Search Results (73,208)

Indoor Environmental Quality (IEQ) plays a vital role in occupant health and productivity. However, current Building Management Systems (BMS) often struggle in sustaining optimal IEQ levels due to limitations in data management and lack of occupant-centric feedback loops. To address these gaps, this research synthesizes the state-of-the-art methods for IEQ monitoring, assessment, and control within Building Automation Systems (BAS), identifying both technological and methodological advancements, as well as highlighting the challenges and potential opportunities for future innovations. Employing the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) methodology, this multi-stage literature review analyzes 176 publications from 1997 to 2024, with a focus on the decade of rapid technological evolution from 2014 to 2024. The review focuses on high-impact journals indexed in Scopus to ensure quality while acknowledging the potential bias inherent in a single-database search. The synthesis reveals a methodological shift in monitoring from sparse, zone-level sensing towards dense, multi-modal systems that incorporate physiological data via wearables and behavioral recognition through computer vision. Assessment techniques are evolving from static models such as the Predicted Mean Vote (PMV) towards adaptive, personalized frameworks supported by Digital Twins and integrated simulations. Furthermore, control logic is transitioning toward Reinforcement Learning and Model Predictive Control to proactively manage occupancy surges and environmental variables. This evolution of monitoring approaches, assessment techniques, and control strategies is represented within the study’s Three-Tiered Developmental Trajectory, providing a novel Body of Knowledge (BOK) for mapping the transition of building systems from reactive tools to autonomous, occupant-centric agents. This study also introduces a Cross-Modal Interaction Matrix to systematically analyze the systemic trade-offs between IEQ domains. Furthermore, by establishing the “Implementation Frontier,” this work identifies the specific technical and ethical bottlenecks, such as “false vacancy” sensing errors, fragmented data silos, and the ethical complexities of high-resolution data collection that prevent academic innovations from becoming industry standards. To bridge these gaps, we conclude that the next generation of “cognitive buildings” must prioritize three pillars: resolving binary sensing limitations, harmonizing data via vendor-neutral APIs, and adopting privacy-preserving architectures to ensure scalable, interoperable, and occupant-centric optimization. Full article

Background: Accurate axillary staging is vital in breast cancer. While dual tracers (Tc-99m + methylene blue dye) are standard for sentinel lymph node biopsy (SLNB), indocyanine green (ICG) offers a cost-effective, safe alternative, especially where nuclear medicine access is limited. Despite growing global use, data from low- and middle-income countries (LMICs) remain scarce. This study presents India’s largest cohort using ICG in SLNB. Methods: We analyzed data from 678 breast cancer patients (2013–2023), of whom 609 underwent SLNB. For analysis, patients were grouped into: isotope + blue dye (control), ICG + blue dye (study group), and ICG alone. False-negative rate (FNR) was evaluated in cases where SLNB was followed by axillary lymph node dissection (ALND). All other outcomes were assessed across the SLNB cohort. Results: In upfront surgery, the study group had an identification rate (IR) of 95.6%, an FNR of 5%, and a median node yield of four, compared to the control group (IR 94.1%, FNR 0%, median of three). Post-neoadjuvant systemic therapy (NAST), the study group outperformed the control (IR 92% vs. 88.2%; both FNR 10%), with higher node yield (three vs. two). From 2021, ICG alone showed 100% IR, 0% FNR (upfront), and 95.6% IR (post-NACT), with high median node retrieval. Overall recurrence was 7.8%; loco-regional recurrence was 3.09%. Conclusions: ICG offers high efficacy, safety, and feasibility as a sole tracer, especially in LMICs. Its integration into SLNB and oncoplastic workflows supports its broader adoption as a practical alternative to radioisotopes in breast cancer surgery. Full article

Construction project delays remain a persistent issue, often exacerbated by variation orders that adversely affect both financial and environmental performance, even in the UK. Although Modern Methods of Construction (MMC) have been increasingly employed to mitigate delays and improve efficiency, limited research has examined how variations affect small- and medium-sized enterprises (SMEs) adopting MMC in the UK construction sector. Given the pivotal role of SMEs and their financial vulnerability, this study examines the key challenges posed by variation orders for SMEs adopting MMC, with the broader aim of enhancing future project performance. Employing a two-stage iterative methodology, the research first identifies challenges through a comprehensive literature review, followed by a questionnaire survey and expert interviews. The resulting data were analysed thematically and statistically using SPSS and subsequently validated through a detailed case study involving interviews and document analysis. The findings highlight three principal clusters of challenges: operational, contractual, and module alteration-related, of which operational issues, particularly cost discrepancies, client approval delays, and rework, exert the most significant influence. The study provides a structured understanding of these interlinked challenges and underscores the need for targeted mitigation strategies to improve productivity and performance among UK construction SMEs engaged in MMC projects. Full article

Against the background of high-quality development and urban renewal in China, old residential communities have become key areas for improving spatial quality and quality of life. We used the entrance pavilion of Baoshengli North Community as a case study to explore how spatial design and layout can meet residents’ psychological and social needs. Adopting a mixed-methods approach, combining field observation, behavioral mapping, a questionnaire (Total = 105), in-depth interviews, and statistical analysis, a post-occupancy evaluation (POE) was conducted on spatial effectiveness and social functions. The results show that user-oriented spatial design, safety, esthetic quality, and inclusive functions significantly enhance residents’ spatial perception, willingness to use the space, and social interaction. Differentiated spatial preferences and potential conflicts among diverse resident groups were also identified. Targeted design interventions can effectively strengthen the connection between spatial use and subjective perception, and participatory and equitable strategies help promote social harmony and justice. This study enriches the post-occupancy evaluation system for the renewal of old communities from psychological and social dimensions, and provides practical references for user-centered, inclusive, and sustainable public space design in urban renewal practices. One limitation of this study is that data were collected over a single period, which restricts the analysis of seasonal impacts on spatial usage. Full article

In recent times, increasing attention has been devoted to the investigation of atmospheric processes through remote sensing in order to improve our understanding of climate dynamics and atmospheric physics. This requires accurate simulation of the spectra emitted by the Earth, from which atmospheric composition and thermodynamic conditions can be retrieved. The FORUM mission focuses on observations of the Earth’s outgoing radiation in the far-infrared spectral region, which has been only sparsely explored due to observational challenges, despite its significant contribution to the characterization of atmospheric processes. As part of the mission activities, dedicated simulations of the measurements expected from the FORUM instrument are required. Different models and codes can be employed for this purpose. Fast radiative transfer models, such as SIGMA-FORUM, efficiently simulate all-sky conditions, whereas detailed line-by-line models, such as KLIMA, have generally been limited to clear-sky applications. In this context, SIGMA-FORUM, an all-sky fast radiative transfer model operating in the 10–2760 cm⁻¹ spectral range and KLIMA, a FORTRAN-based line-by-line algorithm extensively validated under clear-sky conditions, are used to simulate FORUM radiances in both clear and cloudy atmospheres. This study extends the comparison between SIGMA-IASI/F2N and KLIMA to cloudy-sky scenarios by incorporating cloud optical properties into KLIMA using the same parametrization approach adopted in SIGMA-FORUM version 2.4. By combining complementary modeling approaches, this work enables KLIMA to simulate atmospheric radiances under all-sky conditions, thereby broadening its applicability. Full article

Maritime UAV perception must reliably detect and track tiny vessels under harsh specular glare. In practice, detection failures are dominated by two coupled factors: (i) vessels often occupy only a few pixels, causing small-object recall collapse and (ii) sun glint and sea-surface reflections generate over-exposed regions that trigger false positives and unstable associations. This paper presents Resi-YOLO, a system-level pipeline that improves tiny-vessel sensitivity while preserving embedded throughput on a Jetson Orin Nano. At the model level, Resi-YOLO combines a P2-enhanced feature path with CBAM-based glare suppression to strengthen high-resolution semantics and suppress glare-induced artifacts; optional SAHI-style slicing is supported for ultra-high-resolution scenes. At the system level, we adopt a heterogeneous dual-brain deployment, where the Orin Nano performs primary inference and an MCU-based safety-island tracker mitigates delay/jitter via time-stamped measurement replay and IMM-UKF updates. We further define a Glare Severity Score (GSS) to stratify robustness by illumination intensity. Experiments show that Resi-YOLO improves AP_small by 13.1 percentage points over YOLOv8n (18.4% to 31.5%), raises high-glare mAP@0.5 from 41.2% to 53.7%, and runs at 12.8 FPS end-to-end (~100 ms latency) on Jetson Orin Nano, while TensorRT inference-only throughput exceeds 30 FPS. Full article

With the rapid growth of fashion e-commerce, fashion attribute recognition has emerged as a critical research area in computer vision. Existing methods face two primary problems: (1) building multi-task models, leading to complex network architectures; (2) the overlooking of semantic relationships and spatial positional dependencies between fashion attributes. To address these issues, this paper proposes SLAR-Net, a novel hierarchical multi-label classification network that effectively fuses spatial and semantic information for improved recognition performance. Specifically, SLAR-Net adopts a progressive, hierarchical architecture. Firstly, we introduce a lightweight backbone network enhanced with a custom-designed attention mechanism to extract low-level image features. Secondly, we innovatively construct an adjacency matrix to represent the relative spatial orientations of attributes, which is then employed by a graph convolutional network to model mid-level spatial positional features. Thirdly, we design a graph embedding matrix that captures attribute dependency relationships, leveraging a neural network to learn high-level semantic representations. Finally, we propose a custom multi-head attention mechanism to fuse spatial and semantic features, facilitating enhanced feature interaction and improving recognition performance. Experimental results on fashion attribute and benchmark datasets demonstrate that SLAR-Net outperforms state-of-the-art methods in recognition accuracy, validating the effectiveness of the proposed hierarchical architecture and fusion strategy. Full article

The Taskforce on Nature-related Financial Disclosures (TNFD) refers to both the international organizing body and the disclosure framework it developed. Throughout this article, the term TNFD is used to encompass both the organization and the framework to ensure precision while maintaining conciseness. TNFD has emerged as a key mechanism for integrating nature-related risks and opportunities into corporate decision-making, extending the scope of existing environmental, social, and governance (ESG) and climate-related disclosures. As TNFD adoption remains at an early diffusion stage, empirical evidence on its global uptake and sectoral characteristics is still limited, particularly in nature-dependent industries such as forestry and environmental services. This study provides an exploratory mapping of global TNFD adoption patterns using the complete list of TNFD adopting organizations disclosed on the official TNFD platform as of June 2025. A total of 584 organizations across 54 countries were analyzed, with a focused examination of forestry- and environment-related entities. Rather than testing causal relationships, this research adopts a descriptive and structural analytical approach to identify geographic, institutional, and sectoral patterns of adoption. The results reveal a strong concentration of TNFD adoption in developed economies and corporate entities, while forestry-specific adopters remain limited in number. Notably, TNFD adoption does not appear to correlate with forest resource endowment, suggesting that governance capacity and financial disclosure readiness are more influential than ecological conditions. Based on these findings, the study discusses strategic implications for forestry and environmental organizations and proposes a conceptual framework for advancing nature-related financial disclosure in the sector. This research contributes an early-stage empirical foundation for understanding TNFD diffusion and offers practical insights for policymakers, corporations, and researchers seeking to operationalize nature-related disclosure frameworks. Full article

The fashion industry is one of the most resource-intensive sectors, generating major environmental impacts such as greenhouse gas emissions, excessive water and land use, and pollution from waste and microplastics. Fast fashion intensifies these issues through overproduction and overconsumption. However, growing consumer awareness and regulatory pressure are pushing brands to adopt Circular Economy (CE) and sustainability strategies, including resale platforms, recycling programs, and sustainability frameworks. Despite these efforts, their real effectiveness remains uncertain. This study investigates which CE and sustainability strategies are most common among fast fashion companies and how they can mitigate key environmental impacts. Using a Fuzzy Cognitive Maps (FCM) model, the research quantitatively evaluates the effects of various circular and sustainable strategies across the supply chain. Ten key strategies were identified, revealing that isolated actions are often ineffective. Instead, an integrated, systemic approach combining multiple initiatives is essential to achieve meaningful sustainability improvements. Full article

This paper proposes a new fuzzy logic-based rotor flux model reference adaptive system (FLC-MRAS) for rotor speed estimation in induction motor drives, replacing the constant-gain PI controller used in conventional MRAS schemes. The proposed observer simultaneously incorporates both rotor flux and electromagnetic torque errors to enhance estimation accuracy and robustness against load torque disturbances. A nonlinear Mamdani-type fuzzy logic controller (FLC) with two inputs and one output, employing triangular membership functions and seven fuzzy sets, is adopted. The effectiveness and useful operational performance of the proposed approach is examined through extensive simulation cases under various vehicle speed driving profiles and load torque conditions using an indirect vector-controlled induction motor drive. In order to investigate the effective operational performance of a speed estimator, different cases are prepared according to the vehicle requirements. To examine the robustness of the proposed observer under realistic operating conditions, rotor resistance variation is incorporated into the simulation framework. This approach allows assessment of MRAS performance under practical nonlinearities and parameter uncertainties encountered in real applications. Comparative results demonstrate superior speed regulation and speed tracking, reduced estimation error, and faster convergence of the adaptive tuning signal for better speed estimation compared to the PI-MRAS observer. The proposed scheme provides the suitable choice of traction drive adoption for electric vehicle (EV) applications. Full article

Illegal artisanal and small-scale mining (galamsey) and climate stress jointly degrade ecosystems and livelihoods in Ghana. This paper demonstrates how community-driven governance can realign incentives toward environmental stewardship and inclusive livelihoods. Using an explanatory sequential mixed-methods design—quantitative difference-in-differences followed by qualitative case analysis and Participatory Action Research—we evaluate a structured program combining vocational training, financial literacy, environmental stewardship, and governance alignment. We operationalize Environmental, Social, and Governance (ESG) outcomes via transparent composite indices and triangulate survey, administrative, and focus group evidence. The study identifies conditions under which alternative livelihoods reduce participation in illegal mining, strengthen women’s economic agency, and improve adoption of climate-smart practices. Implications include practical guidance for program design (community delivery, matched incentives, oversight), policy (local climate finance and accountability mechanisms), and research (scalable indicators and rigorous impact evaluation in resource-dependent communities). Full article

Landslide hazards occur frequently in the Ya’an region; therefore, accurately identifying and delineating potential landslide areas is crucial for disaster prevention and mitigation. Although deep learning-based detection methods using optical remote sensing imagery are widely adopted, the complex terrain and diverse land cover in this area often result in blurred boundaries and weakened textural features, making it difficult to precisely define spatial extents. To overcome these challenges, this study proposes an improved YOLOv11 model for landslide detection. Building on the YOLOv11 baseline, we designed a novel Multi-Scale Detail Enhancement module and integrated it into the neck network to effectively aggregate shallow-level details with deep-level semantic information, thereby enhancing the model’s ability to represent ambiguous boundaries. Additionally, we incorporated the lightweight SimAM attention mechanism into the backbone network. This mechanism dynamically suppresses background noise based on an energy minimization principle, improving feature discriminability within landslide regions and enabling precise boundary boxes. We conducted validation experiments in the Ya’an region using a custom dataset constructed from high-resolution UAV orthoimagery, comparing our method against mainstream models such as YOLOv8 and YOLOv10. The results show that the proposed improved YOLOv11 model achieves a precision of 90.2%, a recall of 84.8%, and an mAP of 92.7%. This enhanced performance demonstrates the model’s effectiveness in detecting landslides under complex terrain conditions, providing a practical technical reference for efficient hazard screening and dynamic monitoring. Full article

Objectives: To clarify the characteristics of Basosquamous cell carcinoma (BSC), this study compares demographic, clinical, therapeutic, histological, and outcome findings of BSCs with those of basal cell carcinoma (BCC) and squamous cell carcinoma (SCC). Methods: The authors classified various definitions of BSC into three groups: the broadest, modest, and narrowest definitions. This study adopted the narrowest definition (both BCC and SCC features with transition zones in between) due to its wide use, its adoption by the World Health Organization, and the least heterogeneous definition. From 2009 to 2018, 20 consecutive cases of BSC presented in a single institution, along with 130 cases of BCC and 81 cases of SCC. Results: The statistically different parameters of BSC compared to BCC or SCC were age (SCC > BSC, BCC), duration (BSC, BCC > SCC), unclear border (BSC > BCC, SCC), higher NCCN classification (BSC, SCC > BCC), safety margin (SCC > BSC > BCC), operation time (BSC, SCC > BCC), reconstruction (less primary closure in BSC than BCC), microscopic size (BSC, SCC > BCC), perineural invasion (BSC > BCC), free lateral margin (BSC, SCC > BCC), and follow-up period (BSC > BCC, SCC). Regarding outcome, one distant metastasis (6.3%) in BSCs, no aggressive consequences in BCCs, and four local recurrences (11.1%), two lymph node metastases (5.6%), and one distant metastasis (2.7%) in SCCs were observed. Conclusions: In this Asian cohort, BSC has a trend toward higher rates of overall adverse outcomes compared to BCC, although this difference did not reach definitive statistical significance, unlike the findings reported in Caucasian populations. Early detection and appropriate treatment at the individual patient level are warranted to minimize rare but clinically relevant adverse events and reproduce favorable outcomes at the population level. Wide local excision followed by local flaps could be a successful surgical option with an adequate safety margin and double histopathologic intraoperative and postoperative check-up. Full article

As data exploitation continues to demonstrate its value, ontologies, thesauri, and other semantic datasets are increasingly recognized for enabling semantically meaningful data integration across disparate domains. With the proliferation of dataset catalogs, the MOD ontology (Metadata for Ontology Description and publication) was adopted, and an associated API was developed to support the future European Open Science Cloud (EOSC). Their aim is to harmonize catalogs of semantic datasets with respect to metadata vocabularies and access mechanisms, thereby ensuring compliance with the FAIR principles. Within an implementation action involving developers of prominent dataset catalogs, we were selected to integrate the MOD-API into ShowVoc, our platform for publishing and consuming ontologies, thesauri, lexicons, and other Semantic Web datasets. However, ShowVoc already relied on an expressive metadata model, the MDR (acronym for “Metadata Registry”), named after the component responsible for managing the platform’s internal catalog. Due to precise dissemination requirements, the MDR provides multiple abstraction levels and detailed specifications concerning the distributions and formats in which a dataset may be made available. In this article, we report on the challenges that we faced and the trade-offs that we made while reconciling these metadata models, highlighting limitations in the current MOD standard that may inform future enhancements. Full article

This research demonstrates the principle and optimization methodology to create economic and miniaturized high-resolution micro-moisture sensors. The interdigitated fringe electric field-based moisture measurement principle is firstly investigated to sketch the key parameters of printed circuit board (PCB)-based sensors for further performance optimization. Then, a comprehensive study is conducted to analyze parameter variations with conclusions of suggested design rules to achieve higher measurement sensitivity. Two prototypes are designed and manufactured to validate the proposed theoretical contributions. Water droplets are employed to control the ambient relative humidity, which is adopted as the actual moisture variable in this work. A double-correlated sampling circuit is used for capacitance sensing. Both of them demonstrate a linearity of 1% and sensitivity of 0.1 pF/mg levels, but prototype 2 gains a better batch consistency, which is beneficial for commercialization. Further data analysis suggests that the equivalent input–output sensitivity reaches a level of 1.2403 pF/%RH (relative humidity), which is significantly higher than other types of published interdigitated fringe electric field-type moisture sensors. The optimized prototypes also show advantages of miniaturized size, low cost and high consistency, which can potentially impact the industry applications. Full article