Search Results (7,663)

This paper presents the development and implementation of a shopfloor visualization-oriented digitalization framework for heterogeneous industrial equipment, aimed to enhance sustainable performance in manufacturing environments. The proposed solution addresses a critical challenge in modern industry: the integration of legacy and modern equipment into a unified, real-time monitoring and control system. In this paper, a modular and scalable architecture that enables data acquisition from equipment with varying communication protocols and technological maturity was designed and implemented, utilizing Industrial Internet of Things (IIoT) gateways, protocol converters, and Open Platform Communications Unified Architecture (OPC UA). A key contribution of this work is the integration of various data sources into a centralized visualization platform that supports real-time monitoring, anomaly detection, and performance analytics. By visualizing operational parameters—including energy consumption, machine efficiency, and environmental indicators—the system facilitates data-driven decision-making and supports predictive maintenance strategies. The implementation was validated in a real industrial setting, where the solution significantly improved transparency, reduced downtime, and contributed to measurable energy efficiency gains. This research demonstrates that visualization-oriented digitalization not only enables interoperability among heterogeneous assets, but also acts as a catalyst for achieving sustainability goals. The developed methodology and tools provide a replicable model for manufacturing organizations seeking to transition toward Industry 4.0 in a resource-efficient and future-proof manner. Full article

Copper ions (Cu²⁺), indispensable in physiological processes yet toxic at elevated concentrations, require sensitive on-site monitoring. Here, a portable fluorescent sensing film (Y-CDs@BCM) was fabricated by anchoring yellow-emitting carbon dots (Y-CDs) into bacterial cellulose films, which enables rapid and sensitive detection of Cu²⁺ in complex real-world samples. The yellow fluorescent carbon dots (Y-CDs) were synthesized with the aid of o-phenylenediamine and 1-octyl-3-methylimidazolium tetrafluoroborate as precursors, exhibiting excellent fluorescence stability. The fluorescence of Y-CDs was selectively quenched by Cu²⁺ via the inner filter effect (IFE), allowing quantitative analysis with superior sensitivity compared to existing methods. By adding bacterial cellulose (BC) as a solid support, aggregation-induced fluorescence quenching was effectively reduced, and sensor robustness and portability were improved. Through smartphone-based colorimetric analysis, the Y-CDs@BCM sensor enabled rapid, visual interpretation of Cu²⁺ detection (within 1 min). Furthermore, cell viability and in vivo assays confirmed the biocompatibility of Y-CDs, indicating their suitability for biological imaging. This work presents an environmentally friendly, reliable, and practical method for on-site Cu²⁺ monitoring, emphasizing its broad application potential in food safety control and environmental analysis. Full article

Texture, a significant visual attribute in images, plays an important role in many pattern recognition tasks. While Convolutional Neural Networks (CNNs) have been among the most effective methods for texture analysis, alternative architectures such as Vision Transformers (ViTs) have recently demonstrated superior performance on a range of visual recognition problems. However, the suitability of ViTs for texture recognition remains underexplored. In this work, we investigate the capabilities and limitations of ViTs for texture recognition by analyzing 25 different ViT variants as feature extractors and comparing them to CNN-based and hand-engineered approaches. Our evaluation encompasses both accuracy and efficiency, aiming to assess the trade-offs involved in applying ViTs to texture analysis. Our results indicate that ViTs generally outperform CNN-based and hand-engineered models, particularly when using strong pre-training and in-the-wild texture datasets. Notably, BeiTv2-B/16 achieves the highest average accuracy (85.7%), followed by ViT-B/16-DINO (84.1%) and Swin-B (80.8%), outperforming the ResNet50 baseline (75.5%) and the hand-engineered baseline (73.4%). As a lightweight alternative, EfficientFormer-L3 attains a competitive average accuracy of 78.9%. In terms of efficiency, although ViT-B and BeiT(v2) have a higher number of GFLOPs and parameters, they achieve significantly faster feature extraction on GPUs compared to ResNet50. These findings highlight the potential of ViTs as a powerful tool for texture analysis while also pointing to areas for future exploration, such as efficiency improvements and domain-specific adaptations. Full article

This study develops an open, reproducible geospatial workflow to identify high-potential retail locations across Germany using a 1 km census grid and OpenStreetMap points of interest. It combines multi-criteria suitability modelling with spatial autocorrelation and Geographically Weighted Regression (GWR). Using fine-scale demographic and retail data, the results show clear regional differences in how drivers operate. Population density is most influential around large metropolitan areas, while the role of points of interest is stronger in smaller regional towns. A separate gap analysis identified forty grid cells with high suitability but no existing retail infrastructure. These locations are spread across both rural and urban contexts, from peri-urban districts in Baden-Württemberg to underserved municipalities in Brandenburg and Bavaria. The pattern is consistent under different model specifications and echoes earlier studies that reported supply deficits in comparable communities. The results are useful in two directions. Retailers can see places with demand that has gone unnoticed, while planners gain evidence that service shortages are not just an urban issue but often show up in smaller towns as well. Taken together, the maps and diagnostics give a grounded picture of where gaps remain, and suggest where investment could bring both commercial returns and community benefits. This study develops an open, reproducible geospatial workflow to identify high-potential retail locations across Germany using a 1 km census grid and OpenStreetMap points of interest. A multi-criteria suitability surface is constructed from demographic and retail indicators and then subjected to spatial diagnostics to separate visually high values from statistically coherent clusters. “White-spots” are defined as cells in the top decile of suitability with zero (strict) or ≤1 (relaxed) existing shops, yielding actionable opportunity candidates. Global autocorrelation confirms strong clustering of suitability, and Local Indicators of Spatial Association isolate hot- and cold-spots robust to neighbourhood size. To explain regional heterogeneity in drivers, Geographically Weighted Regression maps local coefficients for population, age structure, and shop density, revealing pronounced intra-urban contrasts around Hamburg and more muted variation in Berlin. Sensitivity analyses indicate that suitability patterns and priority cells stay consistent with reasonable reweighting of indicators. The comprehensive pipeline comprising suitability mapping, cluster diagnostics, spatially variable coefficients, and gap analysis provides clear, code-centric data for retailers and planners. The findings point to underserved areas in smaller towns and peri-urban districts where investment could both increase access and business feasibility. Full article

The rapid emergence of generative artificial intelligence (GenAI) has sparked growing interest across educational disciplines, reshaping how knowledge is produced, represented, and assessed. While recent research has increasingly explored the implications of text-based tools such as ChatGPT in education, far less attention has been paid to image-based GenAI tools—despite their particular relevance to fields grounded in visual communication and creative exploration, such as architecture and design. These disciplines raise distinct pedagogical and ethical questions, given their reliance on iteration, authorship, and visual representation as core elements of learning and practice. This exploratory study investigates how architecture and interior architecture students perceive the use of AI-generated images, focusing on ethical responsibility, educational relevance, and career implications. To ensure participants had sufficient exposure to visual GenAI tools, we conducted a series of workshops before surveying 42 students familiar with image generation processes. Findings indicate strong enthusiasm for GenAI image tools, which students viewed as supportive during early-stage design processes and beneficial to their creativity and potential future professional competitiveness. Participants regarded AI use as ethically acceptable when accompanied by transparent acknowledgment. However, acceptance declined in later design stages, where originality and critical judgment were perceived as more central. While limited in scope, this exploratory study foregrounds student voices to offer preliminary insights into evolving conversations about AI in creative education and to inform future reflection on developing ethically and pedagogically responsive curricula across the design disciplines. Full article

Background/Objectives: The objective of this study is to compare the optical and visual quality provided by the advanced monofocal intraocular lens (IOL) ISOPure and the standard monofocal IOL MicroPure in cataract patients, using objective and subjective assessments. Methods: This prospective, single-blind clinical study includes 28 patients with cataracts, bilaterally implanted with either the ISOPure or MicroPure IOL. Eligible eyes had no ocular comorbidities and regular corneal astigmatism ≤ 1.00 D. Three months postoperatively, uncorrected distance and intermediate (UDVA, UIVA) and corrected distance and intermediate (CDVA, DCIVA) visual acuities were measured at 4 m, 80 cm, and 66 cm under photopic (85 cd/m²) and mesopic (3.5 cd/m²) conditions. Photic phenomena, including halo and glare, were evaluated. Objective optical quality was assessed using Objective Scattering Index (OSI), Modulation Transfer Function (MTF), Strehl Ratio (SR), and ocular aberrations. Subjective patient satisfaction was evaluated using Quality of Vision (QoV) and Catquest-9SF questionnaires. Results: Under photopic conditions, logMAR DCIVA at 80 cm, UIVA at 66 cm, and DCIVA at 66 cm were 0.18 ± 0.06, 0.25 ± 0.12, and 0.20 ± 0.13, respectively, for ISOPure, and 0.22 ± 0.06, 0.30 ± 0.09, and 0.25 ± 0.09 for MicroPure (p = 0.05, 0.02, and 0.05, respectively). No significant differences were observed in halo/glare size or intensity, OSI, MTF, or SR. However, statistically significant differences were found in higher-order total aberrations for pupil sizes of 3.0, 4.0 mm, and 5.0 mm. Questionnaires indicated greater satisfaction and functional intermediate vision with ISOPure. Conclusions: The ISOPure IOL offers superior intermediate vision without compromising distance vision, delivering a balanced combination of optical quality, functional performance, and patient satisfaction. Full article

This study investigates the complex interaction between in-game behaviors, post-game emotional expressions, and Game Transfer Phenomena (GTP) among MOBA players. A multidimensional framework is adopted to examine how gaming experiences shape real-world cognition, perception, and behavior through the integration of objective behavioral metrics and affective computing-based emotion recognition. The results indicate that in-game Deaths are negatively associated with altered sensory perceptions—specifically Altered Visual and Auditory Perceptions (AVP and AAP)—suggesting that performance failures may disrupt immersive engagement. In contrast, Assists, as indicators of team-based collaboration, are positively associated with Automatic Mental Processes (AMP), highlighting the cognitive impact of cooperative gameplay. Although no significant mediating effects were observed, emotional dimensions, such as Social Discomfort and Cognitive Focus, offered additional insights into the transfer between in-game and post-game experiences. These findings bridge the gap between virtual and real-world experiences, offering theoretical advancements in GTP research and practical implications for game design, emotional regulation, and psychological interventions. Full article

Glacier retreat due to climate change is accelerating worldwide, yet the phenomenon remains abstract for many people, especially those unfamiliar with mountain environments. The Urban Glaciology experiment, conducted during Milan’s internationally renowned “Fuorisalone” 2024 design event, aimed to bridge this perceptual gap by simulating real glacier melt processes in a busy urban square. Three large ice blocks with contrasting surface conditions (i.e., clean, dirty, and debris-covered) were exposed to springtime urban temperatures, mimicking conditions found on Alpine glaciers during summer. Over one week, the blocks produced a total of 748 L of meltwater, with dirty ice melting up to four times faster than debris-covered ice, consistent with established albedo effects. These results confirmed the thermal analogy between Milan’s spring conditions (+15 to +20 °C) and the ablation season on Alpine glaciers. Visitors observed the differential melting in real time, supported by visual indicators, explanatory panels, immersive virtual experiences, and direct interaction with researchers and students. Informal interviews indicated that more than 60% of participants reported a perceptual shift, recognizing for the first time that urban temperatures can replicate glacier melting conditions. By embedding a science-based installation in a major cultural tourism event, the experiment reached a diverse, non-traditional audience—including tourists, designers, and citizens—and encouraged reflection on the implications of glacier loss. The success of this initiative highlights the potential of replicating similar models in other cities to raise awareness of environmental change through culturally engaging experiences. Full article

Weakly supervised video anomaly detection (WSVAD) aims to identify unusual events using only video-level labels. However, current methods face several key challenges, including ineffective modelling of complex temporal dependencies, indistinct feature boundaries between visually similar normal and abnormal events, and high false alarm rates caused by an inability to distinguish salient events from complex background noise. This paper proposes a novel method that systematically enhances feature representation and discrimination to address these challenges. The proposed method first builds robust temporal representations by employing a hierarchical multi-scale temporal encoder and a position-aware global relation network to capture both local and long-range dependencies. The core of this method is the dual-stream attention-enhanced memory network, which achieves precise discrimination by learning distinct normal and abnormal patterns via dual memory banks, while utilising bidirectional spatial attention to mitigate background noise and focus on salient events before memory querying. The models underwent a comprehensive evaluation utilising solely RGB features on two demanding public datasets, UCF-Crime and XD-Violence. The experimental findings indicate that the proposed method attains state-of-the-art performance, achieving 87.43% AUC on UCF-Crime and 85.51% AP on XD-Violence. This result demonstrates that the proposed “attention-guided prototype matching” paradigm effectively resolves the aforementioned challenges, enabling robust and precise anomaly detection. Full article

Large-scale particle-based fluid simulations present significant computational challenges, particularly in achieving interactive frame rates while maintaining visual quality. Unity3D’s widespread adoption in game development, VR/AR applications, and scientific visualization creates a unique need for efficient fluid simulation within its ecosystem. This paper presents a GPU-accelerated Smoothed Particle Hydrodynamics (SPH) framework implemented in Unity3D that effectively addresses these challenges through several key innovations. Unlike previous GPU-accelerated SPH implementations that typically struggle with scaling beyond 100,000 particles while maintaining real-time performance, we introduce a novel fusion of Count Sort with Parallel Prefix Scan for spatial hashing that transforms the traditionally expensive O(n²) neighborhood search into an efficient O(n) operation, significantly outperforming traditional GPU sorting algorithms in particle-based simulations. Our implementation leverages a Structure of Arrays (SoA) memory layout, optimized for GPU compute shaders, achieving 30–45% improved computation throughput over traditional Array of Structures approaches. Performance evaluations demonstrate that our method achieves throughput rates up to 168,600 particles/ms while maintaining consistent 5.7–6.0 ms frame times across varying particle counts from 10,000 to 1,000,000. The framework maintains interactive frame rates (>30 FPS) with up to 500,000 particles and remains responsive even at 1 million particles. Collision rates approaching 1.0 indicate near-optimal hash distribution, while the adaptive time stepping mechanism adds minimal computational overhead (2–5%) while significantly improving simulation stability. These innovations enable real-time, large-scale fluid simulations with applications spanning visual effects, game development, and scientific visualization. Full article

Endolysins, phage-derived enzymes capable of lysing bacterial cell walls, hold significant promise as novel antimicrobials against resistant Gram-positive and Gram-negative pathogens. In this study, we undertook an integrative approach combining extensive in silico analyses and experimental validation to characterize the novel endolysin LysPALS22. Initially, sixteen endolysin sequences were selected based on documented lytic activity and enzymatic diversity, and subjected to multiple sequence alignment and phylogenetic analysis, which revealed highly conserved catalytic and binding domains, particularly localized to the N-terminal region, underscoring their functional importance. Building upon these sequence insights, we generated three-dimensional structural models using Swiss-Model, EBI-EMBL, and AlphaFold Colab, where comparative evaluation via Ramachandran plots and ERRAT scores identified the Swiss-Model prediction as the highest quality structure, featuring over 90% residues in favored conformations and superior atomic interaction profiles. Leveraging this validated model, molecular docking studies were conducted in PyRx with AutoDock Vina, performing blind docking of key peptidoglycan-derived ligands such as N-Acetylmuramic Acid-L-Alanine, which exhibited the strongest binding affinity (−7.3 kcal/mol), with stable hydrogen bonding to catalytic residues ASP46 and TYR61, indicating precise substrate recognition. Visualization of docking poses using Discovery Studio further confirmed critical hydrophobic and polar interactions stabilizing ligand binding. Subsequent molecular dynamics simulations validated the stability of the LysPALS22–NAM-LA complex, showing minimal structural fluctuations, persistent hydrogen bonding, and favorable interaction energies throughout the 100 ns trajectory. Parallel to computational analyses, LysPALS22 was heterologously expressed in Escherichia coli (E. coli) and Pichia pastoris (P. pastoris), where SDS-PAGE and bicinchoninic acid assays validated successful protein production; notably, the P. pastoris-expressed enzyme displayed an increased molecular weight (~45 kDa) consistent with glycosylation, and achieved higher volumetric yields (1.56 ± 0.31 mg/mL) compared to E. coli (1.31 ± 0.16 mg/mL), reflecting advantages of yeast expression for large-scale production. Collectively, these findings provide a robust structural and functional foundation for LysPALS22, highlighting its conserved enzymatic features, specific ligand interactions, and successful recombinant expression, thereby setting the stage for future in vivo antimicrobial efficacy studies and rational engineering efforts aimed at combating multidrug-resistant Gram-negative infections. Full article

Retinal Pigment Epithelium (RPE), a component of the blood–retinal barrier, plays a pivotal role in maintaining retinal homeostasis and visual function. Dysfunction of the RPE is an early event that triggers photoreceptor death, in Age-related Macular Degeneration (AMD), a multifactorial disorder primarily caused by an imbalance between endogenous antioxidant defenses and reactive oxygen species production. Our in vitro study investigated the hormetic effects of curcumin in human RPE cells (ARPE-19), focusing on its capability to modulate two enzymes related to the onset of AMD: Sirtuin 1 (SIRT1), a NAD+-dependent deacetylase enzyme involved in cellular metabolism, aging, and stress response, and caspase-3, a crucial enzyme in programmed cell death. Curcumin exhibited classic hormetic doseresponses, with low concentrations (5–10 μM) providing cytoprotection while at high doses (≥20 μM) inducing toxicity. Under moderate oxidative stress, acetylated p53 was significantly reduced, indicating SIRT1 activation; curcumin 10 μM restored basal SIRT1 activity, while 5 µM did not. Both concentrations significantly decreased cleaved caspase-3 levels, demonstrating the anti-apoptotic effects of curcumin. Our results reveal curcumin’s hormetic mechanisms of RPE protection and emphasize the critical importance of dose optimization within the hormetic window for AMD therapeutic development. Full article

Background/Objectives: Accurate urinary bladder (UB) volume measurement is essential for diagnosing urinary retention, evaluating post-void residuals, and guiding catheterization decisions. Conventional 2D ultrasound and automated non-visual bladder scanners can be limited by operator variability and systematic errors. Three-dimensional (3D) ultrasound may improve accuracy and reproducibility, but data on handheld, semi-automated devices remain scarce. This study aimed to compare the accuracy, reproducibility, and feasibility of a handheld 3D ultrasound device versus conventional 2D ultrasound for UB volume estimation, using measured voided volume as the reference standard. Methods: Fifty-three healthy male volunteers (mean age 19.6 ± 2.0 years) underwent bladder volume assessment by two novice operators using both methods: 2D ultrasound (manual caliper-based) and handheld 3D ultrasound device (Butterfly iQ). Each operator performed two repeated measurements per method. True voided volume was recorded immediately after scanning. Accuracy was assessed using median differences, percentage error, and Bland–Altman analysis. Intra- and inter-operator reproducibility were evaluated with intraclass correlation coefficients (ICC). Results: Both methods significantly underestimated bladder volume (p < 0.001). The 3D method demonstrated higher accuracy, with a median percentage error of −11.2% to −12.0%, versus −27.6% to −36.7% for 2D. The mean bias ranged from −64.9 mL to −72.3 mL for 3D, compared to −137.4 mL to −191.6 mL for 2D. Intra-operator reproducibility was excellent for all methods (ICC > 0.96). Inter-operator agreement was higher for 3D (ICC = 0.977; bias 7.3 mL) than for 2D (ICC = 0.927; bias −54.2 mL). All scans were completed successfully; however, the 3D device occasionally faced technical errors in large bladder volumes. Conclusions: Handheld 3D ultrasound yielded greater accuracy and inter-operator consistency than conventional 2D ultrasound in healthy adults, with minimal operator input. Both methods underestimated true volume, indicating the need for clinical consideration when interpreting measurements. These findings support broader clinical adoption of handheld 3D ultrasound, particularly in settings with variable sonographic expertise, while highlighting the need for validation in elderly and pathological populations. Full article

This paper presents a comprehensive study of an ESP32 microcontroller-based self-balancing mobile robot system designed in conjunction with an Android app for Bluetooth control. The robot employs an MPU6050 accelerometer/gyroscope to execute dynamic equilibrium control for robotic balance. This study explores the design of a system composed of an ESP32-based dual-platform architecture. The firmware for the ESP32 executes real-time motor control and sensor processing, while the Android application provides the user interface, data visualization, and command transmission. The system achieves stable operation with tilt angle variations of ±2.5° ($\sigma =0.8°$, n = 50 trials) during normal operation with a PID controller tuned to KP = 6.0, KI = 0.1, and KD = 1.5. In experimental tests, control latency was measured at 38–72 ms (mean = 55 ms, $\sigma =12$ ms) over distances of 1–10 m with a robust Bluetooth connection. Extended operational tests indicated the reliability of both autonomous obstacle avoidance mode and manual control exceeding 95%. Key contributions include gyro drift compensation using a progressive calibration scheme, intelligent battery management for operational efficiency, and a dual-mode control interface to facilitate seamless transition between manual and autonomous operation. Processing of real-time telemetry on the Android application allows visualization of important parameters like tilt angle, motor speeds, and sensor readings. This work contributes to a cost-effective mobile robotics platform (total cost: USD 127) through the provision of detailed design specifications, implementation strategies, and performance characteristics. Full article

Soil erosion is a critical environmental issue that leads to land degradation, reduced agricultural productivity, and ecological imbalance. This study aims to assess soil loss under various land surface conditions by developing 11 distinct scenarios using the RUSLE (Revised Universal Soil Loss Equation) model integrated within the Google Earth Engine (GEE) platform. Remote sensing-derived indices including NDVI, EVI, NDWI, SAVI, and BSI were incorporated to represent vegetation cover, moisture, and bare/built-up surfaces. The K, LS, P, and R factors were held constant, allowing the C factor to vary based on each index, simulating real-world landscape differences. Soil loss maps were generated for each scenario, and spatial variability was analyzed using bubble charts, bar graphs, and C-map visualizations. The results show that vegetation-based indices such as NDVI and EVI lead to significantly lower soil loss estimations, while indices associated with built-up or bare surfaces like BSI predict higher erosion risks. These findings highlight the strong relationship between land cover characteristics and erosion intensity. This study demonstrates the utility of integrating satellite-based indices into erosion modeling and provides a scenario-based framework for supporting land management and soil conservation practices. The proposed approach can aid policymakers and land managers in prioritizing conservation efforts and mitigating erosion risk. Moreover, maintaining and enhancing vegetative cover is emphasized as a key strategy for promoting sustainable land use and long-term ecological resilience. Full article