Search Results (1,767)

This research develops and applies the IoTRIM model to assess the economic and operational implications of IoT integration in postal and courier enterprises in Slovakia. Combining a multi-criteria evaluation framework with an extended Cobb–Douglas production function, the analysis captures both readiness levels and their translation into output performance. The IoTRIM assessment reveals heterogeneous distributions of strengths across four strategic and technical pillars, with notable disparities between connectivity, data analytics, and interoperability capacities. Monte Carlo simulations under pessimistic, realistic, and optimistic scenarios highlight divergent digital trajectories among enterprises, with some demonstrating accelerated gains from IoT readiness while others face structural bottlenecks in infrastructure and process integration. Hypothesis testing indicates that while a positive and statistically significant relationship between IoT readiness and output is observed in selected cases, this effect is not universal across all enterprises and scenarios. However, the inclusion of IoT readiness consistently improves the explanatory power of the production function models. The findings underline that digital transformation outcomes depend not only on investment scale but also on systemic absorption capacity, including interoperability, data governance, and organizational alignment. The proposed approach offers both a methodological contribution for measuring digital readiness impacts and practical insights for strategic planning in the postal and courier sector. Full article

Background: Grip control is a critical determinant of fencing performance, requiring both stability and precision. Traditional measures of hand strength, such as dynamometry, provide only a global estimate and cannot capture finger-specific load distribution. Yet, upper-extremity overuse syndromes, tendinopathies of the wrist and digital flexors are common in fencers, underscoring the need for more granular assessments that may inform clinical practice, especially in prehension contexts. Methods: This pilot study included eight elite épée fencers from the Polish National Team (age: 23.9 ± 4.9 years; training experience: >10 years) tested using a novel épée handle instrumented with five force-sensitive resistors (FSRs) embedded beneath each finger. Participants performed two 5-s maximal voluntary contractions (MVCs) for each of the three conditions—Pinch (thumb + index), Trio (middle + ring + small), and Whole (all digits). Standard handheld dynamometry was also performed to provide a global reference measure. Results: Maximal grip strength measured with a dynamometer (65.3 ± 11.7 kgf) was substantially higher than finger-specific forces captured with the FSR handle (14.4 ± 4.4 kgf). Isolated Pinch contractions (83.0 ± 29.2 N) were significantly stronger than their integrated contribution within the Whole-hand condition (54.7 ± 16.3 N; Z = 2.52, p = 0.012), whereas Trio forces did not differ significantly (p = 0.263). On average, radial digits (thumb + index) contributed ~39% and ulnar digits (middle, ring, small) ~61% of Whole output, with the thumb and middle finger producing the largest forces. Conclusions: This pilot study demonstrates the feasibility of using an FSR-instrumented épée handle to capture finger-specific grip contributions in elite fencers. Despite limited statistical power (n = 8), the observed effects provide initial quantitative evidence for sport-specific, digit-level assessment, showing potential clinical utility in detecting maladaptive load-transfer mechanisms and informing rehabilitation and injury-prevention programs. Full article

Terror organizations increasingly utilize the media and especially digital platforms to disseminate strategic messages, particularly during conflicts. This study examines how Hamas employed hostage videos and other related publications as a form of strategic communication during the first 20 months of the 2023–2025 war with Israel. Drawing on qualitative content analysis of 166 media outputs published on Hamas’s official Telegram channel, including videos, infographics, and a few text-based posts, the study identifies five distinct genres: proof of life, revealing the hostages’ fate, rage or call for help, messages to hostage families or the Israeli public, and hostage release videos. Each genre reflects a specific communicative strategy, varying in tone, target audience, emotional appeal, and timing. The findings reveal that Hamas’s media operations are characterized by a high degree of intentionality, with different genres employed to advance political objectives, ranging from negotiation pressure and public mobilization to projecting legitimacy and resilience. The study contributes to the growing literature on terrorism and strategic communication, illustrating how non-state actors leverage visual media and emotional narratives to wage parallel battles over image, perception, and legitimacy. Full article

Stochastic geometry provides a powerful analytical framework for evaluating interference-limited cellular networks with randomly deployed base stations (BSs). While prior studies have examined limited channel state information at the transmitter (CSIT) and low-resolution analog-to-digital converters (ADCs) separately, their joint impact in multi-user multiple-input multiple-output (MIMO) systems remains largely unexplored. This paper investigates a downlink cellular network in which BSs are distributed according to a homogeneous Poisson point process (PPP), employing zero-forcing beamforming (ZFBF) with limited feedback, and receivers are equipped with one-bit ADCs. We derive a tractable approximation for the achievable spectral efficiency that explicitly accounts for both the quantization error from limited feedback and the receiver distortion caused by coarse ADCs. Based on this approximation, we determine the optimal feedback rate that maximizes the net spectral efficiency. Our analysis reveals that the optimal number of feedback bits scales logarithmically with the channel coherence time but its absolute value decreases due to coarse quantization. Simulation results validate the accuracy of the proposed approximation and confirm the predicted scaling behavior, demonstrating its effectiveness for interference-limited multi-user MIMO networks. Full article

To address the limitations of traditional acoustic experimental equipment, such as large volume, discrete modules, and complex operation, this paper proposes and implements a set of digital portable acoustic teaching systems. The hardware component is based on an FPGA, enabling a highly integrated design for signal source excitation and multi-channel synchronous acquisition. It supports the output of various signals, including pulses, sine waves, chirps, and arbitrary waveforms. The software component is developed based on the Qt framework, offering cross-platform compatibility and excellent graphical interaction capabilities. It supports signal configuration, data acquisition, real-time processing, result visualization, and historical playback, establishing a closed-loop experimental workflow of signal excitation–synchronous acquisition–real-time processing–data storage–result visualization. The system supports both local USB connection and remote TCP operation modes, accommodating scenarios such as real-time classroom experiments and cross-regional collaborative teaching. The verification results of three typical experiments, namely, multi-media sound velocity measurement, TDOA hydrophone positioning, and remote acoustic detection, demonstrate that the system performs well in terms of measurement accuracy, positioning stability, and the feasibility of remote detection. This study demonstrates the technical advantages and engineering adaptability of a digital teaching platform in acoustic experimental education. It provides a scalable system solution for cross-regional hybrid teaching models and practice-oriented education under the framework of emerging engineering disciplines. Future work will focus on expanding experimental scenarios, enhancing system intelligence, and improving multi-user collaboration capabilities, aiming to develop a more comprehensive and efficient platform to support acoustic teaching. Full article

This study presents a model-driven approach to the design, calibration, and application of frequency-output pressure sensors integrated within an intelligent system for real-time monitoring of rowing performance. The proposed system captures biomechanical parameters of the “boat–rower” complex across 50 parallel channels with a temporal resolution of 8–12 ms. At the core of the sensing architecture are parametric pressure transducers incorporating strain-gauge primary elements and microelectronic auto-generator circuits featuring negative differential resistance (NDR). These oscillating circuits convert mechanical stress into high-frequency output signals in the 1749.9–1751.9 MHz range, with pressure sensitivities from 0.365 kHz/kPa to 1.370 kHz/kPa. The sensor models are derived using physical energy conversion principles, enabling the formulation of analytical expressions for transformation and sensitivity functions. These models simplify sensitivity tuning and allow clear interpretation of how structural and electronic parameters influence output frequency. The system architecture eliminates the need for analog-to-digital converters and signal amplifiers, reducing cost and power consumption, while enabling wireless ultra high frequency (UHF) transmission of sensor data. Integrated algorithms analyze the influence of biomechanical variables on athlete performance, enabling real-time diagnostics. The proposed model-based methodology offers a scalable and accurate solution for intelligent sports instrumentation and beyond. Full article

Rogowski coils are increasingly being used in electricity metering systems. However, owing to their operating principle, they require an additional active integrating circuit to produce an output voltage or current that is directly proportional to the input current. A signal conditioner has the most significant impact on the overall conversion accuracy of the combined transducer. In this paper, a new measurement system and testing procedure utilizing a digital power meter and arbitrary waveform generator are proposed. This approach enables the characterization of the conversion accuracy of both types of active integrators: voltage-to-voltage and voltage-to-current converters. The conversion error for distorted input voltage harmonics and additional phase shift across a range of frequencies are determined. Instead of using the actual signal from the Rogowski coil during testing —which would be challenging owing to the required high RMS value of the distorted current for its input and difficulties in accurately measuring the RMS values of harmonics and their phase angles in relation to the output voltage or current of the tested converter—an arbitrary waveform generator is used. The input voltage to the active integrating circuit replicates the output voltage of the Rogowski coil: as the harmonic order increases, its RMS voltage rises proportionally. Full article

In Global Navigation Satellite System (GNSS)-denied environments, unmanned aerial vehicles (UAVs) relying on Vision-Based Navigation (VBN) in high-altitude, mountainous terrain face severe challenges due to geometric distortions in aerial imagery. This paper proposes a georeferenced localization framework that integrates orthorectified aerial imagery with Scene Matching (SM) to achieve robust positioning. The method employs a camera projection model combined with Digital Elevation Model (DEM) to orthorectify UAV images, thereby mitigating distortions from central projection and terrain relief. Pre-processing steps enhance consistency with reference orthophoto maps, after which template matching is performed using normalized cross-correlation (NCC). Sensor fusion is achieved through extended Kalman filters (EKFs) incorporating Inertial Navigation System (INS), GNSS (when available), barometric altimeter, and SM outputs. The framework was validated through flight tests with an aircraft over 45 km trajectories at altitudes of 2.5 km and 3.5 km in mountainous terrain. The results demonstrate that orthorectification improves image similarity and significantly reduces localization error, yielding lower 2D RMSE compared to conventional rectification. The proposed approach enhances VBN by mitigating terrain-induced distortions, providing a practical solution for UAV localization in GNSS-denied scenarios. Full article

Floods are among the most frequent and destructive natural hazards worldwide, with increasingly severe socioeconomic consequences due to rapid urbanization, land use changes, and climate variability. While the combination of Geographic Information Systems (GIS) with models such as HEC-RAS has been extensively explored for flood risk management, many existing studies remain limited to one-dimensional (1D) models or use coarse-resolution terrain data, often underestimating flood risk and failing to produce critical multivariate flood characteristics in densely built urban areas. This study applies a two-dimensional (2D) hydraulic modeling framework in HEC-RAS combined with GIS-based spatial analysis, using a high-resolution (1 × 1 m) LiDAR-derived Digital Terrain Model (DTM) and a hybrid mesh refined between 2 × 2 m and 8 × 8 m, with the main contributions represented by the specific application context and methodological choices. A key methodological aspect is the direct integration of synthetic hydrographs with defined exceedance probabilities (10%, 1%, and 0.1%) into the 2D model, thereby reducing the need for extensive hydrological simulations and defining a data-driven approach for resource-constrained environments. The primary novelty is the application of this high-resolution urban modeling framework to a Romanian urban–peri-urban setting, where detailed hydrological observations are scarce. Unlike previous studies in Romania, this approach applies detailed channel and floodplain discretization at high spatial resolution, explicitly incorporating anthropogenic features like buildings and detailed land use roughness for the accurate representation of local hydraulic dynamics. The resulting outputs (inundation extents, depths, and velocities) support risk assessment and spatial planning in the Ungheni locality (Iași County, Romania), providing a practical, transferable workflow adapted to data-scarce regions. Scenario results quantify vulnerability: for the 0.1% exceedance probability scenario (with a calibration accuracy of ±15–30 min deviation for peak flow timing), the flood risk may affect 882 buildings, 42 land parcels, and 13.5 km of infrastructure. This framework contributes to evidence-based decision-making for climate adaptation and disaster risk reduction strategies, improving urban resilience. Full article

In the digital context, how to achieve symmetrical integration between subjective evaluation and structural stability becomes the key to improving the design effect of iterative product optimization. In this paper, we propose an iterative design method for CIHFS-DEMATEL products that incorporates structural symmetry analysis. The method is based on online review mining and constructs a credibility-based interval hesitant fuzzy set (CIHFS) to symmetrically express the ambiguity and credibility differences in the decision-maker’s subjective evaluation. In turn, a novel exact score function called credibility interval hesitant fuzzy score function (CHFSF), incorporating information symmetric weights, is proposed to realize the bidirectional symmetric mapping between subjective fuzzy inputs and objective exact outputs. Subsequently, the CIHFS-DEMATEL model is introduced to identify the causal paths and a symmetric interaction structure between potential users’ demands. Finally, the demand module mapping matrix is constructed to realize the symmetric decision-making closure loop from demand to solution. Taking the “Intelligent Classified Trash Can” as a case study, we verify the superiority of the method in terms of recognition accuracy, rationality of weight allocation, and structural stability. This study emphasizes the structural symmetry between “input–evaluation–output”, which provides a theoretical foundation and practical framework for the optimal design of products with complex multi-source information. Full article

Nitrogen dioxide (NO₂) poses severe risks to human health and the environment, especially in densely populated megacities. Ground-based air quality monitoring stations provide high-temporal-resolution data but are spatially limited, while satellite observations offer broad coverage but measure column densities rather than surface concentrations. To overcome these limitations, this study integrates ground-based observations with satellite-derived NO₂ from Sentinel-5P TROPOMI and GEOS-CF products to estimate ground-level NO₂ in Istanbul using machine learning (ML) approaches. Three ML algorithms (RF, XGB, and CB) were tested on two datasets spanning 2019–2024 at ~1 km resolution, incorporating 20 features, including topographic, meteorological, environmental, and demographic variables. Among models, CB achieved the best performance (R: 0.686, RMSE: 16.23 µg/m³, and MAE: 11.75 µg/m³ in the test dataset) with the Sentinel-5P dataset, successfully capturing spatial and seasonal variations in ground-level NO₂ both quantitatively and qualitatively. SHAP analysis revealed that regarding satellite-derived NO₂, anthropogenic indicators such as population density, road length, and digital elevation model were the most influential features, while meteorological factors contributed secondarily. Despite the lower spatial resolution of GEOS-CF data, both Sentinel-5P and GEOS-CF datasets supported reliable model outputs. This study provides the first ML-based ground-level NO₂ estimation framework for the Istanbul Metropolitan City. Full article

In a global industrial landscape where the digital economy accounts for over 40% of total output, cloud computing technology is reshaping business models at a compound annual growth rate of 19%. This trend has led to an increasing number of cloud computing tasks requiring timely processing. However, most computational tasks are latency-sensitive and cannot tolerate significant delays. This has led to the urgent need for researchers to address the challenge of effectively scheduling cloud computing tasks. This paper proposes a hybrid SAO and RIME optimizer (HSAO) for global optimization and cloud task scheduling problems. First, population initialization based on ecological niche differentiation is proposed to enhance the initial population quality of SAO, enabling it to better explore the solution space. Then, the introduction of the soft frost search strategy and hard frost piercing mechanism from the RIME optimization algorithm enables the algorithm to better escape local optima and accelerate its convergence. Additionally, a population-based collaborative boundary control method is proposed to handle outlier individuals, preventing them from clustering at the boundary and enabling more effective exploration of the solution space. To evaluate the effectiveness of the proposed algorithm, we compared it with 11 other algorithms using the IEEE CEC2017 test set and assessed the differences through statistical analysis. Experimental data demonstrate that the HSAO algorithm exhibits significant advantages. Furthermore, to validate its practical applicability, we applied HSAO to real-world cloud computing task scheduling problems, achieving excellent results and successfully completing the scheduling planning of cloud computing tasks. Full article

Background: The pressure on the transport sector to decarbonise intensifies the need to look beyond the usual recommendations (e.g., walking, cycling, technological innovations). Therefore, strategies to avoid or modify commutes to places of work have long been seen as an option to decarbonise. Recognised for achieving an optimal balance between working from home and working in an office, co-working spaces may also minimise the length of commutes and therefore reduce emissions, traffic congestion, road maintenance, stress experienced by drivers, and other negative externalities of traffic. Methods: This study quantifies the above using a digital model of England and Wales. Two distributions of co-working spaces have been compared in this paper (i.e., one co-working space (i) in each Middle-layer Super Output Area or (ii) at the nearest train station). Results: The overall reduction in travel time and distance exceeds 70% if everyone who commutes by car outside their home MSOA drives to a co-working space. Despite a change in the place of work having no impact on the cold start emissions, substantial emission savings can still be achieved. These range from 35.8% to 92.1% depending on the pollutant, scenario, and distribution of co-working spaces. Full article

Industrial electric drives account for a dominant share of electricity consumption in manufacturing, making their optimal configuration a critical factor for both sustainability and cost reduction. Traditional design approaches based on prototyping and empirical testing are often costly and insufficient for systematically exploring alternative configurations. This study introduces an integrated computational framework that combines digital twin (DT) modeling and virtual commissioning (VC) to enable energy-aware configuration of industrial electric drive systems at early design stages. The methodology employs parameterized component models derived from manufacturer catalog data, implemented in a commercial simulation environment and integrated into an industrial-grade VC platform. Validation is performed on two conveyor-based testbeds, enabling systematic comparison of simulation outputs with physical measurements. The results demonstrate predictive accuracy sufficient to quantify trade-offs in energy consumption, losses, and efficiency across different vendor solutions. Case studies involving belt and strap conveyors highlighted how the framework supports vendor-neutral decision making, revealing nonintuitive optimization trade-offs between minimizing energy consumption and maximizing efficiency. The proposed framework advances sustainable automation by embedding energy analysis directly into commissioning workflows, offering reproducible, scalable, and cross-domain applicability. Its modular design supports transfer to sectors such as renewable energy, transportation, and biomedical mechatronics, where energy efficiency is equally decisive. Full article

Continued population growth demands a significant increase in agricultural production to ensure food security. However, agricultural output is limited by environmental crises and the negative impacts of open-field farm practices. As an alternative, vertical farming techniques, such as aeroponics, can be utilized to optimize the use of resources. However, the uneven size and distribution of spray droplets in aeroponics, issues that affect root development and nutrient delivery, continue to be problematic in spray performance analysis. In aeroponics, nutrient solutions are delivered to plant roots through pressurized nozzles, and the effectiveness of this delivery depends on the spray characteristics. Variations in flow rates directly affect droplet size, density, and coverage, which in turn influence nutrient uptake and crop growth. In this study, the flow rate was adjusted (3, 4.5, and 6 L/min) to quantitatively analyze spray performance using water-sensitive paper (WSP) as a deposit collector via a quick assessment method. Subsequently, image-processing techniques such as threshold segmentation and morphological operations were applied to isolate individual spray droplets on the WSP images. This technique enabled the quantification of the droplet’s coverage area, size, density, and uniformity to effectively evaluate spray performance. One-way ANOVA indicated that all the spray parameters varied significantly with respect to the flow rate (p < 0.05): For example, the average diameters of the droplets increased from 0.73 mm at 3 L/min to 1.29 mm at 6 L/min. The droplets’ densities decreased from 85.53 drops/cm² to 30.00 drops/cm² across the same flow range. The average uniformity index improved from 30.53 to 15.95 as the flow rate increased. These results indicate that the application of WSP is an effective and scalable approach for analyzing spray performance in aeroponics, as WSP can be rapidly digitized with simple tools, such as a cell phone camera, avoiding the limitations of flatbed scanners or specialized imaging systems. Full article