Search Results (6,631)

The human respiratory Central Pattern Generator (CPG) is a complex and tightly regulated network of neurons responsible for the automatic rhythm of breathing. Among the brain nuclei involved in respiratory control, excitatory neurons within the PreBotzinger Complex (PreBötC) are both necessary and sufficient for generating this rhythmic activity. Although several models of the PreBötC circuit have been proposed, a comprehensive analysis of network behavior in response to physiologically relevant external inputs remains limited. In this study, we present a computational model of the PreBötC consisting of 1000 excitatory neurons, divided into two functional subgroups: the rhythm-generating population and the pattern-forming population. To enable real-time closed-loop simulations, we employed parallelized multi-process computing to accelerate network simulation. The network, composed of asynchronous neurons, could produce bursting activity at a eupneic breathing frequency of 0.22 Hz, which could also reproduce the rapid and stable chemoreception of breathing activated in response to hypercapnia. Additionally, it successfully replicated rapid and stable respiratory responses to elevated carbon dioxide levels (hypercapnia), mediated through simulated chemoreception. External inputs from a carbon dioxide sensor were used to modulate the network activity, allowing the implementation of a real-time respiratory control system. These results demonstrate that a network of asynchronous, non-bursting neurons can emulate the behavior of the respiratory CPG and its modulation by external stimuli. The proposed model represents a step toward developing a closed-loop controller for breathing regulation. Full article

Additive manufacturing is driving a significant change in industry, extending beyond prototyping to the inclusion of printed parts in final designs. Stereolithography (SLA) is a polymerization technique valued for producing highly detailed parts with smooth surface finishes. This study presents a hybrid intelligent framework for modeling and optimizing the SLA 3D printer process’s parameters for Acrylonitrile Butadiene Styrene (ABS) photopolymer parts. The nonlinear relationships between the process’s parameters (Orientation, Lifting Speed, Lifting Distance, Exposure Time) and multiple performance characteristics (ultimate tensile strength, yield strength, modulus of elasticity, Shore D hardness, and surface roughness), which represent complex relationships, were investigated. A Taguchi design of the experiment with an L18 orthogonal array was employed as an efficient experimental design. A novel hybrid fuzzy logic–Particle Swarm Optimization (PSO) algorithm, ARGOS (Adaptive Rule Generation with Optimized Structure), was developed to automatically generate high-accuracy Mamdani-type fuzzy inference systems (FISs) from experimental data. The algorithm starts by customizing Modified Learn From Example (MLFE) to create an initial FIS. Subsequently, the generated FIS is tuned using PSO to develop and enhance predictive accuracy. The ARGOS models provided excellent performances, achieving correlation coefficients (R²) exceeding 0.9999 for all five output responses. Once the FISs were tuned, a multi-objective optimization was carried out based on the weighted sum method. This step helped to identify a well-balanced set of parameters that optimizes the key qualities of the printed parts, ensuring that the results are not just mathematically ideal, but also genuinely helpful for real-world manufacturing. The results showed that the proposed hybrid approach is a robust and highly accurate method for the modeling and multi-objective optimization of the SLA 3D process. Full article

Background: Autism Spectrum Disorder (ASD) affects ~1 in 36 children and is increasingly studied as a candidate for non-invasive neuromodulation. Two of the most widely applied modalities are quantitative EEG (QEEG)-guided repetitive transcranial magnetic stimulation (rTMS) and transcranial direct current stimulation (tDCS), both targeting the dorsolateral prefrontal cortex (DLPFC). While both have shown promise, questions remain regarding their relative clinical profiles and scalability. Objective: To conduct a within-cohort reanalysis of QEEG-guided rTMS outcomes in paediatric ASD and to contextualise these findings alongside published reports of home-supervised tDCS. Methods: Individual participant data (n = 56, ages 6–17) from a prospective rTMS cohort were reanalysed, focusing on the Social Responsiveness Scale (SRS-2), Autism Diagnostic Observation Schedule (ADOS-2), Aberrant Behavior Checklist (ABC), Repetitive Behavior Scale–Revised (RBS-R), and QEEG biomarkers. Findings were then situated within a narrative synthesis of published paediatric tDCS trials, which consistently report caregiver-supervised feasibility but did not provide raw, baseline-adjusted data suitable for reanalysis. Results: rTMS was associated with large within-cohort improvements (Hedges’ g ≈ 1.0–1.6), including an 11-point reduction in SRS-2 T-scores, a 12-point reduction in ABC totals, and robust QEEG normalisation (β/γ suppression, α enhancement). Published tDCS studies report moderate, clinically meaningful improvements in social communication, executive functioning, and regulation (Cohen’s d ≈ 0.4–0.6), with excellent adherence and no serious adverse events. Conclusions: rTMS produced robust behavioural and neurophysiological improvements within its cohort, while published tDCS trials demonstrate moderate, feasible benefits in home settings. Because of incomplete baseline data and protocol differences, no direct statistical comparison was possible. These findings suggest complementary roles: rTMS as a high-intensity clinic-based intervention, and tDCS as a scalable, family-centred option. A stepped-care framework that combines both modalities should be considered hypothesis-generating only and requires validation in harmonised, randomised controlled trials. Full article

A prescribed-performance-based sliding mode control method with feed-forward inverse compensation is proposed in this study to improve the micropositioning accuracy and convergence speed of a piezoelectric actuator (PEA). Firstly, the piezo-actuated micropositioning system is described by a Hammerstein structure model, and an inverse Prandtl–Ishlinskii (PI) model was employed to compensate for its hysteresis characteristics. Then, considering modelling errors, inverse compensation errors, and external disturbances, a new prescribed performance function (PPF) with an exponential dynamic decay rate was developed to describe the constrained region of the errors. We then transformed the error into an unconstrained form by constructing a monotonic function, and the sliding variables were obtained by using the transformation error. Based on this, a sliding mode controller with a prescribed performance function (SMC-PPF) was designed to improve the control accuracy of PEAs. Furthermore, we demonstrated that the error can converge to the constrained region and the sliding variables are stable within the switching band. Finally, experiments were conducted to verify the speed and accuracy of the controller. The step-response experiment results indicated that the time taken for SMC-PPC to enter the error window was 8.1 and 2.2 ms faster than that of sliding mode control (SMC) and PID, respectively. The ability of SMC-PPF to improve accuracy was verified using four different reference inputs. These results showed that, for these different inputs, the root mean square error of the SMC-PPF was reduced by over $39.6\%$ and $52.5\%$, compared with the SMC and PID, respectively. Full article

Nowadays, electrochemical sensors have become a popular topic in cosmetics quality control. A simple and stable electrochemical sensor for hydrogen peroxide (H₂O₂) was developed on the basis of a rhodium-modified glassy carbon electrode (Rh/GCE). A quick, one-step, reproducible, and cost-effective electrodeposition procedure was applied to modify GCE with Rh nanoparticles. The sensor shows a high selectivity for H₂O₂ at a low applied potential of –0.1 V (vs. Ag/AgCl, 3 M KCl), with an excellent stability and good repeatability (RSD = 3.2%; n = 5). The modified electrode Rh/GCE demonstrates a high sensitivity of 172.24 ± 1.95 μA mM^–1 cm^–2 (n = 3), a linear response to H₂O₂ between 5 and 1000 µM, and a detection limit estimated to be 1.2 µM. Furthermore, Rh/GCE has been successfully used to measure H₂O₂ concentrations in hair dye and antiseptic solution, yielding satisfactory recovery rates. These findings highlight the potential of the Rh/GCE for the reliable quantitative detection of H₂O₂ in complex cosmetics matrices. Full article

As high-energy-consumption underground public space, subway stations are responsible for a particularly significant proportion of air-conditioning energy use, especially during the cooling season, making the investigation of cooling-load characteristics highly important. However, the determination of independent influencing factors in different situations has not yet reached a consensus, and the role of interaction effects is lacking, which hinders the development of energy-saving strategies. For this purpose, this study proposes a sensitivity analysis framework based on 10 typical influencing factors from thermal parameters, meteorological parameters, internal heat disturbances, and indoor environmental setpoints. An input set was generated by integrating equal-step parameter discretization and Saltelli quasi-MonteCarlo sampling. A database containing 11,264 samples was constructed through an EnergyPlus–Python co-simulation method. Based on the Sobol global sensitivity analysis, the key influencing factors of subway station cooling load were identified and quantified, and the impact of these 10 factors was systematically analyzed. Results show that occupant density (S_i^T = 0.5605) and fresh air volume (S_i^T = 0.4546) are the dominant factors, contributing more than 50% of the load variance. In contrast, the characteristics of an underground structure significantly weaken the influence of the building-envelope heat transfer coefficient (S_i^T = 0.1482) and soil temperature (S_i^T = 0.0884). Furthermore, five groups of strong interaction effects were identified in this study, including occupant density–fresh air volume (S_ij = 0.1094), revealing a nonlinear load response mechanism driven by multi-parameter coupling. This research provides a theoretical foundation and quantitative tool for the refined design and optimized dynamic coupled operation of underground transportation hubs. Full article

Since it is difficult to obtain the positions of dynamic loads on structures, this paper suggests a new method to identify the locations of dynamic loads step-by-step based on the correlation coefficients of dynamic responses. First, a recognition model for dynamic load position based on a finite-element scheme is established, with the finite-element domain divided into several regions. Second, virtual loads are applied at the central points of these regions, and acceleration responses are calculated at the sensor measurement points. Third, the maximum correlation coefficient between the calculational and measured accelerations is obtained, and the dynamic load is located in the region with the virtual load corresponding to the maximum correlation coefficient. Finally, this region is continuously subdivided with the refined mesh until the dynamic load is pinpointed in a sufficiently small area. Different virtual load construction methods are proposed according to different types of loads. The frequency response function, unresolvable for the actual problem due to the unknown location of the real dynamic load, can be transformed into a solvable form, involving only known points. This transformation simplifies the analytical process, making it more efficient and applicable to analysis of the dynamic behavior of the system. The identification of the dynamic load position in the entire structure is then transformed into a sub-region approach, focusing on the area where the dynamic load acts. Simulations for case studies are conducted to demonstrate that the proposed method can effectively identify positions of single and multiple dynamic loads. The correctness of the theory and simulation model is verified with experiments. Compared to recent methods that use machine learning and neural networks to identify positions of dynamic loads, the approach proposed in this paper avoids the heavy computational cost and time required for data training. Full article

Advancements in flexible, low-cost, and recyclable alternatives to transparent conductive oxides (TCOs) are critical challenges in the sustainability of third-generation solar cells. This work introduces a copper mesh-based transparent electrode for dye-sensitized solar cells, replacing conventional fluorine doped-tin oxide (FTO)-coated glass to simultaneously reduce spectral reflection losses, enhance mechanical flexibility, and enable material recyclability. Titanium dioxide (TiO₂) photoanodes were synthesized and directly deposited onto the mesh via a single-step, low-energy ball milling process, which eliminates TiO₂ paste preparation and high-temperature annealing while reducing fabrication time from over three hours to 30 min. Structural and surface analyses confirmed the deposition of high-purity anatase-phase TiO₂ with strong adhesion to the mesh branches, enabling improved dye loading and electron injection pathways. Optical studies revealed higher visible light absorption for the copper mesh compared to FTO in the visible range, further enhanced upon TiO₂ and Ru-based dye deposition. Electrochemical measurements showed that TiO₂/Cu mesh electrodes exhibited significantly higher photocurrent densities and faster photo response rates than bare Cu mesh, with dye-sensitized Cu mesh achieving the lowest charge transfer resistance in impedance analysis. Techno–economic and sustainability assessments revealed a decrease of 7.8% in cost and 82% in CO₂ emissions associated with the fabrication of electrodes as compared to conventional TCO electrodes. The synergy between high conductivity, transparency, mechanical durability, and a scalable, recyclable fabrication route positions this architecture as a strong candidate for next-generation dye-sensitized solar modules that are both flexible and sustainable. Full article

The transition to sustainable energy systems is essential for attaining global carbon neutrality targets. Demand-side flexibility for carbon mitigation is investigated, and a low-carbon operational strategy tailored for industrial park energy systems is proposed. Demand response (DR) is classified into price-based and alternative categories, with respective models developed utilizing a price elasticity matrix and accounting for electricity-to-heat conversion. Integrated energy system (IES) involvement in the carbon trading market is incorporated through a stepped carbon pricing mechanism to regulate emissions. A mixed-integer linear programming model is constructed to characterize IES operations under ladder-type carbon pricing and DR frameworks. The model is resolved via the off-the-shelf commercial solver, facilitating effective optimization of dispatch over multiple time intervals and complex market interactions. Case study findings indicate that implementing stepped carbon pricing alongside DR strategies yields a 44.45% reduction in carbon emission costs, a 9.85% decrease in actual carbon emissions, and a 10.62% reduction in total system costs. These results offer a viable approach toward sustainable development of IES, achieving coordinated improvements in economic efficiency and low-carbon performance. Full article

Objective: To evaluate the real-world safety and efficacy of macitentan (MACI) in patients with systemic autoimmune rheumatic diseases (SARDs) and refractory digital ulcers (DUs). Methods: We conducted a retrospective observational study of 42 patients treated with MACI (10 mg/day) on a compassionate-use basis across Spanish reference hospitals. Given the cohort’s heterogeneity, a two-step analysis was performed: a global assessment of all patients, followed by a subgroup analysis restricted to those with systemic sclerosis (SSc) or fulfilling very early SSc (VEDOSS) criteria to explore predictors of response. Efficacy was defined as complete healing, partial response, or a lack of response based on physician assessment. Safety was evaluated through analysis of adverse events. Results: In the global cohort, MACI demonstrated a high rate of complete ulcer healing (82.9%) at the 3-month follow-up, with a significant reduction in median ulcer count (p < 0.001). Subgroup analysis within the SSc/VEDOSS cohort (n = 36) revealed that the presence of gastrointestinal involvement (GI) and a higher baseline DUs were significant predictors of a poorer therapeutic response (p = 0.022 and p = 0.028). The drug was well-tolerated; adverse events were infrequent and rarely led to treatment discontinuation. Conclusions: In this real-world refractory population, MACI was associated with rapid DU healing and a favorable safety profile. GI and higher ulcer burden predicted diminished treatment response in SSc patients. These results support the use of MACI as a valuable therapeutic option for severe digital vasculopathy in SARDs, although further prospective studies are warranted to confirm these observations. Full article

The severe vertical motion of high-speed multihull vessels significantly impairs their seakeeping performance, making the design of effective anti-motion controllers crucial. However, existing controllers, predominantly designed based on deterministic dynamic models, suffer from limitations such as insufficient robustness, reliance on empirical knowledge, structural complexity, and suboptimal performance, which hinder their practical applicability. To address this, this paper proposes a robust decoupled vertical motion controller based on the step response inversion method and incorporating an Extended State Observer (ESO) uncertainty compensation term. The control algorithm is designed leveraging the equivalent noise bandwidth theory to account for the stochastic characteristics of pitch/heave motion, with ESO compensation introduced to enhance robustness. The stability of the closed loop system is rigorously proven through theoretical analysis. Simulation results demonstrate that the proposed algorithm significantly suppresses the amplitudes of both pitch and heave motions. Full article

This study experimentally compares classical PID and three adaptive control strategies (including a novel adaptive control strategy developed by the authors) for stabilizing the crankshaft speed of a diesel engine (ADCR Euro 4). The tests were performed on a dynamometer with alternator-induced step loads. All tests were performed at a constant engine crankshaft speed using National Instruments instrumentation and custom LabVIEW-based software for real-time monitoring. Metrics included response time (RT), overshoot (OV), and steady-state error (SSE), each based on ten repetitions with reported standard deviations. Results show that the competitive adaptive algorithm reduced RT by ~20%, OV by ~15%, and SSE by ~10% compared to PID. These results confirm that adaptive control, especially the competitive strategy, provides high precision and fast disturbance rejection, bridging the gap between simulation-based studies and industrial diesel engine applications. These results highlight the potential of adaptive control in applications such as air–fuel ratio control, turbocharger pressure control, knock detection, and fuel optimization. Full article

The environmental crisis and the accelerated depletion of natural resources require strategies that balance economic growth and environmental protection in accordance with the principles of sustainable development. In this context, the mining industry, despite playing an important role in economic development, has significant negative impacts. The application of sustainable practices helps to mitigate these impacts. This study evaluates the effectiveness of rehabilitation measures applied in the abandoned mine of Punta Gorda, Cuba, using an integrated system of socio-environmental and economic indicators. The methodology, based on a procedure for socio-environmental and economic management in mine rehabilitation, comprises six stages and fourteen steps, including the valuation of ecosystem goods and services, economic valuation, and monitoring of rehabilitation results. Key results show a 5.95% improvement in economic efficiency in 2023, partial recovery of the ecosystem after rehabilitation, and improved health of mine workers. The study emphasizes the importance of multidimensional assessment tools to align mine rehabilitation with the Sustainable Development Goals (SDGs), particularly SDG 11, and highlights the role of corporate social responsibility in improving the well-being of the mining community. The proposed framework provides a replicable model for sustainable mine rehabilitation, emphasizing the integration of economic, social, and environmental indicators. Full article

Given the environmental hazards of 2,4,6-trinitrophenol (TNP) and the limitations of existing detection methods, sodium-doped fluorescent carbon dots (Na-CDs) were successfully synthesized via a one-step hydrothermal method using citric acid and ascorbic acid as carbon sources. Compared with undoped carbon quantum dots, Na-CDs exhibited nearly identical surface functional groups but significantly enhanced fluorescence stability and markedly improved selective responsiveness toward TNP. Accordingly, a Na-CD-based fluorescent probe was developed for the highly selective detection of TNP. Results demonstrated a good linear relationship between the relative fluorescence intensity change (F₀ − F)/F₀ and TNP concentration ranging from 7 × 10⁻⁷ to 2 × 10⁻⁵ mol/L, with a detection limit of 3.5 × 10⁻⁸ mol/L. When applied to detect TNP in local river water samples, the method achieved recoveries of 95.40–104.0%, confirming its reliability for real-world environmental sample analysis. This study develops a novel, sensitive, and highly selective approach for monitoring TNP in environmental systems. Full article

Glucosinolates (GSLs) are nitrogen- and sulfur-containing secondary metabolites central to the defense, development, and environmental responsiveness of Brassicaceae species. While the enzymatic steps and transcriptional networks underlying GSL biosynthesis have been extensively characterized, mounting evidence reveals that chromatin-based processes add a critical, yet underexplored, layer of regulatory complexity. Recent studies highlight the roles of DNA methylation, histone modifications, and non-coding RNAs in modulating the spatial and temporal expression of GSL biosynthetic genes and their transcriptional regulators in response to developmental cues and environmental signals. This review provides a comprehensive overview of GSL classification, biosynthetic pathway architecture, transcriptional regulation, and metabolite transport, with a focus on emerging epigenetic mechanisms that shape pathway plasticity. We also discuss how these insights may be leveraged in precision breeding and epigenome engineering, including the use of CRISPR/dCas9-based chromatin editing and epigenomic selection, to optimize GSL content, composition, and stress resilience in cruciferous crops. Integrating transcriptional and epigenetic regulation thus offers a novel framework for the dynamic control of specialized metabolism in plants. Full article