Search Results (2,619)

This paper presents an approach integrating Large Language Models (LLMs), specifically GPT-4 and the open-source DeepSeek-R1, into Geographic Information System (GIS) workflows to enhance the accessibility, flexibility, and efficiency of spatial analysis tasks. We designed and implemented a system capable of interpreting natural language instructions provided by users and translating them into automated GIS workflows through dynamically generated Python scripts. An interactive graphical user interface (GUI), built using CustomTkinter, was developed to enable intuitive user interaction with GIS data and processes, reducing the need for advanced programming or technical expertise. We conducted an empirical evaluation of this approach through a comparative case study involving typical GIS tasks such as spatial data validation, data merging, buffer analysis, and thematic mapping using urban datasets from Pesaro, Italy. The performance of our automated system was directly compared against traditional manual workflows executed by 10 experienced GIS analysts. The results from this evaluation indicate a substantial reduction in task completion time, decreasing from approximately 1 h and 45 min in the manual approach to roughly 27 min using our LLM-driven automation, without compromising analytical quality or accuracy. Furthermore, we systematically evaluated the system’s factual reliability using a diverse set of geospatial queries, confirming robust performance for practical GIS tasks. Additionally, qualitative feedback emphasized improved usability and accessibility, particularly for users without specialized GIS training. These findings highlight the significant potential of integrating LLMs into GISs, demonstrating clear advantages in workflow automation, user-friendliness, and broader adoption of advanced spatial analysis methodologies. Full article

Accurate and detailed tissue characterization is a central goal in medical diagnostics, often requiring the combination of multiple imaging modalities. This study presents a multimodal imaging system that integrates mid-infrared (MIR) scanning with fluorescence imaging to enhance the chemical specificity and spatial resolution in biological samples. A motorized mirror allows rapid switching between MIR and fluorescence modes, enabling efficient, co-registered data acquisition. The MIR modality captures label-free chemical maps based on molecular vibrations, while the fluorescence channel records endogenous autofluorescence for additional biochemical contrast. Applied to mouse brain tissue, the system enabled the clear differentiation of gray matter and white matter, supported by the clustering analysis of spectral features. The addition of autofluorescence imaging further improved anatomical segmentation and revealed fine structural details. In mouse skin, the approach allowed the precise mapping of the layered tissue architecture. These results demonstrate that combining MIR scanning and fluorescence imaging provides complementary, label-free insights into tissue morphology and chemistry. The findings support the utility of this approach as a powerful tool for biomedical research and diagnostic applications, offering a more comprehensive understanding of tissue composition without relying on staining or external markers. Full article

This study conducts a comprehensive bibliometric analysis of research applying data envelopment analysis (DEA) to the evaluation of sustainability and performance in complex socioeconomic systems between 2010 and mid-2025. DEA has become an increasingly valuable tool for measuring efficiency, benchmarking practices, and supporting decision-making in contexts where sustainability challenges intersect with economic, environmental, and governance dimensions. To capture global research dynamics, we extracted and merged bibliographic data from Web of Science and Scopus, analyzing publication trends, thematic clusters, co-authorship networks, citation structures, and keyword co-occurrences using bibliometric tools such as VOSviewer and Bibliometrix. Our findings reveal a consistent growth trajectory of the field, with research outputs peaking in 2020 and subsequently diversifying across multiple thematic areas. Conceptual mapping highlights two dominant domains: (i) policy, governance, and planning and (ii) environmental, ecological, and management applications, both linked through the overarching theme of sustainable development. The analysis further underscores the geographic diversity of contributions, the concentration of knowledge in key publication outlets, and the increasing connectivity of international collaboration networks. By identifying thematic gaps and underexplored intersections, this study emphasizes the need for more interdisciplinary approaches that integrate bibliometric insights with practical sustainability outcomes. The results provide a structured overview of the field’s evolution, offering researchers and policymakers a valuable reference point for advancing DEA applications in sustainability research. Full article

The emergence of Generative Artificial Intelligence (GenAI) is reshaping logistics and supply chain operations, offering new opportunities to improve efficiency, accuracy, and responsiveness. In the automotive manufacturing sector, where high-volume throughput and precision are critical, the integration of AI technologies into warehouse management represents a strategic advancement. This study presents a case analysis of the implementation of AI-driven reception processes at an Automotive facility in Blaj, Romania. The research focuses on the transition from manual operations to automated recognition using industrial-grade imaging systems integrated with enterprise resource planning platforms. The integrated approach used combines Value Stream Mapping, quantitative performance analysis, and statistical validation using the Wilcoxon Signed-Rank Test. The results reveal a substantial reduction in reception time up to 79% and significant cost savings across various operational scales with improved data accuracy and minimized logistics failures. To support broader industry adoption, the study proposes a Cleaner Logistics and Supply Chain Model, incorporating principles of sustainability, ethical compliance, and continuous improvement. This model serves as a strategic framework for organizations seeking to align AI adoption with long-term operational resilience and environmental responsibility. The findings validate the operational and financial advantages of AI-enabled warehousing management in achieving sustainable digital transformation in logistics. Full article

This study introduces a novel photocatalyst derived from pumpkin peel bio-waste, calcined at 200 °C and incorporated with magnetite nanoparticles to form a hybrid PK-P/Fe₃O₄ catalyst. The material was characterized using X-ray diffraction (XRD), diffuse reflectance spectra (DRS), and scanning electron microscopy (SEM) with energy-dispersive X-ray spectroscopy (EDX) mapping to confirm its structure and elemental distribution. The catalyst was applied for the photo-Fenton degradation of Synozol Red KHL dye under natural solution conditions (pH 5.7). Optimal parameters were achieved with a 20 mg/L catalyst and 200 mg/L H₂O₂, resulting in complete dye removal within 25 min of irradiation. The PK-P/Fe₃O₄ catalyst exhibited excellent reusability, retaining 72% removal efficiency after 10 successive cycles. Kinetic analysis confirmed a first-order model, while thermodynamic evaluation revealed a non-spontaneous, endothermic process with a low activation energy barrier, indicating energy-efficient dye degradation. These findings highlight the potential of bio-waste-derived PK-P/Fe₃O₄ as a sustainable, low-cost, and highly effective catalyst for treating dye-polluted wastewater under photo-Fenton conditions. Full article

Single nucleotide polymorphism (SNP) involves plenty of genetic disorders in organisms that can be passed down to the next generation or cause the stimulant signal that leads to early mortality in infants, especially within humankind. In medical field, real-time polymerase chain reaction (RT-PCR) is the most popular method for disease diagnosis. The investigation of genetic maps for the prediction of inherited illnesses needs the collaboration of sequencing technique and genome analysis. Although these methods are popular now, the cost for each test is quite high. Moreover, there is the requirement of extra machines and skillful technician or specialist level. Among these popular methods, the allele-specific polymerase chain reaction (AS-PCR), allele-specific loop isothermal mediated amplification (AS-LAMP), and allele-specific recombinase polymerase amplification (AS-RPA) are brought up for screening the nucleotide differences in the genetic sequence which will be noticed in this review as their availability, novelty, and potential for quick distinguishing of disease caused by SNP. Point-of-care testing (POCT) is a system built in a portable size but can perform the entire process of SNP recognition. Along with that, the POCT is intersected with the mentioned amplification methods and the genetic material preparation steps to become a united framework for higher efficiency and accuracy and lower cost. According to that, this review will focus on three common amplification techniques and their combination with POCT in the upstream and downstream process to genotype SNP related to human diseases. Full article

Neuromorphic circuits emulate the brain’s massively parallel, energy-efficient, and robust information processing by reproducing the behavior of neurons and synapses in dense networks. Memristive technologies have emerged as key enablers of such systems, offering compact and low-power implementations. In particular, locally active memristors (LAMs), with their ability to amplify small perturbations within a locally active domain to generate action potential-like responses, provide powerful building blocks for neuromorphic circuits and offer new perspectives on the mechanisms underlying neuronal firing dynamics. This paper introduces a novel second-order locally active memristor (LAM) governed by two coupled state variables, enabling richer nonlinear dynamics compared to conventional first-order devices. Even when the capacitances controlling the states are equal, the device retains two independent memory states, which broaden the design space for hysteresis tuning and allow flexible modulation of the current–voltage response. The second-order LAM is then integrated into a FitzHugh–Nagumo neuron circuit. The proposed circuit exhibits oscillatory firing behavior under specific parameter regimes and is further investigated under both DC and AC external stimulation. A comprehensive analysis of its equilibrium points is provided, followed by bifurcation diagrams and Lyapunov exponent spectra for key system parameters, revealing distinct regions of periodic, chaotic, and quasi-periodic dynamics. Representative time-domain patterns corresponding to these regimes are also presented, highlighting the circuit’s ability to reproduce a rich variety of neuronal firing behaviors. Finally, two unknown system parameters are estimated using the Aquila Optimization algorithm, with a cost function based on the system’s return map. Simulation results confirm the algorithm’s efficiency in parameter estimation. Full article

Display-field communication (DFC) is an imperceptible screen-to-camera technology that embeds and recovers data from the frequency domain of an image frame. Conventional DFC requires a reference frame for each data frame to estimate the channel, a method that, while reliable, is not bandwidth-efficient. Similarly, iterative DFC requires the transmission of pilot symbols for channel estimation. In this paper, we propose an implicit DFC (iDFC) scheme that eliminates the need for reference frames by estimating them using the first-order statistics of the received image. The system employs discrete Fourier-transform-based subcarrier mapping and adds data directly to the frequency coefficients of the host image. At the receiver, statistical estimation enables blind channel equalization without sacrificing the data rate. The simulation results show that iDFC achieves an achievable data rate (ADR) of up to $1.52\times {10}^5$ bps, a significant enhancement of approximately 97% and 11% compared to conventional and iterative DFC schemes, respectively. Furthermore, the analysis reveals a critical trade-off between communication robustness and visual imperceptibility; allocating 70% of signal power to the image maintains high visual quality but results in a symbol error rate (SER) floor of $1.5\times {10}^{-1}$, whereas allocating only 10% improves the SER to below ${10}^{-2}$ at the cost of visible artifacts. The findings also identify QPSK as the optimal modulation order that maximizes the data rate, showing that higher-order schemes can be detrimental due to system impairments such as signal clipping. The proposed iDFC scheme presents a more efficient and robust solution for high-capacity DFC applications by balancing the competing demands of data throughput and visual fidelity. Full article

Injection strategies play a crucial role in determining hydrogen engine performance. The diversity of these strategies and the limited number of comparative studies highlight the need for further investigation. This study focuses on the analysis, parameter selection, and comparison of single early and late direct injection, single injection with ignition occurring during injection (the so-called jet-guided operation), and dual injection in a hydrogen spark-ignition engine. The applicability and effectiveness of these injection strategies are assessed using contour maps, with ignition timing and start of injection as coordinates representing equal levels of key engine parameters. Based on this approach, injection and ignition settings are selected for a range of engine operating modes. Simulations of engine performance under different load conditions are carried out using the selected parameters for each strategy. The results indicate that the highest indicated thermal efficiencies are achieved with single late injection, while the lowest occur with dual injection. At the same time, both dual injection and jet-guided operation provide advantages in terms of knock suppression, peak pressure reduction, and reduced nitrogen oxide emissions. Full article

Taro (Colocasia esculenta) is the fifth most cultivated root crop in the world. During the asexual reproduction of taro, the frequent mutation of somatic cells leads to high genetic diversity. With the continuous increase in the amount of taro germplasm resources collected, efficiently and accurately genotyping taro has become a major problem. The identification of taro resources using penta-primer amplification refractory mutation system single-nucleotide polymorphisms (SNP-PARMS) is a relatively efficient method. After resequencing 29 taro resources in this study, approximately 86.95 million SNPs were obtained. Then, 252 specific SNP loci were screened. Based on these 252 specific SNP loci, 36 pairs of PARMS-SNP markers were formed. Among them, 9 pairs of PARMS-SNP markers with a sample loss rate > 15% were eliminated, and finally 27 pairs of PARMS-SNP markers were determined. The average values of minimal allele frequency (MAF), polymorphic information content (PIC), gene diversity (GD), and heterozygosity of these markers are 0.63, 0.34, 0.49, and 0.45, respectively. We analyzed the population structure and the evolutionary group, and the results showed that the 72 taro resources could be divided into 6 groups. The clustering result of the 72 taro resources based on phenotypic traits showed a potential congruence with the result of grouping in the evolutionary tree, with only a few differences detected between the two classifications. Using these markers, DNA fingerprint maps of 72 taro resources were constructed, and all taro resources were differentiated. Some resources show potential similarities in DNA fingerprint maps, as well is in their phenotypic traits, confirming the validity of the fingerprint. The study’s findings serve as a reference for the analysis of the genetic diversity of taro resources. Full article

Real-time rendering is increasingly used in augmented and virtual reality (AR/VR), interactive design, and product visualisation, where materials must prioritise efficiency and consistency rather than the extreme accuracy required in offline rendering. In parallel, the growing demand for personalised and customised products has created a need for digital materials that can be generated in-house without relying on expensive commercial systems. To address these requirements, this paper presents a low-cost digitisation workflow based on photometric stereo. The system integrates a custom-built scanner with cross-polarised illumination, automated multi-light image acquisition, a dual-stage colour calibration process, and a node-based reconstruction pipeline that produces albedo and normal maps. A reproducible evaluation methodology is also introduced, combining perceptual colour-difference analysis using the CIEDE2000 ($\Delta E_{00}$) metric with angular-error assessment of normal maps on known-geometry samples. By openly providing the workflow, bill of materials, and implementation details, this work delivers a practical and replicable solution for reliable material capture in real-time rendering and product customisation scenarios. Full article

Insider threats remain a persistent challenge in cybersecurity, as malicious or negligent insiders exploit legitimate access to compromise systems and data. This study presents a bibliometric review of 325 peer-reviewed publications from 2015 to 2025 to examine how machine learning (ML) and deep learning (DL) techniques for insider threat detection have evolved. The analysis investigates temporal publication trends, influential authors, international collaboration networks, thematic shifts, and algorithmic preferences. Results show a steady rise in research output and a transition from traditional ML models, such as decision trees and random forests, toward advanced DL methods, including long short-term memory (LSTM) networks, autoencoders, and hybrid ML–DL frameworks. Co-authorship mapping highlights China, India, and the United States as leading contributors, while keyword analysis underscores the increasing focus on behavior-based and eXplainable AI models. Symmetry emerges as a central theme, reflected in balancing detection accuracy with computational efficiency, and minimizing false positives while avoiding false negatives. The study recommends adaptive hybrid architectures, particularly Bidirectional LSTM–Variational Auto-Encoder (BiLSTM-VAE) models with eXplainable AI, as promising solutions that restore symmetry between detection accuracy and transparency, strengthening both technical performance and organizational trust. Full article

Background: Alzheimer’s disease (AD) is a progressive neurodegenerative condition marked by cognitive decline and memory loss. Despite advancements in AI-driven neuroimaging analysis for AD detection, clinical deployment remains limited due to challenges in model interpretability and usability. Explainable AI (XAI) frameworks such as XRAI offer potential to bridge this gap by providing clinically meaningful visualizations of model decision-making. Methods: This study developed a comprehensive, clinically deployable AI system for AD severity classification using 2D brain MRI data. Three deep learning architectures MobileNet-V3 Large, EfficientNet-B4, and ResNet-50 were trained on an augmented Kaggle dataset (33,984 images across four AD severity classes). The models were evaluated on both augmented and original datasets, with integrated XRAI explainability providing region-based attribution maps. A web-based clinical interface was built using Gradio to deliver real-time predictions and visual explanations. Results: MobileNet-V3 achieved the highest accuracy (99.18% on the augmented test set; 99.47% on the original dataset), while using the fewest parameters (4.2 M), confirming its efficiency and suitability for clinical use. XRAI visualizations aligned with known neuroanatomical patterns of AD progression, enhancing clinical interpretability. The web interface delivered sub-20 s inference with high classification confidence across all AD severity levels, successfully supporting real-world diagnostic workflows. Conclusions: This research presents the first systematic integration of XRAI into AD severity classification using MRI and deep learning. The MobileNet-V3-based system offers high accuracy, computational efficiency, and interpretability through a user-friendly clinical interface. These contributions demonstrate a practical pathway toward real-world adoption of explainable AI for early and accurate Alzheimer’s disease detection. Full article

The China Railway Express (China-Europe container railway freight transport) is pivotal to Eurasian freight, yet its transcontinental railway faces escalating cascading risks. We develop a coupled map lattice (CML) model representing the physical infrastructure layer and the operational traffic layer concurrently to quantify and mitigate cascading failures. Twenty critical stations are identified by integrating TOPSIS entropy weighting with grey relational analysis in dual-layer networks. The enhanced CML embeds node-degree, edge-betweenness, and freight-flow coupling coefficients, and introduces two adaptive cargo-redistribution rules—distance-based and load-based for real-time rerouting. Extensive simulations reveal that network resilience peaks when the coupling coefficient equals 0.4. Under targeted attacks, cascading failures propagate within three to four iterations and reduce network efficiency by more than 50%, indicating the vital function of higher importance nodes. Distance-based redistribution outperforms load-based redistribution after node failures, whereas the opposite occurs after edge failures. These findings attract our attention that redundant border corridors and intelligent monitoring should be deployed, while redistribution rules and multi-tier emergency response systems should be employed according to different scenarios. The proposed methodology provides a dual-layer analytical framework for addressing cascading risks of transcontinental networks, offering actionable guidance for intelligent transportation management of international intermodal freight networks. Full article

There has been significant interest in deploying unmanned aerial vehicles (UAVs) for their ability to perform precise and rapid remote mapping and inspection of critical environmental assets for structural health monitoring. This case study investigates the use of UAV-based LiDAR and photogrammetry at Melbourne Water’s Western Treatment Plant (WTP) to routinely monitor high-density polyethylene floating covers on anaerobic lagoons. The proposed approach integrates LiDAR and photogrammetry data to enhance the accuracy and efficiency of generating digital elevation models (DEMs) and orthomosaics by leveraging the strengths of both methods. Specifically, the photogrammetric images were orthorectified onto LiDAR-derived DEMs as the projection plane to construct the corresponding orthomosaic. This method captures precise elevation points directly from LiDAR, forming a robust foundation dataset for DEM construction. This streamlines the workflow without compromising detail, as it eliminates the need for time-intensive photogrammetry processes, such as dense cloud and depth map generation. This integration accelerates dataset production by up to four times compared to photogrammetry alone, while achieving centimetre-level accuracy. The LiDAR-derived DEM achieved higher elevation accuracy with a root mean square error (RMSE) of 56.1 mm, while the photogrammetry-derived DEM achieved higher in-plane accuracy with an RMSE of up to 35.4 mm. An analysis of cover deformation revealed that the floating cover had elevated rapidly within the first two years post-installation before showing lateral displacement around the sixth year, which was also evident from a significant increase in wrinkling. This approach delivers valuable insights into cover condition that, in turn, clarifies scum accumulation and movement, thereby enhancing structural integrity management and supporting environmental sustainability at WTP by safeguarding methane-rich biogas for renewable-energy generation and controlling odours. The findings support the ongoing collaborative industry research between Monash University and Melbourne Water, aimed at achieving comprehensive structural and prognostic health assessments of these high-value assets. Full article