Search Results (1,010)

In recent years, coal-combustion-related air pollution has declined markedly, whereas tropospheric ozone (O₃) pollution has emerged as a growing environmental concern. Long-term exposure to O₃ can severely impact human health and ecosystems, constraining socioeconomic development. The Fenwei Plain has complex topographical conditions and a relatively simple industrial structure, and at present, O₃ is one of the main pollutants affecting air quality in this region. Therefore, studying the distribution of O₃ pollution in the Fenwei Plain can provide a reference for developing plans to control O₃ pollution in the area, which is important for safeguarding local public health and economic development. Currently, the number of pollutant monitoring stations in China is limited, spatially discontinuous, and significantly affected by environmental factors, making it difficult to obtain high-precision, large-scale observational data. Satellite-based remote sensing provides broad spatial coverage and is free from topographic constraints, thereby serving as an effective complement to ground-based monitoring networks. This provides important technical support for studying the distribution characteristics of O₃ pollution and its associated health risks. This study focuses on the Fenwei Plain, utilizing machine learning models to estimate continuous O₃ concentrations from 2015 to 2022 and analyze the spatiotemporal distribution of O₃. Based on this, an assessment and analysis of the health risks associated with near-surface O₃ exposure in the study area will be conducted, incorporating the population exposed in the Fenwei Plain and individuals with chronic obstructive pulmonary disease (COPD). Full article

With the acceleration of urbanization, the coupling relationship between the built environment and urban safety hazards has become increasingly prominent. Irrational spatial structures and resource allocations may aggravate safety hazards and negatively affect residents’ quality of life, thus requiring urgent scientific evaluation and optimization. However, existing studies mostly focus on linear correlation analysis, which makes it difficult to reveal the complex nonlinear mechanisms among multidimensional environmental factors. Taking Cracow (Kraków), Poland as the study area, this research utilizes multi-source spatial data to quantify environmental features such as transportation, socioeconomic conditions, visual landscapes, and public services, in order to uncover their role in the formation of safety hazards. An XGBoost-based safety hazard prediction model is constructed, and SHAP interpretability analysis, together with two-dimensional partial dependence plots (2D PDPs), are introduced to systematically explore the synergistic gains, marginal effects, and resource allocation thresholds of key variables. The results indicate that variables such as average housing price, distance to the nearest police station, and average population density contribute significantly to hazard prediction, and that certain combinations of variables exhibit strong synergistic effects in reducing hazards within medium-range intervals. The study concludes that integrating machine learning with interpretability analysis can not only effectively identify the spatial features associated with high levels of safety hazards, but also provide quantifiable and actionable optimization pathways for urban planning and safety hazard governance. This research further underscores the role of managing urban safety hazards as a key pillar in the sustainable development of cities by linking safety hazard modeling with spatial governance strategies that promote inclusive, resilient, and livable urban environments. Full article

Soil microorganisms are important decomposers in soil, and they play important roles in litter degradation, nutrient cycle and balance, soil physicochemical property improvement, and soil fertility maintenance. To understand the influence of Eucalyptus plantations on the growth, reproduction, and activity of soil microorganisms in severely degraded land, the Leizhou Peninsula in tropical China was selected as the research area. The vegetation restoration types of Eucalyptus urophylla × grandis planted in its severely degraded red soil areas (ES: Eucalyptus–shrub, EG: Eucalyptus–grass, and ED: Eucalyptus–Dicranopteris pedata (Houtt.) Nakaike) were studied, and the nearby natural vegetation types (S: shrub, G: grass, and D: Dicranopteris pedata) served as control groups. The microbial characteristics of different vegetation restoration types were compared, and the influence of Eucalyptus plantations on the growth, reproduction, and activity of soil microorganisms in severely degraded red soil areas was discussed by setting up sample plots for investigation, sample determination, and statistical analysis. The structure of soil microorganisms differed significantly between Eucalyptus vegetation restoration (ER) and natural vegetation restoration without Eucalyptus (NER). Key organic decomposers, including bacterial genera such as Candidatus Solibacter (ER: 1.2 ± 0.4% vs. NER: 0.9 ± 0.1%), Candidatus Koribacter (ER: 1.0 ± 0.4% vs. NER: 0.7 ± 0.1%), and Edaphobacter (ER: 0.9 ± 0.1% vs. NER: 0.4 ± 0.1%), as well as fungal genera such as Rhizophagus (ER: 0.1 ± 0.0% vs. NER: 0.0 ± 0.0%), Paxillus (ER: 0.1 ± 0.0% vs. NER: 0.0 ± 0.0%), and Pisolithus (ER: 0.1 ± 0.0% vs. NER: 0.0 ± 0.0%), exhibited a significantly higher relative richness and a broader distribution in ER compared to NER (p < 0.05). Soil microbial biomass carbon, nitrogen and phosphorus (MBC, MBN, MBP), community structure (keystone taxa and symbiosis network complexity), and functional genes (for growth, reproduction, and decomposition) in ER, especially in ES, were significantly higher than in NER. This study illustrated that Eucalyptus plantations, especially ES types, can promote the growth and reproduction of soil organic decomposers, improve microbial metabolic and biological activities, and increase functional diversity and interactions among microorganisms, thus accelerating the cycle of soil carbon, nitrogen, and phosphorus nutrients, improving soil quality and fertility, and accelerating the recovery of degraded soil fertility. In areas with serious soil degradation and where natural vegetation restoration is difficult, planting Eucalyptus, especially while guiding the understory vegetation to develop into the shrub vegetation type, is an effective vegetation restoration model. Full article

Water is a vital resource for human survival, economic development, and environmental sustainability. It is essential to agriculture, energy production, public health, and biodiversity preservation. Efficient water management is even more important in areas that are prone to scarcity. This paper presents a systematic review of the management of water resources in South Africa, a country characterized by significant water scarcity challenges compounded by its socio-economic and ecological needs. South Africa’s limited freshwater resources are under extreme stress due to its semi-arid climate, unequal rainfall distribution, expanding population, and industrial needs. The nation’s water security has also been made more difficult by historical injustices, climatic fluctuations, and decaying infrastructure. Through a systematic review of 60 scholarly articles published between 2011 and 2025 in the Web of Science database, this study discusses the historical context of water management in South Africa, including the legacy of apartheid-era policies and their impact on access to water. It also examines current management practices, governance structures involving national and local authorities, the role of key institutions such as the Department of Water and Sanitation (DWS), climate change impact on water availability, population growth and urbanization, inequality and access, and challenges in South Africa’s water resources management (WRM). In particular, this review highlights the integration of scientific water quality and biostability assessment into the Integrated Water Resources Management (IWRM) framework in order to produce actionable insights that enhance resilience, sustainability, and equity in WRM. Furthermore, it explores future strategies for sustainable WRM, emphasizing the importance of IWRM, community participation, technological innovation, and climate change adaptation. Through this comprehensive analysis, the paper aims to contribute to a deeper understanding of the complexities and opportunities in ensuring water security for all South Africans. Full article

The increasing prevalence of Arabic text in digital media offers significant potential for sentiment analysis. However, challenges such as linguistic complexity and limited resources make Arabic sentiment analysis (ASA) particularly difficult. In addition, explainable artificial intelligence (XAI) has become crucial for improving the transparency and trustworthiness of artificial intelligence (AI) models. This paper addresses the integration of XAI techniques in ASA through a scoping review of developments. This study critically identifies trends in model usage, examines explainability methods, and explores how these techniques enhance the explainability of model decisions. This review is crucial for consolidating fragmented efforts, identifying key methodological trends, and guiding future research in this emerging area. Online databases (IEEE Xplore, ACM Digital Library, Scopus, Web of Science, ScienceDirect, and Google Scholar) were searched to identify papers published between 1 January 2016 and 31 March 2025. The last search across all databases was conducted on 1 April 2025. From these, 19 peer-reviewed journal articles and conference papers focusing on ASA with explicit use of XAI techniques were selected for inclusion. This time frame was chosen to capture the most recent decade of research, reflecting advances in deep learning and the transformer-based and explainable AI methods. The findings indicate that transformer-based models and deep learning approaches dominate in ASA, achieving high accuracy, and that local interpretable model-agnostic explanations (LIME) is the most widely used explainability tool. However, challenges such as dialectal variation, small or imbalanced datasets, and the black box nature of advanced models persist. To address these challenges future research directions should include the creation of richer Arabic sentiment datasets, the development of hybrid explainability models, and the enhancement of adversarial robustness. Full article

Evidence of possible cerebellar involvement in spatial processing, place learning and other types of higher order functions comes mainly from clinical observations, as well as from mutant mice and lesion studies. The latter, in particular, have reported deficits in spatial learning and memory following surgical or neurotoxic cerebellar ablation. However, the low specificity of such manipulations has often made it difficult to precisely dissect the cognitive components of the observed behaviors. Likewise, due to conflicting data coming from lesion studies, it has not been possible so far to conclusively address whether a cerebellar dysfunction is sufficient per se to induce learning deficits, or whether concurrent damage to other regulatory structure(s) is necessary to significantly interfere with cognitive processing. In the present study, the immunotoxin 192 IgG-saporin, selectively targeting cholinergic neurons in the basal forebrain and a subpopulation of cerebellar Purkinje cells, was administered to adult rats bilaterally into the basal forebrain nuclei, the cerebellar cortices or both areas combined. Additional animals underwent injections of the toxin into the lateral ventricles. Starting from two–three weeks post-lesion, the animals were tested on paradigms of motor ability as well as spatial learning and memory and then sacrificed for post-mortem morphological analyses. All lesioned rats showed no signs of ataxia and no motor deficits that could impair their performance in the water maze task. The rats with discrete cerebellar lesions exhibited fairly normal performance and did not differ from controls in any aspect of the task. By contrast, animals with double lesions, as well as those with 192 IgG-saporin given intraventricularly did manifest severe impairments in both reference and working memory. Histo- and immunohistochemical analyses confirmed the effects of the toxin conjugate on target neurons and fairly similar patterns of Purkinje cell loss in the animals with cerebellar lesion only, basal forebrain-cerebellar double lesions and bilateral intraventricular injections of the toxin. No such loss was by contrast seen in the basal forebrain-lesioned animals, whose Purkinje cells were largely spared and exhibited a normal distribution pattern. The results suggest important functional interactions between the ascending regulatory inputs from the cerebellum and those arising in the basal forebrain nuclei that would act together to modulate the complex sensory–motor and cognitive processes required to control whole body movement in space. Full article

Electric discharge machining processes are prominent in the fastest-growing industries because of their accuracy, achievable complex workpiece shapes, and cost-effectiveness. Furthermore, the machining of high-quality difficult-to-machine alloys is becoming critical in the aerospace, manufacturing, and defence industries. While the optimisation of EDM parameters is essential for improving machining outcomes, it is also important to consider the trade-offs between different performances metrics, such as material removal rate and part accuracy. Part accuracy in terms of dimensional and geometric deviations from nominal values was rarely considered in the literature, if not by the authors. Balancing these factors remains a challenge in the field of EDM. Therefore, this work aims to carry out a multi-objective optimisation of both MRR and part accuracy. A Ni-based alloy (Inconel-625) was used that is widely used in creep-resistant turbine blades and vanes and turbine disks in gas turbine engines for aerospace and defence industries. Four performance indices were optimised simultaneously: two related to the performance of the EDM process and two connected with the form deviations of the manufactured surfaces. Multi-hole copper electrodes having different diameters and three process parameters were varied during the experimental tests. Grey relational analysis and the Adaptive Neuro-Fuzzy Inference System method were used for optimisation. Grey relational analysis found that the following values of the process parameter—0.16 mm of multi-hole electrode diameter, 12 Amperes of Peak current, 200 µs of pulse on time and 0.2 kg/m² as dielectric pressure—produce the optimal performance, i.e., a material removal rate of 0.099 mm³/min, an electrode wear rate of 0.0002 g/min, a circularity deviation of 0.0043 mm and a cylindricity deviation of 0.027 mm. From the experimental examination using multi-hole electrodes, it is concluded that the material removal rate increases and the electrode wear rate decreases because of the availability of higher spark discharge areas between the electrode and work material interface. The Adaptive Neuro-Fuzzy Inference System models showed minimum mean percentage error and, therefore, better performance in comparison with regression models. Full article

Probabilistic roadmap (PRM) methods are widely used for robot navigation in both 2D and 3D environments; however, a major drawback is that the raw paths tend to be jagged. Executing a trajectory along such paths can lead to significant overshoots and tight turns, making it difficult to achieve a near-optimal solution under motion constraints. This paper presents an enhanced PRM-based path planning approach designed to improve path quality and computational efficiency. The method integrates advanced sampling strategies, adaptive neighbor selection with spatial data structures, and multi-stage path post-processing. In particular, shortcut smoothing and polynomial fitting are used to generate smoother trajectories suitable for motion-constrained robots. The proposed hybrid sampling scheme biases sample generation toward critical regions—near obstacles, in narrow passages, and between the start and goal—to improve graph connectivity in challenging areas. An adaptive k-d tree-based connection strategy then efficiently builds a roadmap using variable connection radii guided by PRM* theory. Once a path is found using an any-angle graph search, post-processing is applied to refine it. Unnecessary waypoints are removed via line-of-sight shortcuts, and the final trajectory is smoothed using a fitted polynomial curve. The resulting paths are shorter and exhibit gentler turns, making them more feasible for execution. In simulated complex scenarios, including narrow corridors and cluttered environments, the advanced PRM achieved a 100% success rate where standard PRM frequently failed. It also reduced calculation time to 30% and peak turning angle by up to 50% compared to conventional methods. The approach supports dynamic re-planning: when the environment changes, the roadmap is efficiently updated rather than rebuilt from scratch. Furthermore, the use of an adaptive k-d tree structure and incremental roadmap updates leads to an order-of-magnitude speedup in the connection phase. These improvements significantly increase the planner’s path quality, runtime performance, and reliability. Quantitative results are provided to substantiate the performance gains of the proposed method. Full article

Artificial intelligence (AI), particularly machine learning (ML), is equipping laser micro/nano processing with significant intelligent capabilities, demonstrating exceptional performance in areas such as manufacturing process modeling, process parameter optimization, and real-time anomaly detection. This transformative potential is driving the development of next-generation laser micro/nano processing technologies. The key challenges confronting traditional laser manufacturing stem from the complexity of laser–matter interactions, resulting in difficult-to-control processing outcomes and the accumulation of micro/nano defects across multi-step processes, ultimately triggering catastrophic process failures. This review provides an in-depth exploration of how machine learning effectively addresses these challenges through the integration of data-driven modeling with physics-driven modeling, coupled with intelligent in situ monitoring and adaptive control techniques. Systematically, we summarize current representative breakthroughs and frontier advances at the intersection of machine learning and laser micro/nano processing research. Furthermore, we outline potential future research directions and promising application prospects within this interdisciplinary field. Full article

Micro- and nanopatterning is crucial for advanced photonic, electronic, and sensing devices. Yet achieving large-area periodic nanostructures with a 75 nm half-pitch on low-cost laboratory systems remains difficult, because conventional near-ultraviolet laser interference lithography (LIL) suffers from Gaussian-beam non-uniformity and a narrow exposure latitude. Here, we report a cost-effective deep-ultraviolet (DUV) dual-beam LIL system based on a 266 nm laser and diffractive flat-top beam shaping, enabling large-area patterning of periodical nanostructures. At this wavelength, a moderate half-angle can be chosen to preserve a large beam-overlap region while still delivering 150 nm period (75 nm half-pitch) structures. By independently tuning the incident angle and beam uniformity, we pattern one-dimensional (1D) gratings and two-dimensional (2D) arrays over a Ø 1.0 cm field with critical-dimension variation < 5 nm (1σ), smooth edges, and near-vertical sidewalls. As a proof of concept, we transfer a 2D pattern into Si to create non-metal-coated nanodot arrays that serve as surface-enhanced Raman spectroscopy (SERS) substrates. The arrays deliver an average enhancement factor of ~1.12 × 10⁴ with 11% intensity relative standard deviation (RSD) over 65 sampling points, a performance near the upper limit of all-dielectric SERS substrates. The proposed method overcomes the uneven hotspot distribution and complex fabrication procedures in conventional SERS substrates, enabling reliable and large-area chemical sensing. Compared to electron-beam lithography, the flat-top DUV-LIL approach offers orders-of-magnitude higher throughput at a fraction of the cost, while its centimeter-scale uniformity can be scaled to full wafers with larger beam-shaping optics. These attributes position the method as a versatile and economical route to large-area photonic metasurfaces and sensing devices. Full article

Unmanned aerial vehicle (UAV) image object detection has become an increasingly important research area in computer vision. However, the variable target shapes and complex environments make it difficult for the model to fully exploit its features. In order to solve this problem, we propose a UAV image object detection method based on a backbone feature reuse detection network, named BFRDNet. First, we design a backbone feature reuse pyramid network (BFRPN), which takes the model characteristics as the starting point and more fully utilizes the multi-scale features of backbone network to improve the model’s performance in complex environments. Second, we propose a feature extraction module based on multiple kernels convolution (MKConv), to deeply mine features under different receptive fields, helping the model accurately recognize targets of different sizes and shapes. Finally, we design a detection head preprocessing module (PDetect) to enhance the feature representation fed to the detection head and effectively suppress the interference of background information. In this study, we validate the performance of BFRDNet primarily on the VisDrone dataset. The experimental results demonstrate that BFRDNet achieves a significant improvement in detection performance, with the mAP increasing by 7.5%. To additionally evaluate the model’s generalization capacity, we extend the experiments to the UAVDT and COCO datasets. Full article

The rugged terrain and dense vegetation in the mountainous area of Luding after the strong earthquake have made geologic hazards hidden and difficult to verify, and there are limitations in the fine-resolution monitoring of small-scale landslides, especially in the area covered by high vegetation. Currently, there is a lack of research on the application of L-band LuTan-1 (LT-1) for landslide detection in the dense vegetation-covered area of the Luding strong earthquake zone, and it is necessary to carry out the analysis of the detection capability of LT-1 for small-scale landslide hazards under the complex terrain and dense vegetation area. In this study, the Stacking-InSAR method was employed using LT-1 and Sentinel-1 satellites to conduct deformation monitoring and landslide detection in the Luding seismic area and to investigate the small-scale landslide detection capability of LT-1 in vegetation-covered areas. The results show that LT-1 and Sentinel-1 identified 23 landslide hazards, and their obvious deformation and landslide characteristics indicate that they are still in an unstable state with a continuous deformation trend. At the same time, through the detection analysis of LT-1’s landslide detection capability under high vegetation cover and small-scale landslide detection capability, the results show that the long wavelength LT-1 can be more effective in landslide hazard identification and monitoring than the short wavelength, and LT-1 with high spatial resolution can be more refined to depict the landslide deformation characteristics in space, which demonstrates the great potential of LT-1 in the refinement of landslide detection. It shows the significant potential of the LT-1 satellite data in landslide detection. Finally, the effects of geometric distortion on landslide detection under different satellite orbits are analyzed, and it is necessary to adopt the combined monitoring method of elevating and lowering orbits for landslide detection to ensure the integrity and reliability of landslide detection. This study highlights the capability of the LT-1 satellite in monitoring landslides in complex mountainous terrain and underscores its potential for detecting small-scale landslides. The findings also offer valuable insights for future research on landslide detection using LT-1 data in similar challenging environments. Full article

Leishmaniasis is difficult to control due to clinical and vector diversity associated with the complex life cycle of Leishmania parasites, which are transmitted by sandflies. This study investigated the presence of Leishmania DNA in sandfly vectors, their blood meal sources, and their distribution in relation to environmental and climatic variables in four municipalities in Piauí state, Brazil. Between 2020 and 2022, sandflies were collected, morphologically identified, and analyzed for the presence of parasite DNA and blood meal sources (PCR, sequencing). Climate data were correlated with the density of collected insects. Among the 10,245 specimens collected, Lutzomyia longipalpis (54.87%) and Nyssomyia whitmani (30.41%) were the most abundant in the collection areas. Leishmania braziliensis DNA was detected in Lu. longipalpis, while L. braziliensis and Leishmania infantum DNAs were recovered from Ny. whitmani. Homo sapiens was the main blood meal source (~73%). Vector density was associated with humidity, temperature, and precipitation in Teresina and Pedro II, with significant results for Ny. whitmani. In conclusion, Lu. longipalpis, widely adapted to anthropized environments, can act as a potential vector of the etiological agent of cutaneous leishmaniasis in Teresina and Oeiras. In Pedro II, the detection of L. infantum DNA in Ny. whitmani suggests a possible role of this species in the transmission cycle of visceral leishmaniasis, reinforcing the complex ecoepidemiology of Leishmania spp. in Piauí. Full article

Using systematic literature observations, this study explains how 3D printing technology is being applied to innovative sustainable construction (Systematic Literature Review). Additive manufacturing, also referred to as 3D printing technology, has greatly increased productivity and adoption in the building sector. The utilization of eco-friendly materials, enhancing sustainable building practices, and the environmental impact of 3D printing technology in comparison to conventional techniques are the three primary areas of attention for this study. By reducing material waste through additive manufacturing methods, 3D printing technology may employ alternative resources like fly ash, geopolymers, and limestone calcined clay (LC3) cement, which lowers carbon emissions considerably, according to observation data. This technology also speeds up the construction process, saves costs, and enables complex architectural designs that are difficult to achieve with conventional methods. There are still a number of issues, though, such as the high upfront expenditures of supplies and equipment and the long-term robustness of the molded structures that are produced. Nevertheless, 3D printing has enormous potential to transform building methods into more effective and ecologically friendly ones as a result of technological advancements and growing knowledge of desirability. This research provides valuable insights for stakeholders in supporting wider application of this technology to achieve sustainable development goals. Full article

Background: the Skill, Rule, and Knowledge (S-R-K) model is a framework used to describe and analyze human behaviour and decision-making in complex environments based on the nature of the task and kind of cognitive control required. The S-R-K model is particularly useful in fields like human factor engineering, system design, and safety-critical industries because it helps to understand human errors and how they relate to different levels of cognitive control. However, the S-R-K model is still qualitative and lacks specific and quantifiable metrics for determining what kind of cognitive control a person is using at any given time. This aspect makes difficult to directly measure and compare performance across the three levels. This study aimed therefore to characterize the S-R-K model from a neurophysiological perspective by analyzing the operator’s cerebral cortical activity. Methods: in this study, participants carried out experimental tasks able to replicate the Skill (tracking task), Rule (rule-based navigation) and Knowledge conditions (unfamiliar situations). Results: participants’ Electroencephalogram (EEG) was recorded during tasks execution and then Global Field Power (GFP) was estimated in the different EEG frequency bands. Brodmann areas (BAs) and EEG features were then used to characterize the S-R-K pattern over the cerebral cortex and as inputs to build up the machine learning-based model to estimate participants’ cognitive control behaviours while dealing with tasks. Conclusions: the results demonstrate the possibility of objectively measuring the different S, R and K levels in terms of brain activations. Furthermore, such evidence is consistent with the scientific literature in terms of cognitive functions corresponding to the different levels of cognitive control. Full article