Search Results (1,145)

Persimmon polyphenols (PP) are natural polyphenols with high reactivity and strong deodorization potential; however, their practical application in odor control is limited by their poor solubility. In this study, natural deep eutectic solvents (NADESs) were employed for the green extraction of PP, and the capabilities of extracts on the removal of ammonia (NH₃) and hydrogen sulfide (H₂S) were investigated. In addition, the underlying mechanisms were explored by integrating spectroscopic analysis, molecular dynamics simulations, and quantum chemical calculations. The results showed that chloride-citric acid (CC-CA) was the optimal system in both PP extraction and sustained NH₃ removal, while the betaine-urea (B-U) system was more effective for H₂S removal. NH₃ removal was governed by acid-base neutralization, with the resulting ammonium species being further stabilized within the PP-regulated NADES hydrogen-bond network. In contrast, H₂S interacted with the solvent network not only through acid-base neutralization but also via Van der Waals forces and hydrophobic contacts. Our data supported that NADESs enhanced the deodorization performance of PP through cooperative microenvironment regulation rather than irreversible chemical conversion. This work highlighted that NADESs could not only function as highly efficient extraction media for polyphenols, but also active platforms for enhancing selective gas-capture capability for polyphenols. Furthermore, it provided a new strategy for the rational design of green, persimmon-derived deodorants. Full article

Single-phase ground faults account for more than 80% of total faults in distribution networks. However, the introduction of distributed generation complicates power grid topology, leading to strong nonlinearity and non-stationarity in the zero-sequence current. This limits the accuracy of traditional faulted line selection methods. To address this problem, a CNN–ResNet-based method for faulted line selection for single-phase ground faults in distribution networks is proposed. Firstly, a 10 kV arc ground fault simulation test platform is built to analyze the nonlinear distortion characteristics of fault current. The WOA–VMD algorithm, optimized by permutation entropy, is used to denoise the zero-sequence current signal. The Gram Angular Difference Field (GADF) is then adopted to convert the one-dimensional signal into a two-dimensional image that retains its temporal characteristics. A hybrid deep learning model is constructed by fusing the one-dimensional time-domain features extracted by CNN and the two-dimensional time-frequency image features extracted by ResNet34. Matlab/Simulink simulations and physical experimental verification demonstrate that the proposed method achieves a training accuracy of over 97%, with zero misjudgments recorded in 15 arc grounding fault tests, representing a significant improvement in accuracy compared with existing diagnostic algorithms. It can adapt to complex scenarios such as high-resistance grounding and changes in neutral point grounding mode, effectively improving the accuracy and robustness of faulted line selection and providing technical support for the safe operation of distribution networks. Full article

The increasing penetration of rooftop photovoltaic (PV) systems has introduced significant challenges to voltage regulation and power quality within low voltage (LV) distribution networks. Reverse power flows during periods of high solar generation and low local demand can lead to overvoltage issues, voltage unbalance, and increased neutral-to-ground potential. This paper presents a comprehensive review of voltage regulation challenges and mitigation strategies for PV-rich distribution networks. The review consolidates findings from recent literature, focusing on traditional methods such as on-load tap changers and reactive power compensation, as well as modern techniques including smart inverter functionalities, community energy storage, static compensators, and advanced coordinated control schemes. A detailed examination of the suitability and limitations of these approaches in the Australian regulatory and network context is provided. The literature review demonstrates that previous work has mainly considered generic LV regulation issues without explicit four-wire MEN modelling or detailed LV–MV time series impact analysis. As a response to the lack of detailed practical analysis, a detailed three-phase four-wire LV–MV modelling and case study analysis, which illustrates the technical implications of high PV penetration on a representative Australian LV feeder, has been completed. The network is modelled using a three-phase four-wire unbalanced load flow formulation, explicitly incorporating the neutral conductor and multiple earthed neutral (MEN) system configuration. Results demonstrate pronounced voltage rise and unbalance during midday generation periods, highlighting the need for distributed and adaptive voltage-management solutions. The paper concludes by identifying key research gaps and future directions for voltage regulation in Australian distribution networks, emphasizing the importance of low voltage visibility, coordinated control architectures, and the integration of emerging distributed energy resources. The novelty of this work lies in combining a focused review of state-of-the-art with respect to management of voltage regulation in the presence of high penetration of distributed PV generation with a detailed three-phase four-wire LV–MV modelling framework and time-series case study of a representative Australian residential feeder, which illustrates the practical implications of increasing PV penetration. Full article

Telemedicine at the national scale must balance clinical quality, privacy, latency, and sustainability. This study aims to develop a system architecture and methodology for low-energy edge AI combined with green data center routing to reduce energy per consultation while maintaining clinical-grade performance. The results present (1) an energy-aware edge inference stack for physiological sensing and video triage; (2) a carbon-aware, service level agreement (SAL)-constrained routing strategy across regional data centers using software-defined networking and dynamic workload placement; (3) a techno-environmental methodology linking patient-level service key performance indexes to energy neutrality factor, grams CO₂e per encounter, and latency–reliability envelopes; and (4) national rollout playbooks covering network tiers (household/clinic/edge/cloud), facilities upgrades, and governance. Scenarios in urban, peri-urban, and rural/remote environments show 37–62% energy savings and 28–49% carbon reductions relative to cloud-only baselines, with median end-to-end latency ≤120 ms for triage and ≤40 ms for vitals alarms, meeting the World Health Organization and the International Telecommunication Union latency expectations for eHealth. Trade-offs, risks (drift, network volatility), and policy levers (green SLAs, data residency, open standards) are evaluated to scale sustainable telemedicine without compromising safety or equity. Full article

Microbial volatile organic compounds (VOCs) mediate rhizosphere plant-microbe interactions, yet their integrated effects on plant microbiome assembly and host transcriptional regulation remain unresolved. Here we address this gap by investigating how two common VOCs, acetoin (AC) and 2,3-butanediol (BD), influence growth, rhizosphere communities, and root gene expression in the medicinal plant Pseudostellaria heterophylla using a split-pot system. Bacterial and fungal communities were monitored across three developmental stages via amplicon sequencing, alongside root transcriptome profiling during tuber enlargement. Contrasting with widely reported growth-promoting effects of microbial VOCs, both compounds significantly reduced tuber number and biomass. Bacterial communities remained taxonomically stable, shaped primarily by species replacement, with modest VOC responses but clear shifts across developmental stages. Fungal communities exhibited marked compositional restructuring and greater treatment sensitivity, particularly under BD. Neutral community modeling indicated predominantly stochastic bacterial assembly, while fungal assembly—especially under BD—showed stronger influence of deterministic processes. BD associated with broader transcriptional reprogramming than AC, including downregulation of photosynthesis, specialized metabolism, and defense pathways. Cross-omics network analysis revealed discriminant genera (e.g., Granulicella, Harposporium) that correlated strongly with host genes involved in stress response, development, and epigenetic regulation, with fungal taxa showing tighter associations with host expression than bacteria. Together, these findings establish a mechanistic framework for how microbial VOCs shape rhizosphere communities and host responses, with implications for microbiome-based strategies in medicinal plant cultivation. Full article

Biological soil crusts (BSCs) are critical ecological components in arid lands. Their formation and stability hinge on the assembly and interactive networks of cyanobacteria-led bacterial communities. Yet, how different functional cyanobacteria shape the underlying microbial structure and assembly rules is poorly understood. Here, we cultivated artificial algal crusts using two representative cyanobacteria: the nitrogen-fixing Leptolyngbya sp. and the non-nitrogen-fixing Microcoleus vaginatus (M. vaginatus CM01). A total of six treatments were established based on the presence or absence of spraying with in situ BSCs leachate: a control group without inoculation of algae or bacteria (soil, S); a treatment group sprayed only with bacterial suspension (soil + bacteria, SB); a treatment group sprayed only with M. vaginatus CM01 (soil + M. vaginatus CM01, SM); a treatment group co-inoculated with both BSCs leachate and M. vaginatus CM01 (soil + M. vaginatus CM01 + bacteria, SMB); a treatment group inoculated only with Leptolyngbya sp. CT01 (soil + Leptolyngbya sp. CT01, SL); and a treatment group co-inoculated with Leptolyngbya sp. CT01 and biocrust leachate (soil + Leptolyngbya sp. CT01 + bacteria, SLB). By integrating 16S rRNA gene sequencing, neutral community modeling (NCM), and structural equation modeling (SEM), we dissected differences in Cyano-BSCs development, bacterial community composition, co-occurrence networks, and assembly mechanisms. Inoculation with M. vaginatus CM01 (SM, SMB) superiorly promoted Cyano-BSCs development: the SM group achieved the highest coverage (23.33%), while the SMB group showed marked increases in organic matter (OM, 4.10 g·kg⁻¹) and chlorophyll a (Chla, 13.40 μg·g⁻¹), alongside a >5-fold rise in bacterial, cyanobacterial, and nitrogen-fixation gene abundances versus controls. The mechanism centers on extracellular polymeric substances (EPS) secreted by M. vaginatus, which homogenized the microenvironment, suppressed stochastic bacterial dispersal (NCM, SM: R² = 0.698), and enhanced deterministic selection. This process forged a highly cooperative network (89.74% positive links, average degree 34.71) that directionally enriched Cyanobacteria (relative abundance 40.40%). The Shannon index of Cyano-BSCs from the group (SMB) reached 7.72 ± 0.09, reflecting high microbial community diversity. SEM confirmed M. vaginatus directly regulated bacterial assembly (path coefficient = 0.59, p < 0.05) and indirectly improved the soil environment (path coefficient = 0.64, p < 0.05), establishing a “cyanobacteria-community-environment” feedback loop. Conversely, the Leptolyngbya sp. groups (SL, SLB), despite enriching nitrogen-fixing bacteria and fungi, exhibited low carbon fixation efficiency (notably 1.26 g·kg⁻¹ OM in SL) and lack of EPS; communities remained stochastic (NCM, SL: R² = 0.751) with no effective regulatory pathway—a pattern mirrored in S and SB groups. Our findings demonstrate that M. vaginatus acts as a core engineer of biological soil Cyano-BSCs formation via an “EPS-mediated habitat filtering—functional group enrichment—cooperative network assembly” cascade, enforcing deterministic community construction. Leptolyngbya sp., with limited niche-constructing ability, fails to exert comparable control. This work provides a targeted framework for the artificial restoration of Cyano-BSCs in arid zones. Full article

Ecological network optimization can enhance ecological connectivity, regional ecological stability, and carbon sink capacity. Current research on ecological networks employs single-perspective optimization, which overlooks the synergistic requirements between network topological characteristics and the dual carbon goals. It lacks a comprehensive, systemic optimization framework. Focusing on the Beijing–Tianjin–Hebei region, the work constructs an ecological network by integrating ecosystem services, morphological spatial pattern analysis (MSPA), and circuit theory. A framework integrating barrier removal, structural robustness, and carbon sink enhancement is proposed, incorporating ecological barrier identification, complex network theory, and carbon offset patterns for multi-objective structural and functional optimization. The optimized network is evaluated using structural metrics, robustness analysis, and carbon sequestration validation. The network comprises 41 ecological sources and 102 corridors, exhibiting a dense northwest and sparse southeast distribution. Ecological barriers totaling 565.56 km² are removed to improve connectivity in the region. An edge-addition strategy introduces 12 nodes and 49 edges, enhancing connectivity, stability, and carbon sink capacity. Restoration priorities are set with the phased objectives of removing barriers, connecting topological weak points, and optimizing low-value carbon offset areas. Shifting the focus from structural connectivity to integrated function, the work contributes a methodological framework for advancing ecological security and carbon neutrality in urban agglomerations. Full article

With the global energy structure transitioning toward low-carbon and sustainable development, improving the stability and predictability of renewable energy generation has become a key challenge for achieving carbon neutrality goals. However, photovoltaic power output exhibits significant variability and uncertainty, and accurate power forecasting is of great significance for optimizing grid dispatch, improving renewable energy integration capacity, and reducing system reserve requirements. Therefore, this paper proposes a multi-stage prediction model that integrates Ensemble Empirical Mode Decomposition (EEMD), Improved Whale Optimization Algorithm-based Variational Mode Decomposition (IWOA-VMD), and an Improved Sparrow Search Algorithm (ISSA)-optimized Bidirectional Gated Recurrent Unit (BiGRU) network. Specifically, EEMD is first used to decompose the photovoltaic power sequence to extract Intrinsic Mode Functions (IMFs); then, the residual IMF is further decomposed using IWOA-optimized VMD to enhance low-frequency modeling capability; next, ISSA adaptively optimizes the hidden layer dimensions and learning rate of the BiGRU; Finally, each component is predicted individually, and the overall power sequence is reconstructed. Experimental results based on publicly available real photovoltaic data demonstrate that the proposed model outperforms BiGRU and several hybrid models in terms of MAE and RMSE. The research findings contribute to improving the accuracy of photovoltaic power forecasting, thereby providing technical support for the low-carbon transition and sustainable development of energy systems. Full article

The accelerating transition toward carbon neutrality necessitates the synergistic integration of power and hydrogen systems to mitigate renewable intermittency; however, coordinating regulatory policies with the operational flexibility of these coupled systems remains a critical challenge under deep uncertainty. Motivated by this gap, this study develops a bilevel carbon price-coupled optimization framework for integrated power–hydrogen systems, aiming to coordinate environmental policy design with operational scheduling under deep uncertainty. The upper-level model represents the decision-making of a market regulator that determines the optimal carbon price and emission allowances to maximize overall social welfare, while the lower-level model captures the coordinated operation of electricity and hydrogen subsystems that minimize total dispatch cost, including renewable utilization, electrolyzer conversion, and fuel-cell recovery.To address stochastic variations in renewable generation and load demand, a Distributionally Robust Optimization (DRO) formulation is introduced using Wasserstein ambiguity sets, ensuring decision feasibility against worst-case probability distributions. The bilevel structure is efficiently solved via a Benders–Column-and-Constraint Generation (CCG) algorithm, which decomposes policy and operation layers into tractable subproblems with provable convergence. Case studies on a 33-bus integrated power–hydrogen network demonstrate that the proposed framework effectively balances economic efficiency and carbon reduction. Results show that the optimal carbon price of approximately 45 $/tCO₂ achieves a 27% emission reduction with only a 9% cost increase, revealing a near-optimal social welfare equilibrium. Hydrogen subsystems operate flexibly, with electrolyzer utilization increasing by 30% and storage cycling deepening by 15%, enabling enhanced renewable absorption. Sensitivity analyses confirm that the DRO layer reduces operational risk by 4% compared with stochastic optimization, validating robustness against distributional shifts. The study provides a rigorous and computationally efficient paradigm for policy-coordinated decarbonization, highlighting the synergistic role of carbon pricing and cross-energy scheduling in the next generation of resilient low-carbon energy systems. Full article

University–employer alliances have expanded as a strategy to foster innovation, employability, and knowledge transfer; however, their growth often results in instrumental arrangements oriented toward short-term metrics (agreements, hours, deliverables) that weaken curricular transformation and Social Responsibility. This article proposes a governance architecture to design and audit sustainable Collaborative Education, understood as a technologically mediated multi-actor network organized by a shared principle of Social Responsibility. The method operates in two moves: (i) a conceptual ordering that uses the substance–accidents distinction and a formative telos to subordinate organizational and technological means to the educational purpose; and (ii) the translation of concepts into decision domains (who decides, with what evidence, under what risks, and with what safeguards), positioning Technological Mediation as governance infrastructure rather than a neutral support. The proposal delivers three managerial outputs: (a) a hierarchy of seven support entities (metaphysical question, Social Responsibility, projects and strategies, institutional management, institutional development, stakeholders, and benefits); (b) governance principles (primacy of purpose, multi-actor accountability, justifiable distribution of benefits and risks, and deliberative traceability); and (c) a compact matrix and checklist applicable through document auditing and platform design review, without requiring field data collection. Taken together, the framework shows how employer-side corporate governance can align incentives, rules of evidence, and data use to enable co-responsibility and avoid capture, strengthening the sustainability of collaboration over time across organizational contexts. Full article

Decarbonisation of urban areas is essential to reaching climate neutrality, as cities house half the global population and account for over 70% of carbon emissions. However, applying innovative approaches, such as establishing positive energy districts (PEDs), remains challenging due to stakeholder engagement and funding constraints, largely driven by knowledge gaps and a lack of best practices. This study examines barriers, facilitators and lessons learnt from six case studies in Europe, Canada and Singapore through a mixed-methods approach, including stakeholder interviews, grey literature analysis and a semi-structured review. Findings highlight district heating networks, heat pumps and photovoltaics as key technologies, with regional variations. While Mediterranean regions prioritise solar energy, northern climates employ a diverse range of solutions, including geothermal and seasonal storage. Political commitment and funding enable progress, whereas regulatory gaps and stakeholder misalignment hinder it. The study underscores the need for sharing best practices to enable PED implementation. Full article

This paper introduces a hybrid two-stage registration framework for reconstructing three-dimensional (3D) kidney anatomy from macroscopic slices, using CT-derived models as the geometric reference standard. The approach addresses the data-scarcity and high-distortion challenges typical of macroscopic imaging, where fully learning-based registration (e.g., VoxelMorph) often fails to generalize due to limited training diversity and large nonrigid deformations that exceed the capture range of unconstrained convolutional filters. In the proposed pipeline, the Optimal Cross-section Matching (OCM) algorithm first performs constrained global alignment—translation, rotation, and uniform scaling—to establish anatomically consistent slice initialization. Next, a lightweight deep-learning refinement network, inspired by VoxelMorph, predicts residual local deformations between consecutive slices. The core novelty of this architecture lies in its hierarchical decomposition of the registration manifold: the OCM acts as a deterministic geometric anchor that neutralizes high-amplitude variance, thereby constraining the learning task to a low-dimensional residual manifold. This hybrid OCM + DL design integrates explicit geometric priors with the flexible learning capacity of neural networks, ensuring stable optimization and plausible deformation fields even with few training examples. Experiments on an original dataset of 40 kidneys demonstrated that the OCM + DL method achieved the highest registration accuracy across all evaluated metrics: NCC = 0.91, SSIM = 0.81, Dice = 0.90, IoU = 0.81, HD95 = 1.9 mm, and volumetric agreement ${\mathrm{DC}}_{\mathrm{Vol}}$ = 0.89. Compared to single-stage baselines, this represents an average improvement of approximately 17% over DL-only and 14% over OCM-only, validating the synergistic contribution of the proposed hybrid strategy over standalone iterative or data-driven methods. The pipeline maintains physical calibration via Hough-based grid detection and employs Bézier-based contour smoothing for robust meshing and volume estimation. Although validated on kidney data, the proposed framework generalizes to other soft-tissue organs reconstructed from optical or photographic cross-sections. By decoupling interpretable global optimization from data-efficient deep refinement, the method advances the precision, reproducibility, and anatomical realism of multimodal 3D reconstructions for surgical planning, morphological assessment, and medical education. Full article

The rapid development of medical technologies requires architects to implement a future-proofing approach while designing medical facilities, despite the inherent uncertainty of long-term change. This challenge is particularly visible within hospital emergency departments (HEDs), which play a critical role as first-contact units and life-saving infrastructures. Due to their specific function, HEDs are a challenging environment for implementing new solutions, as they rely on proven frameworks designed to ensure continuity of care and operational efficiency. This raises the key question: how can modern technologies and architectural strategies streamline workflows in HEDs without overwhelming medical staff? Considering current challenges, an equally important factor in the development of emergency departments is their preparedness for crisis situations, such as pandemics, war threats and natural disasters. How can architectural design enable the implementation of given design strategies, aiming to ensure opportunities for development while simultaneously preparing for all-hazard scenarios? The authors gathered existing trends and solutions aimed at preparing hospital emergency departments for future challenges: positive/neutral, such as technological development, but also negative, such as currently ongoing war threats or risk of the next pandemic. Despite the apparent thematic extremity, certain systematic architectural solutions using a transdisciplinary approach may be the answer to these occurrences. The mentioned architectural solutions and factors were synthesized and subjected to design-oriented review based on existing case studies of a few Polish hospitals, which are simultaneously studied as case studies for broader doctoral research in the field of effectiveness assessment. The selected Polish hospital emergency departments are used as an illustrative, analytical reference to support the interpretation and synthesis of the reviewed literature. The contextual analysis enables the identification of transferable, design-oriented strategies relevant to broader emergence medicine architecture and applicable within European units. Examples from Polish units in particular are used as reference and background for discussion, rather than as empirical case studies. The study provides an overview of contemporary and future-oriented solutions in hospital architecture, focusing on the impact and feasibility within the hospital emergency departments. The synthesis highlights the importance of designing flexible spaces prepared for future technological advances, such as oversized service shafts, increased floor heights, and modular layouts. Additionally, the study focuses on the spatial connotations of emerging technologies like medical robotics, their maintenance areas and possible challenges. All of this is interrelated to social, demographic, and economic trends. These include the development of hospital networks, the evolving patient profile, inter-hospital information flow, and the growing role of highly specialized medical units. In terms of rapid challenges like wars or armed threats, factors revealed within the review indicate levels of HED readiness to face the conflict, mainly in terms of surge capacity but also structural durability and reserve resources. The post-pandemic context, in turn, assumes rapid expansion of the hospital into temporary and flexible structures and reversible zoning allowing for patient segregation and separation. Together, these insights outline pathways for creating resilient, adaptable, and efficient emergency care environments resilient to unforeseen challenges. Considering future scenarios of emergency departments, two main scenarios were identified: “the hospital of the future”, continuing overall development and adapting to rapid technological innovations, and “the crisis-resilient hospital”, resistant to various crisis scenarios, such as pandemics or war threats. The optimal development of the unit assumes both openness to technological changes and preparation of key zones for all-hazard scenarios. This review aims to synthesize architectural implications of technological and socio-demographic changes, not to provide a full empirical study. Adopting an exploratory framework, the review refers to technological innovations and crisis preparedness as external drivers shaping the spatial organization of hospital emergency departments and their adaptability to future challenges. Because of various inhibitors (economic, political, hierarchical), not all hospitals can introduce the described improvements, but the synthesis may serve as a knowledge source for future investments. The review was also conducted to support design decisions under conditions of uncertainty. The choice to address all the external factors collectively was induced to provide transferability of solutions and coherence of possible scenarios, which may happen simultaneously. Full article

Polymer functionalization is rapidly emerging as a transformative strategy for enhancing nanocatalysts by reprogramming the catalytic interface, rather than simply modifying the active phase. This approach leverages the unique tunability of polymers through their chemistry, thickness, permeability, charge density, and ionic/electronic conductivity to stabilize nanophases, regulate local microenvironments, and manage mass transport. These properties significantly improve catalytic activity, selectivity, and long-term durability. This review provides an in-depth examination of key construction strategies for polymer-functionalized nanocatalysts, categorizing them into six primary platforms: neutral functional polymers, ionomers/polyelectrolytes, conductive polymers, crosslinked networks/hydrogels, hybrid polymers, and framework polymers. Additionally, we explore recent advances in electrocatalysis, photocatalysis, and thermocatalysis, addressing challenges such as the trade-off between protection and accessibility, polymer stability under extreme conditions, and the need for standardized reporting of polymer descriptors. By framing polymers as programmable interfacial materials, this review highlights their potential to unlock significant improvements in catalytic performance across various catalytic systems. Full article

Background/Objectives: This study examines psychological well-being and mental health in Caribbean Colombian urban populations through methodological triangulation, integrating traditional statistical analysis with network analysis to develop a comprehensive understanding of protective and risk factors. Methods: A cross-sectional study was conducted with 412 participants from Barranquilla and Cartagena. Instruments included Ryff’s Psychological Well-being Scale, Keyes’ Social Well-being Scale, Self-Reporting Questionnaire (SRQ), family APGAR, and perceived social support scales. Data were analyzed using correlational analysis, multiple regression models, and network analysis to achieve methodological triangulation. Results: Traditional analysis revealed that social acceptance (β = −0.248), negative emotions (β = −0.268), and family crises (β = 3.272) were significant predictors, explaining 42.2% of mental health variance. Network analysis confirmed these findings through centrality measures, showing social acceptance and social coherence as central nodes. The triangulation between methods validated four integrative hypotheses: differential perceived social support, social coherence as a culturally sensitive protective factor, social support as moderator/mediator of family crises, and the autonomy paradox in collectivist contexts. Notably, the autonomy paradox hypothesis was not empirically supported; autonomy showed a neutral or slightly protective profile, indicating possible cultural adaptation in these urban settings. Conclusions: Methodological triangulation between traditional and network approaches provides evidence for multidimensional well-being models in Caribbean Colombian contexts. Social acceptance and family functionality emerge as central protective factors, while family crises constitute primary risk factors. The convergence between analytical methods strengthens the validity of findings and suggests the need for culturally adapted interventions that consider the specificity of collectivist urban contexts in the Colombian Caribbean. Full article