Search Results (24,644)

Modern, electrically operated heat pumps are characterized by a high degree of efficiency and represent an attractive alternative to conventional heating systems. However, the noise emissions from heat pumps installed outside can lead to increasing noise pollution in densely populated residential areas, which represents an obstacle to widespread use. As part of a research project, a heat pump mock-up was built based on an outdoor unit in the Fraunhofer IBP. With this mock-up, investigations have now been carried out with a prototypical silencer–resonator concept. The aim was to reduce the sound power on the outlet side of the heat pump mock-up. To estimate the effect of this silencer–resonator concept for heat pumps, FEM simulations were first carried out using COMSOL Multiphysics^® with a simplified model. The simulation results validated the silencer–resonator concept for heat pumps and indicated the considerable potential for sound reduction. A measurement was then set up, with which different silencer lengths and absorber thicknesses in the silencer were tested. The measured sound attenuation was higher than the simulated values. The results showed that porous absorbers with sufficient thickness can achieve effective performance in the mid-frequency range. A maximum sound power reduction of 5.7 dB was achieved with the 0.15 m absorber. Additionally, Helmholtz resonators were implemented to attenuate the low-frequency range and tonal peaks. With these resonators sound attenuation was increased to 7.7 dB. Full article

This research experimentally investigates the association between multidimensional risk factors and economic growth, quantified by GDP as a partial indicator of advancement towards economically relevant Sustainable Development Goals (SDGs). This research experimentally investigates the correlation between multidimensional risk variables and economic growth, quantified by GDP as a partial indicator of advancement towards economically relevant Sustainable Development Goals (SDGs) in Saudi Arabia, particularly in alignment with the objectives of Saudi Vision 2030. This study utilizes annual data from 1990 to 2024 and employs the Autoregressive Distributed Lag (ARDL) bounds testing approach to examine the short-run and long-run relationships between economic growth, as measured by GDP, and five key risk dimensions: governance effectiveness, financial development, environmental pressure, human capital, and oil price volatility, which act as proxies for risk dimensions. The main contribution of this study is the integration of these governance, financial, environmental, human capital, and oil price risk factors into a single ARDL framework for Saudi Arabia from 1990 to 2024, using GDP growth as a proxy for progress toward SDGs within the Saudi Vision 2030 context, addressing gaps in prior studies that focus on individual determinants. The empirical evidence indicates a long-term cointegration relationship among the variables. Our findings indicate that government effectiveness and investment in human capital are important positive factors associated with long-term economic growth, thereby validating the importance of institutional improvements and educational expenditures. In contrast, fluctuations in oil prices and environmental pressures are linked to adverse association, highlighting issues related to resource dependency and ecological degradation. Financial development exhibits a negative long-run association, indicating potential inefficiencies or diminishing returns in loan distribution. The study offers essential policy recommendations, such as expediting digital governance reforms, allocating financial resources to non-oil SMEs (SDG 8), aligning educational curricula with labor market demands, and implementing stricter environmental regulations to separate economic growth from emissions. Full article

Background: Attention Deficit Hyperactivity Disorder (ADHD) is associated with executive function deficits—such as planning, organization, and prospective memory—that impair autonomy and daily functioning, increase family stress, and create challenges in educational contexts. These consequences underscore the need for accessible and ecologically valid interventions addressing the cognitive, familial, and educational dimensions. Traditional approaches often lack ecological validity, and pharmacological treatment shows a limited impact on functional cognition. Objectives: This protocol outlines a feasibility study of DiverAcción, a web-based telerehabilitation system designed to enhance functional cognition through interactive and gamified tasks integrated into a comprehensive healthcare programme. Methods: A quasi-experimental feasibility study before and after the study will recruit 30 participants aged 9 to 17 years with ADHD. The study comprises an initial face-to-face session for instructions and baseline assessment (T0), followed by twelve supervised online sessions over six weeks. Therapeutic support is provided via integrated chat, email, and two scheduled videoconference check-ins. Feasibility outcomes: include recruitment, adherence, retention, usability (SUS), acceptability (TAM), satisfaction, user-friendly design, therapeutic alliance (WAI-I), and professionals’ attitudes toward technology (e-TAP-T). Exploratory measures: include parental self-efficacy (BPSES), parenting stress (PSI-4-SF), ADHD symptomatology (SNAP-IV), executive functioning (BRIEF-2), time management (Time-S), emotional regulation (ERQ-CA), prospective memory (PRMQ-C), and health-related quality of life (KIDSCREEN-52). Analyses emphasize descriptive statistics for feasibility metrics (recruitment, adherence, retention, dropout and fidelity). Assessments are conducted post-intervention (T1) and at three-month follow-up (T2) and analyzed relative to baseline using repeated-measures ANOVA or Friedman tests, depending on data distribution. Conclusions: This feasibility protocol will provide preliminary evidence on the usability, acceptability, and implementation of DiverAcción. Findings will guide refinements and inform the design of a subsequent randomized controlled trial. Full article

In response to the urgent need for decarbonising the transport sector, this paper analyses the thermal performance of a battery electric minibus under cold ambient conditions. Thermal simulation models of the vehicle cabin and its electric heating circuits for both driver and passenger areas were developed using Modelica and validated with measurement data at −7 °C and 0 °C. The model showed good agreement with the measurements, with cabin temperature deviations within ±1.6 K and heating power deviations below 6%. Results show that the existing electric-only heating system is, in the automatic heating mode selected, insufficient to reach the target cabin temperature of 23 °C, as the optional fuel-powered heater was omitted to ensure fully zero-emission operation. To address this, an extended heating system with an additional heat exchanger was implemented in the simulation, which improved the overall cabin temperature level and also its spatial variation. However, it also increased the heating power demand by 43% at −7 °C (from 4.8 kW to 6.8 kW) and by 17% at 0 °C (from 4.8 kW to 5.6 kW). An additional heat loss analysis revealed that approx. 65–75% of all thermal losses occur through the window areas. Future improvements should therefore focus on optimising the heating strategy and enhancing cabin and heating system insulation to reduce energy demand while maintaining or even improving passenger comfort. Full article

Soil erosion is largely driven by climate change and land use dynamics. The objective of this study is to assess the dynamic variation in erosion under the combined effects of precipitation and land use change in the Tigrigra watershed, located in the mountainous region of the Middle Atlas. The RUSLE (Revised Universal Soil Loss Equation) model is used in the methodological approach to estimate soil loss based on various parameters such as precipitation, soil, topography, land cover, and conservation practices. Geographic Information Systems (GIS) and remote sensing tools are essential for applying this method. In addition, the CA-Markov model (cellular automata), which models and predicts land use changes over time, is used to project future land cover scenarios that influence soil erosion dynamics. The research focuses on four previous periods (1991–2000, 2001–2010, 2011–2015, and 2016–2023), as well as a future period (2024–2050), considering two climate scenarios, RCP 2.6 and RCP 4.5. Precipitation data from local weather stations and the CMIP5 climate model were used to calculate the R factor (precipitation erosivity). Land cover analysis was performed using Landsat satellite images (30 m resolution) integrated into the CA-Markov model to calculate the C factor (land cover management). The results show that erosion has gradually decreased over both past and future periods, mainly due to variations in precipitation and vegetation cover. It should be noted that the period from 1991–2000 to 2016–2023 shows higher erosion compared to the future periods, with a maximum value of 17.83 t/ha/year recorded between 1991 and 2000. For the future period 2024–2050, a continuous decrease in erosion is observed under both scenarios, with an average value of 15.30 t/ha/year for the RCP2.6 scenario and 15.86 t/ha/year for the RCP4.5 scenario, with erosion remaining slightly higher under RCP4.5. Overall, erosion decreases across both historical (1991–2023) and projected (2024–2050) periods due to reduced rainfall erosivity. The northern part of the basin is particularly prone to erosion due to the low vegetation cover. The results indicate that areas susceptible to erosion require conservation measures to reduce soil loss. Implementing sustainable agricultural practices is crucial for maintaining long-term soil health and preventing degradation. However, some limitations of the study, such as the lack of data on conservation practices and daily precipitation, might affect the overall robustness of the findings. Full article

Severe surface deformation induced by super-large mining height longwall extraction poses a significant threat to the safe operation of high-voltage transmission towers. In this study, a 330 kV straight-line transmission tower located above the 122104 working face of the Caojiatan Coal Mine was selected as a case study to investigate tower stability under mining-induced surface deformation and to develop corresponding protection technologies. An integrated monitoring system combining instantaneous and long-term measurements was established to characterize surface movement throughout the mining process. The results indicate that the maximum surface subsidence reached 7300 mm, while the maximum inclination and curvature attained 50 mm/m and 0.62 mm/m², respectively, reflecting intense deformation of the overlying ground. Numerical simulations based on ANSYS 2021R1 were conducted to systematically evaluate the effects of surface inclination, compressive deformation, and tensile deformation on the structural response of the transmission tower. The critical deformation thresholds leading to structural failure were identified as 30 mm/m for inclination, −7.2 mm/m for horizontal compression, and 7.7 mm/m for horizontal tension. Based on these findings, a comprehensive protection system was proposed, integrating tower body reinforcement, combined foundation reconstruction, surface subsidence monitoring, dynamic jacking-based rectification, and foundation grouting reinforcement. The proposed scheme was successfully implemented in field practice. Monitoring results demonstrate that, after reinforcement and rectification, differential settlement of the tower foundation was controlled within 20 mm, and tower inclination remained below 1‰. This ensured uninterrupted underground mining operations and continuous power transmission within the Caojiatan Coal Mine corridor. The outcomes of this study provide a practical reference for the protection of high-voltage transmission towers under similar mining conditions. Full article

Reconstructing original cusp dimensions in worn molars represents a fundamental challenge across dentistry, anthropology, and paleontology, as dental wear obscures critical morphological information. In this proof-of-concept study, we present a standardized machine learning pipeline for predicting original cusp height, specifically the horn tips of the enamel–dentine junction (EDJ), in worn lower molars using three-dimensional morphometric data from micro-computed tomography (micro-CT). We analyzed 40 permanent lower first (M1) and second (M2) molars from four hominin groups, systematically evaluated across three wear stages: original, moderately worn (worn1), and severely worn (worn2). Morphometric variables including height, area, and volume were quantified for each cusp, with Random Forest and multiple linear regression models developed individually and combined through ensemble methods. To mimic realistic reconstruction scenarios while preserving a known ground truth, models were trained on unworn specimens (original EDJ morphology) and tested on other teeth after digitally simulated wear (worn1 and worn2). Predictive performance was evaluated using root mean square error (RMSE) and coefficient of determination (R²). Our results demonstrate that under moderate wear (worn1), the ensemble models achieved normalized RMSE values between 11% and 17%. Absolute errors typically below 0.25 mm for most cusps, with R² values up to ~0.69. Performance deteriorated under severe wear (worn2), particularly for morphologically variable cusps such as the hypoconid and entoconid, but generally remained within sub-millimetric error ranges for several structures. Random Forests and linear models showed complementary strengths, and the ensemble generally offered the most stable performance across cusps and wear states. To enhance transparency and accessibility, we provide a comprehensive, user-friendly software pipeline including pre-trained models, automated prediction scripts, standardized data templates, and detailed documentation. This implementation allows researchers without advanced machine learning expertise to explore EDJ-based reconstruction from standard morphometric measurements in new datasets, while explicitly acknowledging the limitations imposed by our modest and taxonomically unbalanced sample. More broadly, the framework represents an initial step toward predicting complete crown morphology, including enamel thickness, in worn or damaged teeth. As such, it offers a validated methodological foundation for future developments in cusp and crown reconstruction in both clinical and evolutionary dental research. Full article

This work presents a binary correlation-based ultrasonic ranging system that supports versatile signal modulation schemes. It transmits ultrasonic waves with distinct characteristics in consecutive measurement operations and employs a set of signal detectors to identify the originating excitation of the received echo signals, thereby effectively mitigating cross-measurement interference. Design equations supporting diverse modulation schemes are presented, and the factors affecting system performance are discussed. Experimental results obtained with the developed hardware system demonstrate its ability to perform accurate measurements over extended distances and under low signal-to-noise ratio conditions. Furthermore, the system effectively distinguishes echo signals generated by different excitation waves. Full article

Firefighting requires high and multidimensional fitness to ensure operational readiness and public safety. In Portugal, there is limited data regarding firefighters’ fitness and exercise programs to improve readiness are lacking. This study evaluated the effects of a 6-month minimal-equipment exercise program on the physical fitness of firefighter recruits. Thirty-five male subjects (23.0 ± 2.72 years) were assessed at baseline,3 and 6 months for body composition, handgrip strength, running speed, cardiovascular endurance, anaerobic power, and upper- and lower-body strength. The intervention entailed daily sessions with 15 min of continuous running (50–65% HRmax) and active stretching, followed by alternating routines, including endurance running, free weights, interval sprints, calisthenics, and drills. A repeated-measures ANOVA and Bonferroni adjusted post hoc comparisons identified time-based changes. Significant improvements occurred across all fitness variables. Body fat fell by 8.4% and VO₂max increased (p < 0.001), surpassing occupational thresholds required for extended suppression tasks. Bench press and sit-up performance improved by 88% and 81%, respectively, while countermovement jump showed double-digit gains (13%), all of which can translate directly to hose advancement, victim rescue, and forcible entry. These results highlight that resource-constrained departments can implement effective, low-cost exercise programs for enhancing pivotal fitness components, supporting policy initiatives to include structured training throughout firefighters’ careers. Full article

Background: Intranasal drug delivery is a preferred route for emergency administration of naloxone in opioid overdose due to its rapid onset of action and ease of use. However, limited knowledge exists on the delivery efficiency and safety of nasal sprays in neonates, particularly in life-threatening situations such as coma states where breathing is compromised. This study presents a physiology-based simulation of spray deposition and runoff loss in a 10-day-old infant nose model. Methods: Spray characteristics, including droplet size distribution, exiting velocity, and plume angle, were measured and implemented in ANSYS Fluent droplet tracking model. Naloxone film thickness was measured on ex vivo porcine nasal mucosa at varying angles and used in the Eulerian Wall-Film model. Simulations were conducted in a 10-day-old nose geometry across multiple doses (0.25, 0.50, 1.0, and 2.0 mL) in supine and 45° inclined postures to quantify regional deposition, liquid film translocation, and pharyngeal runoff. Results: While a 0.25 mL spray was fully retained in the nasal passages, higher doses exceeded the mucosal holding capacity and caused significant runoff. Runoff into the pharynx was 18.5% and 10.1% for the spray volume of 0.50 mL in the 45° back tilt and supine positions, respectively. The 1.0 mL spray caused 55.1% and 53.5% runoff in the 45° back tilt and supine positions, while the 2.0 mL spray caused 77.5% and 76.8% runoff in the 45° back tilt and supine positions, respectively. Conclusions: These findings highlight the critical influence of spray volume on drug delivery outcomes in neonates and provide quantitative guidance for optimizing intranasal naloxone administration in emergency pediatric care. Full article

Millimeter-wave (mmWave)/terahertz (THz) devices relying on conventional semiconductor technologies face significant performance bottlenecks, constraining their use in next-generation electronic systems. To address these challenges, this work demonstrates high-performance THz Schottky barrier diodes (SBDs) based on aligned carbon nanotube (ACNT) arrays, and the realization of a D-band second-harmonic frequency multiplier. The ACNT-SBDs exhibit superior electrical and radio-frequency (RF) characteristics, achieving a forward current density of 0.14 mA·μm⁻¹ at −1.3 V and an intrinsic cutoff frequency (f_C) of 506 GHz. The developed small-signal model of diodes shows close agreement with measurements, with S-parameter relative errors below 0.7% from 100 MHz to 67 GHz. The implemented 154 GHz D-band multiplier achieved a maximum output power of −18.97 dBm and a minimum conversion loss of 27.92 dB, outperforming previously reported frequency multipliers based on carbon nanotubes or two-dimensional (2D) materials. This study not only establishes the outstanding high-frequency response, nonlinear efficiency, and integration potential of ACNT-based devices but also provides a promising technical pathway for future THz communication and sensing applications. Full article

In this paper, two improvements in precipitation classification have been performed. Supervised machine learning has demonstrated considerable performances in classification tasks. However, supervised machine learning can only be applied to labeled data. In some cases, large amounts of unlabeled data contain valuable information for better classification. In the classification of precipitation intensities from satellite images, unlabeled data constitute the majority and remain largely unexplored. To exploit both labeled and unlabeled data, a Semi-Supervised Support Vector Machine (S3VM) is implemented as the first improvement for classification results. The labeling of the limited available data is derived from radar measurements covering a small portion of the Meteosat Second Generation Satellite observations. The results show that the S3VM model outperforms the standard SVM model, with up to a 15% improvement in classification accuracy compared to the standard SVM. To achieve the second improvement, the S3VM was combined with the Firefly Algorithm (FFA) to optimize its hyperparameters. This hybridization (S3VM-FFA) enabled an even more robust performance. A comparative study showed that the S3VM-FFA approach yielded highly satisfactory results, achieving a 17% improvement in classification compared to the SVM results. Based on these classifications, precipitation quantities at different scales are estimated. Similarly to the classification results, statistical evaluation parameters indicate that the S3VM-FFA outperforms both the standard SVM and the conventional S3VM. Full article

Control measures for pine wilt disease (PWD) are widely implemented, yet multi-year field comparisons that track biodiversity trajectories across contrasting management regimes remain limited. We conducted a 3-year (2023–2025) replicated study across nine pine-forest sites in Gyeongsangnam-do, Republic of Korea, comparing three management regimes (Clear-cut, Fumigation/Aerial, Unmanaged) to evaluate regime-associated patterns in ground-active beetle diversity, activity density, and community composition while considering understory vegetation cover. Regime-associated differences were consistent but dynamic: Unmanaged stands generally supported higher richness and Shannon diversity (H′), Clear-cut stands showed the lowest diversity immediately after harvest, and Fumigation/Aerial stands maintained the highest activity density. Assemblage composition separated strongly among regimes within each year, and indicator taxa highlighted regime-associated assemblage states, notably Pheropsophus jessoensis (Fumigation/Aerial), Carabus tuberculosus (Clear-cut), and Blindus strigosus (Unmanaged). Because regimes were assigned at the site level and were partially confounded by geographic region, we interpreted these outcomes as region-structured, regime-associated patterns rather than strictly causal effects. We recommend integrating PWD management with retention forestry (e.g., partial canopy and deadwood retention) and routine biodiversity monitoring to reconcile effective disease suppression with the long-term conservation of forest biodiversity. Full article

Recent studies have reported a rapid increase in short-duration, high-intensity rainfall over the Seoul Metropolitan Area (SMA), primarily associated with mesoscale convective systems (MCSs), highlighting the need for high-resolution and multi-platform observations for accurate forecasting. To address this challenge, the Korea Meteorological Administration (KMA) established the Korean Precipitation Observation Program (KPOP), an intensive observation network integrating radar, wind lidar, wind profiler, and storm tracker measurements. This study introduces the design and implementation of the KPOP network and evaluates its observational and forecasting value through a heavy rainfall event that occurred on 17 July 2024. Wind lidar data and weather charts reveal that a strong low-level southwesterly jet and enhanced moisture transport from the Yellow Sea played a key role in sustaining a quasi-stationary, line-shaped rainband over the metropolitan region, leading to extreme short-duration rainfall exceeding 100 mm h⁻¹. To investigate the impact of KPOP observations on numerical prediction, preliminary data assimilation experiments were conducted using the Korean Integrated Model-Regional Data Assimilation and Prediction System (KIM-RDAPS) with WRF-3DVAR. The results demonstrate that assimilating wind lidar observations most effectively improved the representation of low-level moisture convergence and spatial structure of the rainband, leading to more accurate simulation of rainfall intensity and timing compared to experiments assimilating storm tracker data alone. These findings confirm that intensive, high-resolution wind observations are critical for improving initial analyses and enhancing the predictability of extreme rainfall events in densely urbanized regions such as the SMA. Full article

The expansion of renewables in modern power systems and the coordinated development of upstream and downstream industrial chains are promoting a shift on the utility side from traditional settlement by energy toward operation driven by data and models. Industrial electricity consumption data exhibit pronounced multi-scale temporal structures and sectoral heterogeneity, which makes unified long-term load and generation forecasting while maintaining accuracy, interpretability, and scalability a challenge. From a modern system identification perspective, this paper proposes a System Identification in Adaptive Bayesian Framework (SIABF) for medium- and long-term industrial load forecasting based on daily freeze electricity time series. By combining daily aggregation of high-frequency data, frequency domain analysis, sparse identification, and long-term extrapolation, we first construct daily freeze series from 15 min measurements, and then we apply discrete Fourier transforms and a spectral complexity index to extract dominant periodic components and build an interpretable sinusoidal basis library. A sparse regression formulation with ${\ell }_1$ regularization is employed to select a compact set of key basis functions, yielding concise representations of sector and enterprise load profiles and naturally supporting multivariate and joint multi-sector modeling. Building on this structure, we implement a state-space-implicit physics-informed Bayesian forecasting model and evaluate it on real data from three representative sectors, namely, steel, photovoltaics, and chemical, using one year of 15 min measurements. Under a one-month-ahead evaluation using one year of 15 min measurements, the proposed framework attains a Mean Absolute Percentage Error (MAPE) of 4.5% for a representative PV-related customer case and achieves low single-digit MAPE for high-inertia sectors, often outperforming classical statistical models, sparse learning baselines, and deep learning architectures. These results should be interpreted as indicative given the limited time span and sample size, and broader multi-year, population-level validation is warranted. Full article