Search Results (8,773)

This study reports the synthesis and comprehensive spectroscopic characterization of 4-indolylcyanamide (4ICA), a novel indole-derived infrared (IR) probe designed for assessing local microenvironments in biological systems. 4ICA was synthesized via a two-step procedure with an overall yield of 43%, and its structure was confirmed using high-resolution mass spectrometry and ¹HNMR. Fourier Transform Infrared (FTIR) spectroscopy revealed that the cyanamide group stretching vibration of 4ICA exhibits exceptional solvent-dependent frequency shifts, significantly greater than those of conventional cyanoindole probes. A strong linear correlation was observed between the vibrational frequency and the combined Kamlet–Taft parameter, underscoring the dominant role of solvent polarizability and hydrogen bond acceptance in modulating its spectroscopic behavior. Quantum chemical calculations employing density functional theory (DFT) with a conductor-like polarizable continuum model (CPCM) provided further insight into the solvatochromic shifts and suppression of Fermi resonance in high-polarity solvents such as DMSO. Additionally, IR pump–probe measurements revealed short vibrational lifetimes (~1.35 ps in DMSO and ~1.13 ps in ethanol), indicative of efficient energy relaxation. With a transition dipole moment nearly twice that of traditional nitrile-based probes, 4ICA demonstrates enhanced sensitivity and signal intensity, establishing its potential as a powerful tool for site-specific environmental mapping in proteins and complex biological assemblies using nonlinear IR techniques. Full article

This study introduces an innovative self-regulating intelligent optimal balancing control framework for inverted pendulum-type mechatronic platforms, designed to enhance reference tracking accuracy and improve disturbance rejection capability. The control procedure is synthesized by synergistically integrating a baseline Linear Quadratic Regulator (LQR) with a fuzzy controller via a customized linear decomposition function (LDF). The LDF dissociates and transforms the LQR control law into compounded state tracking error and tracking error derivative variables that are eventually used to drive the fuzzy controller. The principal contribution of this study lies in the adaptive modulation of these compounded variables using reconfigurable tangent hyperbolic functions driven by the cubic power of the error signals. This nonlinear preprocessing of the input variables selectively amplifies large errors while attenuating small ones, thereby improving robustness and reducing oscillations. Moreover, a model-free online self-tuning law dynamically adjusts the variation rates of the hyperbolic functions through dissipative and anti-dissipative terms of the state errors, enabling autonomous reconfiguration of the nonlinear preprocessing layer. This dual-level adaptation enhances the flexibility and resilience of the controller under perturbations. The robustness of the designed controller is substantiated via tailored experimental trials conducted on the Quanser rotary pendulum platform. Comparative results show that the prescribed scheme reduces pendulum angle variance by 41.8%, arm position variance by 34.6%, and average control energy by 28.3% relative to the baseline LQR, while outperforming conventional fuzzy-LQR by similar margins. These results show that the prescribed controller significantly enhances disturbance rejection and tracking accuracy, thereby offering a numerically superior control of inverted pendulum systems. Full article

The flexible job shop scheduling problem (FJSP) with transportation resources such as automated guided vehicles (AGVs) is prevalent in manufacturing enterprises. Multi-type AGVs are widely adopted to transfer jobs and realize the collaboration of different machines, but are often ignored in current research. Therefore, this paper addresses the FJSP with multi-type AGVs (FJSP-MTA). Considering the difficulties caused by the introduction of transportation and the NP-hard nature, the artificial bee colony (ABC) algorithm is adopted as a fundamental solution approach. Accordingly, a Q-learning hybrid multi-objective ABC (Q-HMOABC) algorithm is proposed to deal with the FJSP-MTA. First, to minimize both the makespan and total energy consumption (TEC), this paper proposes a novel mixed-integer linear programming (MILP) model. In Q-HMOABC, a three-layer encoding strategy based on operation sequence, machine assignment, and AGV dispatching with type selection is used. Moreover, during the employed bee phase, Q-learning is employed to update all individuals; during the onlooker bee phase, variable neighborhood search (VNS) is used to update nondominated solutions; and during the scout bee phase, a restart strategy is adopted. Experimental results demonstrate the effectiveness and superiority of Q-HMOABC. Full article

In response to escalating global concerns about waste generation throughout the product life cycle, the Circular Economy (CE) has emerged as a central alternative to the dominant linear economic model. The integration of CE principles into supply chain management is manifested in Circular Supply Chain Management (CSCM), offering a novel perspective on supply chain sustainability. Despite the growing research interest in developing CSCM to enhance supply chain sustainability, assessment approaches of this concept are notably absent in the literature. This study addresses this gap by focusing on the assessment and performance measurement of circular practices in the context of supply chains. At first, the research presents a bibliometric analysis to delve into the performance and science mapping of CSCM assessment, providing a comprehensive view of the scientific landscape. Subsequently, a content analysis is then used to identify current assessment approaches, focusing on frameworks, methodologies, barriers, enablers, and CE strategies. The study proposes a conceptual model based on the SCOR framework, including core categories such as enablers (business model, technology, collaboration, design) and results (material, water, energy flows) represented by the Rs strategies. This model contributes to bridging theoretical gaps and guiding practitioners and policymakers in the design, evaluation, and implementation of circular supply chains. Full article

Ammonia borane is a promising hydrogen storage material due to its high hydrogen content, but its use as hydrogen carrier under thermal stimuli involves the production of several byproducts, such as borazine, reducing hydrogen purity and the overall efficiency. This work is focused on the use of high-boiling-point amines to modulate ammonia borane decomposition, aiming to enhance hydrogen release and suppress volatile N_xB_y species. Kissinger’s equation kinetics revealed that amines significantly influence the decomposition mechanism, and TGA-IR investigation showed a maximum of 2.4 wt.% of pure hydrogen release in the presence of triphenyl amine. Furthermore, the experimental data herein discussed, together with a computational study of activation energies, allowed us to derive a detailed mechanism that leads to a foundation for further advancement in the exploitation of ammonia borane as a hydrogen carrier, suggesting that the formation of linear species is anchored to amine over the release of borazine and production of poly borazine-like species. Full article

High energy consumption in Chinese rural residential buildings, caused by rudimentary construction methods and the poor thermal performance of building envelopes, poses a significant challenge to national sustainability and “dual carbon” goals. To address this, this study proposes a comprehensive modeling and analysis framework integrating an improved Bio-inspired Black-winged Kite Optimization Algorithm (IBKA) with Support Vector Regression (SVR). Firstly, to address the limitations of the original B-inspired BKA, such as premature convergence and low efficiency, the proposed IBKA incorporates diversification strategies, global information exchange, stochastic behavior selection, and an NGO-based random operator to enhance exploration and convergence. The improved algorithm is benchmarked against BKA and six other optimization methods. An orthogonal experimental design was employed to generate a dataset by systematically sampling combinations of influencing factors. Subsequently, the IBKA-SVR model was developed for energy consumption prediction and analysis. The model’s predictive accuracy and stability were validated by benchmarking it against six competing models, including GA-SVR, PSO-SVR, and the baseline SVR and so forth. Finally, to elucidate the model’s internal decision-making mechanism, the SHAP (SHapley Additive exPlanations) interpretability framework was employed to quantify the independent and interactive effects of each influencing factor on energy consumption. The results indicate that: (1) The IBKA demonstrates superior convergence accuracy and global search performance compared with BKA and other algorithms. (2) The proposed IBKA-SVR model exhibits exceptional predictive accuracy. Relative to the baseline SVR, the model reduces key error metrics by 37–40% and improves the R² to 0.9792. Furthermore, in a comparative analysis against models tuned by other metaheuristic algorithms such as GA and PSO, the IBKA-SVR consistently maintained optimal performance. (3) The SHAP analysis reveals a clear hierarchy in the impact of the design features. The Insulation Thickness in Outer Wall and Insulation Thickness in Roof Covering are the dominant factors, followed by the Window-wall Ratios of various orientations and the Sun space Depth. Key features predominantly exhibit a negative impact, and a significant non-linear relationship exists between the dominant factors (e.g., insulation layers) and the predicted values. (4) Interaction analysis reveals a distinct hierarchy of interaction strengths among the building design variables. Strong synergistic effects are observed among the Sun space Depth, Insulation Thickness in Roof Covering, and the Window-wall Ratios in the East, West, and North. In contrast, the interaction effects between the Window-wall Ratio in the South and other variables are generally weak, indicating that its influence is approximately independent and linear. Therefore, the proposed bio-inspired framework, integrating the improved IBKA with SVR, effectively predicts and analyzes residential building energy consumption, thereby providing a robust decision-support tool for the data-driven optimization of building design and retrofitting strategies to advance energy efficiency and sustainability in rural housing. Full article

In this article, we establish the existence of global mild solutions to the Vlasov–Fokker–Planck equation with local alignment forces under specular reflection boundary conditions in the low-regularity function space $L_k^1{L}_T^{\infty }{L}_v^2$. A key difficulty is that the macroscopic averaged velocity u does not directly possess a dissipative structure in the equation. To overcome this, we rely on the dissipation $u-b$ from the linear part, combined with the dissipation of the macroscopic component b derived from the associated macroscopic equation. Moreover, since no direct energy functional is available for u, we fully exploit the dissipative mechanisms of both $u-b$ and b when handling the estimates for the nonlinear terms. Full article

Accurate prediction of the state of charge (SOC) of a battery pack is essential to improve the operational efficiency and safety of energy storage systems. In this paper, we propose a novel lithium-ion battery (Lib) pack SOC prediction framework that combines redundant control correlation downscaling with Adaptive Error Variation Weighting Mechanism (AVM) fusion mechanisms. By integrating redundancy feature selection based on correlation analysis with global sensitivity analysis, the dimensionality of the input features was reduced by 81.25%. The AVM merges BiGRU’s ability to model short-term dynamics with Informer’s ability to capture long-term dependencies. This approach allows for complementary information exchange between multiple models. Experimental results indicate that on both monthly and quarterly slice datasets, the RMSE and MAE of the fusion model are significantly lower than those of the single model. In particular, the proposed model shows higher robustness and generalization ability in seasonal generalization tests. Its performance is significantly better than the traditional linear and classical filtering methods. The method provides reliable technical support for accurate estimation of SOC in battery management systems under complex environmental conditions. Full article

A new energy storage unit, which is fed by a piezoelectric wind energy harvester, is explored. The outputs of a three-phase piezoelectric wind energy device have been initially recorded from the laboratory experiments. Following the records of voltage outputs, the power ranges of the device were measured at several hundred microwatts. The main issue of piezoelectric voltage generation is that voltage waveforms of piezoelectric materials have high total harmonic distortion (THD) with incredibly high subharmonics and superharmonics. Therefore, such a material reply causes a certain power loss at the output of the wind energy generator. In order to fix this problem, we propose a combination of a rectifier and a storage system, where they can operate compatibly under high THD rates (i.e., 125%). Due to high THD values, current–voltage characteristics are not linear-dependent; indeed, because of capacitive effect of the piezoelectric (i.e., lead zirconium titanite) material, harvested power from the material is reduced by nearly a factor of 20% in the output. That also negatively affects the storage on the Li-based battery. In order to compensate, the output waveform of the device, the waveforms, which are received from the energy-harvester device, are first rectified by a full-wave rectifier that has a maximum power point tracking (MPPT) unit. The SOC values prove that almost 40% of the charge is stored in 1.2 s under moderate wind speeds, such as 6.1 m/s. To conclude, a better harvesting performance has been obtained by storing the energy into the Li-ion battery under a current–voltage-controlled boost converter technique. Full article

Non-mechanical contact distance measurement solutions are becoming more and more necessary in various industries, including building monitoring, automotive, and aviation industries. Inductive proximity sensor (IPS) technology is becoming a more popular solution in the field of short distances. Because of its small size, dependability, and measurement capabilities, IPS is a good option. Separate circuits are used in the classical structures to generate the excitation signal for the sensor coil and measure the response signal. The response signal’s amplitude is typically measured. This article proposes an IPS model that uses frequency response as its basis for operation. A microcontroller and embedded technology are used to implement a small IPS structure. This includes the circuit for determining distance, as well as the signal generator used to excite the sensor coil. In essence, an LC circuit is employed, which at the unit step has a damped oscillatory response by nature. Periodically injecting energy into the LC circuit, however, causes it to enter a persistent oscillatory state. The full experimental model is implemented and presented in the article, illustrating how the distance can be measured with a 33 µm accuracy within the 10 mm range with the help of the nonlinear relationship between frequency and distance and the linear drift of frequency with temperature. Full article

This study investigated differences in energy-expenditure (EE) modeling between badminton players of varying competitive levels during aerobic training. It evaluated the impact of sensor quantity and sample size on prediction model accuracy and generalizability, providing evidence for personalized training-load monitoring. Fifty badminton players (25 elite, 25 enthusiasts) performed treadmill running, cycling, rope skipping, and stair walking. Data were collected using accelerometers (waist, wrists, ankles), a heart rate monitor, and indirect calorimetry (criterion EE). Multiple machine learning models (Linear Regression, Bayesian Ridge Regression, Random Forest, Gradient Boosting) were employed to develop EE prediction models. Performance was assessed using R², mean absolute percentage error (MAPE), and root mean square error (RMSE), with further evaluation via the Triple-E framework (Effectiveness, Efficiency, Extension). Elite athletes demonstrated stable, coordinated movement patterns, achieving the best values for R² and the smallest errors using minimal core sensors (typically dominant side). Enthusiasts required multi-site sensors to compensate for greater execution variability. Increasing sensors beyond three yielded no performance gains; optimal configurations involved 2–3 core accelerometers combined with heart rate data. Expanding sample size significantly enhanced model stability and generalizability (e.g., running task R² increased from 0.49 (N = 20) to 0.95 (N = 40)). Triple-E evaluation indicated that strategic sensor minimization coupled with sufficient sample size maximized predictive performance while reducing computational cost and deployment burden. Competitive level significantly influences EE modeling requirements. Elite athletes are suited to a “low-sensor, small-sample” scenario, whereas enthusiasts necessitate a “multi-sensor, large-sample” strategy. Full article

To validate a positive relationship between air temperature (T) and atmospheric substances (S/G, a ratio of diffuse solar radiation to global solar radiation) found at four typical stations on the Earth, and a further investigation was conducted. Based on the analysis of long-term solar radiation, atmospheric substances, and air temperature at 29 representative stations of baseline surface radiation network (BSRN) in the world, the relationships and the mechanisms between air temperature and atmospheric substances were studied in more detail. A universal non-linear relationship between T and S/G was still found, which supported the previous relationship between T and S/G. This further revealed that a high (or low) air temperature is strongly associated with large (or small) amounts of atmospheric substances. The mechanism is that all kinds of atmospheric substances can keep and accumulate solar energy in the atmosphere and then heat the atmosphere, causing atmospheric warming at the regional and global scales. Therefore, it is suggested to reduce the direct emissions of all kinds of atmospheric substances (in terms gases, liquids and particles, and GLPs) from the natural and anthropogenic sources, and secondary formations produced from atmospheric compositions via chemical and photochemical reactions (CPRs) in the atmosphere, to slow down the regional and global warming through our collective efforts, by all mankind and all nations. Air temperature increased at most BSRN stations and many sites in China, and decreased at a small number of BSRN stations during long time scales, revealing that the mechanisms of air temperature change were very complex and varied with region, atmospheric substances, and the interactions between solar radiation, GLPs, and the land. Full article

Background: Optimal energy and protein delivery during the early phase of severe trauma remains unclear. Observational studies frequently contradict the findings of randomized controlled trials, raising concerns about confounding factors. The aim of this study is to assess nutritional adequacy and daily variability in the energy gaps and its impact on outcomes using innovative statistical methods. Methods: Prospective observational study enrolled severely injured patients in the ICU at the Level 1 trauma center between October 2023 to April 2025. To describe the evolution of calorie and protein deficits during the first 10-day ICU stay, we utilized a linear mixed-effects model to estimate each patient’s individual energy gap trajectory. Results: 286 patients were analyzed. Median APACHE II and ISS score was 16.0 (12.0–20.0) and 22.0 (18.0–27.0), respectively. Mortality rate was 35.3%. Patients received 68.3% of prescribed calories and 76.8% of proteins. Admission energy deficit, rate of caloric intake, and their interaction are associated with ICU mortality. Increased day-to-day energy variability was associated with longer duration of mechanical ventilation (HR = 0.55, 95% CI: 0.31–0.99; p = 0.047). Patients who achieved better caloric (HR = 0.68, 95% CI: 0.48–0.98, p = 0.036) and protein (HR = 0.29, 95% CI: 0.09–0.96, p = 0.043) nutrition had a lower hazard of developing nosocomial infection. Conclusions: This study supports the 2023 ESPEN guidelines, showing that achieving the recommended energy and protein intake during the early phase of severe trauma is linked to lower mortality rates, shorter mechanical ventilation time, and reduced risk of nosocomial infections. Full article

The mechanisms of freak wave generation in 3D island-reef topography are investigated. Four types of freak waves are investigated, based on the wavelet transform for examining the characteristics of freak waves and their mechanism. The freak waves come from a three-dimensional experimental terrain model in a random wave. The wavelet energy spectrum, scale-averaged and time-averaged wavelet spectrum are considered. A new parameter (scale-centroid wavelet spectrum) is defined, based on the wavelet transform algorithm, to quantitatively analyze and further estimate the energy transfer process. The results suggest that the occurrence of freak waves is associated with the gradual alignment of the phases of wave components. The nonlinear interaction in terms of wavelet cross-bispectrum implies that wave–wave interaction, especially with high-frequency components, is obviously enhanced during a freak wave occurrence. The energy transforms to a high frequency during a freak wave occurrence. The current result forms a definite indication that the occurrence of freak waves is caused by the combined effects of linear superposition and nonlinear interactions. Linear superposition begins to take effect long before the freak wave occurs, whereas nonlinear interactions primarily occur during the shorter period just before the freak wave forms. It provides an important reference for the prediction of abnormal waves. Full article

Carbyne, a linear chain of carbon atoms, possesses extraordinary properties but has remained elusive due to its extreme instability. While encapsulation within carbon nanotubes stabilizes carbyne, a lack of synthetic control over its location has prevented practical use. Here, we introduce a spatially localized plasma jet technique that enables the guided spatially selective self-assembly of carbyne encapsulated within multiwalled carbon nanotube (carbyne@MWCNT) hybrids on graphite surfaces. This method uses intense, localized plasma energy to simultaneously grow nanotubes and synthesize carbyne within them, where the nanotube structure and carbyne encapsulation are governed by the localized heat flux distribution. Beyond confirming carbyne formation via its characteristic Raman mode, we discover its second-order vibrational spectrum, confirming anharmonic interactions between the chain and its nanotube container. This spatial control can be used to architect functional carbyne@MWCNT arrays, whose potential applications are discussed in detail. Full article