Search Results (4,034)

Laser cladding processing efficiency is often limited by low powder utilization. To address this, our study elucidates the mechanism by which powder feeding parameters influence powder stream convergence, aiming to optimize these parameters. A three-dimensional model of a wide-band symmetrical nozzle was developed using a Computational Fluid Dynamics—Discrete Element Method (CFD-DEM) coupling method to simulate the gas–solid flow. Single-factor tests and experimental validation confirmed the model’s reliability. The results identify carrier gas flow as the key parameter controlling the focal length and powder concentration, while the powder feed rate primarily governs the concentration on the focal plane. These findings provide a theoretical foundation for optimizing laser cladding parameters to enhance powder utilization. Full article

Quantitative trading systems require predictive models that simultaneously deliver accurate forecasts, calibrated uncertainty quantification, and actionable risk measures. This paper proposes an information-theoretic semiparametric regression framework combining a convolutional neural network–Transformer (CNN–Transformer) network for nonlinear temporal dependencies with a Gaussian process (GP) prior for residual autocorrelation and calibrated predictive distributions. Three theoretical results are established: an identifiability theorem guarantees joint recoverability of the nonparametric and GP components; a consistency theorem showing that the penalised maximum likelihood estimator converges at a rate $n^{-1/(2+d_{\mathrm{eff}})}$; and a coverage theorem proving asymptotic nominal coverage of the GP’s credible intervals. The framework enables an entropy-regulated trading module where predictive differential entropy informs position sizing via an uncertainty-penalised Kelly criterion, Kullback–Leibler divergence quantifies model uncertainty, and CVaR-constrained optimisation controls the tail risk. Simulations show the method outperforms the CNN, long short-term memory (LSTM), Transformer, XGBoost, random forest, least absolute shrinkage and selection operator (LASSO), and standard GP regression approaches. Backtesting on four Chinese A-share stocks yielded annualised returns of 15.9–22.4% with Sharpe ratios of 0.49–0.62, maximum drawdowns below 15%, and daily 95% CVaR reductions of 28–31% relative to a full-Kelly baseline, confirming both predictive accuracy and risk management effectiveness. Full article

Background: Parents of children with attention-deficit/hyperactivity disorder (ADHD) and sleep problems can experience challenges and negative health effects. The aim of this study was to explore parents’ own health-related experiences as their child with ADHD and sleep problems underwent a sleep intervention with a weighted blanket. Methods: A convergent mixed methods design was undertaken. Sociodemographic and questionnaire data were collected from 68 parents at baseline and at the 16-week follow-up. Paired-samples t-tests were used to analyze the data. An inductive qualitative content analysis was used to analyze interviews with 21 parents after the follow-up. An integrative analysis was performed and assessed for confirmation, expansion, or disconfirmation. Results: At the follow-up, parents reported improvements in their own health status (EQ-5D-3L—index 0.83 ± 0.15 vs. 0.87 ± 0.13; p = 0.034), in well-being (Outcome Rating Scale—individual 7.08 ± 2.22 vs. 7.55 ± 1.82; p = 0.045), and in family life (the Brief Child and Family Phone Interview—family comfort score 5.62 ± 1.62 vs. 5.14 ± 1.66; p = 0.003). Parents’ health-related experiences were described as: (1) having a sense of well-being, including being well rested, sustaining energy, reaching a state of calm, and finding hope, (2) balancing family life, including reclaiming personal sphere and nurturing relationships, and (3) managing everyday life, including keeping to the daily schedule and dealing with household chores. The integrative analysis resulted in the overarching themes of health through: (1) inner strength (confirmed), (2) recovery (expanded), (3) close relationships (confirmed), and (4) social engagements (expanded). Conclusions: The findings suggest that sleep interventions for children with ADHD and sleep problems may also be associated with positive changes in aspects of parents’ health, well-being, and family life. Full article

Unmanned Aerial Vehicle Ad Hoc Networks (UAVANETs) are characterized by highly dynamic topology changes and unstable link conditions, which necessitate deep collaboration between transport-layer congestion control and network-layer routing decisions to ensure service quality. However, the existing layered architecture of Software-Defined Networking (SDN) results in a significant separation between routing information and congestion control mechanisms, rendering traditional protocols ineffective in handling severe performance fluctuations caused by highly dynamic route switching. The significant disconnect between network-layer route planning and transport-layer congestion control strategies in Software-Defined Unmanned Aerial Vehicle Ad Hoc Networks (SD-UAVANETs) leads to degraded transmission performance of BBR (Bottleneck Bandwidth and Round-trip propagation time) under high-dynamic route switching scenarios. As such, this paper proposes an in-band network telemetry (INT)-based cross-layer optimization scheme for BBR, named SDN-BBR. Firstly, a lightweight real-time route switching detection mechanism based on INT is designed. Secondly, a QoS inequality model before and after path switching is established, deriving the critical bandwidth of the new path and integrating it into the BBR algorithm to accelerate convergence and avoid congestion. Finally, the BBR state machine is redesigned to achieve cross-layer information fusion and coordinated control, thereby optimizing transmission performance. Experimental results show that the proposed scheme reduces convergence time by 69.8% and increases throughput by 73.9% in low-bandwidth to high-bandwidth switching scenarios; decreases packet loss rate by 86.8% and reduces delay by 8.3% in high-bandwidth to low-bandwidth switching scenarios; and improves throughput by 12.3%, lowers packet loss rate by 21%, and reduces delay by 7.9% in multi-traffic flow concurrent scenarios. The scheme significantly enhances the transmission performance of BBR in highly dynamic routing environments of SD-UAVANET. Full article

The rapid development of space exploration demands real-time backhaul of massive sensing payload data in space-ground integrated telemetry, tracking, and command (TT&C) networks. However, traditional narrow-band TT&C links suffer from severe congestion during massive data backhaul. Since most TT&C applications are inherently task-oriented and do not require pixel-perfect data reconstruction, we propose a task-oriented joint resource allocation framework based on semantic communications. Specifically, we introduce an adaptive semantic split computing mechanism that extracts and transmits only compact, decision-critical features instead of raw bitstreams, fundamentally mitigating the bandwidth bottleneck. The joint optimization of computation offloading, semantic splitting, and continuous on-board computing allocation is formulated as a stochastic mixed-integer nonlinear programming (MINLP) problem. We propose a decoupled algorithm based on Hierarchical Multi-Agent Proximal Policy Optimization (HMAPPO) to solve it. An outer layer employs multi-agent reinforcement learning (MARL) for distributed discrete decision-making, while an inner layer utilizes a Karush–Kuhn–Tucker (KKT)-based solver for continuous space-based computing allocation. This bi-level architecture overcomes the curse of dimensionality and mathematically guarantees zero-violation of physical capacity constraints. Simulations demonstrate that HMAPPO rapidly converges and sustains a high weighted success rate under heavy traffic congestion, significantly improving system utility compared to state-of-the-art baselines. Full article

Robust numerical frameworks are essential for the simulation, design, monitoring, and control of membrane-based separation units, particularly under highly nonlinear and industrially relevant operating conditions. In this context, a comprehensive phenomenological and numerical framework is proposed for the simulation of hollow-fiber membrane modules, incorporating coupled mass, momentum (through pressure drop), and energy transport equations. The governing equations are discretized using a rigorous orthogonal collocation formulation, and the performances of two numerical solution strategies are systematically investigated for the first time to allow the in-line and real-time implementation of the model: a steady-state approach based on the Newton–Raphson method with careful treatment of initial estimates, and a pseudotransient formulation. Particularly, an original and consistent numerical treatment is introduced for the energy balance at boundaries where the permeate flow vanishes, enabling the stable incorporation of thermal effects and Joule–Thomson phenomena. The results clearly show that the steady-state Newton–Raphson approach provides the best overall performance in terms of computational efficiency, numerical robustness, and accuracy when physically consistent initial profiles are employed. In particular, the combination of a linear initial guess and a numerical mesh constituted of four collocation points yielded the most favorable balance between convergence speed, numerical robustness, and accuracy for the base-case sensitivity analysis. For monitoring-oriented applications, the numerical choice should be weighted primarily toward computational performance once physical consistency and convergence criteria are satisfied, rather than toward maximum mesh-refinement accuracy. In this context, small differences in internal-fiber profiles can be compensated through real-time permeance estimation and are negligible when compared with measurement uncertainty in real industrial processes. Under extreme operating conditions involving low concentrations, low flow rates, and highly permeable species, the pseudotransient formulation proved to be a reliable auxiliary strategy, enabling robust convergence when suitable initial guesses were not readily available. The proposed framework is validated against experimental data from the literature and subjected to extensive convergence and sensitivity analyses, providing a reliable basis for simulation and for assessing computational feasibility in in-line and real-time monitoring-oriented applications. A full demonstration of digital-twin integration, online parameter updating, reduced-order coupling, and closed-loop control is beyond the scope of the present study and will be addressed in future work. Full article

This study recognizes education as an investment and estimates the private rates of return to upper secondary education in Greece, overall, by type (general or vocational) and by gender. Earnings data were collected through primary research using stratified sampling from the private sector of the economy. The analysis is based on the Mincer method and is complemented by machine learning methods, including Support Vector Regression, Random Forests, and Extreme Gradient Boosting. The empirical analysis shows that investing in upper secondary education (general and vocational) is profitable. The private rates of return in upper general secondary education are higher than those in vocational education, and female graduates exhibit higher returns than male graduates. Machine learning models achieve modest improvements in predictive performance, as reflected in higher adj. R² values and lower prediction errors. However, the estimated rates of return remain broadly consistent with those obtained from the Mincer method. This convergence suggests that the Mincer specification captures the core structural relationship between education and earnings, while machine learning models primarily enhance predictive accuracy without substantially altering the estimated economic returns. This finding highlights the robustness of the traditional econometric framework and clarifies the complementary role of machine learning techniques in empirical labor economics. Full article

Coupling effects and communication delays present major challenges for distributed formation control of multi-UAV formations. This work characterizes coupling effects and integrates them into cooperative control synthesis under delay conditions. A leader state observer is introduced to reconstruct the leader’s state via neighboring information, reducing reliance on direct links and improving communication robustness. A delay aware cooperative control law with coupling effects is then developed, and Lyapunov–Krasovskii analysis establishes matrix inequality conditions to ensure stability. The key innovation lies in actively exploiting communication coupling to accelerate the error convergence rate and ensure formation tracking under communication delays. Theoretical analysis, grounded in the Lyapunov stability theorem, elucidates the mechanism by which coupling effects accelerate the error convergence rate. The effectiveness of the proposed method is validated through simulations of leader–follower formations. Full article

This paper proposes a multi-strategy Secretary Bird Optimization Algorithm (MS-SBOA) for solving global optimization problems and 3D wireless sensor network deployment. While preserving the original two-phase search framework of SBOA, the proposed algorithm achieves a dynamic balance between global exploration and local exploitation through the synergistic integration of multiple enhancement strategies, including a hybrid initialization scheme combining Latin hypercube sampling and quasi-opposition-based learning, a success-history-based adaptive parameter learning mechanism, a finance-inspired market-state trading operator, and an elite-guided population regulation strategy. Experimental results on the IEEE CEC2020 and CEC2022 benchmark test suites demonstrate that MS-SBOA significantly outperforms nine comparative algorithms, including VPPSO, IAGWO, and QHSBOA, under both 10-dimensional and 20-dimensional settings. The proposed algorithm exhibits superior optimization accuracy, faster convergence speed, and stronger robustness. Statistical analyses using the Wilcoxon rank-sum test and the Friedman mean rank test further confirm that the observed performance improvements are statistically significant. Moreover, MS-SBOA is applied to three-dimensional wireless sensor network (3D WSN) deployment optimization problems, where the average coverage rates reach 76.22% and 82.32% for 30-node and 50-node deployment scenarios, respectively. The resulting node distributions are more uniform, and the computational efficiency is improved compared with competing algorithms. Full article

Standard 16000-8 of the International Organization for Standardization (ISO 16000-8) specifies the assessment of ventilation performance using age-of-air concepts and tracer gas techniques. Since its publication in 2007, ventilation systems and assessment practices have evolved considerably, driven by increased use of mixed-mode and decentralized ventilation and advances in modeling and measurement technologies. This review examines how ISO 16000-8 can be modernized to harmonize with adjacent ventilation and indoor air quality standards while remaining applicable to contemporary systems and emerging approaches. A structured literature search of Web of Science and Google Scholar identified 76 studies (2007–2026) that engage with ISO 16000-8, age-of-air metrics, or tracer gas-based assessment. The literature was synthesized qualitatively using the framework of Preferred Reporting Items for Systematic reviews and Meta-Analyses (PRISMA), classifying studies into performance assessment, measurement–simulation convergence, and standardization discourse. The synthesis shows that while the conceptual foundations of ISO 16000-8 remain valid, assumptions of homogeneous mixing and steady-state conditions are often violated in real buildings, leading to inconsistent application of age-of-air indicators. Field and laboratory studies under point-source conditions demonstrate reduced ventilation effectiveness of 0.73–0.82 in classrooms and 0.5–1.4 in various indoor environments, instead of ≈1 for perfect mixing. Spatial heterogeneity is also observed in mixed-mode systems, with an efficiency around 0.5. In decentralized and façade-integrated systems, air exchange effectiveness deviates from theoretical expectations, indicating inhomogeneous air renewal and short-circuiting. Field measurements show configuration-dependent discrepancies in air exchange rates (e.g., carbon dioxide vs. perfluorocarbon tracer methods under varying door positions), while wind induces time-varying infiltration. Collectively, the literature demonstrates systematic violations of well-mixed and steady-state assumptions underpinning ISO 16000-8. Fragmentation between ventilation performance standards and indoor air quality regulation limits practical uptake. Emerging experimental, numerical, and data-driven methods complement ISO 16000-8, provided applicability domains and uncertainties are addressed. The review concludes that ISO 16000-8 should be modernized toward a harmonized, performance-based framework integrating diverse ventilation systems and assessment technologies. Full article

Achieving sustainable design requires balancing environmental performance, resource efficiency, functional feasibility, and aesthetic acceptance throughout the product life cycle. However, traditional design approaches often struggle to quantitatively integrate subjective aesthetic evaluation with objective sustainability indicators such as energy consumption, carbon emissions, and material recyclability. To address this challenge, this study proposes a semantic-guided multi-objective optimization framework for sustainable design that integrates cross-modal aesthetic evaluation with life cycle environmental performance assessment. The proposed framework employs a Contrastive Language–Image Pre-training (CLIP)-based semantic evaluation mechanism to translate abstract sustainability and aesthetic concepts into quantifiable design features, enabling consistent assessment across diverse design solutions. These semantic features are further optimized using a multi-objective evolutionary optimization strategy to simultaneously minimize energy consumption and carbon emissions while maximizing material recovery and design quality. Life cycle environmental indicators derived from OpenLCA datasets are incorporated into the optimization process to ensure practical sustainability relevance. The experimental results demonstrate that the proposed framework achieves a superior performance compared with benchmark optimization methods. Specifically, carbon emission equivalents are reduced to as low as 12.3 kg CO₂e, material recovery rates exceed 92%, and total computational energy consumption is reduced by more than 40% relative to comparative models. In addition, the framework shows strong stability and convergence efficiency while maintaining a high aesthetic evaluation accuracy in high-quality design ranges. The findings indicate that the proposed approach provides an effective pathway for integrating aesthetic value with environmental responsibility in sustainable design practice. This framework supports low-carbon and resource-efficient product development and offers practical insights for sustainable manufacturing, circular design, and environmentally conscious innovation. Full article

The rapid expansion of the Internet of Vehicles (IoV) necessitates high-capacity backhaul connectivity, yet the deployment of such networks under strict hardware and power constraints poses significant computational challenges for network optimization. To address this challenge, this paper investigates a joint transmit–receive beamforming optimization problem for narrowband wireless backhaul in IoV networks under constant-modulus constraints. Unlike ideal digital architectures, we focus on cost-effective analog phase shifters, which introduce strictly non-convex constant-modulus constraints, rendering the optimization problem mathematically intractable for standard solvers. Since the resulting problem is highly non-convex, we develop two structured numerical methods: an iterative alternating optimization (AO) method and a joint optimization (JO) method, where AO employs auxiliary WMMSE-guided alternating updates together with constant-modulus projection, while JO jointly updates both beamformers over the constant-modulus feasible set. We compare their achievable sum-rate performance with that of a CDO-based benchmark and analyze their dominant computational costs through representative Big-O complexity expressions. Furthermore, we examine the effect of SVD-based and random feasible initializations on empirical convergence behavior, runtime, and final achievable performance. Simulation results demonstrate that the proposed computational methods significantly improve achievable sum-rate performance compared with the CDO benchmark. Moreover, SVD-based initialization provides a more structured starting point and generally leads to better convergence behavior and lower runtime than random feasible initialization. The empirical timing results further show that AO exhibits faster empirical convergence and requires lower runtime, whereas JO achieves better final sum-rate performance after more iterations. Full article

Background/Objectives: The human brain is responsible for controlling various physiological functions, and hence, the presence of tumors in the brain is a major concern in the medical field. The correct identification and categorization of tumors in the brain using Magnetic Resonance Imaging (MRI) is a major requirement for the diagnosis and treatment of a tumor. The proposed research will focus on designing a CNN model that is optimized for tumor image classification. Methods: This research proposes an optimized CNN model featuring strategically placed dropout layers and hyperparameter optimization. This study uses a dataset of 640 MRI scans (320 tumor and 320 non-tumor) collected from a private hospital in Saudi Arabia. The proposed method utilizes a learning rate of 0.001 in combination with the Adam optimizer to ensure stable and efficient convergence. Its performance was benchmarked against established architectures, including VGG-19, Inception V3, ResNet-10, and ResNet-50, with evaluation based on classification accuracy and computational cost. Results: The experimental results show that the optimized CNN proposed in this work performs much better than the deeper architectures. The network reached a maximum training accuracy of 97.77% and a final test accuracy of 95.35% with a small test loss of 0.2223. The test accuracy of the optimized VGG-19 and Inception V3 networks was much lower, with a training time per epoch that was several orders of magnitude higher. The validation stability of the proposed network was high (92.25% to 95.35%) during the final stages of training. Conclusions: The conclusion drawn from this study is that hyperparameter optimization and strategic regularization are more advantageous for tumor classification using MRI images than the mere depth of the model. The accuracy of 95.35% with low computational complexity makes this lightweight CNN model a feasible solution for real-time applications. Full article

Salt cavern CO₂ storage (SCCS) technology represents a crucial pathway for achieving large-scale carbon sequestration. However, its long-term operation faces the challenge of cavern shrinkage due to surrounding rock creep, which directly impacts storage safety and stability. Despite its importance, there is currently a lack of research focusing on the proactive control of SCCS cavern shrinkage and its collaborative optimization with operational economy. To fill this gap, this paper first investigated the effects of the stress state (f₁), height-to-diameter ratio (f₂), symmetry factor (f₃), and cavern volume (f₄) on the volumetric shrinkage rate through numerical simulations of regular caverns and univariate sensitivity analysis. The sensitivity ranking and quantitative relationships of these factors were clarified as $f_1(2.31)>f_4(0.309)>f_2(0.166)>f_3(0)$. Subsequently, a multi-objective nonlinear optimization model was established, and the primal-dual interior-point method was adopted as the solution algorithm. Using actual cavern data as a case study for the solution, the results demonstrate that the optimization model converges stably in approximately 1.1 s. The resulting optimal gas injection allocation scheme achieves a 14.77% improvement in the comprehensive score compared to the baseline scheme. This study provides a theoretical basis and a practical tool for the rapid generation of SCCS gas injection allocation schemes. Full article

Time-reversible synchronization (TRS) of nonlinear oscillators is a recently proposed technique that ensures super-exponential convergence of dynamics between master and slave systems, which is beneficial in many real-time applications. Nevertheless, this approach has not been demonstrated in any real-time embedded system to practically verify it and quantitatively estimate its advantages. Furthermore, previous studies did not consider the application of time-reversible synchronization to a wide, practically relevant class of chaotic systems with piecewise-linear nonlinearity. To fill these gaps, in this work, we developed an FPGA-based time-reversible synchronization controller for the analog Chua circuit and its digital counterpart. To achieve complete synchronization, we first reconstructed dynamical equations of the circuit. Then, we performed a rigorous theoretical analysis of synchronization possibility between analog and digital systems by each single variable. Next, we implemented the digital model of the Chua circuit in the MyRIO-1900 FPGA using the reconstructed dynamical model and showed its capability of digital-to-analog and analog-to-digital conventional Pecora–Carroll (PC) synchronization. Then, an algorithm of time-reversible synchronization on MyRIO-1900 was tested, achieving complete synchronization at the predefined normalized RMSE level of 0.01, requiring an average of 8.0 fewer points and a median of 10.1 fewer points than the PC synchronization. Finally, we implemented a proof-of-concept version of a capacitive sensor based on the analog Chua circuit with an FPGA-based observer using PC synchronization or the TRS algorithm with a heuristic selection of a starting point. Our experiments reveal that when using the TRS algorithm, the time needed to detect a pre-selected 3% level of capacitance change is reduced by a mean factor of 4 and a median factor of 4.9 in comparison with the conventional PC synchronization. This allows for using the developed solution in applications where the synchronization rate is crucial, including chaos-based sensing, communication, and monitoring. Full article