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by Yuteng Wang, Lin Zhao, Xianhong Meng, Lunyu Shang, Zhaoguo Li, Hao Chen, Mingshan Deng, Yingying An and Yuanpu Liu

Remote Sens. 2025, 17(17), 2985; https://doi.org/10.3390/rs17172985 (registering DOI) - 27 Aug 2025

Abstract

The Three-River Source Region (TRSR) of the Tibetan Plateau (TP) is a critical headwater area with complex alpine terrain and significant climate variability. Accurately simulating 2 m air temperature (T₂) in this region remains challenging for models such as the Weather Research and Forecasting (WRF) model. This study integrated remote sensing data into the WRF model to improve T₂ simulations over the TRSR. Two simulations were conducted for 2020: a control simulation with default static vegetation parameters (EXP_control) and a sensitivity simulation with realistic vegetation and associated surface albedo of 2020 from the Global Land Surface Satellite (GLASS) datasets (EXP_glass). Results showed that incorporating the GLASS-derived datasets significantly alleviated the cold bias in simulated T₂ during winter and spring, while maintaining comparable performance in summer and autumn. The EXP_glass run achieved better agreement with observations (R = 0.98, p < 0.01) and reduced root-mean-square error (RMSE) by 36.4% compared to EXP_control. Energy balance analysis indicated that the GLASS-derived datasets enhanced solar energy absorption and increased net radiation. Consequently, EXP_glass produced greater turbulent heat fluxes and warmer surface skin temperature (TSK) and T₂. This study underscores the importance of accurate land surface characterization and highlights the utility of remote sensing data for improving regional climate model performance in high-altitude regions. Full article

26 pages, 3815 KB

Open AccessArticle

Deep Learning Method Based on Multivariate Variational Mode Decomposition for Classification of Epileptic Signals

by Shang Zhang, Guangda Liu, Shiqing Sun and Jing Cai

Brain Sci. 2025, 15(9), 933; https://doi.org/10.3390/brainsci15090933 (registering DOI) - 27 Aug 2025

Abstract

Background/Objectives: Epilepsy is a neurological disorder that severely impacts patients’ quality of life. In clinical practice, specific pharmacological and surgical interventions are tailored to distinct seizure types. The identification of the epileptogenic zone enables the implementation of surgical procedures and neuromodulation therapies. Consequently, accurate classification of seizure types and precise determination of focal epileptic signals are critical to provide clinicians with essential diagnostic insights for optimizing therapeutic strategies. Traditional machine learning approaches are constrained in their efficacy due to limited capability in autonomously extracting features. Methods: This study proposes a novel deep learning framework integrating temporal and spatial information extraction to address this limitation. Multivariate variational mode decomposition (MVMD) is employed to maintain inter-channel mode alignment during the decomposition of multi-channel epileptic signals, ensuring the synchronization of time–frequency characteristics across channels and effectively mitigating mode mixing and mode mismatch issues. Results: The Bern–Barcelona database is employed to classify focal epileptic signals, with the proposed framework achieving an accuracy of 98.85%, a sensitivity of 98.75%, and a specificity of 98.95%. For multi-class seizure type classification, the TUSZ database is utilized. Subject-dependent experiments yield an accuracy of 96.17% with a weighted F1-score of 0.962. Meanwhile, subject-independent experiments attain an accuracy of 87.97% and a weighted F1-score of 0.884. Conclusions: The proposed framework effectively integrates temporal and spatial domain information derived from multi-channel epileptic signals, thereby significantly enhancing the algorithm’s classification performance. The performance on unseen patients demonstrates robust generalization capability, indicating the potential clinical applicability in assisting neurologists with epileptic signal classification. Full article

(This article belongs to the Section Computational Neuroscience, Neuroinformatics, and Neurocomputing)

14 pages, 2177 KB

Open AccessArticle

Low-Frequency Band Gap Expansion of Acoustic Metamaterials Based on Multi-Mode Coupling Effect

by Yudong Wu, Zhiyuan Wu, Wang Yan, Shiqi Deng, Fangjun Zuo, Mingliang Yang and Weiping Ding

Crystals 2025, 15(9), 764; https://doi.org/10.3390/cryst15090764 (registering DOI) - 27 Aug 2025

Abstract

To address the problem of low-frequency broadband vibration and noise encountered in engineering, a method for expanding the low-frequency band gap of locally resonant acoustic metamaterials is proposed based on the multi-mode coupling effect. A computational method for the band gap characteristics of second-order multi-mode acoustic metamaterials has been derived. By incorporating the vibrational modes obtained from band structure calculations, a systematic investigation of the formation mechanisms of multiple band gaps was conducted, revealing that the emergence of these multiple band gaps stems from the coupled resonance between elastic waves and distinct vibrational modes of the local resonator units. Furthermore, the influence of design parameter variations on the bandgap was investigated, and the strategy of realizing low-frequency multi-order bandgaps by increasing the order of local resonance units was examined. Finally, vibration tests were conducted on the second-, third-, and fourth-order multi-mode coupled acoustic metamaterials. The results demonstrated that these materials exhibit an expanded vibration band gap within the low-frequency range, and the measured frequency response aligns closely with the theoretical calculations. This type of acoustic metamaterial offers viable applicability for controlling low-frequency broadband vibrations. Full article

(This article belongs to the Special Issue Functional Acoustic Metamaterials)

13 pages, 2442 KB

Open AccessArticle

Sustainable Green Synthesis of Fe₃O₄ Nanocatalysts for Efficient Oxygen Evolution Reaction

by Erico R. Carmona, Anandhakumar Sukeri, Ronald Nelson, Cynthia Rojo, Arnoldo Vizcarra, Aliro Villacorta, Felipe Carevic, Ricard Marcos, Bernardo Arriaza, Nelson Lara, Tamara Martinez and Lucas Patricio Hernández-Saravia

Nanomaterials 2025, 15(17), 1317; https://doi.org/10.3390/nano15171317 - 27 Aug 2025

Abstract

This work focuses on the sustainable green synthesis of magnetic iron oxide nanoparticles (Fe₃O₄NPs) using bioreductants derived from orange peel extracts for application in the efficient oxygen evolution reactions (OER). The synthesized catalysts were characterized using X-ray diffraction analysis, field emission scanning electron microscopy (FESEM), energy dispersive X-ray analysis (EDS), transmission electron microscopy (TEM), Fourier transform infrared spectroscopy (FTIR), and UV–visible spectroscopy. The Fe₃O₄NPs exhibit a well-defined spherical morphology with a larger Brunauer–Emmett–Teller surface area and a significant electrochemically active surface area. The green synthesis using orange peel extracts leads to an excellent electrocatalytic activity of the apparent spherical Fe₃O₄NPs (diameter of 9.62 ± 0.07 nm), which is explored for OER in an alkaline medium (1.0 M KOH) using linear-sweep and cyclic voltammetry techniques. These nanoparticles achieved a benchmark current density of 10 mA cm⁻² at a low overpotential of 0.3 V versus RHE, along with notable durability and stability. The outstanding OER electrocatalytic activity is attributed to their unique morphology, which offers large surface area and an ideal porous structure that enhances the adsorption and activation of reactive species. Furthermore, structural defects within the nanoparticles facilitate efficient electron transfer and migration of these species, further accelerating the OER process. Full article

(This article belongs to the Special Issue Hydrogen Production and Evolution Based on Nanocatalysts)

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24 pages, 1928 KB

Open AccessReview

Alkali Activation of Glass for Sustainable Upcycling: An Overview

by Giulia Tameni and Enrico Bernardo

Ceramics 2025, 8(3), 108; https://doi.org/10.3390/ceramics8030108 - 27 Aug 2025

Abstract

The recycling of glass presently poses several challenges, predominantly to the heterogeneous chemical compositions of various glass types, along with the waste glass particle size distribution, both of which critically influence the efficiency and feasibility of recycling operations. Numerous studies have elucidated the potential of converting non-recyclable glass waste into valuable materials thanks to the up-cycling strategies, including stoneware, glass wool fibres, glass foams, glass-ceramics, and geopolymers. Among the promising alternatives for improving waste valorisation of glass, alkali-activated materials (AAMs) emerge as a solution. Waste glasses can be employed both as aggregates and as precursors, with a focus on its application as the sole raw material for synthesis. This overview systematically explores the optimisation of precursor selection from a sustainability standpoint, specifically addressing the mild alkali activation process (<3 mol/L) of waste glasses. The molecular mechanisms governing the hardening process associated with this emerging class of materials are elucidated. Formulating sustainable approaches for the valorisation of glass waste is becoming increasingly critical in response to the rising quantities of non-recyclable glass and growing priority on circular economy principles. In addition, the paper highlights the innovative prospects of alkali-activated materials derived from waste glass, emphasising their emerging roles beyond conventional structural applications. Environmentally relevant applications for alkali-activated materials are reported, including the adsorption of dyes and heavy metals, immobilisation of nuclear waste, and an innovative technique for hardening as microwave-assisted processing. Full article

(This article belongs to the Special Issue Ceramics in the Circular Economy for a Sustainable World)

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21 pages, 5823 KB

Open AccessArticle

Electrical Power Optimization of Cloud Data Centers Using Federated Learning Server Workload Allocation

by Ashkan Safari and Afshin Rahimi

Electronics 2025, 14(17), 3423; https://doi.org/10.3390/electronics14173423 (registering DOI) - 27 Aug 2025

Abstract

Cloud Data Centers (CDCs) are the foundation of the digital economy, enabling data storage, processing, and connectivity for different academia/industry/commerce activities and digital services worldwide. As a result, their consistent power supply and reliable performance are critical factors; however, few works have considered power consumption optimization based on intelligent workload allocation. To this end, the proposed paper presents a Federated Learning (FL)-based server workload allocation model for optimal power optimization. In this strategy, the servers are modeled based on their Central Processing Unit (CPU), memory, storage, and network usage. A global server is considered as the global model responsible for final workload allocation decisions. Each server acts as a client in the federated learning framework, sharing its derived parameters with the global model securely and federatedly. Finally, after ten epochs of the system running, the model could optimize the system, decrease the overall power consumption, and reduce the workload pressure in each server by distributing it to other servers. The model is evaluated using different Key Performance Indicators (KPIs), and an appendix is provided, including the full performance results, workload shifting logs, and server resource status. Overall, the suggested FL allocator model shows promise in significantly lowering power consumption and alleviating server workload efficiently. Full article

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11 pages, 671 KB

Open AccessArticle

Investigation of the Electrical Mechanism in an Ag/pSiO₂/Si MIS Heterojunction: Effect of the Oxidation Temperature

by Hassen Nouri, Karim Choubani, Rachid Ouertani and Mohamed Ben Rabha

Crystals 2025, 15(9), 763; https://doi.org/10.3390/cryst15090763 (registering DOI) - 27 Aug 2025

Abstract

In this work, we investigate the electrical properties of a metal–insulator–semiconductor (MIS) heterojunction based on porous silicon dioxide (Ag/pSiO₂/Si). The porous silicon (PS) films were elaborated by electrochemical anodization under specific experimental conditions to obtain a porosity of about 55%. Porous silicon (PS) was oxidized by IR-RTP at different oxidation temperatures (Tox) ranging from 200 to 950 °C under an oxygen atmosphere. The morphology of the samples was analyzed using a scanning electron microscope (SEM). Ag/Al and Ag contacts were screen printed on the back and front sides of the heterojunction, respectively. Both the series and shunt resistances were derived from dark current–voltage (I–V) characteristics related to the various Ag/pSiO₂/Si heterojunctions. In this context, the reflectance was also measured at different oxidation temperatures to investigate its correlation with the series resistance (Rs) and shunt resistance (Rsh). The optimum electrical performance was obtained for an oxidation temperature close to 400 °C. Depending on the pSiO₂ thickness, two conduction mechanisms were highlighted within the devices. For a Tox below 200 °C, as well as for the non-oxidized devices, the conduction mechanism is governed by the tunneling current through the pSiO₂ film. However, when the Tox increases and exceeds 200 °C, the pSiO₂ thickness increases, leading to the switching of the conduction mechanism to a thermionic instead of a tunneling effect mechanism. Full article

(This article belongs to the Special Issue Metal Oxides: Crystal Structure, Synthesis and Characterization (2nd Edition))

28 pages, 4872 KB

Open AccessReview

High-Entropy Alloys and Their Derived Compounds as Electrocatalysts: Understanding, Preparation and Application

by Xianjie Yuan, Xiangdi Yin, Yirui Zhang and Yuanpan Chen

Materials 2025, 18(17), 4021; https://doi.org/10.3390/ma18174021 (registering DOI) - 27 Aug 2025

Abstract

High-entropy alloy (HEA) catalysts have attracted significant attention from researchers. In many cases, HEAs exhibit high activity and selectivity for catalytic reactions due to four “core effects”: high entropy effect, lattice distortion effect, slow diffusion effect, and mixing effect. However, a systematic summary of HEA catalyst design and understanding is lacking. In this review, the reasons for the outstanding performance of HEA catalysts are first discussed from multiple perspectives, such as excellent mechanical properties, ultra-high-performance stability, and the potential for compositional optimization. Furthermore, to deepen our understanding of HEA catalysts, the rational design of HEA catalysts is introduced, covering design principles, element selection, and the use of algorithms for prediction. Next, several common preparation methods for HEAs are introduced, including chemical co-reduction, solution combustion, mechanical alloying, and sol–gel methods. Finally, the research progress of HEA catalysts in hydrogen evolution reactions, oxygen evolution reactions, and oxygen reduction reactions is presented. Unlike existing reviews, this work establishes a unified framework connecting HEA fundamentals (entropy effects), computational design, scalable synthesis, and application-specific performance, while identifying underexplored pathways like lattice-oxygen-mediated mechanisms (LOM) for future research. Full article

(This article belongs to the Section Metals and Alloys)

21 pages, 1419 KB

Open AccessArticle

Prediction of Concealed Water Body Ahead of Construction Tunnels Based on Temperature Patterns and Artificial Neural Networks

by Zidong Xu, Shuai Zhang, Jun Hu and Liang Li

Sustainability 2025, 17(17), 7728; https://doi.org/10.3390/su17177728 (registering DOI) - 27 Aug 2025

Abstract

Concealed water bodies within surrounding rock formations pose a serious threat to tunnel construction. To address this risk, this study integrates physics-based heat conduction theory with deep learning, unlike existing methods that treat temperature as isolated data points or rely solely on empirical models. The approach introduces three key innovations: (a) analytical temperature–location relationships for water body characterization; (b) pseudo-temporal modeling of spatial sequences and (c) physics-guided neural architecture design. First, a steady-state heat conduction model is established to characterize axial temperature distribution patterns caused by concealed water bodies during excavation. From this, quantitative relationships between temperature anomalies and the location and size of the water bodies are derived. Next, a deep learning model, ST-HydraNet, is proposed to treat tunnel axial temperature data as a pseudo-time series for hazard prediction. Experimental results demonstrate that the model achieves high accuracy (91%) and perfect precision (1.0), significantly outperforming existing methods. These findings show that the proposed framework provides a non-invasive, interpretable, and robust solution for real-time hazard detection, with strong potential for integration into intelligent tunnel safety systems. By enabling earlier and more reliable detection, the model directly enhances construction safety, economic efficiency, and environmental sustainability. Full article

12 pages, 1094 KB

Open AccessArticle

Human-Derived H3N2 Influenza A Viruses Detected in Pigs in Northern Italy

by Laura Soliani, Ada Mescoli, Irene Zanni, Laura Baioni, Giovanni Alborali, Ana Moreno, Silvia Faccini, Carlo Rosignoli, Giorgia De Lorenzi, Laura Fiorentini, Camilla Torreggiani, Benedetta Cordioli, Alice Prosperi, Andrea Luppi and Chiara Chiapponi

Viruses 2025, 17(9), 1171; https://doi.org/10.3390/v17091171 - 27 Aug 2025

Abstract

In recent years, the four main swine influenza A virus (IAV-S) subtypes circulating in swine in the EU have been H1avN1, H1huN2, H1N1pdm09, and H3N2. The latter emerged in 1984 from a reassortment event between a human seasonal H3N2 and H1avN1, and is currently detected at low prevalence in swine in Italy. Here, we describe nine H3N2 IAV-S isolates belonging to three novel genotypes, first detected in Italy in 2021, likely resulting from reassortment events between swine and human IAVs. The first genotype was characterized by a hemagglutinin (H3 HA) of human seasonal origin, a neuraminidase (N2 NA) derived from H1huN2 strains circulating in Italian swine, and an avian-like internal gene cassette (IGC). The second genotype differed in its IGC constellation: PB2, PB1, PA and NP segments were of pandemic origin (pdm09), while NS and M segments derived from the Eurasian avian-like lineage. The third genotype combined a human-derived H3, a Gent/84-derived N2, and a pdm09-origin IGC, as well as an avian-like NS. This study aimed to characterize the genetic features of these novel H3huN2 and assess their epidemiological relevance, with implications for surveillance and control, improving preparedness and mitigating the risks posed by zoonotic influenza viruses. Full article

(This article belongs to the Special Issue Influenza and Other Respiratory Viruses: Prevention, Diagnosis, Treatment: 2nd Edition)

22 pages, 589 KB

Open AccessReview

Occupational Therapy Interventions in Mental Health During Lockdown: A Scoping Review

by Laura-María Compañ-Gabucio, Gema Moreno-Morente, Verónica Company-Devesa, Laura Torres-Collado and Manuela García-de-la-Hera

Healthcare 2025, 13(17), 2136; https://doi.org/10.3390/healthcare13172136 - 27 Aug 2025

Abstract

Lockdown derived from the COVID-19 pandemic posed significant challenges to mental health care, prompting the adaptation of therapeutic practices. The objective of this study was to describe the characteristics and objectives of occupational therapy (OT) interventions conducted in the field of mental health during the COVID-19 lockdown. A scoping review was conducted following PRISMA-ScR guidelines. A systematic search was carried out in the following databases: PubMed, Scopus, Embase, OTSeeker, PsycINFO, and Web of Science. We included randomized or non-randomized intervention studies, published in English or Spanish, that explored OT interventions in mental health during the COVID-19 period and/or lockdown. Data were extracted using pre-designed tables in accordance with the recommendations of the Cochrane Handbook. We included seven articles. OT interventions were conducted via video call (n = 4) and in person (n = 3). These were carried out with adults, adolescents, and children, lasting from 1 to 32 weeks, with the number of sessions ranging from 7 to 22 and lasting 20 to 90 min. The most frequently addressed outcomes were quality of life (n = 4), anxiety or depression (n = 4), and sleep (n = 4). During lockdown, OT interventions were mainly applied via telerehabilitation with the aim of increasing activity participation and addressing emotional issues. These results could help occupational therapists to implement mental health interventions when in-person application is compromised. Full article

19 pages, 658 KB

Open AccessReview

Cardiac Cell and Animal Models for Duchenne Muscular Dystrophy in the Era of Gene Therapy and Precision Medicine

by Hidenori Moriyama and Toshifumi Yokota

Cells 2025, 14(17), 1326; https://doi.org/10.3390/cells14171326 - 27 Aug 2025

Abstract

Duchenne muscular dystrophy (DMD) is a lethal inherited muscle disease caused by mutations in the DMD gene, and the development of gene therapies targeting DMD is rapidly progressing. Patient-derived induced pluripotent stem cells and animal models that mimic patient-specific mutations have significantly contributed to the advancement of precision medicine based on individual genetic profiles. Currently, no approved disease-specific therapy exists for DMD cardiomyopathy, which remains one of the leading causes of death in DMD patients. Therefore, the development of effective cardiac therapies represents a critical milestone in DMD research. In this review, we provide an overview of essential cellular and animal models used in DMD research, with a specific focus on the heart. We describe their key characteristics, advantages, and limitations. It is considered that a comprehensive and strategic integration of these models—based on a clear understanding of their respective strengths and weaknesses—will be important for advancing the development and clinical application of targeted therapies for DMD cardiomyopathy. Full article

19 pages, 4880 KB

Open AccessArticle

Research of Spatial-Temporal Variation and Correlation of Water Storage and Vegetation Coverage in the Loess Plateau

by Zehui Wang, Yinli Bi, Fei Yang, Junxi Zheng, Yanru Yang and Sichen Zhang

Remote Sens. 2025, 17(17), 2983; https://doi.org/10.3390/rs17172983 (registering DOI) - 27 Aug 2025

Abstract

As a region with functions such as energy production and as an ecological barrier, the Loess Plateau plays a vital role in China. This study examines the spatiotemporal changes in water storage and vegetation cover and their correlations. The changes in water storage were calculated using GRACE data and the GLDAS-NOAH model, while vegetation changes were derived from MODIS data. The results showed that the groundwater inventory decreased by 7.80 mm/a and the land inventory decreased by 9.72 mm/a. Surface water storage capacity increased by 1.92 mm/a. From west to east, terrestrial and groundwater storage decrease, reflecting overall losses, but surface water storage remains positive. By analyzing the FVC, it can be observed that since 2006, vegetation coverage has shown an overall increasing trend, with the highest value occurring in 2018. There has been a remarkably increase in vegetation coverage in most areas, while there was a decrease in vegetation coverage along the borders of Qinghai Province and northern Shaanxi Province. By conducting a correlation analysis, it can be found that the correlation coefficients between terrestrial water storage, surface water storage, and groundwater storage changes and vegetation coverage are −0.85, 0.60, and −0.93, respectively, indicating that increased vegetation coverage leads to reduced groundwater and terrestrial water storage. The results also indicate that there are significant spatial differences in the monthly correlations and maximum lag months between water storage and vegetation coverage. In addition, through discussing the driving factors of water storage changes in the Loess Plateau, we consider that the Grain for Green Project and mining activities may be the two major drivers of these changes. This study is highly important and valuable to the study of changes in water reserves in the Loess Plateau, as well as ecological protection and environmental assessment in the Loess Plateau. Full article

(This article belongs to the Special Issue New Advances of Space Gravimetry in Climate and Hydrology Studies)

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34 pages, 1768 KB

Open AccessArticle

A Pilot-Scale Evaluation of Duckweed Cultivation for Pig Manure Treatment and Feed Production

by Marie Lambert, Reindert Devlamynck, Marcella Fernandes de Souza, Pieter Vermeir, Katleen Raes, Mia Eeckhout and Erik Meers

Plants 2025, 14(17), 2680; https://doi.org/10.3390/plants14172680 - 27 Aug 2025

Abstract

Livestock-intensive regions in Europe face dual challenges: nutrient surpluses and a high dependency on import of high-protein feedstocks. This study proposes duckweed (Lemnaceae) as a potential solution by recovering nutrients from manure-derived waste streams while producing protein-rich biomass. This study evaluated the performance of duckweed treatment systems at a pig manure processing facility in Belgium. Three outdoor systems were monitored over a full growing season under temperate climate conditions. Duckweed cultivated on constructed wetland effluent showed die-off and low protein content, while systems supplied with diluted liquid fraction and nitrification–denitrification effluent achieved consistent growth, yielding 8 tonnes of dry biomass/ha/year and 2.8 tonnes of protein/ha/year. Average removal rates were 1.2 g N/m²/day and 0.13 g P/m²/day. Growth ceased after approximately 100–120 days, likely due to rising pH and electrical conductivity, suggesting ammonia toxicity and salt stress. Harvested duckweed had a high protein content and a total amino acid profile suitable for broilers, though potentially limiting in histidine and methionine for pigs or cattle. Additionally, promising energy and protein values for ruminants were measured. Although high ash and fibre contents may limit use in monogastric animals, duckweed remains suitable as part of a balanced feed. Its broad mineral profile further supports its use as a circular, locally sourced feed supplement. Full article

(This article belongs to the Special Issue Duckweed: Research Meets Applications—2nd Edition)

22 pages, 1523 KB

Open AccessArticle

Cultural Heritage Sites as a Facilitator for Place Making in the Context of Smart City: The Case of Geelong

by Elika Tousi, Surabhi Pancholi, Md Mizanur Rashid and Chin Koi Khoo

Urban Sci. 2025, 9(9), 337; https://doi.org/10.3390/urbansci9090337 (registering DOI) - 27 Aug 2025

Abstract

This study examines the role of cultural heritage sites as facilitators of place making within the evolving paradigm of smart city development. As cities worldwide adopt data-driven models of governance, integrating cultural identity and heritage becomes increasingly critical. This research addresses the conceptual and practical gap in understanding how heritage can support inclusive, sustainable, and meaningful urban transformation in smart city contexts. To do so, it selects Geelong in Australia as a case study. The study then employs a qualitative methodology drawing on semi-structured interviews with experts and professionals across urban planning, architecture, sustainability, and heritage management. Thematic analysis derived five key themes: heritage as an identity anchor, digital technologies enhancing cultural narratives, community engagement, adaptive reuse, and economic-policy integration. Findings highlight that heritage sites are dynamic assets that foster community identity, historical continuity, and digital storytelling. Digital tools enhance the visibility and accessibility of heritage, while adaptive reuse strategies align cultural preservation with environmental sustainability and economic growth. The resulting conceptual and assessment framework positions heritage both as a cultural and functional urban asset, offering actionable insights for planners, policymakers, and designers aiming to create smart cities that are not only technologically advanced but also socially inclusive and culturally grounded. Full article

(This article belongs to the Special Issue Urban Trends: Cities, Housing Markets, Regional Dynamics and Tourism)

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