and data reporting, enabling quantitative benchmarking across materials and architectures. To support meaningful cross-platform comparison, representative performance envelopes—including conductivity, limit of detection, sensitivity, selectivity strategies, and operational stability—are critically benchmarked across major CP families and sensing modalities. Finally, we explore future directions such as organic mixed ionic–electronic conductors, biohybrid and living polymer interfaces, Artificial Intelligence-driven modeling, and sustainable transient electronics. Full article

This study examines whether sustainability information disclosure (SID) in the Arab Gulf acts as a substantive strategic tool that enhances corporate outcomes or merely serves as a symbolic gesture to maintain legitimacy. Using data from 92 listed firms across the Gulf Cooperation Council (GCC) from 2020 to 2023, the study distinguishes between the level (volume) and quality (credibility) of disclosure. It examines their respective impacts on return on assets (ROA), return on equity (ROE), and financial reporting quality. The results reveal a consistent positive association between disclosure levels and financial performance, suggesting that volume-based corporate environmental, social, and governance (ESG) reporting may support short-term legitimacy and market confidence. In contrast, disclosure quality shows weaker and less consistent effects, highlighting a potential disconnect between visibility and substance. This pattern reflects the strategic use of disclosure for symbolic compliance in the GCC, where ESG reporting is often adopted to satisfy external expectations rather than to support internal transformation or long-term value creation. The findings position sustainability disclosure as an underleveraged tool for strategic knowledge management. While current practices enhance legitimacy, they fall short of driving performance gains through internal learning or reporting integrity. Policy implications include the need for harmonised disclosure frameworks, mandatory assurance standards, and improved alignment with international ESG guidelines to strengthen the credibility and impact of corporate sustainability communication in emerging markets. Full article

Nanofertilizers have attracted increasing attention as an approach to improve the low nutrient use efficiency of conventional fertilizers, in which only a limited fraction of applied nitrogen, phosphorus, and potassium is ultimately taken up by crops. Beyond their capacity to minimize nutrient losses, nanofertilizers have attracted increasing attention for their possible role in addressing environmental issues, including soil eutrophication and the contamination of groundwater systems. Owing to their nanoscale characteristics, including large specific surface area and enhanced adsorption capacity, these materials enable more precise nutrient delivery to the rhizosphere and sustained release over extended periods, while also influencing soil–plant–microbe interactions. In this review, nanofertilizers are classified into six major categories—macronutrient-based, micronutrient-based, organic, controlled-release, composite, and nano-enhanced formulations—and representative examples and preparation routes are summarized, including green synthesis approaches and conventional chemical methods. The agronomic mechanisms associated with nanofertilizer application are discussed, with emphasis on enhanced nutrient uptake, modification of soil physicochemical properties, and shifts in microbial community composition. Reported studies indicate that nanofertilizers can increase crop yield across different crop species and formulations, while also contributing to improved nutrient cycling. Despite these advantages, several limitations continue to restrict their broader adoption. These include uncertainties regarding long-term environmental behavior, relatively high production costs compared with conventional fertilizers, and the absence of well-defined regulatory and safety assessment frameworks in many regions. Overall, this review highlights both the opportunities and challenges associated with nanofertilizer application and points to the need for further development of cost-effective formulations and standardized evaluation systems that account for their distinct environmental interactions. Full article

Machine learning (ML) has become an increasingly important tool in concrete engineering which has significantly altered the method of prediction and optimization of concrete properties, enabling more efficient, accurate, and sustainable processes. However, the inherent variability of concrete is a significant challenge to the generalization and performance of ML models. This study is a review that explores the effect of the variability of concrete material on the reliability and accuracy of predictions by ML. To explain the influence of these sources of variability on mechanical and durability related behaviors, the paper groups the sources of variability into four major groups, namely composition, microstructure, curing conditions, and environmental factors. A broad range of machine learning paradigms—including supervised learning, unsupervised learning, reinforcement learning (RL), and hybrid physics-informed approaches—is examined with respect to their robustness against data heterogeneity and distributional shifts. The weaknesses and advantages of the two types of algorithms are highlighted with regard to forecasting fresh and hardened concrete properties and the optimization of the mix design. Based on this synthesis, the review identifies key unresolved challenges, including the lack of standardized multi-source datasets, limited transferability of models across experimental settings, and insufficient reporting of preprocessing and normalization practices. Full article

The cultivation of blueberry (Vaccinium spp.) has undergone an unprecedented global expansion, driven by its nutraceutical value and the diversification of production zones across the Americas, Europe, and Asia. Its consolidation as a strategic crop has prompted intensive scientific activity aimed at optimizing every stage of management from propagation and physiology to harvest, postharvest, and environmental sustainability. However, the available evidence remains fragmented, limiting the integration of results and the formulation of knowledge-based, comparative production strategies. The objective of this systematic review was to synthesize scientific and technological advances related to the integrated management of blueberry cultivation, incorporating physiological, agronomic, technological, and environmental dimensions. The PRISMA 2020 methodology (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) was applied to ensure transparency and reproducibility in the search, selection, and analysis of scientific literature indexed in the Scopus database. After screening, 367 articles met the inclusion criteria and were analyzed comparatively and thematically. The results reveal significant progress in propagation using hydrogel and micropropagation techniques, efficient fertigation practices, and the integration of climate control operations within greenhouses, leading to improved yield and fruit quality. Likewise, non-thermal technologies, edible coatings, and harvest automation enhance postharvest quality and reduce losses. In terms of sustainability, the incorporation of water reuse and waste biorefinery has emerged as key strategies to reduce the environmental footprint and promote circular systems. Among the main limitations are the lack of methodological standardization, the scarce economic evaluation of innovations, and the weak linkage between experimental and commercial scales. It is concluded that integrating physiology, technology, and sustainability within a unified management framework is essential to consolidate a resilient, low-carbon, and technologically advanced fruit-growing system. Full article

This study provides a detailed technical and sustainability comparison of the conventional CNC machining and additive manufacturing routes for an aerospace bearing bracket. The work integrates material selection, process parameterization, build simulation, and environmental–economic assessment within a single framework. For the CNC route, machining of Al 7175-T7351 is characterized through process sequencing, tooling requirements, and waste generation. For the Laser Powder Bed Fusion (LPBF) route, two build strategies, single-part distortion-minimized and multi-part volume-optimized, are developed using Siemens NX for orientation optimization and Atlas3D for thermal and recoater collision simulations. The mechanical properties of Al 7175-T7351 and Scalmalloy^® are compared to justify material selection for aerospace applications. Both the experimental and simulation-derived process metrics are reported, including the build time, support mass, energy consumption, distortion tolerances, and buy-to-fly (B2F) ratio. CNC machining exhibited a B2F ratio of 1:7, with cradle-to-gate CO₂ emissions of ~11,000 g and an energy consumption exceeding 100 kWh per component. In contrast, both LPBF strategies achieved a B2F ratio of 1:1.2, reducing CO₂ emissions by over 90% and energy consumption by up to 63%. Build volume optimization further reduced the LPBF unit cost by over 50% relative to the CNC machining. Use-phase analysis in an aviation context indicated estimated lifetime fuel savings of 776,640 L and the avoidance of 2328 tons of CO₂ emissions. The study demonstrates how simulation-guided build preparation enables informed sustainability-driven decision-making for manufacturing route selection in aerospace applications. Full article

C-reactive protein (CRP) has emerged as a crucial link between systemic and neuroinflammatory processes, though its role across neurological autoimmune disorders remains incompletely understood. Pathologies such as multiple sclerosis (MS), neuromyelitis optica spectrum disorder (NMOSD), Guillain–Barré syndrome (GBS), and myasthenia gravis (MG) share chronic, dysregulated inflammation resulting from loss of immune tolerance. Their pathogenesis arises from interactions among genetic susceptibility, environmental factors, and gut microbiota alterations that trigger autoreactive immune cascades through molecular mimicry, ectopic antigen expression, or paraneoplastic cross-reactivity. These immune pathways sustain inflammation and promote neuroaxonal injury. CRP, synthesized mainly by hepatocytes in response to interleukin-6 (IL-6), functions as both an effector and reporter of inflammation, linking systemic immune activation to neuroinflammatory damage. Elevated CRP levels correlate with unfavorable outcomes, including accelerated disability in MS, IL-6-mediated astrocyte injury in NMOSD, respiratory failure in GBS, and crisis susceptibility in MG. Composite indices such as the CRP-to-albumin ratio are emerging as refined prognostic markers, though interpretation is limited by non-specificity and biological variability. This review integrates current evidence on CRP’s mechanistic roles, clinical associations, and translational potential in neuroinflammatory disorders, combining molecular, clinical, and imaging perspectives to refine its role within inflammation-driven neurodegeneration. Full article

Soil amendments are widely applied to improve soil fertility and structure, yet their performance in cold regions is constrained by low accumulated temperatures, frequent freeze–thaw (FT) cycles, and permafrost sensitivity. In this review, ‘cold regions’ refers to high-latitude and high-altitude areas characterized by long winters and seasonally frozen soils and/or permafrost. We screened the peer-reviewed literature using keyword-based searches supplemented by backward/forward citation tracking; studies were included when they assessed amendment treatments in cold region soils and reported measurable changes in physical, chemical, biological, or environmental indicators. Across organic, inorganic, biological, synthetic, and composite amendments, the most consistent benefits are improved aggregation and nutrient retention, stronger pH buffering, and the reduced mobility of potentially toxic elements. However, effectiveness is often site-specific and may be short-lived, and unintended risks—including greenhouse gas emissions, contaminant accumulation, and thermal disturbances—can offset gains. Cold-specific constraints are dominated by limited thermal regimes, FT disturbance, and the trade-off between surface warming for production and permafrost protection. We therefore propose integrated countermeasures: prescription-based amendment portfolios tailored to soils and seasons; the prioritization and screening of local resources; coupling with engineering and land surface strategies; a minimal cold region MRV loop; and the explicit balancing of agronomic benefits with environmental safeguards. These insights provide actionable pathways for sustainable agriculture and ecological restoration in cold regions under climate change. Full article

Digital twins, understood as computational replicas of poultry production systems updated in real time by sensor data, are increasingly invoked as transformative tools for precision livestock farming and sustainable agriculture. They are credited with enhancing feed efficiency, reducing greenhouse gas emissions, enabling disease detection earlier and improving animal welfare. Yet close examination of the published evidence reveals that these promises rest on a surprisingly narrow empirical foundation. Across the available literature, no peer reviewed study has quantified the full lifecycle carbon footprint of digital twin infrastructure in poultry production. Only one field validated investigation reports a measurable improvement in feed conversion ratio attributable to digital optimization, and that study’s design constrains its general applicability. A standardized performance assessment framework specific to poultry has not been established. Quantitative evaluations of reliability are scarce, limited to a small number of studies reporting data loss, sensor degradation and cloud system downtime, and no work has documented abandonment timelines or reasons for discontinuation. The result is a pronounced gap between technological aspiration and verified performance. Progress in this domain will depend on small-scale, deeply instrumented deployments capable of generating the longitudinal, multidimensional evidence required to substantiate the environmental and operational benefits attributed to digital twins. Full article

Food waste is a persistent global concern, requiring behavioral and systemic responses from consumers. The current study investigated the effect of sociodemographic factors and digital activism on food waste prevention behavior. Data from 390 respondents living in Italy (65% females, from 18 to 75 years old, grouped into four generations) were collected through an online survey covering these sections: sociodemographic variables, digital activism, knowledge, attitudes, and food waste behaviors. A Food Waste Prevention Index (FWPI) was computed to assess self-reported adherence to waste-reducing practices, and differences across three groups identified through tertiles were tested. Women displayed higher levels of digital activism; Gen Z was the most engaged generation in seeking information about food, while interest in food issues declined with age. Gender, geographical area, and dietary orientation significantly influenced food waste prevention, with women, rural residents, and individuals adopting flexitarian or vegetarian diets tending towards more virtuous behavior (higher FWPI). According to digital activism, less virtuous waste behavior (lower FWPI) was associated with a lower social media and apps usage frequency. Furthermore, higher FWPI individuals self-reported stronger sensitivity to sustainability-related topics such as circular economy, short food chains, and ethical or environmental motivations for vegetarianism. Overall, awareness and digital activism may synergistically foster more responsible food consumption, and targeted communication and digital tools can effectively support household food waste reduction strategies. Full article