Search Results (22,068)

Particulate matter, particularly PM_2.5, poses a significant threat to public health due to its ability to disperse widely and its detrimental impact on the respiratory and circulatory systems upon inhalation. Consequently, it is imperative to maintain regular monitoring and assessment of particulate matter levels to anticipate air pollution events and promptly mitigate their adverse effects. However, predicting air quality is inherently complex, given the multitude of variables that influence it. Deep learning models, renowned for their ability to capture nonlinear relationships, offer a promising approach to address this challenge, with hybrid architectures demonstrating enhanced performance. This study aims to develop and evaluate a hybrid model integrating Convolutional Neural Networks (CNNs) and Recurrent Neural Networks (RNNs) for forecasting PM_2.5 levels in India, Milan, and Frankfurt. A comparative analysis with established deep learning and machine learning techniques substantiates the superior predictive capabilities of the proposed CNN-RNN model. The findings underscore its potential as an effective tool for air quality prediction, with implications for informed decision-making and proactive intervention strategies to safeguard public health. Full article

With intensifying watershed pollution pressures and growing ecological vulnerability, scientifically revealing and enhancing the water environmental carrying capacity is crucial for ensuring the long-term health of the basin and the sustainable socioeconomic development of the region. However, the dynamic regulatory mechanisms linking narrow-sense and broad-sense water environmental carrying capacity remain poorly understood, limiting the development of integrated management strategies. This study systematically investigated the changing trends of both the narrow-sense and broad-sense water environmental carrying capacity in the Harbin section of the Songhua River basin through model calculations, along with the regulatory mechanisms of its key influence indicators. The results of the study on the carrying capacity of the water environment in the narrow sense show that permanganate, total phosphorus, and ammonia nitrogen exhibited partial carrying capacity across water periods, while dissolved oxygen decreased during flat and dry periods, with only limited capacity remaining at the Ash River estuary and in the Hulan River. The biochemical oxygen demand in the Ash River was consistently overloaded, and total nitrogen showed insufficient capacity except during the abundant water period. Broad-sense analysis indicated that improving urbanization quality, water supply infrastructure, and drinking water safety could effectively reduce future overload risks, with projections suggesting a transition from critical to loadable levels by 2030, though latent threats persist. Correlation analysis between narrow- and broad-sense indicators informed targeted control strategies, including stricter regulation of nitrogen- and phosphorus-rich industrial discharges, restoration of aquatic vegetation, and periodic dredging of riverbed sediments. This work is the first to dynamically integrate pollutant and socio-economic indicators through a hybrid modelling framework, providing a scientific basis and actionable strategies for improving water quality and achieving sustainable management in the Songhua River Basin. Full article

In the era of digital–real integration, open-source collaboration has become a strategic pathway for accelerating the innovation catch-up of China’s industrial software. This study employs an exploratory multi-case design, focusing on the China Automotive Operating System open-source project and the FastCAE open-source domestic CAE software integrated development platform to examine how open-source strategies shape collaborative mechanisms and innovation outcomes. The analysis reveals that firms adopt both formal (behavioral and outcome coordination) and informal (relationship and empowerment coordination) strategies, fostering high-level complementary collaboration in data, technology, institution, and human resources. These mechanisms significantly enhance R&D efficiency and quality, drive technological innovation, and create new market innovation, thereby improving collaborative performance. The study contributes to theory by linking open-source-driven digital–real integration with industrial software innovation catch-up and offers practical governance recommendations for strengthening China’s industrial software autonomy and ecosystem sustainability. Full article

With the growing adoption of electric vehicles, optimizing the user experience of charging infrastructure has become critical. However, extracting actionable insights from the vast number of user reviews remains a significant challenge, impeding demand-driven operational planning for charging stations and degrading the user experience. This study leverages three pre-trained language models to perform sentiment classification and multi-level topic identification on 168,129 user reviews from Beijing, facilitating a comprehensive understanding of user feedback. The experimental results reveal significant task-model specialization: RoBERTa-WWM excels in sentiment analysis (accuracy = 0.917) and fine-grained topic identification (Micro-F1 = 0.844), making it ideal for deep semantic extraction. Conversely, ELECTRA, after sufficient training, demonstrates a strong aptitude for coarse-grained topic summarization, highlighting its strength in high-level semantic generalization. Notably, the models offer capabilities beyond simple classification, including autonomous label normalization and the extraction of valuable information from comments with low information density. Furthermore, integrating textual and spatial analyses revealed striking patterns. We identified an urban–rural emotional gap—suburban users are more satisfied despite fewer facilities—and used geographically weighted regression (GWR) to quantify the spatial differences in the factors affecting user satisfaction in Beijing’s districts. We identified three types of areas requiring differentiated strategies, as follows: the northwestern region is highly sensitive to equipment quality, the central urban area has a complex relationship between supporting facilities and satisfaction, and the emerging adoption area is more sensitive to accessibility and price factors. These findings offer a data-driven framework for charging infrastructure planning, enabling operators to base decisions on real-world user feedback and tailor solutions to specific local contexts. Full article

Artificial intelligence (AI) is now the computational core of smart building automation, acting across the entire cyber–physical stack. This review surveys peer-reviewed work on the integration of AI with indoor environmental quality (IEQ) and energy performance, distinguishing itself by presenting a holistic synthesis of the complete technological evolution from IoT sensors to generative AI. We uniquely frame this progression within a human-centric architecture that integrates digital twins of both the building (DT-B) and its occupants (DT-H), providing a forward-looking perspective on occupant comfort and energy management. We find that deep reinforcement learning (DRL) agents, often developed within physics-calibrated digital twins, reduce annual HVAC demand by 10–35% while maintaining an operative temperature within ±0.5 °C and CO₂ below 800 ppm. These comfort and IAQ targets are consistent with ASHRAE Standard 55 (thermal environmental conditions) and ASHRAE Standard 62.1 (ventilation for acceptable indoor air quality); keeping the operative temperature within ±0.5 °C of the setpoint and indoor CO₂ near or below ~800 ppm reflects commonly adopted control tolerances and per-person outdoor air supply objectives. Regarding energy impacts, simulation studies commonly report higher double-digit reductions, whereas real building deployments typically achieve single- to low-double-digit savings; we therefore report simulation and field results separately. Supervised learners, including gradient boosting and various neural networks, achieve 87–97% accuracy for short-term load, comfort, and fault forecasting. Furthermore, unsupervised models successfully mine large-scale telemetry for anomalies and occupancy patterns, enabling adaptive ventilation that can cut sick building complaints by 40%. Despite these gains, deployment is hindered by fragmented datasets, interoperability issues between legacy BAS and modern IoT devices, and the computer energy and privacy–security costs of large models. The key research priorities include (1) open, high-fidelity IEQ benchmarks; (2) energy-aware, on-device learning architectures; (3) privacy-preserving federated frameworks; (4) hybrid, physics-informed models to win operator trust. Addressing these challenges is pivotal for scaling AI from isolated pilots to trustworthy, human-centric building ecosystems. Full article

In order to improve image visual quality in high dynamic range (HDR) scenes while avoiding motion ghosting artifacts caused by exposure time differences, innovative image sensors captured two registered extreme-exposure-ratio (EER) image pairs with complementary and symmetric exposure configurations for high dynamic range imaging (HDRI). However, existing multi-exposure fusion (MEF) algorithms suffer from luminance inversion artifacts in overexposed and underexposed regions when directly combining such EER image pairs. This paper proposes a neural network-based framework for HDRI based on attention mechanisms and edge assistance to recover missing luminance information. The framework derives local luminance representations from a convolution kernel perspective, and subsequently refines the global luminance order in the fused image using a Transformer-based residual group. To support the two-stage process, multi-scale channel features are extracted from a double-attention mechanism, while edge cues are incorporated to enhance detail preservation in both highlight and shadow regions. The experimental results validate that the proposed framework can alleviate luminance inversion in HDRI when inputs are two EER images, and maintain fine structural details in complex HDR scenes. Full article

This paper explores how blockchain technology, widely known as the backbone of cryptocurrencies, can be harnessed to address limitations of traditional water quality monitoring (WQM) systems. Blockchain offers a decentralized, tamper-proof ledger that enables secure, transparent, and traceable data management across distributed networks. When applied to water quality monitoring, blockchain facilitates real-time data acquisition, enhances data integrity, and enables smart contracts for automated regulatory compliance and alerts. These features not only improve the accuracy and efficiency of WQM systems but also build public trust in the reported data. Key insights from current research and pilot applications highlight blockchain’s capacity to integrate with IoT devices for real-time sensing, support adaptive water governance, and empower local stakeholders through decentralized control and transparent access to information. The implications for policy and practice are significant: blockchain-based WQM can support stronger regulatory enforcement, encourage cross-sector collaboration, and provide a robust digital foundation for sustainable water management in smart cities and rural areas alike. As such, this review paper positions blockchain as a transformative tool in the digital transition toward more resilient and equitable water management systems. Full article

Corporate carbon emissions reporting is central to climate accountability, yet significant gaps remain in transparency, completeness, and methodological rigor. This study introduces the Carbon Integrity Index (CIX), a structured framework for assessing disclosure quality through ten indicators covering Scopes 1, 2, and 3. Unlike existing standards focused on reporting requirements, the CIX evaluates how well emissions are reported, addressing methodological transparency, scope coverage, and treatment of uncertainty. Applied to 2022 sustainability reports from companies listed in Spain’s IBEX 35 index, the framework reveals an average score of 5.7/10, with 69% of firms achieving passing results. While Scope 2 reporting was generally robust (mean: 0.82), Scope 3 disclosures—often representing the majority of emissions—and uncertainty assessments were systematically weak (mean: 0.08). Findings provide empirical support for legitimacy and institutional theory, showing how formal compliance can mask performative compliance that limits meaningful accountability. Sectoral differences suggest that institutional pressures and operational complexity shape divergent transparency pathways, raising concerns that universal standards may entrench reporting disparities. The CIX offers regulators, investors, and companies a practical tool for distinguishing symbolic from substantive disclosure, enabling more informed decision-making and strengthening the role of reporting in driving the transition to net-zero business models. Full article

This study investigates the synergistic effects of single- and binary-additive systems on the morphology and nucleation mechanism of Sn-Pb alloy electrodeposited coatings. Scanning electron microscopy (SEM), energy-dispersive spectroscopy (EDS), linear sweep voltammetry (LSV), electrochemical impedance spectroscopy (EIS), and chronoamperometry were applied in order to obtain more information on the action mechanisms of single-additive systems (cinnamaldehyde, PEG-2000, gelatin, vanillin) and binary ones (0.1 g/L cinnamaldehyde + 0.2 g/L PEG-2000) in Sn-Pb electroplating. Results showed that the use of binary-additive systems based on cinnamaldehyde and PEG-2000 significantly improved coating quality, leading to a smooth and uniform surface, dense grains, and a near-eutectic composition (Sn 63.10 wt.%, Pb 36.90 wt.%). This was because the composite additive, through synergistic effects, exhibited the highest cathodic polarization and the largest charge transfer resistance (189.20 Ω cm²), thus inhibiting the electrodeposition process of Sn²⁺ and Pb²⁺. Chronoamperometry revealed that, unlike single additives (PEG-2000 or cinnamaldehyde), the binary-additive system promoted a transition of nucleation mode to instantaneous nucleation, accompanied by a decrease in the peak current and an extension of the corresponding time. This study provides a theoretical basis and experimental support for understanding the nucleation mode of Sn-Pb electroplating, as well as optimizing the synergistic mechanism of additives. Full article

Background/Objectives: Pediatric acquired brain injury (ABI) often results in persistent challenges that extend beyond motor impairments, affecting quality of life (QoL), social participation, and engagement in physical activity. Given the complexity and chronicity of these outcomes, there is a pressing need for multidimensional interventions grounded in the International Classification of Functioning, Disability and Health (ICF). Sport-based exercise interventions, when developmentally adapted and tailored to individual interests, may promote intrinsic motivation, peer connection, and sustainable engagement—factors especially relevant in pediatric ABI populations, who often experience reduced physical activity and social isolation. However, standardized, replicable protocols specifically tailored to this population remain scarce. This study presents the protocol for a randomized controlled trial evaluating the effects of a 16-week sport-based intervention on QoL, social participation, physical activity engagement, and motor functioning tailored for adolescents with pediatric ABI. Methods: Participants will be randomly assigned to an intervention group or a control group receiving usual care. The intervention consists of one weekly 60-minute session, led by trained professionals in adapted physical activity and pediatric neurorehabilitation. It combines sport-based motor skill training, cooperative games, and group activities specifically tailored to each child’s developmental level, motor abilities, and preferences. Outcomes will be assessed at baseline and following the 16-week intervention period, focusing on QoL, participation, physical activity engagement, and motor functioning. Discussion: This study introduces a structured, child-centered model that bridges clinical rehabilitation and community-based sport. By integrating motor and psychosocial targets through a group sport-based intervention, it aims to enhance recovery across ICF domains. Findings may inform interdisciplinary practice and support the development of sustainable strategies to promote long-term engagement and well-being in adolescents with ABI. Full article

Germplasm resources embody the genetic diversity of plants and form the foundation for breeding and the ongoing improvement of elite cultivars. The establishment of germplasm banks, along with their systematic evaluation, constitutes a critical step toward the conservation, sustainable use, and innovative utilization of these resources. Liriodendron, a rare and endangered tree genus with species distributed in both East Asia and North America, holds considerable ecological, ornamental, and economic significance. However, a standardized evaluation system for Liriodendron germplasm remains unavailable. In this study, 297 Liriodendron germplasm accessions were comprehensively evaluated using 34 phenotypic traits and whole-genome resequencing data. Substantial variation was observed in most phenotypic traits, with significant correlations identified among several characteristics. Cluster analysis based on phenotypic data grouped the accessions into three distinct clusters, each exhibiting unique distribution patterns. This classification was further supported by principal component analysis (PCA), which effectively captured the underlying variation among accessions. These phenotypic groupings demonstrated high consistency with subsequent population structure analysis based on SNP markers (K = 3). Notably, several key traits exhibited significant divergence (p < 0.05) among distinct genetic clusters, thereby validating the coordinated association between phenotypic variation and molecular markers. Genetic diversity and population structure were assessed using 4204 high-quality single-nucleotide polymorphism (SNP) markers obtained through stringent filtering. The results indicated that the Liriodendron sino-americanum displayed the highest genetic diversity, with an expected heterozygosity (He) of 0.18 and a polymorphic information content (PIC) of 0.14. In addition, both hierarchical clustering and PCA revealed clear population differentiation among the accessions. Association analysis between three phenotypic traits (DBH, annual height increment, and branch number) and SNPs identified 25 highly significant SNP loci (p < 0.01). Of particular interest, the branch number-associated locus SNP_17_69375264 (p = 1.03 × 10⁻⁵) demonstrated the strongest association, highlighting distinct genetic regulation patterns among different growth traits. A minimal set of 13 core SNP markers was subsequently used to construct unique DNA fingerprints for all 297 accessions. In conclusion, this study systematically characterized phenotypic traits in Liriodendron, identified high-quality and core SNPs, and established correlations between key phenotypic and molecular markers. These achievements enabled differential analysis and genetic diversity assessment of Liriodendron germplasm, along with the construction of DNA fingerprint profiles. The results provide crucial theoretical basis and technical support for germplasm conservation, accurate identification, and utilization of Liriodendron resources, while offering significant practical value for variety selection, reproduction and commercial applications of this species. Full article

Capsicum is one of the most economically significant vegetable crops worldwide, owing to its high content of bioactive compounds with nutritional, pharmacological, and industrial relevance. However, research has focused on C. annuum, often disregarding local diversity and secondary gene pools, which may contain hidden variation for quality traits. Therefore, this study evaluated the genetic and phenotypic diversity of 283 accessions from the Colombian germplasm collection in the agrobiodiversity hotspot of northwest South America, representing all five domesticated species of the genus. A total of 18 morphological, physicochemical, and biochemical fruit traits were assessed, including texture, color, capsaicinoid, and carotenoid content. The phenotypic data were integrated with genomic information obtained through genotyping-by-sequencing (GBS) using the C. annuum reference genome and a multispecies pangenome. Fixed-and-Random-Model-Circulating-Probability-Unification (FarmCPU) and Bayesian-information-and-Linkage-disequilibrium-Iteratively-Nested-Keyway (BLINK) genome-wide association studies (GWAS) were performed on both alignments, respectively, leading to the identification of complex polygenic architectures with 144 and 150 single nucleotide polymorphisms (SNPs) significantly associated with key fruit quality traits. Candidate genes involved in capsaicinoid biosynthesis were identified within associated genomic regions, terpenoid and sterol pathways, and cell wall modifiers. These findings highlight the potential of integrating pangenomic resources with multi-omics approaches to accelerate Capsicum improvement programs and facilitate the development of cultivars with enhanced quality traits and increased agro-industrial value. Full article

The complexity of fake information and the inefficiency of parameter optimization in detection models present dual challenges for current detection technologies. Therefore, this paper proposes a hybrid detection model named MIBKA-CNN-BiLSTM, which significantly improves detection accuracy and efficiency through a triple-strategy enhancement of the Black Kite Optimization Algorithm (MIBKA) and an optimized dual-channel deep learning architecture. First, three improvements are introduced in the MIBKA. The population initialization process is restructured using circle chaotic mapping to enhance parameter space coverage. The conventional random perturbation is replaced by a random-to-elite differential mutation strategy (DE/rand-to-best/1) to balance global exploration and local exploitation. Moreover, a logarithmic spiral opposition-based learning (LSOBL) mechanism is integrated to dynamically explore the opposition solution space. Second, a CNN-BiLSTM dual-channel feature extraction network is constructed, with hyperparameters such as the number of convolutional kernels and LSTM units optimized by MIBKA to enable adaptive model structure alignment with task requirements. Finally, a high-quality fake information dataset is created based on social media platforms, including CCTV. The experimental results show that our model achieves the highest accuracy on the self-built dataset, which is 3.11% higher than the optimal hybrid model. Additionally, on the Weibo21 dataset, our model’s accuracy and F1-score increased by 1.52% and 1.71%, respectively, compared to the average values of all baseline models. These findings offer a practical and effective approach for detecting lightweight and robust false information. Full article

Soil degradation poses a significant threat to the sustainability of agroecosystems, particularly in mountainous regions where environmental conditions are highly variable and management practices are often suboptimal. In this context, soil quality assessment emerges as a key tool for guiding sustainable land use and informing decision-making processes. This study aimed to develop and spatially evaluate a Soil Quality Index (SQI) tailored to the northeast sector of Jangas district, Ancash, Peru. A total of 24 soil indicators were initially considered and reduced using Spearman’s correlations to avoid multicollinearity. Depending on the weighting strategy applied, the final SQI configurations incorporated between 14 and 15 indicators. Two weighting strategies—Principal Component Analysis (PCA) and Expert Opinion (EO)—were combined with linear and non-linear (sigmoidal) scoring functions, resulting in four distinct SQI configurations. The spatial performance of each index was tested using Geographically Weighted Regression Kriging (GWRK), incorporating covariates like NDMI, elevation, slope, and aspect. The SQI constructed using PCA combined with non-linear scoring achieved the highest performance, effectively minimizing skewness and while achieving the highest predictive accuracy under GWRK. By contrast, although the EO-based index with linear scoring demonstrated similar statistical robustness, it failed to achieve comparable effectiveness in terms of spatial predictive accuracy. The SQIs generated offer a practical framework for local institutions to identify and prioritize areas requiring intervention. Through the interpretation of complex soil data into accessible, spatially explicit maps, these indices facilitate the targeted application of inputs—such as organic amendments in low-SQI zones—and support the implementation of improved management practices, including crop rotation and soil conservation, without necessitating advanced technical expertise. Full article

In this preliminary study, the thermoregulatory response in individuals diagnosed with Parkinson’s disease was investigated by infrared thermography. Parkinson’s disease is a complex neurodegenerative disorder primarily known for motor impairments, significantly reducing the quality of life of affected people. However, in most cases, such disease is accompanied or preceded by non-motor symptoms, including autonomic dysfunction. As in the case of neurovegetative dysautonomia, this dysfunction involves a malfunction of the autonomic nervous system, which also plays a key role in thermoregulation. In general, such conditions are not always easy to detect; a valid method could be represented by the vasomotor response of the skin to cold stimuli. In this context, infrared thermography can provide insights into the thermoregulatory patterns associated with autonomic dysfunction, representing a valuable tool for non-invasive assessment of Parkinson’s research. Early biomarkers of the disease can be obtained through changes in skin temperature, allowing for timely intervention and management. The study was conducted on a cohort of 16 subjects (8 patients with Parkinson’s disease and 8 healthy controls), who were monitored with infrared images captured from their hands, following a specific protocol established by a preliminary analysis. Experimental results revealed that thermography can detect focal points and regions exhibiting either hyper- or hypothermia across the skin surface and muscular regions. This capability allows for extracting and categorizing precise medical data, which could inform future research aimed at identifying early markers of the disease. However, as this is a preliminary observational study, no diagnostic claims are made, and further investigations on larger cohorts with controlled comorbidities are needed. Full article