Search Results (4,185)

To promote the development of long-distance high-speed underwater optical wireless communication (UWOC) based on visible light, this study proposes a high-bandwidth UWOC system based on micro-light-emitting-diodes (micro-LEDs) adopting the Non-Return-to-Zero On-Off Keying (NRZ-OOK) modulation. The numerical simulations reveal that optimizing the structural parameters of gallium nitride (GaN)-based micro-LED through dimensional scaling and quantum well layer reduction may significantly enhance optoelectronic performance, including modulation bandwidth and luminous efficiency. Moreover, experimental validation demonstrated maximum real-time data rates of 420 Mbps, 290 Mbps, and 250 Mbps at underwater distances of 2.3 m, 6.9 m, and 11.5 m, respectively. Furthermore, the underwater audio communication was successfully implemented at an 11.5 m UWOC distance at an ultra-low level of incoming optical power (12.5 µW) at the photodetector (PD) site. The channel characterization yielded a micro-LED-specific attenuation coefficient of 0.56 dB/m, while parametric analysis revealed wavelength-dependent degradation patterns, exhibiting positive correlations between both attenuation coefficient and bit error rate (BER) with operational wavelength. This study provides valuable insights for optimizing underwater optical systems to enhance real-time environmental monitoring capabilities and strengthen security protocols for subaquatic military communications in the future. Full article

Enhancing urban air quality and thermal comfort involves addressing multifaceted environmental and design challenges. Investigating the effects of urban morphological and building geometrical parameters on enhancing air quality and thermal comfort is a multifaceted problem, influenced by different parameters. This study aims to develop optimized design solutions for university buildings and courtyards to enhance outdoor thermal comfort and reduce CO₂ concentration levels as an indicator of air quality. Consequently, the methodology involved a combination of field monitoring at two university faculties in Egypt and a computational parametric methodology using Rhino 3D+Grasshopper(V8) for enhancing thermal comfort, reducing CO₂ concentration levels, and improving wind velocity. The in situ measurements revealed significantly high CO₂ levels (780 ppm) and wind speed (3.8 m/s). The parametric methodology’s findings revealed a substantial reduction in the Universal Thermal Climate Index (UTCI) by 2.04 to 10.3 °C, a decrease in CO₂ concentration by 57 to 197 ppm, and an increase in wind speed by 0.4 to 4.07 m/s. The most suitable vegetation ratio for trees within narrow courtyard designs was found to be 30%. This ratio effectively enhances thermal comfort (UTCI) and reduces CO₂ concentrations, while also maintaining adequate airflow and avoiding excessive obstruction of natural ventilation within the courtyard. These findings provide valuable guidance for optimizing courtyard designs in hot arid climates. Full article

To improve the tracking accuracy and the adaptability of intelligent vehicles in various road conditions, an adaptive model predictive controller combining reinforcement learning is proposed in this paper. Firstly, to solve the problem of control accuracy decline caused by a fixed prediction time domain, a low-computational-cost adaptive prediction horizon strategy based on a two-dimensional Gaussian function is designed to realize the real-time adjustment of prediction time domain change with vehicle speed and road curvature. Secondly, to address the problem of tracking stability reduction under complex road conditions, the Deep Q-Network (DQN) algorithm is used to adjust the weight matrix of the Model Predictive Control (MPC) algorithm; then, the convergence speed and control effectiveness of the tracking controller are improved. Finally, hardware-in-the-loop tests and real vehicle tests are conducted. The results show that the proposed adaptive predictive horizon controller (DQN-AP-MPC) solves the problem of poor control performance caused by fixed predictive time domain and fixed weight matrix values, significantly improving the tracking accuracy of intelligent vehicles under different road conditions. Especially under variable curvature and high-speed conditions, the proposed controller reduces the maximum lateral error by 76.81% compared to the unimproved MPC controller, and reduces the average absolute error by 64.44%. The proposed controller has a faster convergence speed and better trajectory tracking performance when tested on variable curvature road conditions and double lane roads. Full article

One of the nanoscale technologies that shows its capability of implementing integrated digital circuits with low power, high speed, and high density is quantum-dot cellular automata (QCA). The fundamental device for designing and implementing circuits in QCA is majority logic. In this paper, a novel energy-efficient QCA design of three-input AND/OR logic functions is proposed. This design can perform both AND and OR logic operations using the same structure with an achievement of 58% and 64% approximate reductions in power consumption compared to majority-based structures, and 31% and 32% approximate reductions in power consumption compared to the best available circuits, respectively. In addition, other physical constraints such as area and latency are improved and have better or similar results compared to the best existing circuits. The proposed circuit can be considered as a fundamental and better alternative to the majority gate for energy-efficient circuit design in QCA. This will pave the way for developing efficient large-scale QCA-based sequential and combinational circuits. Full article

As internal combustion engines (ICEs) remain dominant in maritime transport, reducing their greenhouse gas (GHG) emissions is critical to meeting IMO’s decarbonization targets. Ammonia (NH₃) has gained attention as a carbon-free fuel due to its zero CO₂ emissions and high hydrogen density. However, its low flame speed and high ignition temperature pose combustion challenges. This study investigates the combustion and emission performance of NH₃-diesel dual-fuel engines, applying Response Surface Methodology (RSM) for multi-objective optimization of key operating parameters: ammonia fraction (AF: 0–30%), engine speed (1200–1600 rpm), and altitude (0–2000 m). Experimental results reveal that increasing AF led to a reduction in Brake Thermal Efficiency (BTE) from 39.2% to 37.4%, while significantly decreasing NOₓ emissions by 82%, Total hydrocarbon emissions (THC) by 61%, and CO₂ emissions by 36%. However, the ignition delay increased from 8.2 to 10.8 crank angle degrees (CAD) and unburned NH₃ exceeded 6500 ppm, indicating higher incomplete combustion risks at high AF. Analysis of variance (ANOVA) confirmed AF as the most influential factor, contributing up to 82.3% of the variability in unburned NH₃ and 53.6% in NOₓ. The optimal operating point, identified via desirability analysis, was 20% AF at 1200 rpm and sea level altitude, achieving a BTE of 37.4%, NOₓ of 457 ppm, and unburned NH₃ of 6386 ppm with a desirability index of 0.614. These findings suggest that controlled NH₃ addition, combined with proper speed tuning, can significantly reduce emissions while maintaining engine efficiency in dual-fuel configurations. Full article

Accurate, real-time, and cost-effective detection and counting of pre-weaning piglets are critical for improving piglet survival rates. However, achieving this remains technically challenging due to high computational demands, frequent occlusion, social behaviors, and cluttered backgrounds in commercial farming environments. To address these challenges, this study proposes a lightweight and attention-enhanced piglet detection and counting network based on an improved YOLOv8n architecture. The design includes three key innovations: (i) the standard C2f modules in the backbone were replaced with an efficient novel Multi-Scale Spatial Pyramid Attention (MSPA) module to enhance the multi-scale feature representation while a maintaining low computational cost; (ii) an improved Gather-and-Distribute (GD) mechanism was incorporated into the neck to facilitate feature fusion and accelerate inference; and (iii) the detection head and the sample assignment strategy were optimized to align the classification and localization tasks better, thereby improving the overall performance. Experiments on the custom dataset demonstrated the model’s superiority over state-of-the-art counterparts, achieving 88.5% precision and a 93.8% $mA{P}_{0.5}$. Furthermore, ablation studies showed that the model reduced the parameters, floating point operations (FLOPs), and model size by 58.45%, 46.91% and 56.45% compared to those of the baseline YOLOv8n, respectively, while achieving a 2.6% improvement in the detection precision and a 4.41% reduction in the counting MAE. The trained model was deployed on a Raspberry Pi 4B with $ncnn$ to verify the effectiveness of the lightweight design, reaching an average inference speed of <87 ms per image. These findings confirm that the proposed method offers a practical, scalable solution for intelligent pig farming, combining a high accuracy, efficiency, and real-time performance in resource-limited environments. Full article

This study introduces YOLOv13-Cone-Lite, an enhanced algorithm based on YOLOv13s, designed to meet the stringent accuracy and real-time performance demands for traffic cone detection in autonomous formula racing cars on enclosed tracks. We improved detection accuracy by refining the network architecture. Specifically, the DS-C3k2_UIB module, an advanced iteration of the Universal Inverted Bottleneck (UIB), was integrated into the backbone to boost small object feature extraction. Additionally, the Non-Maximum Suppression (NMS)-free ConeDetect head was engineered to eliminate post-processing delays. To accommodate resource-limited onboard terminals, we minimized superfluous parameters through structural reparameterization pruning and performed 8-bit integer (INT8) quantization using the TensorRT toolkit, resulting in a lightweight model. Experimental findings show that YOLOv13-Cone-Lite achieves a mAP₅₀ of 92.9% (a 4.5% enhancement over the original YOLOv13s), a frame rate of 68 Hz (double the original model’s speed), and a parameter size of 8.7 MB (a 52.5% reduction). The proposed algorithm effectively addresses challenges like intricate lighting and long-range detection of small objects and offers the automotive industry a framework to develop more efficient onboard perception systems, while informing object detection in other closed autonomous environments like factory campuses. Notably, the model is optimized for enclosed tracks, with open traffic generalization needing further validation. Full article

Mass gatherings such as the annual Hajj pilgrimage in Makkah, Saudi Arabia, generate extreme, short-term anthropogenic emission loads with significant air quality and public health implications. This study assesses the spatiotemporal dynamics of key atmospheric pollutants—including nitrogen dioxide (NO₂), carbon monoxide (CO), sulfur dioxide (SO₂), formaldehyde (HCHO), and aerosols—across Makkah and its holy sites before and during the Hajj seasons of 2023 and 2024. Using high-resolution Sentinel-5P TROPOMI satellite data, pollutant fields were reconstructed at 100 m spatial resolution via cloud-based geospatial analysis on the Google Earth Engine. During Hajj 2023, spatially resolved NO₂ concentrations ranged from 15.4 μg/m³ to 38.3 μg/m³ with an average of 24.7 μg/m³, while SO₂ during the 2024 event peaked at 51.2 μg/m³ in key hotspots, occasionally exceeding World Health Organization (WHO) guideline values. Aerosol index values showed episodic surges (up to 1.43), particularly over transportation corridors, parking areas, and logistics facilities. CO concentrations reached values as high as 1069.8 μg/m³ in crowded zones, and HCHO concentrations surged up to 9.99 μg/m³ during peak periods. Quantitative correlation analysis revealed that during Hajj, atmospheric chemistry diverged from urban baseline: the NO₂–SO₂ relationship shifted from strongly negative pre-Hajj (r = −0.74) to moderately positive during the event (r = 0.35), while aerosol–HCHO correlations intensified negatively from r = −0.23 pre-Hajj to r = −0.50 during Hajj. Meteorological analysis indicated significant positive correlations between wind speed and NO₂ (r = 0.35) and wind speed and CO (r = 0.35) during 2024, demonstrating that extreme emission rates overwhelmed typical dispersive processes. Relative humidity was positively correlated with aerosol loading (r = 0.37), pointing to hygroscopic growth patterns. These results quantitatively demonstrate that Hajj drives a distinct, event-specific pollution regime, characterized by sharp increases in key pollutant concentrations, altered inter-pollutant and pollutant–meteorology relationships, and spatially explicit hotspots driven by human activity and infrastructure. The integrated satellite–meteorology workflow enabled near-real-time monitoring in a data-sparse environment and establishes a scalable framework for evidence-based air quality management and health risk reduction in mass gatherings. Full article

Regulatory changes related to the policy of reducing CO₂ emissions from natural gas are leading to an increase in the share of hydrogen in gas transmission and utilization systems. In this context, the impact of the change in composition on thermal radiation zones should be assessed for flaring during startups, scheduled shutdowns, maintenance, and emergency operations. Most existing models are calibrated for hydrocarbon flare gases. This study assesses how the CH₄–H₂ blends affect thermal radiation zones using a developed solver based on the Brzustowski–Sommer methodology with composition-dependent fraction of heat radiated (F) and range-dependent atmospheric transmissivity. Five blends, 0–50% (v/v) H₂, were analyzed for a 90 m stack at wind speeds of 3 and 5 m·s⁻¹. Comparisons were performed at constant molar (standard volumetric) throughput to isolate composition effects. Adding H2 contracted the radiation zones and reduced peak ground loads. Superposition analysis for a multi-flare layout indicated that replacing one 100% (v/v) CH₄ flare with a 10% (v/v) H₂ blend reduced peak ground radiation. Emission-factor analysis (energy basis) showed reductions of 3.24/3.45% at 10% (v/v) H₂ and 7.01/7.44% at 20% (v/v) H₂ (LHV/HHV); at 50% (v/v) H₂, the decrease reached 22.18/24.32%. Hydrogen blending provides coupled safety and emissions co-benefits, and the developed framework supports screening of flare designs and operating strategies as blends become more prevalent. Full article

The recycling of nickel–cadmium batteries poses a significant environmental challenge due to cadmium’s high biotoxicity. This study proposes a green method for recovering cadmium from cadmium oxide (CdO) using carbon (coal) in the presence of a molten binary flux (KCl:NaCl = 0.507:0.493, melting point 667 °C). The flux’s relatively low density and conductivity enable cadmium reduction beneath and through the flux layer. Brown coal (5–25 mm) served as the reductant. The reduction of cadmium from cadmium oxide with carbon (brown coal) took place in the temperature range from 667 °C to 700 °C. To enhance the process, electrovortex flows (EVF) were employed—generated by the interaction between non-uniform AC electric currents and their self-induced magnetic fields resembling conditions in a fluidised bed reactor. The graphite crucible acted as both one of the electrodes, with a graphite rod as the second electrode. As Cd and CdO are denser than both the flux and coal, the reduction proceeded below the flux layer. The flux facilitated CdO transport to the reductant, speeding up the reaction. X-ray diffraction (XRD) and scanning electron microscopy (SEM) confirmed the formation of metallic cadmium beneath and within the flux layer. This method demonstrates the feasibility of flux-assisted cadmium recovery without prior mixing and offers a foundation for further optimisation of sustainable battery recycling. Full article

Functional glass surfaces with tunable wettability are of growing interest in optical, biomedical, and architectural applications. In this study, we investigate the influence of femtosecond laser processing parameters—including power, scanning speed, and repetition rate—on the surface morphology, wettability, and optical properties of Panda glass. Laser structuring generated microscale ablation features and increased surface roughness (arithmetic mean height, Sa, rising from ~0.02 µm for pristine glass to ~1.85 µm under optimized conditions). The treated surfaces exhibited enhanced hydrophobicity, with static water contact angles up to ~82° and sliding angles exceeding 50°, indicating significant droplet pinning. Optical characterization further showed a reduction in transmittance at 550 nm from ~92% (pristine) to ~68% after laser treatment, consistent with increased scattering by surface textures. These findings demonstrate that femtosecond laser processing is an effective mask-free method to enhance the hydrophobicity of glass surfaces and establish clear process–structure–property relationships, providing guidance for future optimization toward superhydrophobic performance. Full article

In modern industrial manufacturing, the mechanical properties of large bearing housing castings are critical to equipment reliability and lifespan. Traditional prediction methods relying on experimental testing and empirical formulas face challenges such as high costs, limited samples, and inadequate generalization capabilities. This study presents a machine learning approach for predicting mechanical properties of ZG270-500 cast steel, integrating multivariate data (chemical composition, process parameters) to establish an efficient predictive model. Utilizing real-world production data from a certain foundry and forging plant, the research implemented preprocessing steps including outlier handling, data balancing, and normalization. A systematic comparison was conducted on the performance of four algorithms: Backpropagation Neural Network (BPNN), Support Vector Regression (SVR), Random Forest (RF), and Extreme Gradient Boosting (XGBoost). The results indicate that under small-sample conditions, the SVR model outperforms other models, achieving a coefficient of determination (R²) between 0.85 and 0.95 on the test set for mechanical properties. The root mean square errors (RMSE) for yield strength, tensile strength, elongation, reduction in area, and impact energy are 7.59 MPa, 7.52 MPa, 0.68%, 1.47%, and 5.51 J, respectively. Experimental validation confirmed relative errors between predicted and measured values below 4%. SHAP value analysis elucidated the influence mechanisms of key process parameters (e.g., pouring speed, normalization holding time) and elemental composition on mechanical properties. This research establishes an efficient data-driven approach for large casting performance prediction and provides a theoretical foundation for guiding process optimization, thereby addressing the research gap in performance prediction for large bearing housing castings. Full article

Background/Objectives: Femoral neck and proximal femur fractures in the elderly can result from low-energy trauma due to osteoporotic changes and contribute significantly to increased morbidity and mortality. Despite various treatment options, closed reduction and internal fixation (CRIF) with intramedullary nails has become the predominant approach. While a minimally invasive approach reduces complications and speeds recovery, this outcome is not always feasible in practice. The primary surgical goal remains achieving a stable and precise fracture reduction, favoring CRIF when possible. Our study aims to evaluate the clinical, radiological, and functional outcomes of patients operated on using the Wide-Awake Local Anesthesia No Tourniquet (WALANT) technique. Methods: Patients who underwent surgery for intertrochanteric femur fractures between June 2019 and June 2021 were analyzed. Patients who were between 75 and 90 years old and had undergone surgery with a proximal femoral nail (PFN) were included in the study. Patients were excluded if they required general anesthesia, if an acceptable reduction could not be achieved with the PFN, if they did not attend the last follow-up examination, or if the follow-up period was <4 years. Patients were functionally assessed using the Harris hip score at the 6th month and at the last follow-up and using the visual analog scale at the surgery, at the 4th hour after surgery, and at the time of discharge. For radiological assessment, the classification of reduction quality and the measurement of the tip–apex distance were used. Results: Forty patients (22F/18M) were included in the study. Their mean age was 83.0 ± 2.9 years. The mean time from trauma to surgery was 6.8 ± 2.3 h. Patients were mobilized on average 1.53 ± 0.8 h after surgery, and the mean hospitalization time was 27.4 ± 8.1 h. No statistically significant decrease in hemoglobin value was observed before or after surgery (p = 0.476). The Harris hip score was 73.3 ± 3.2 at the 6th month postoperatively and 74.9 ± 2.5 at the last follow-up (p = 0.296). The reduction quality was found to be poor in only two patients. Conclusions: The WALANT technique’s promising results in terms of pain management, blood loss control, and early mobilization show that it is a viable alternative to conventional anesthesia methods in geriatric hip fractures. Full article

Extreme pantograph–catenary contact force (PCCF) oscillations pose a serious challenge to the stable coupling between pantograph and catenary in high-speed railway systems. This paper introduces an active compensation control framework CPO-LQR-BC-SAC, which combines optimized Linear Quadratic Regulator (LQR) baseline control with behavior cloning (BC) and Soft Actor-Critic (SAC) deep reinforcement learning. First, the Crowned Porcupine Optimization algorithm (CPO) is used to offline tune the LQR weighting matrix, producing a high-performance CPO-LQR controller that significantly reduces PCCF fluctuation. Next, a dual model-based offline control law provides “expert” adjustments that further suppress extreme contact force values. Observing that superimposing these offline-tuned actions onto real-time CPO-LQR outputs yields further suppression gains, we developed the BC-SAC compensatory controller to provide corrective control actions. In this scheme, expert actions guide the SAC policy via a behavior cloning loss term in its loss function, and a decaying imitation weight ensures a balance between imitation and exploration. Simulation results demonstrate that, compared to both CPO-LQR and the idealized offline control law, the proposed CPO-LQR-BC-SAC framework achieves over 77% reduction in PCCF standard deviation and exhibits the ability to generalize across different pantograph types, confirming its effectiveness and robustness as a practical solution for mitigating extreme PCCF oscillations. Full article

Fuel layered-distribution sintering (FLDS) is a technology that can effectively reduce fuel consumption and achieve a more uniform temperature distribution within the sintering bed compared to traditional iron ore sintering. In this study, the melting quality index, combined with the maximum temperature and the duration of melting temperature, are used as performance indicators to investigate the effects of coke size and oxygen content on sintering characteristics under layered fuel distribution conditions. The results indicate that increasing the oxygen content can enhance the velocity of the flame front in the sinter pot, thereby accelerating the sintering process. However, excessive oxygen content may lead to fluctuations in the quality of the sinter. Small coke sizes provide higher melting quality in the upper region of the sinter pot, while large coke sizes perform better in the lower region. For a 600 mm sintering bed layer, an oxygen enrichment time of 6 min with oxygen concentration of 27% and coke particle diameter of 2.0 mm can balance sintered ore quality, sintering time, and flame front speed, ensuring the yield of sintered ore. These findings provide an effective pathway for energy saving and emission reduction in iron ore sintering plants. Full article