Search Results (6,415)

The growing mismatch between ecosystem service (ES) supply and demand underscores the importance of thoroughly understanding their spatiotemporal patterns and key drivers to promote ecological civilization and sustainable development at the regional level in China. This study investigates six key ES indicators across mainland China—habitat quality (HQ), carbon sequestration (CS), water yield (WY), sediment delivery ratio (SDR), food production (FP), and nutrient delivery ratio (NDR)—by integrating a suite of analytical approaches. These include a spatiotemporal analysis of trade-offs and synergies in supply, demand, and their ratios; self-organizing maps (SOM) for bundle identification; and interpretable machine learning models. While prior research studies have typically examined ES at a single spatial scale, focusing on supply-side bundles or associated drivers, they have often overlooked demand dynamics and cross-scale interactions. In contrast, this study integrates SOM and SHAP-based machine learning into a dual-scale framework (grid and city levels), enabling more precise identification of scale-dependent drivers and a deeper understanding of the complex interrelationships between ES supply, demand, and their spatial mismatches. The results reveal pronounced spatiotemporal heterogeneity in ES supply and demand at both grid and city scales. Overall, the supply services display a spatial pattern of higher values in the east and south, and lower values in the west and north. High-value areas for multiple demand services are concentrated in the densely populated eastern regions. The grid scale better captures spatial clustering, enhancing the detection of trade-offs and synergies. For instance, the correlation between HQ and NDR supply increased from 0.62 (grid scale) to 0.92 (city scale), while the correlation between HQ and SDR demand decreased from −0.03 to −0.58, indicating that upscaling may highlight broader synergistic or conflicting trends missed at finer resolutions. In the spatiotemporal interaction network of supply–demand ratios, CS, WY, FP, and NDR persistently show low values (below –0.5) in western and northern regions, indicating ongoing mismatches and uneven development. Driver analysis demonstrates scale-dependent effects: at the grid scale, HQ and FP are predominantly influenced by socioeconomic factors, SDR and WY by ecological variables, and CS and NDR by climatic conditions. At the city level, socioeconomic drivers dominate most services. Based on these findings, nine distinct supply–demand bundles were identified at both scales. The largest bundle at the grid scale (B3) occupies 29.1% of the study area, while the largest city-scale bundle (B8) covers 26.5%. This study deepens the understanding of trade-offs, synergies, and driving mechanisms of ecosystem services across multiple spatial scales; reveals scale-sensitive patterns of spatial mismatch; and provides scientific support for tiered ecological compensation, integrated regional planning, and sustainable development strategies. Full article

This study evaluates the risk assessment and hazard identification of hydrothermal liquefaction (HTL)-derived bio-oil from the MARINES project, which converts military organic waste into fuel. The high oxygen content (35–50 wt%), acidic pH (2–4), and viscosity (10–1000 cP) of bio-oils pose unique challenges, including oxidative polymerization, corrosion, and micro-explosions during combustion. Key hazards include storage instability, particulate emissions (20–30% higher than diesel), and aquatic toxicity (LC50 < 10 mg/L for phenolics). Mitigation strategies such as inert gas blanketing, preheating, and spill containment are proposed. While offering renewable fuel potential, HTL bio-oil demands rigorous safety protocols for military/industrial deployment, warranting further experimental validation. Full article

Background/Objectives: Communication disorders in childhood, including expressive, receptive, pragmatic, and fluency impairments, have been consistently linked to mental health challenges such as anxiety, depression, and behavioural difficulties. However, existing research remains fragmented across diagnostic categories and developmental stages. This scoping review aimed to synthesise empirical evidence on the relationship between communication disorders and mental health outcomes in children and adolescents and to identify key patterns and implications for practice and policy. Methods: Following the PRISMA Extension for Scoping Reviews (PRISMA-ScR) and Arksey and O’Malley’s framework, this review included empirical studies published in English between 2000 and 2024. Five databases were searched, and ten studies met the inclusion criteria. Data were charted and thematically analysed to explore associations across communication profiles and emotional–behavioural outcomes. Results: Four interconnected themes were identified: (1) emotional and behavioural manifestations of communication disorders; (2) social burden linked to pragmatic and expressive difficulties; (3) family and environmental stressors exacerbating child-level challenges; and (4) a lack of integrated care models addressing both communication and mental health needs. The findings highlight that communication disorders frequently co-occur with emotional difficulties, often embedded within broader social and systemic contexts. Conclusions: This review underscores the need for developmentally informed, culturally responsive, and interdisciplinary service models that address both communication and mental health in children. Early identification, family-centred care, and policy reforms are critical to reducing inequities and improving outcomes for this underserved population. Full article

The root of Plumbago indica L. is commercially available in herbal markets in both crude and powdered forms. P. indica root is a key ingredient in numerous polyherbal formulations. However, P. indica has two closely related species, P. zeylanica L. and P. auriculata Lam. Since only P. indica is traditionally used in Thai polyherbal products, adulteration with other species could potentially compromise the therapeutic efficacy and overall effectiveness of these formulations. To address this issue, a MassARRAY iPLEX assay was developed to accurately identify and differentiate P. indica from its closely related species. Five single nucleotide polymorphism (SNP) sites—positions 18, 112, 577, 623, and 652—within the internal transcribed spacer (ITS) region were selected as genetic markers for species identification. The assay demonstrated high accuracy in identifying P. indica and was capable of detecting the species at DNA concentrations as low as 0.01 ng/µL. Additionally, the assay successfully identified P. zeylanica in commercial crude drug samples, highlighting potential instances of adulteration. Furthermore, it was able to distinguish P. indica in mixed samples containing P. indica, along with either P. zeylanica or P. auriculata. The developed MassARRAY iPLEX assay proves to be a reliable and effective molecular tool for authenticating P. indica raw materials. Its application holds significant potential for ensuring the integrity of herbal products by preventing misidentification and adulteration. Full article

With the continuous expansion of data center scales worldwide, the problem of energy consumption has become increasingly prominent. To address the multi-parameter control challenge in environmental temperature regulation for large data center computer rooms, achieve precise control of hot-aisle temperatures in data centers, and reduce energy waste, this paper designs a multi-parameter model-free adaptive control (MMFAC) algorithm suitable for computer room environmental temperatures. The algorithm integrates the model-free adaptive control (MFAC) algorithm with a weight matrix to perform scaling transformations. Considering the large parameter space of the MFAC controller and the dynamic complexity of data center temperature control systems, compact-form dynamic linearization (CFDL) technology and optimization mathematical methods are used to simplify the parameter identification of the pseudo-Jacobian matrices and the calculation of control quantities for the regulation devices. Simulation experiments based on measured data from a data center show that the proposed algorithm can calculate control quantities for equipment such as air conditioners according to real-time environmental parameter measurements and drive each device based on these control quantities. Meanwhile, the algorithm can reduce errors in key parameters by adjusting the weight matrix. Comparative tests with other control algorithms show that the algorithm has faster response in temperature control and smaller control errors, verifying the effectiveness and application prospects of the algorithm in data center temperature control. Full article

The Sierra Minera de Cartagena-La Unión, located in southeast of the Iberian Peninsula, has been significantly impacted by historical mining activities, which resulted in environmental degradation, including acid mine drainage (AMD) and heavy metal contamination. This study evaluates the potential of PRISMA hyperspectral imagery for multi-temporal mapping of AMD-related minerals in two mining-affected drainage basins: Beal and Gorguel. Key minerals indicative of AMD—iron oxides and hydroxides (hematite, jarosite, goethite), gypsum, and aluminium-bearing clays—were identified and mapped using band ratios applied to PRISMA data acquired over five dates between 2020 and 2024. Additionally, Sentinel-2 data were incorporated in the analysis due to their higher temporal resolution to complement iron oxide and hydroxide evolution from PRISMA. Results reveal distinct temporal and spatial patterns in mineral distribution, influenced by seasonal precipitation and climatic factors. Jarosite was predominant after torrential precipitation events, reflecting recent AMD deposition, while gypsum exhibited seasonal variability linked to evaporation cycles. Goethite and hematite increased in drier conditions, indicating transitions in oxidation states. Validation using X-ray diffraction (XRD), laboratory spectral curves, and a larger time-series of Sentinel-2 imagery demonstrated strong correlations, confirming PRISMA’s effectiveness for iron oxides and hydroxides and gypsum identification and monitoring. However, challenges such as noise, striping effects, and limited image availability affected the accuracy of aluminium-bearing clay mapping and limited long-term trend analysis. Full article

Hydrogen is widely recognized as a key enabler of the clean energy transition, but the lack of safe, efficient, and scalable storage technologies continues to hinder its broad deployment. Conventional hydrogen storage approaches, such as compressed hydrogen storage, cryo-compressed hydrogen storage, and liquid hydrogen storage, face limitations, including high energy consumption, elevated cost, weight, and safety concerns. In contrast, solid-state hydrogen storage using carbon-based adsorbents has gained growing attention due to their chemical tunability, low cost, and potential for modular integration into energy systems. This review provides a comprehensive evaluation of hydrogen storage using carbon-based materials, covering fundamental adsorption mechanisms, classical materials, emerging architectures, and recent advances in computationally AI-guided material design. We first discuss the physicochemical principles driving hydrogen physisorption, chemisorption, Kubas interaction, and spillover effects on carbon surfaces. Classical adsorbents, such as activated carbon, carbon nanotubes, graphene, carbon dots, and biochar, are evaluated in terms of pore structure, dopant effects, and uptake capacity. The review then highlights recent progress in advanced carbon architectures, such as MXenes, three-dimensional architectures, and 3D-printed carbon platforms, with emphasis on their gravimetric and volumetric performance under practical conditions. Importantly, this review introduces a forward-looking perspective on the application of artificial intelligence and machine learning tools for data-driven sorbent design. These methods enable high-throughput screening of materials, prediction of performance metrics, and identification of structure–property relationships. By combining experimental insights with computational advances, carbon-based hydrogen storage platforms are expected to play a pivotal role in the next generation of energy storage systems. The paper concludes with a discussion on remaining challenges, utilization scenarios, and the need for interdisciplinary efforts to realize practical applications. Full article

This work was conducted within the framework of French university reforms undertaken since 2022. Regardless of learning level and target audience, project-based learning has proved its effectiveness as a teaching strategy for many years. The novelty of the present contribution lies in the gamification of this learning method. A popular game, Trivial Pursuit, was adapted to enable students to acquire knowledge in a playful manner while preparing for upcoming technical challenges. Various technical subjects were chosen to create new cards for the game. A total of 180 questions and their answers were created. The colored tokens were then used to trace manufactured products. This teaching experiment was conducted as part of a project-based learning program with third-year Bachelor students (Electrical Engineering and Industrial Computing Department). The game components associated with the challenge proposed to the students comprised six key elements: objectives, challenges, mechanics, components, rules, and environment. Within the framework of the Industry 4.0 concept, this pedagogical activity focused on the knowledge, understanding, development, and application of an RFID (Radio Frequency Identification) system demonstrating the capabilities of this technology. This contribution outlines the various stages of the work assigned to the students. An industrial partner was also involved in this work. Full article

The upcoming deployment of sixth-generation (6G) wireless networks promises to significantly outperform 5G in terms of data rates, spectral efficiency, device densities, and, most importantly, latency and security. To cope with the increasingly complex network traffic, Network Traffic Classification (NTC) will be essential to ensure the high performance and security of a network, which is necessary for advanced applications. This is particularly relevant in the Internet of Things (IoT), where resource-constrained platforms at the edge must manage tasks like traffic analysis and threat detection. In this context, balancing classification accuracy with computational efficiency is key to enabling practical, real-world deployments. Traditional payload-based and packet inspection methods are based on the identification of relevant patterns and fields in the packet content. However, such methods are nowadays limited by the rise of encrypted communications. To this end, the research community has turned its attention to statistical analysis and Machine Learning (ML). In particular, Convolutional Neural Networks (CNNs) are gaining momentum in the research community for ML-based NTC leveraging statistical analysis of flow characteristics. Therefore, this paper addresses CNN-based NTC in the presence of encrypted communications generated by the rising Quick UDP Internet Connections (QUIC) protocol. Different models are presented, and their performance is assessed to show the trade-off between classification accuracy and CNN complexity. In particular, our results show that even simple and low-complexity CNN architectures can achieve almost 92% accuracy with a very low-complexity architecture when compared to baseline architectures documented in the existing literature. Full article

Papaya ripeness identification is a key task in orchard management. To achieve efficient deployment of this task on edge computing devices, this paper proposes a lightweight detection model, ABD-YOLO-ting, based on YOLOv8. First, the width factor of YOLOv8n is adjusted to construct a lightweight backbone network, YOLO-Ting. Second, a low-computation ADown module is introduced to replace the standard downsampling structure, aiming to enhance feature extraction efficiency. Third, an enhanced BiFPN is integrated into the neck structure to achieve efficient multi-scale feature fusion. Finally, to strengthen the model’s capability in identifying small objects, the dynamic detection head DyHead is introduced to improve ripeness recognition accuracy. On a self-constructed Japanese quince orchard dataset, ABD-YOLO-ting achieves a mAP50 of 94.7% and a mAP50–95 of 77.4%, with only 1.47 M parameters and 5.4 G FLOPs, significantly outperforming mainstream models such as YOLOv5, YOLOv8, and YOLOv11. On edge devices, the model achieves a well-balanced trade-off between detection speed and accuracy, demonstrating strong potential for practical applications in intelligent harvesting and orchard management. Full article

This study investigates the impact and mechanisms of digital inclusive finance (DIF) on high-quality economic development in China. Drawing on panel data from 281 prefecture-level cities between 2011 and 2021, we employ a Spatial Durbin Model (SDM) to analyze both the direct effects and spatial spillovers of DIF. The results indicate that (1) DIF has a significantly positive effect on high-quality development, which remains robust after conducting various stability and endogeneity tests; (2) DIF strongly contributes to economic upgrading in eastern regions, while its impact is weaker or even negative in central and western regions, revealing notable regional disparities exist; (3) a key finding is the identification of a double-threshold effect, suggesting that the positive influence of DIF only emerges when financial and industrial development surpass certain thresholds; (4) results from the two-regime SDM further show that spillover effects are more prominent in non-central cities than in central ones; and (5) mechanism analysis reveals that DIF facilitates high-quality growth primarily by promoting industrial structure upgrading. These findings underscore the importance of region-specific policy strategies to enhance the role of DIF and reduce spatial disparities in development across China. Full article

The identification of structural damage types remains a key challenge in electromechanical impedance/admittance (EMI/EMA)-based structural health monitoring realm. This paper proposed a damage classification approach for concrete structures by using integrating discrete wavelet transform (DWT) decomposition of EMA signatures with supervised machine learning. In this approach, the EMA signals of arranged piezoelectric ceramic (PZT) patches were successively measured at initial undamaged and post-damaged states, and the signals were decomposed and processed using the DWT technique to derive indicators including the wavelet energy, the variance, the mean, and the entropy. Then these indicators, incorporated with traditional ones including root mean square deviation (RMSD), baseline-changeable RMSD named RMSDk, correlation coefficient (CC), and mean absolute percentage deviation (MAPD), were processed by a support vector machine (SVM) model, and finally damage type could be automatically classified and identified. To validate the approach, experiments on a full-scale reinforced concrete (RC) slab and application to a practical tunnel segment RC slab structure instrumented with multiple PZT patches were conducted to classify severe transverse cracking and minor crack/impact damages. Experimental and application results cogently demonstrated that the proposed DWT-based approach can precisely classify different types of damage on concrete structures with higher accuracy than traditional ones, highlighting the potential of the DWT-decomposed EMA signatures for damage characterization in concrete infrastructure. Full article

North Africa has a poorly and unevenly known hoverfly fauna. Xanthogramma Schiner, 1861 (Syrphinae, Syrphini) is represented in this territory by some scattered records of four species, Xanthogramma dives (Rondani, 1857), Xanthogramma evanescens Becker & Stein, 1913 (endemic to North Africa), Xanthogramma marginale (Loew, 1854), and Xanthogramma pedissequum (Harris, 1776). After examination of old Xanthogramma material collected in Tanger, Morocco, from the ‘Museo Nacional de Ciencias Naturales, Madrid, Spain (MNCN)’, specimens with distinctive morphology were spotted and found to be different from a syntype of X. evanescens collected in the same locality. Consequently, we revised all the available material of Xanthogramma from North Africa, characterised a new species, proposed a lectotype for X. evanescens, and provided an identification key to the North African species of this genus. The new species is also found in Tunisia and differs from X. evanescens in facial width, colour of the thoracic pleura, length of mesonotum hairs, wing pollinosity, and shape of the yellow maculae on tergum 2. Full article

A new species of Aprostocetus (Hymenoptera: Eulophidae), Aprostocetus bipolaris sp. nov., is recognized to be fortuitously present on a population of the invasive Eucalyptus (E. grandis × E. urophylla) gall wasp Ophelimus bipolaris Chen & Yao, in Guangxi, China. To classify this species, an integrated approach of morphological characteristics and molecular data was applied. The morphology of the new species is described and illustrated, and an identification key for female and male adults is also presented. Regarding phylogenetic analyses, the position of A. bipolaris sp. nov. within the Aprostocetus group of genera was reaffirmed based on 28S and COI gene sequences. All these lines of evidence indicate that A. bipolaris sp. nov. is a new species. Full article

Autoimmune diseases such as systemic lupus erythematosus and Sjögren’s syndrome show pronounced sex disparities in prevalence, severity, and clinical outcomes, with females disproportionately affected. Emerging evidence highlights sex-based differences in immune and inflammatory responses as key contributors to this bias. Genetic factors—including sex chromosomes, skewed X chromosome inactivation, and sex-biased microRNAs—as well as sex hormones and pregnancy modulate gene expression and immune cell function in a sex-specific manner. Additionally, sex hormone-dependent epigenetic modifications influence the transcription of critical immune regulators. These genetic and hormonal factors collectively shape the activation, differentiation, and effector functions of diverse immune cell types. Environmental factors—including infections, gut microbiota, environmental chemicals and pollutants, and lifestyle behaviors such as diet, smoking, UV exposure, alcohol and caffeine intake, physical activity, and circadian rhythms—further modulate immune function and autoimmune disease pathogenesis in a sex-dependent manner. Together, these mechanisms contribute to the heightened risk and distinct clinical features of autoimmunity in females. A deeper understanding of sex-biased immune regulation will facilitate the identification of novel biomarkers, enable patient stratification, and inform the development of sex-specific diagnostic and therapeutic strategies for autoimmune diseases. Full article