Search Results (23,847)

The primary aim of this study is to develop an effective decision-support system for managing crises related to the release of hazardous airborne substances. Such incidents, which can arise from industrial accidents or intentional releases, necessitate the rapid identification of contaminant sources to enable timely response measures. This work focuses on a novel approach that integrates a modified Sandpile model with advection and employs the (1 + 1)-Evolution Strategy to solve the inverse problem of source localization. The initial section of this paper reviews existing methods for simulating atmospheric dispersion and reconstructing source locations. In the following sections, we describe the architecture of the proposed system, the modeling assumptions, and the experimental framework. A key feature of the method presented here is its reliance solely on concentration measurements obtained from a distributed network of sensors, eliminating the need for prior knowledge of the source location, release time, or emission strength. The system was validated through a two-stage process using synthetic data generated by a Gaussian dispersion model. Preliminary experiments were conducted to support model calibration and refinement, followed by formal tests to evaluate localization accuracy and robustness. Each test case was completed in under 20 min on a standard laptop, demonstrating the algorithm’s high computational efficiency. The results confirm that the proposed (1 + 1)-ES Sandpile model can effectively reconstruct source parameters, staying within the resolution limits of the sensor grid. The system’s speed, simplicity, and reliance exclusively on sensor data make it a promising solution for real-time environmental monitoring and emergency response applications. Full article

Background/Objectives: Gliomas are the most common tumours of the central nervous system (CNS), classified into grades I to IV based on their malignancy. Genetic and epigenetic alterations play a crucial role in glioma progression. DNA methyltransferases (DNMTs) are vital enzymes responsible for DNA methylation, with DNMT1 and DNMT3 catalysing the addition of a methyl group to the 5-carbon of cytosine in CpG dinucleotides. Targeting DNMTs with DNA methyltransferase inhibitors (DNMTi) has become a promising therapeutic approach in tumour treatment. In this study, in silico screening tools were employed to evaluate potential inhibitors of DNMT1, DNMT3A, and DNMT3B for the treatment of glioblastoma multiforme (GBM). Methods: The Gene2Drug platform was used to screen compounds and rank them based on their capacity to dysregulate DNMT genes. PRISM viability assays were performed on 68 cell lines, and DepMap data were analyzed to assess the antitumor activities of these compounds and their target genes. Candidate drug similarity was evaluated using DSEA, and compounds with p < 1 × 10⁻³ were considered statistically significant. Gene-compound interactions for DNMT1, DNMT3A, and DNMT3B were confirmed using Expression Public 24Q2, while Prism Repositioning Public data were analyzed via DepMap. Results: Glioblastoma cell lines showed sensitivity to compounds including droperidol, demeclocycline, benzthiazide, ozagrel, pizotifen, tracazolate, norcyclobenzaprine, monocrotaline, dydrogesterone, 6-benzylaminopurine, and nifedipine. SwissTargetPrediction was utilised to identify alternative molecular targets for selected compounds, revealing high-probability matches for droperidol, pizotifen, tracazolate, monocrotaline, dydrogesterone, and nifedipine. Conclusions: Integrating computational approaches with biological insights and conducting tissue-specific and experimental validations may significantly enhance the development of DNMT-targeted therapies for gliomas. Full article

The transmission of viruses and bacteria via surfaces remains a persistent challenge for healthcare systems, leading to high public health costs and significant environmental impact due to the widespread use and disposal of single-use products. This study aims to evaluate the feasibility of using surface-covering films, based on biopolymers and inorganic nanoparticles, with strong antiviral and antibacterial properties, as a strategy to prevent infection transmission while offering a sustainable alternative to disposable materials. To this end, we developed a sprayable chitosan-based solution embedded with copper oxide nanoparticles (CH.CA@Cu). The solution demonstrated antibacterial activity against both Gram-positive and Gram-negative bacteria as well as virucidal activity, predominantly within one minute of exposure, against a wide range of viruses. After spraying various materials, the resulting film surfaces exhibited excellent adherence and uniform coverage, maintaining their integrity after contact. A field trial conducted in high-traffic environments confirmed the coating’s effectiveness. This long-lasting antiviral action supports their implementation, since the coated surface can continuously deactivate viruses regardless of infective doses of exposure, thereby reducing viral transmission. These findings will expand biopolymers’ current applicability while guiding us toward the adoption of green and eco-friendly technologies, thus reducing waste production. Full article

In recent years, increasing attention has been paid to the impact of air pollution on the neuropsychological development of children and adolescents. However, a comprehensive overview of global research trends and thematic structures in this field remains lacking. This study applies bibliometric methods to systematically analyze 1441 English-language publications from 2000 to 2024, retrieved from the Web of Science Core Collection and Scopus. Using CiteSpace 6.4.R1, VOSviewer 1.6.20, and RStudio Bibliometrix (RStudio version: 2025.05.1+496, R version: 4.5.0, Bibliometrix package version: 5.0.0), we conducted a multidimensional visualization of publication trends, contributing countries and institutions, interdisciplinary integration, author collaborations, and keyword clustering. Results show a marked increase in research output in recent years, with the United States, China, and Spain leading in publication number and international collaboration. Key research themes include particulate pollution, prenatal and early-life exposure, and neuropsychological disorders such as attention-deficit hyperactivity and autism, alongside mechanisms like oxidative stress and neuroinflammation. This study builds a knowledge framework for the field, offering insights for scholars and evidence-based guidance for policymakers to support interventions that protect the neuropsychological health of the younger population. Full article

Background/Objectives: Precision medicine has transformed how we manage cancer patients. As treatments and drug targets become more complex, the associated diagnostic technologies must also evolve to actualize the benefit of these therapeutic innovations. Digital and computational pathology (DP/CP) play a pivotal role in this evolution, offering enhanced analytical techniques and addressing workflow constraints in pathology labs. This study aims to understand clinicians’ awareness, utilization, and willingness to adopt DP/CP-based tools, as well as the role they perceive themselves playing in the adoption of CP-based tests. Methods: A double-blinded, online quantitative survey was conducted among 101 U.S.-based medical oncologists. Results: Awareness of DP/CP varied among clinicians, with only 17% identifying as very aware. Subsequently, the current utilization of CP-based tests is also low. Despite this, clinicians are optimistic about the potential benefits of DP/CP, including reduced turnaround times, improved therapy selection, and more consistent slide review. To achieve full adoption, clinicians recognize that barriers must be addressed, including cost, regulatory guidance and, to a lesser extent, concerns with the “black box” nature of CP algorithms. While the focus for the adoption of DP has centered on pathologists, clinicians anticipate playing a more significant role in the adoption of CP-based tests. Finally, clinicians demonstrated clear willingness to utilize a CP-based CDx, with 90% of respondents identifying as potential adopters. Conclusions: This study highlights a positive outlook for the adoption of DP/CP among clinicians, despite varied awareness and low current utilization. Clinicians recognize the potential benefits of DP/CP but also acknowledge barriers to adoption. Addressing these barriers through education, regulatory approval, and collaboration with pathologists and biopharma is essential for successfully integrating DP/CP technologies into clinical practice. Full article

Accurate modeling of non-cooperative space targets remains a significant challenge, particularly under complex illumination conditions. A hybrid virtual–real framework is proposed that integrates photometric compensation, 3D reconstruction, and visibility determination to enhance the robustness and accuracy of monocular-based modeling systems. To overcome the breakdown of the classical photometric constancy assumption under varying illumination, a compensation-based photometric model is formulated and implemented. A point cloud–driven virtual space is constructed and refined through Poisson surface reconstruction, enabling per-pixel depth, normal, and visibility information to be efficiently extracted via GPU-accelerated rendering. An illumination-aware visibility model further distinguishes self-occluded and shadowed regions, allowing for selective pixel usage during photometric optimization, while motion parameter estimation is stabilized by analyzing angular velocity precession. Experiments conducted on both Unity3D-based simulations and a semi-physical platform with robotic hardware and a sunlight simulator demonstrate that the proposed method consistently outperforms conventional feature-based and direct SLAM approaches in trajectory accuracy and 3D reconstruction quality. These results highlight the effectiveness and practical significance of incorporating virtual space feedback for non-cooperative space target modeling. Full article

Background/Objectives: Family physicians are key stakeholders in the implementation of cancer prevention strategies, including risk factor assessment, lifestyle counseling, and early detection. Despite this, integration of personalized prevention into routine practice remains limited. This study aimed to explore family physicians’ perspectives on barriers, training needs, and collaboration opportunities in cancer prevention. Methods: A mixed-methods study was conducted using an exploratory sequential design. The qualitative phase involved semi-structured interviews with 12 family physicians from the North-West Region of Romania. Thematic analysis was employed to identify main challenges and opportunities. Findings informed the development of a structured online survey completed by 50 family physicians. Descriptive and comparative statistical analyses were applied to assess trends and subgroup differences. Results: Interviews and survey data revealed multiple barriers to cancer prevention in primary care: insufficient consultation time, limited access to diagnostic tools, administrative workload, and low patient health literacy. Physicians reported moderate familiarity with personalized prevention but expressed strong interest in further training, particularly through flexible and interactive learning formats. Collaboration with cancer centers was considered suboptimal; participants emphasized the need for streamlined referral pathways and improved communication. Conclusions: The study highlights systemic and educational gaps affecting cancer prevention efforts in family medicine. Tailored training programs, digital integration with cancer centers, and targeted policy adjustments are needed to enhance prevention capacity within primary care. Full article

The immobilization of titanium dioxide (TiO₂) nanostructures on conductive supports offers a promising strategy to overcome the intrinsic limitations of a wide band gap, poor visible-light absorption, and rapid charge recombination in photocatalysis. Herein, a rutile TiO₂ nanorods (TiO₂NRs) array was directly grown on carbon cloth (CC) via a hydrothermal method by using titanium tetrachloride (TiCl₄) seed solutions of 0.1, 0.3, and 0.5 M, designated as TiO₂NR0.1/CC, TiO₂NR0.3/CC, and TiO₂NR0.5/CC, respectively. Structural analysis confirmed that the TiO₂ NRs array is vertically aligned, and phase=pure rutile NRs strongly adhered to CC. The optical characterization revealed broadened absorption in the visible wavelength region and progressive band gap narrowing with the increasing seeding concentration. Photoluminescence (PL) spectra showed pronounced quenching in the fabricated TiO₂NRs/CC samples, especially with TiO₂NR0.3/CC exhibiting the lowest PL intensity, indicating suppressed charge recombination. Electrochemical impedance spectroscopy further demonstrated reduced charge transfer resistance, and TiO₂NR0.3/CC achieved the most efficient electron transport kinetics. Photocatalytic tests at λ ≥ 400 nm irradiation confirmed the enhanced hydrogen evolution performance of TiO₂NR0.3/CC. The hydrogen yield of 2.66 mmol h⁻¹ g⁻¹ of TiO₂NR0.3/CC was 4.03-fold higher than that of TiO₂NRs (0.66 mmol h⁻¹ g⁻¹), along with excellent cyclic stability across three runs. Additionally, TiO₂NR0.3/CC achieved 90.2% degradation of methylene blue within 60 min, with a kinetic constant of 0.0332 min⁻¹ and minimal activity loss after three cycles. These results highlight the synergistic integration of TiO₂ NRs with CC in achieving a durable, recyclable, and efficient photocatalytic platform for sustainable hydrogen generation and wastewater remediation. Full article

A novel methodology for hybrid energy management in aquaculture is introduced, aimed at enhancing self-sufficiency and optimizing grid-related cash flows. Wind and solar energy generation are modeled using calibrated turbine performance curves and PVGIS data, respectively, with a photovoltaic capacity of 120 kWp. The system also incorporates a 250 kW small hydroelectric plant and a wood drying kiln that utilizes surplus wind energy. This study conducts a comparative analysis between HY4RES, a research-oriented simulation model, and HOMER Pro, a commercially available optimization tool, across multiple hybrid energy scenarios at two aquaculture sites. For grid-connected configurations at the Primary site (base case, Scenarios 1, 2, and 6), both models demonstrate strong concordance in terms of energy balance and overall performance. In Scenario 1, a peak power demand exceeding 1000 kW is observed in both models, attributed to the biomass kiln load. Scenario 2 reveals a 3.1% improvement in self-sufficiency with the integration of photovoltaic generation, as reported by HY4RES. In the off-grid Scenario 3, HY4RES supplies an additional 96,634 kWh of annual load compared to HOMER Pro. However, HOMER Pro indicates a 3.6% higher electricity deficit, primarily due to battery energy storage system (BESS) losses. Scenario 4 yields comparable generation outputs, with HY4RES enabling 6% more wood-drying capacity through the inclusion of photovoltaic energy. Scenario 5, which features a large-scale BESS, highlights a 4.7% unmet demand in HY4RES, whereas HOMER Pro successfully meets the entire load. In Scenario 6, both models exhibit similar load profiles; however, HY4RES reports a self-sufficiency rate that is 1.3% lower than in Scenario 1. At the Secondary site, financial outcomes are closely aligned. For instance, in the base case, HY4RES projects a cash flow of 54,154 EUR, while HOMER Pro estimates 55,532 EUR. Scenario 1 presents nearly identical financial results, and Scenario 2 underscores HOMER Pro’s superior BESS modeling capabilities during periods of reduced hydroelectric output. In conclusion, HY4RES demonstrates robust performance across all scenarios. When provided with harmonized input parameters, its simulation results are consistent with those of HOMER Pro, thereby validating its reliability for hybrid energy management in aquaculture applications. Full article

Sustainability is gaining relevance across organizations, yet significant challenges remain in how it is implemented and translated into daily operations. This paper examines how Lean Six Sigma can be used to address operational challenges while also supporting the integration of sustainability objectives in industrial contexts. The study is based on a project conducted in a fish processing plant, aiming to increase production capacity and reduce delays. Using the DMAIC framework, the team addressed key bottlenecks through demand-based workload leveling, earlier production planning, and targeted maintenance to improve equipment performance. These actions led to measurable gains in throughput, resource use, and schedule reliability. In parallel, they contributed to sustainability outcomes, including reduced rework, lower waste, and improved working conditions. The results suggest that Lean Six Sigma, typically focused on performance, can also act as a platform for embedding sustainability into existing routines. The findings offer insight into how performance-driven approaches can support sustainability transitions in process-intensive industries. Full article

Land Surface Temperature (LST) is a critical variable for understanding land–atmosphere interactions and is widely applied in urban heat monitoring, evapotranspiration estimation, near-surface air temperature modeling, soil moisture assessment, and climate studies. MODIS LST products, with their global coverage, long-term consistency, and radiometric calibration, are a major source of LST data. However, frequent data gaps caused by cloud contamination and atmospheric interference severely limit their applicability in analyses requiring high spatiotemporal continuity. This study presents a seamless MODIS LST reconstruction framework that integrates multi-source data fusion and a multi-stage optimization strategy. The method consists of three key components: (1) topography- and land cover-constrained spatial interpolation, which preliminarily fills orbit-induced gaps using elevation and land cover similarity criteria; (2) pixel-level LST reconstruction via random forest (RF) modeling with multi-source predictors (e.g., NDVI, NDWI, surface reflectance, DEM, land cover), coupled with HANTS-based temporal smoothing to enhance temporal consistency and seasonal fidelity; and (3) Poisson-based image fusion, which ensures spatial continuity and smooth transitions without compromising temperature gradients. Experiments conducted over two representative regions—Huainan and Jining—demonstrate the superior performance of the proposed method under both daytime and nighttime scenarios. The integrated approach (Step 3) achieves high accuracy, with correlation coefficients (CCs) exceeding 0.95 and root mean square errors (RMSEs) below 2K, outperforming conventional HANTS and standalone interpolation methods. Cross-validation with high-resolution Landsat LST further confirms the method’s ability to retain spatial detail and cross-scale consistency. Overall, this study offers a robust and generalizable solution for reconstructing MODIS LST with high spatial and temporal fidelity. The framework holds strong potential for broad applications in land surface process modeling, regional climate studies, and urban thermal environment analysis. Full article

Historically, the Mediterranean Sea has been an area of cultural exchange and maritime commerce. One out of many submerged archaeological sites is the Roman shipwreck that was discovered in 2006 off the coast of Santo Stefano al Mare, in the Ligurian Sea, Italy. The wreck was dated to the 1st century B.C. and consists of a well-preserved cargo ship of Roman amphorae that were likely used for transporting wine. In this study, we present the results of the first underwater survey of the wreck using an Autonomous Underwater Vehicle (AUV) industrialized by Graal Tech. The AUV was equipped with a NORBIT WBMS multibeam sonar, a 450 kHz side-scan sonar, and inertial navigation systems. The AUV conducted multiple high-resolution surveys on the wreck site and the collected data were processed using geospatial analysis methods to highlight local anomalies directly related to the presence of the Roman shipwreck. The main feature was an accumulation of amphorae, covering an area of approximately 10 × 7 m with a maximum height of 1 m above the seabed. The results of this interdisciplinary work demonstrated the effectiveness of integrating AUV technologies with spatial analysis techniques for underwater archaeological applications. Furthermore, the success of this mission highlighted the potential for broader applications of AUVs in the study of the seafloor, such as monitoring seabed movements related to offshore underground energy storage or the identification of objects lying on the seabed, such as cables or pipelines. Full article

Against the backdrop of advancing ecological civilization and increasing public interest in reconnecting with nature, this study examines fireflies—iconic insects cherished for their natural charm—as valuable landscape resources. This study was conducted in Dalingshan Forest Park, Dongguan (Pearl River Delta), using the Forest Science Trail as a sampling site. Surveys combining line transect and point count methods were employed to analyze firefly species composition, adult activity patterns, and flight characteristics. Key findings include: (1) Four species were identified—Asymmetricata circumdata, Pygoluciola qingyu, Aquatica analis, and Luciola satoi—three of which were observed along the trail; (2) Adults appeared sporadically after 19:00, with peak activity occurring between 19:30 and 20:00, showing minor interspecific variation; (3) Although flight height varied slightly among species, most activities concentrated within 0–1.5 m, corresponding to herbaceous and shrub layers; (4) Distinct flight patterns were observed: A. circumdata displayed prolonged intermittent flights, while P. qingyu and L. satoi exhibited shorter perching-based flights. These results provide a scientific basis for firefly habitat conservation, biodiversity promotion, and the sustainable integration of firefly landscapes into nature education and ecotourism. Full article

Organic manure and grass mulching are widely recognized as modifiers of soil microbial communities and nutrient dynamics; however, the combined effects of these practices on nitrogen fractionation and microbial functionality in orchard ecosystems remain poorly understood. This study conducted a comprehensive evaluation of soil nitrogen fractions, enzymatic activity, microbial diversity and functional traits in walnut orchards under three management practices: organic manure (OM), grass mulching combined with manure (GM), and chemical fertilization (CF) in China’s Loess Plateau. The results revealed that OM and GM significantly enhanced soil nutrient pools, with GM elevating total nitrogen by 1.96-fold, soil organic carbon by 97.79%, ammonium nitrogen by 128%, and nitrate nitrogen by 54.56% relative to CF. Furthermore, the OM significantly increased the contents of total hydrolysable nitrogen, amino sugar nitrogen, amino acid nitrogen, ammonia nitrogen, hydrolysable unidentified nitrogen, non-acid-hydrolyzable nitrogen compared to the CF and GM treatments. Meanwhile, ASN and AN had significant effects on mineral and total nitrogen. The OM and GM had higher activities of leucine aminopeptidase enzymes (LAP), α-glucosidase enzyme, β-glucosidase enzyme (βG), and N-acetyl-β-D-glucosidase enzyme (NAG). Microbial community analysis revealed distinct responses to different treatments: OM and GM enhanced bacterial Shannon index, while suppressing fungal diversity, promoting the relative abundance of copiotrophic bacterial phyla such as Proteobacteria and Chloroflexi. Moreover, GM favored the enrichment of lignocellulose-degrading Ascomycota fungi. Functional annotation indicated that Chemoheterotrophy (43.54%) and Aerobic chemoheterotrophy (42.09%) were the dominant bacterial metabolic pathways. The OM significantly enhanced the abundance of fermentation-related genes. Additionally, fungal communities under the OM and GM showed an increased relative abundance of saprotrophic taxa, and a decrease in the relative abundances of potential animal and plant pathogenic taxa. The Random forest model further confirmed that βG, LAP, and NAG, as well as Basidiomycota, Mortierellomycota, and Ascomycota served as pivotal mediators of soil organic nitrogen fraction. Our findings demonstrated that combined organic amendments and grass mulching can enhance soil N retention capacity, microbial functional redundancy, and ecosystem stability in semi-arid orchards. These insights support the implementation of integrated organic management as a sustainable approach to enhance nutrient cycling and minimize environmental trade-offs in perennial fruit production systems. Full article

With the rapid development of Internet of Vehicles (IoV) technology, massive data are continuously integrated into intelligent transportation systems, making efficient computing resource allocation a critical challenge for enhancing network performance. Due to the dynamic and real-time characteristics of IoV tasks, existing static offloading strategies fail to effectively cope with the complexity caused by network fluctuations and vehicle mobility. To address this issue, this paper proposes a task offloading algorithm based on the dynamic adjustment of the parallel factor in directed acyclic graphs (DAG), referred to as Dynamic adjustment of Parallel Factor (DPF). By leveraging edge computing, the proposed algorithm adaptively adjusts the parallel factor according to the dependency relationships among subtasks in the DAG, thereby optimizing resource utilization and reducing task completion time. In addition, the algorithm continuously monitors network conditions and vehicle states to dynamically schedule and offload tasks according to real-time system requirements. Compared with traditional static strategies, the proposed method not only significantly reduces task delay but also improves task success rates and overall system efficiency. Extensive simulation experiments conducted under three different task load conditions demonstrate the superior performance of the proposed algorithm. In particular, under high-load scenarios, the DPF algorithm achieves markedly better task completion times and resource utilization compared to existing methods. Full article