Search Results (19,243)

Cellulose is the most promising abundant renewable polymer material with the highest potential for the future low-carbon biorefineries. However, its utilization in industry is limited by the structural recalcitrance as a result of organization of crystalline domains, fibrillar architecture hierarchy and intramolecular and intermolecular hydrogen bonding which is responsible for access restriction for the catalysts and consequent cleavage of the glycosidic bonds. Therefore, efficient depolymerization of cellulose is of paramount importance as a step in biomass conversion into the low molecular products. In this review, the recent advances in cellulose depolymerization are discussed. The chemical, enzymatic, thermal, thermochemical, mechanochemical, oxidative and hybrid catalytic method is thoroughly discussed. Attention is paid to the mechanism of the depolymerization reaction steps as glycosidic bond activation as hydrolytic, radical mediated, and energy assisted pathways. Selectivity and conversion efficiency based on substrate morphology, solvent system and catalyst design are also discussed. Further, there is a comparison of key performance metrics which are relevant for the industrial process as product yield, carbon efficiency, energy demand, stability of the catalyst, solvent recyclability and impact to the environmental lifecycle. The pros and cons of the various methods are also represented. Processes based on mineral acids enable rapid conversion. However, they suffer from corrosion, waste handling issues and degradation by-products. On the other hand, enzymatic depolymerization processes offer relatively high selectivity but they are limited in terms of feedstock sensitivity and slow reaction kinetics. The downstream valorization mechanisms are also described with the result being that no single available technology is capable of satisfying all industrial requirements. Thus, future progress expects integrated circular processes where advanced catalysis, process intensification and digital optimization strategies take place. Full article

Hydrogen production and storage constitute a promising technology in the path towards a global energy scenario featured by renewable energy penetration, decarbonization, sustainable development and resilience. In particular, so-called green hydrogen is generated from renewable energy sources, generally produced in an electrolyzer by means of Proton Exchange Membrane (PEM) water electrolysis. To make these expectations reality, experimental and real-world facilities are required, dealing with challenging aspects such as new technologies and integration of equipment. Thus, this paper presents the implementation and operation of a pilot plant for green hydrogen generation and storage based on a commercial 30 kW PEM electrolyzer. The renewable source is a photovoltaic generator of 60.6 kW which supplies the hydrogen generator through an inverter. Furthermore, the deployment of a supervisory system entirely based on open-source technologies is reported. The equipment employed and the supervisory system developed in this work exhibit a level of complexity and scale that is uncommon in the literature. Therefore, this article is a novelty in the literature and aims to contribute to the advancement of green hydrogen production and storage by providing experimental data and descriptions of a fully functional plant operating under real-world conditions. The achieved results under real operation conditions prove the successful implementation of the pilot plant as well as the suitability of the supervisory system to effectively track the most relevant variables. Full article

As the complexity, scale, and nonlinearity of modern engineering optimization problems continue to increase, traditional optimization algorithms face significant challenges in achieving high solution accuracy, fast convergence, and robust performance. To address these issues, this paper proposes a Rural Tourism Migration-based Improved Equilibrium Optimizer (RTM-IEO), aiming to enhance the global search capability and adaptive balance between exploration and exploitation. Specifically, an adaptive lens imaging opposition-based learning strategy is introduced to effectively expand the search space and maintain population diversity. A dynamic elite-guided elimination mechanism is designed to strengthen exploitation capability and accelerate convergence by reconstructing inferior individuals using high-quality solutions. In addition, a multi-stage rural tourism migration strategy is developed to dynamically regulate the search behavior across different optimization phases, enabling a more flexible and efficient search process. The effectiveness of the proposed algorithm is comprehensively validated on the CEC2021 and CEC2022 benchmark suites, where RTM-IEO demonstrates superior performance in terms of convergence accuracy, convergence speed, and robustness compared with several representative state-of-the-art algorithms. The statistical superiority of the proposed method is further confirmed through Friedman mean ranking and Wilcoxon rank-sum tests. To further evaluate its practical applicability, RTM-IEO is applied to the sustainable economic dispatch problem of a microgrid integrating renewable energy sources, including wind power and photovoltaic generation, along with energy storage systems and controllable units. The optimization objective simultaneously considers economic cost minimization and sustainable operation requirements, such as improving renewable energy utilization and reducing dependence on fossil-fuel-based generation. Experimental results indicate that the proposed method achieves a significant reduction in daily operating cost (exceeding 52% compared with benchmark algorithms), while effectively promoting low-carbon energy utilization and enhancing overall system sustainability. Overall, the proposed RTM-IEO provides an efficient and reliable optimization framework for addressing complex global optimization problems, particularly in scenarios requiring a coordinated balance between economic performance and sustainable development. Full article

Multimodal recommender systems are challenged by heterogeneous modality noise and coarse-grained feature fusion. Specifically, existing frequency-domain methods typically apply symmetric filtering across modalities, ignoring their distinct spectral characteristics. Consequently, symmetric filtering cannot simultaneously satisfy the denoising requirements of visual features and the semantic preservation requirements of textual features, leading to suboptimal multimodal representations. Meanwhile, current fusion strategies mainly operate at the instance level with static modality weights, lacking flexibility to dynamically adjust feature channels for user-specific collaborative contexts. To address these issues, this paper proposes MFA-GCN, a multimodal recommendation framework that combines asymmetric spectral filtering, multiview graph enhancement, and behavior-guided channel attention. For visual modalities, a multiscale frequency-domain module integrating 1D convolution and self-attention is adopted to suppress high-frequency disturbances while preserving informative structures. For textual modalities, a lightweight complex-domain scaling strategy is introduced to adjust spectral energy while maintaining semantic consistency. In addition, auxiliary user–user and item–item graphs are constructed to supplement sparse user–item interactions and provide richer collaborative signals. A behavior-guided channel attention mechanism is further used to dynamically refine multimodal representations. Experiments on three public Amazon datasets demonstrate that MFA-GCN consistently outperforms several representative baselines. Full article

As the largest inter-basin water diversion project in eastern China, the Eastern Route of China’s South-to-North Water Diversion Project (ER-SNWDP) plays a crucial role in alleviating water shortages and ensuring regional ecological security. However, large-scale water diversion that uses natural lakes as impounded lakes across different basins has impacted on the structure and function of the original ecosystems. To explore the changes in the food web and ecosystem structure of the impounded lakes during different operation periods of the ER-SNWDP, we constructed Ecopath models for Hongze Lake in 2010–2011 (pre-operation), 2017–2018 (initial operation), and 2023–2024 (operational period). Our results showed that the trophic energy flow in Hongze Lake was dominated by the detrital food chain, with the highest trophic level ranging from 3.06 to 3.50. Energy flows at trophic levels I and II accounted for a high proportion of the total throughput, and the interactions between trophic levels were relatively simple, indicating that Hongze Lake is approaching a mature ecosystem. Compared with the pre-operation period, the average trophic level, food chain length, and energy conversion efficiency of Hongze Lake declined during the initial operation period, but rebounded during the operational period, though still remaining lower than the pre-operation period. Ecosystem stability followed a similar trajectory: the total primary production/total respiration (TPP/TR) and the system omnivory index (SOI) indicated that ecosystem maturity decreased during the initial operation and increased during the operational period. Fishing activities had negative effects on most functional groups during the pre-operation and initial operation periods, whereas the negative effects from zooplankton and non-native species groups increased during the operational period. Based on changes in the food web structure and ecosystem of Hongze Lake across different water diversion periods, we suggest that the management of Hongze Lake should establish precautionary fishing management measures targeting the effects of filter-feeding functional groups and non-native species, optimize the species and quantities of restocking initiatives, prioritize the protection of critical habitat integrity, and implement long-term ecological monitoring. Full article

As power systems continue to expand and grow in complexity, power flow calculation remains a fundamental task in power system analysis and operation. Conventional methods rely on iterative solvers and detailed grid models, yet are often hindered by non-convergence and unreliable modeling assumptions. To address these limitations, this paper introduces a deep learning-based approach that integrates graph neural networks (GNNs) and recurrent neural networks (RNNs) for power flow calculation. The proposed model captures spatial dependencies through graph convolutional layers and temporal dynamics through recurrent layers, enabling accurate prediction of node voltage magnitudes, phase angles, and branch power flows. To enhance transparency, SHAP (Shapley Additive exPlanations)-based feature attribution and multi-modal visualizations are employed to interpret the model’s predictions. Experimental results on the IEEE 9-bus, 39-bus, and 118-bus systems demonstrate prediction errors within 4% and a computational speedup of approximately 40-fold over traditional Newton–Raphson methods. Beyond technical performance, these results suggest that the proposed method can support more efficient and reliable grid operation, thereby contributing to the integration of renewable energy, enhancement of grid resilience, and advancement of sustainable energy systems. Full article

The aim of this article is to identify how research on smart buildings has evolved in the context of the energy transition, with particular emphasis on energy flexibility, grid interaction, market integration, and policy barriers. The study addresses a gap in previous reviews, which have often focused on individual technological domains, building automation, or smart-readiness assessment, while paying less attention to the conditions under which smart buildings become active energy-system resources. A systematic review protocol based on the PRISMA logic was combined with bibliometric mapping and qualitative synthesis. Bibliographic data were retrieved from Scopus on 28 February 2026 and covered 663 English-language journal articles published between 2015 and February 2026. A core set of 63 studies was selected through explicit cluster-based and relevance-based criteria for in-depth qualitative synthesis. The results show a gradual shift from component-level efficiency research towards system-level studies in which smart buildings are analyzed as flexible demand-side assets, distributed energy nodes, and participants in emerging market mechanisms. At the same time, the evidence base remains uneven: many studies rely on simulation or case-specific modeling, while empirical validation, interoperability, occupant behavior, business models, and regulatory implementation remain less mature. The article contributes by distinguishing observed bibliometric patterns from conceptual interpretation and by integrating technological, economic, behavioral, and regulatory evidence into a framework explaining the persistent implementation gap in smart building deployment. Full article

Deep-mining operations are increasingly challenged by severe thermal hazards, which have become a critical bottleneck for achieving safe, efficient, and sustainable mineral extraction. While research on deep-mine cooling and heat hazard mitigation has proliferated, the field lacks a systematic, critical review that explicitly examines these advances through the lens of sustainability science. To address this gap, this study conducted a comprehensive bibliometric analysis of 432 publications (1994–2024) retrieved from the Web of Science Core Collection. The methodology employs Bibliometrix, Vosviewer, and CiteSpace to map the intellectual landscape, research hotspots, and evolving frontiers of the field. The results reveal a clear three-stage development trajectory and identify China, the USA, South Africa, and Canada as leading contributors, with national research emphases on ventilation, energy conservation, and refrigeration, respectively. Crucially, keyword clustering and burst detection uncover a notable paradigm shift: the focus has moved from isolated cooling techniques toward integrated, multi-objective strategies—including geothermal energy co-exploitation, phase-change material applications, and system-level energy optimization—signaling a growing alignment with resource efficiency and low-carbon mining principles. However, a critical finding is that the literature remains predominantly techno-centric, overwhelmingly evaluating performance through operational energy savings while largely neglecting life-cycle environmental impacts, holistic sustainability assessment metrics, and the influence of policy drivers. This review thus not only provides a structured overview of the domain, but, more importantly, exposes these critical knowledge gaps. We argue that future research must pivot toward a multi-dimensional sustainability framework that integrates technical, economic, and environmental dimensions, thereby guiding the next generation of research toward truly sustainable deep-mining practices. Full article

Advanced oxidation processes using titanium dioxide (TiO₂) have emerged as a promising approach for the photocatalytic degradation of contaminants in water and have drawn extensive research attention despite limited translation of this technology to large-scale applications. The limitations of this technology include immobilization of the photocatalyst, scalability, and compatibility with available light sources. Using 3D printing to immobilize TiO₂-based photocatalysts, we systematically evaluated the rates of photocatalytic degradation of methylene blue (MB) with different light-emitting diode (LED) ultraviolet (UV) light sources and modified TiO₂-based photocatalytic materials. The UV LED lights successfully decreased the MB concentrations with half-lives ranging from 0.9 to 2.4 h, with relative photocatalytic performance of UVA-365 > UVA-395 > UVC-280. The photocatalytic degradation rates under UV LEDs were slower (0.9–2.4 h) than those achieved using a low-pressure mercury UV-C lamp (0.5 h) and were also lower than those observed under solar simulated lights (0.6 h). The TiO₂ modified by an alkyl silane entity and embedded in a polylactic acid polymeric system with 3D printing exhibited the fastest methylene blue (MB) removal among the three TiO₂-based structures evaluated, with a half-life of 0.6 h compared to the 1.6–17.7 h for the other materials. This research demonstrated that 3D printing enables the integration of functionalized photocatalysts, and, when paired with low-cost, low-energy UV LED lights, can achieve environmentally relevant rates of performance. Ultimately, these findings represent an incremental step toward improving the performance of 3D-printed photocatalytic materials. Full article

Edge-side acoustic monitoring enables animal sound recognition in remote environments, but microcontroller deployment remains constrained by feature extraction, numerical consistency, memory, latency, and energy consumption. This study presents a sensor-based tiny machine learning (TinyML) acoustic monitoring system on an Arduino Nano 33 BLE Sense Rev2 platform, integrating onboard pulse-density modulation (PDM) microphone acquisition, Mel-frequency cepstral coefficient (MFCC) feature extraction, deployment-side standardization, 8-bit integer (INT8) neural-network inference, and edge-side decision output. To reduce training-to-deployment feature drift, consistent frame parameters, mirrored C++ feature operators, and exported standardization parameters are used to align personal-computer-side and microcontroller-side feature representations. A source-isolated seven-class protocol was constructed for six target animal classes and one compound background-noise class. In the single-run baseline comparison, the proposed multilayer perceptron achieved 98.28% test accuracy and 97.21% test macro-F1, while the ten-seed stability analysis yielded 98.64% ± 0.26% test accuracy and 97.87% ± 0.38% test macro-F1. The deployed INT8 model occupied approximately 26.9 KB, with a post-window latency of about 303 ms. System-level input power was 0.783–0.825 W, corresponding to an estimated autonomy of 7.63–8.03 h under the reference battery setting. Full article

Surgical smoke generated by energy-based instruments during minimally invasive surgery severely degrades intraoperative visibility in laparoscopic procedures, prolonging operation time and elevating surgical risk. Although deep-learning desmoking methods have improved spatial clarity, most operate frame-by-frame and produce temporal artifacts—flicker, brightness drift, and color instability—that hinder clinical adoption. To our knowledge, no prior framework has jointly addressed spatial restoration and temporal consistency within a unified surgical smoke removal pipeline. We proposed DCP-TS, a unified spatiotemporal framework that coupled a Dark Channel Prior (DCP)-guided conditional generative adversarial network (cGAN) with an inference-time module integrating optical flow alignment, exponential moving-average luminance smoothing, and adaptive gamma correction. A key novelty was that this stabilizer was smoke-aware and operated entirely at inference time, requiring no retraining or post-processing, which distinguished it from generic video temporal-consistency methods. On laparoscopic colorectal surgery videos, DCP-TS achieved a PSNR of 23.39 dB, SSIM of 0.62, NIQE of 4.17, and BRISQUE of 23.66, outperforming DehazeFormer and Colores et al. across all metrics. Temporal analysis showed an approximate 28% reduction in inter-frame luminance variation, and a double-blind reader study with five experienced laparoscopic surgeons confirmed substantial improvements in brightness stability (4.37 vs. 2.86) and overall perceptual quality (4.18 vs. 3.51 on a 5-point Likert scale). The system ran at 22 fps with ~3.9 GB GPU memory on standard operating-room hardware, supporting real-time intraoperative deployment. DCP-TS demonstrated that physics-guided spatiotemporal modeling could transform frame-by-frame desmoking into a clinically promising, perceptually more continuous video stream. Full article

Façades play a decisive role in shaping the visual and symbolic character of historic urban environments. Recent European funding schemes promoting energy-efficient retrofitting have accelerated interventions on building envelopes. Although aligned with decarbonization objectives, these processes are generating significant chromatic and material transformations that risk eroding the visual coherence and cultural sustainability of consolidated urban areas. In the historic Ensanches of San Sebastián, the replacement of traditional envelope systems with new cladding solutions is leading to the loss of the architectural style of some facades and altering their materials, textures, and colors. A progressive “contagion effect” has been identified, whereby dissonant chromatic schemes—often associated with the proliferation of so-called “zebra blocks”, residential buildings with façades clad in alternating black and white stripes that have proliferated in recent urban developments—are replicated across adjacent buildings, gradually weakening spatial continuity and the genius loci of the neighborhood. In response to this phenomenon, this research develops a systematic methodology to analyze, quantify, and anticipate chromatic transformation in consolidated urban fabrics. The study combines historical morphological analysis, classification of architectural periods, and chromatic mapping of recent façade interventions. Based on this framework, a CARI, Chromatic Alteration Risk Index is proposed to evaluate the potential impact of façade alterations on urban chromatic coherence. Drawing on an epidemiological framework, the methodology enables the identification of critical transformation clusters, the assessment of contagion dynamics, and the definition of regulatory thresholds for color and material interventions. By integrating perceptual criteria, urban morphology, and spatial distribution patterns, the study moves beyond descriptive diagnosis and offers a transferable tool for municipal planning. The proposed approach supports the proactive regulation of façade rehabilitation processes, balancing energy efficiency objectives with the preservation of collective memory, material identity, and urban sensory quality. This study proposes a quantitative model of “urban chromatic contagion” to assess how façade color interventions propagate within a neighborhood. We define the Chromatic Integration Percentage (CIP) and the Chromatic Alteration Risk Index (CARI) of the analyzed area. Results indicate that poorly regulated façades show higher chromatic dissonance (low CIP) and act as contagion hotspots, while a clear risk gradient emerges: highly protected buildings present lower risk, whereas mixed typologies and recent rehabilitations concentrate higher CARI values. The model supports preventive urban color management by identifying areas at risk before visible alteration. Full article

The increasing frequency of extreme weather events, particularly hailstorms, driven by climate change, poses growing threats to the resilience, environmental sustainability, and long-term performance of photovoltaic (PV) systems. This study evaluates the environmental impacts of a 12 kWp rooftop PV installation in Brescia, northern Italy, through a comparative Life Cycle Assessment (LCA) of three system configurations: a standard unprotected system (Scenario A), one equipped with a retractable polycarbonate hail-protection panel with automated weather-sensor activation (Scenario B), and one using thicker reinforced front-glass modules (Scenario C). The analysis follows a cradle-to-gate plus operational maintenance phase (30-year horizon, excluding end-of-life) system boundary and employs the ReCiPe 2016 Midpoint (H) methodology across 18 environmental impact categories. A novel integration of the Social Discount Rate (SDR) to the LCA framework—constituting a Discounted LCA (D-LCA)—incorporates both temporal discounting and risk dimensions into the environmental evaluation. A structured PESTEL-based risk taxonomy is applied to derive scenario-specific SDRs, with the Environmental risk category as the key differentiator between configurations. The static LCA identifies Scenario A as the lowest-impact option, while the D-LCA framework reverses this ranking: Scenario C achieves the highest Net Present Value of Emissions, followed by Scenario A. A negative NPV-E for Scenario B reflects the temporal cost of a large, front-loaded construction debt rather than absolute environmental harm. D-LCA framework should be interpreted as a complement to the full 18-category static LCIA profile, not a replacement. These results demonstrate that risk-informed D-LCA provides a more policy-relevant environmental sustainability assessment than static LCA for long-lived energy infrastructure subject to climate-driven operational risks. Full article

Against the backdrop of the global energy transition towards clean, low-carbon sources and China’s “carbon peak, carbon neutrality” strategic goals, distributed photovoltaic (PV) power generation is being integrated into distribution networks at large scale and with a high penetration level. This trend profoundly changes the configuration and operational characteristics of traditional distribution networks, posing challenges in system planning, operation control, power quality, and economics. This paper innovatively treats the step-up transformers of multiple distributed PV stations as a “distributed generation collection network” that requires coordinated optimization and constructs an integer linear programming (ILP) model aimed at minimizing the total life-cycle cost. The model deeply integrates engineering practice, incorporates nonlinear construction, installation, operation, and maintenance costs related to cluster size, as well as power transmission costs proportional to distance, and it employs piecewise cost functions to accurately capture economies of scale. This research achieves a system-level coordination framework that moves beyond single-device optimization, reducing system costs for step-up transformer deployment in distributed PV stations under complex terrain conditions. Full article

Buildings account for 40% of global energy consumption and one-third of greenhouse gas emissions. Renewable energy systems (RESs), such as solar photovoltaic (PV) and geothermal heat pumps, are critical technological solutions for decarbonization. Despite the growing literature, existing reviews lack a comprehensive synthesis integrating machine learning (ML), Internet of Things (IoT), and Building Information Modeling (BIM). Following the PRISMA protocol, this paper presents a systematic review of 41 studies published between 2012 and 2025. The review evaluates four primary domains: RES performance, building energy prediction, HVAC optimization, and occupancy-aware management. Quantitative findings reveal that solar PV-integrated buildings achieve electricity cost reductions of 35–64%, while ML-enhanced energy prediction models attain accuracies up to R² = 0.989. Critical research gaps are identified, including the scarcity of real-time sensor integration and geographically inclusive multi-climate datasets. Ultimately, this review contributes a structured synthesis of effective technologies, a comparative analysis of methodological approaches (ML, simulation, hybrid), and actionable future directions. It provides practical guidance for researchers and policymakers toward achieving net-zero energy buildings. This study serves as a definitive reference for the development of sustainable, low-energy built environments. Full article