Search Results (295)

With the development of inspection robot control technology, wheel-legged robots are increasingly used in complex underground space inspection. To address low stability during obstacle crossing in unstructured terrains, a motion control strategy integrating an improved CPG algorithm and a biological reflex mechanism is proposed. It introduces an adaptive coupling matrix, augmented with the Lyapunov function, and vestibular/stumbling reflex models for real-time motion feedback. Simulink–Adams virtual prototypes and single-wheeled leg experiments (on the left front leg) were used to verify the system. Results show that the robot’s turning oscillation was ≤±0.00593 m, the 10° tilt maintained a stable center of mass at 10.2° with roll angle fluctuations ≤±5°, gully-crossing fluctuations ≤±0.01 m, and pitch recovery ≤2 s. The experiments aligned with the simulations, proving that the strategy effectively suppresses vertical vibrations, ensuring stable and high-precision inspection. Full article

In this study, we consider monthly load forecasting, which is an essential decision for energy infrastructure planning and investment. This study focuses on the Texas power grid, where electricity consumption has surged due to rising industrial activity and the increased construction of data centers driven by growing demand for AI. Based on an extensive exploratory data analysis, we identify key characteristics of monthly electricity demand in Texas, including an accelerating upward trend, strong seasonality, and temperature sensitivity. In response, we propose a regression-based forecasting model that incorporates a carefully designed set of input features, including a nonlinear trend, lagged demand variables, a seasonality-adjusted month variable, average temperature of a representative area, and calendar-based proxies for industrial activity. We adopt a rolling forecasting approach, generating 12-month-ahead forecasts for both 2023 and 2024 using monthly data from 2013 onward. Comparative experiments against benchmarks including Holt–Winters, SARIMA, Prophet, RNN, LSTM, Transformer, Random Forest, LightGBM, and XGBoost show that the proposed model achieves superior performance with a mean absolute percentage error of approximately 2%. The results indicate that a well-designed regression approach can effectively outperform even the latest machine learning methods in monthly load forecasting. Full article

Many low-temperature applications, such as rocket engines and liquefied natural gas (LNG) transport pumps, necessitate ultra-low-temperature operational environments. In these conditions, the properties of lubricating oils and greases are significantly influenced by temperature, leading to the widespread adoption of solid lubrication. Currently, there is no international research regarding the influence of bearing coatings on the subsurface stress distribution in raceways. The Lundberg–Palmgren (L-P) theory states that subsurface stress variations govern bearing lifespan. Therefore, this paper utilizes existing formulas and Python programming to calculate the subsurface stress field of the inner raceway in a MoS₂ solid-lubricated angular contact ball bearing. Furthermore, it analyzes the impacts of factors such as coating material properties, slide-to-roll ratio, traction coefficient, and load on its subsurface stress field. The results reveal that for solid-lubricated ball bearings, as the load increases, the maximum subsurface stress shifts closer to the center of the contact area, and the maximum subsurface shear stress becomes more concentrated. As the traction coefficient increases, the stress on the XZ-plane side increases and its position moves closer to the surface, while the opposite trend is observed on the other side. Additionally, the maximum value of the subsurface von Mises stress is approximately 0.64P₀, and the maximum value of the orthogonal shear stress component τ_yz in the subsurface is approximately 0.25P₀. Full article

The use of high-pressure grinding rolls (HPGRs) is increasing in the ore industries as advanced technology is available for this type of comminuting. There are important parameters in these devices, which have many effects on productivity. One of the main reasons for damage on the rolls and, therefore, decreases in the machine’s productivity and efficiency is surface wear. This phenomenon must be carefully understood so that it can be controlled as much as possible through the readjustment and optimization of the effective parameters. In this research, the wear mechanism of HPGRs in a production line for iron ore concentrate was investigated. The results showed that there was greater wear at the center of the rolls and that changes to the chemical and physical properties of the incoming iron compared to the design condition reduced the rolls’ lives. The results showed a failure to perform appropriate mechanical adjustment and improper repair and maintenance. Full article

The model explicitly incorporates boundary conditions that account for the complex interplay between sections experiencing varying degrees of reduction. This interaction significantly influences the overall deformation behavior and force loading. The control effect is associated with boundary conditions determined by the unevenness of the compression, which have certain quantitative and qualitative characteristics. These include additional loading, which is less than the main load, which implements the process of plastic deformation, and the ratio of control loads from the entrance and exit of the deformation site. According to this criterion, it follows from experimental data that the controlling effect on the plastic deformation site occurs with a ratio of additional and main loading in the range of 0.2–0.8. The next criterion is the coefficient of support, which determines the area of asymmetry of the force load and is in the range of 2.00–4.155. Furthermore, the criterion of the regulating force ratio at the boundaries of the deformation center forming a longitudinal plastic shear is within the limits of 2.2–2.5 forces and 1.3–1.4 moments of these forces. In this state, stresses and deformations of the plastic medium are able to realize the effects of plastic shaping. The force effect reduces with an increase in the unevenness of the deformation. This is due to a change in height of the longitudinal interaction of the disparate sections of the strip. There is an appearance of a new quality of loading—longitudinal plastic shear along the deformation site. The unbalanced additional force action at the entrance of the deformation source is balanced by the force source of deformation, determined by the appearance of a functional shift in the model of the stress state of the metal. The developed theory, using the generalized method of an argument of functions of a complex variable, allows us to characterize the functional shift in the deformation site using invariant Cauchy–Riemann relations and Laplace differential equations. Furthermore, the model allows for the investigation of material properties such as the yield strength and strain hardening, influencing the size and characteristics of the identified limit state zone. Future research will focus on extending the model to incorporate more complex material behaviors, including viscoelastic effects, and to account for dynamic loading conditions, more accurately reflecting real-world milling processes. The detailed understanding gained from this model offers significant potential for optimizing mill roll designs and processes for enhanced efficiency and reduced energy consumption. Full article

In complex marine environments, ramp-based recovery systems for autonomous underwater vehicles (AUVs) often encounter engineering challenges such as reduced docking accuracy and success rate due to disturbances in the capture window attitude. In this study, a desktop-scale physical experimental platform for recovery compensation was designed and constructed. The system integrates attitude feedback provided by an attitude sensor and dual-motor actuation to achieve active roll and pitch compensation of the capture window. Based on the structural and geometric characteristics of the platform, a dual-channel closed-loop control strategy was proposed utilizing midpoint tracking of the capture window, accompanied by multi-level software limit protection and automatic centering mechanisms. The control algorithm was implemented using a discrete-time PID structure, with gain parameters optimized through experimental tuning under repeatable disturbance conditions. A first-order system approximation was adopted to model the actuator dynamics. Experiments were conducted under various disturbance scenarios and multiple control parameter configurations to evaluate the attitude tracking performance, dynamic response, and repeatability of the system. The results show that, compared to the uncompensated case, the proposed compensation mechanism reduces the $MSE$ by up to 76.4% and the $MaxAE$ by 73.5%, significantly improving the tracking accuracy and dynamic stability of the recovery window. The study also discusses the platform’s limitations and future optimization directions, providing theoretical and engineering references for practical AUV recovery operations. Full article

In this paper, a new mechanical vibration technology applied to continuous casting production is studied, which is used to break the dendrite at the solidification front, expand the equiaxed dendrite zone, and improve the center quality of the billet. The exciting force of this vibration technology is provided by a new type of vibration equipment (Vibration roll) independently developed and designed. Firstly, an investigation is conducted into the impacts of vibration acceleration, vibration frequency, and the contact area between the Vibration roll (VR) and the billet surface on the internal stress distribution within the billet shell, respectively. Secondly, the billet with and without vibration treatment was sampled and analyzed through industrial tests. The results show that the area ratio of equiaxed dendrites in transverse specimens treated with vibration technology was 11.96%, compared to 6.55% in untreated specimens. Similarly, for longitudinal samples, the linear ratio of equiaxed dendrites was observed to be 34.56% in treated samples and 22.95% in untreated samples. Compared to the specimens without mechanical vibration, the billet treated with mechanical vibration exhibits an increase in the area ratio and linear ratio of equiaxed dendrite ratio by 5.41% and 11.61%, respectively. Moreover, the probability of bridging at the end of solidification of the billet treated by vibration technology was significantly reduced, and the central porosity and shrinkage cavities of the billet were significantly improved. This study provides the first definitive evidence that the novel mechanical vibration technology can enhance the quality of the billet during the continuous casting process. Full article

As a core component of machining tools and vehicles, the load-bearing and transmission performance of rolling bearings is directly related to product processing quality and driving safety, highlighting the critical importance of fault detection. To address the nonlinearity, non-stationary modulation, and low signal-to-noise ratio (SNR) observed in bearing vibration signals, we propose a fault feature extraction method based on spectral kurtosis and Hilbert envelope demodulation. First, spectral kurtosis is employed to determine the center frequency and bandwidth of the signal adaptively, and a bandpass filter is constructed to enhance the characteristic frequency components. Subsequently, the envelope spectrum is extracted through the Hilbert transform, allowing for the precise identification of fault characteristic frequencies. In the fault diagnosis stage, a multidimensional feature vector is formed by combining the kurtosis index with the amplitude ratios of inner/outer race characteristic frequencies, and fault pattern classification is accomplished using a Least-Squares Support Vector Machine (LS-SVM). To evaluate the effectiveness of the proposed method, experiments were conducted on the bearing datasets from Case Western Reserve University (CWRU) and the Machine Failure Prevention Technology (MFPT) Society. The experimental results demonstrate that the proposed method surpasses other comparative approaches, achieving identification accuracies of 95% and 100% for the CWRU and MFPT datasets, respectively. Full article

In various applications, IoT temporal data play a crucial role in accurately predicting future trends. Traditional models, including Rolling Window, SVR-RBF, and ARIMA, suffer from a potential accuracy decrease because they generally use all available data or the most recent data window during training, which can result in the inclusion of noisy data. To address this critical issue, this paper proposes a new forecasting technique called Adaptive Holt–Winters (AHW). The AHW approach utilizes two models grounded in an exponential smoothing methodology. The first model is trained on the most current data window, whereas the second extracts information from a historical data segment exhibiting patterns most analogous to the present. The outputs of the two models are then combined, demonstrating enhanced prediction precision since the focus is on the relevant data patterns. The effectiveness of the AHW model is evaluated against well-known models (Rolling Window, SVR-RBF, ARIMA, LSTM, CNN, RNN, and Holt–Winters), utilizing various metrics, such as RMSE, MAE, p-value, and time performance. A comprehensive evaluation covers various real-world datasets at different granularities (daily and monthly), including temperature from the National Climatic Data Center (NCDC), humidity and soil moisture measurements from the Basel City environmental system, and global intensity and global reactive power from the Individual Household Electric Power Consumption (IHEPC) dataset. The evaluation results demonstrate that AHW constantly attains higher forecasting accuracy across the tested datasets compared to other models. This indicates the efficacy of AHW in leveraging pertinent data patterns for enhanced predictive precision, offering a robust solution for temporal IoT data forecasting. Full article

The production of AISI 304 stainless steel (a corrosion-resistant alloy prone to solidification defects from high alloy content) particularly benefits from twin-roll strip casting—a short-process green technology enabling sub-rapid solidification (the maximum cooling rate exceeds 1000 °C/s) control for high-performance steels. However, the internal phenomena within its molten pool remain exceptionally challenging to monitor. This study developed a multiscale numerical model to simulate coupled fluid flow, heat transfer, and solidification in AISI 304 stainless steel twin-roll strip casting. A quarter-symmetry 3D model captured macroscopic transport phenomena, while a slice model resolved mesoscopic solidification structure. Laboratory experiments had verified that the deviation between the predicted temperature field and the measured average value (1384.3 °C) was less than 5%, and the error between the solidification structure simulation and the electron backscatter diffraction (EBSD) data was within 5%. The flow field and flow trajectory showed obvious recirculation zones: the center area was mainly composed of large recirculation zones, and many small recirculation zones appeared at the edges. Parameter studies showed that, compared with the high superheat (110 °C), the low superheat (30 °C) increased the total solid fraction by 63% (from 8.3% to 13.6%) and increased the distance between the kiss point and the bottom of the molten pool by 154% (from 6.2 to 15.8 mm). The location of the kiss point is a key industrial indicator for assessing solidification integrity and the risk of strip fracture. In terms of mesoscopic solidification structure, low superheat promoted the formation of coarse columnar crystals (equiaxed crystals accounted for 8.9%), while high superheat promoted the formation of equiaxed nucleation (26.5%). The model can be used to assist in the setting of process parameters and process optimization for twin-roll strip casting. Full article

Background: The COVID-19 pandemic necessitated immediate regulatory vaccine approvals to facilitate timely global access. The prevailing differences in economies and resources and the varying maturity of the regulatory systems worldwide resulted in different levels of capacity to ensure vaccine quality, safety, and efficacy. In addition to the Emergency Use Authorization or equivalent by some advanced regulatory agencies, the WHO issued Emergency Use Listings (EULs), among other tools, to streamline and expedite regulatory approvals globally. This study aimed to assess the regulatory strategies and best practices adopted during the COVID-19 vaccine approvals and gather lessons for future emergency preparedness. Methods: A mixed-method approach employing qualitative desk reviews and a cross-sectional study collected data from 194 national regulatory authorities (NRAs) across all WHO regions. Results: Three main approval processes were identified: procurement-driven, reliance-based, and independent evaluations. Wealthier countries with more mature regulatory systems were found to spend a longer time issuing approvals, primarily due to being the initial assessors of the vaccines’ quality, safety, and efficacy. Furthermore, various regulatory flexibilities and best practices centered around regulatory reliance, rolling reviews, fast-tracking reviews, and employing digital tools were identified. Notably, the WHO’s EULs were essential in facilitating the timely approval of vaccines globally, including in low- and middle-income countries. Conclusions: The findings suggest a significant turn in vaccine regulation theories and practice, emphasizing balancing speed with scientific validity. This necessitates the creation of thorough provisions for emergency preparedness, regulatory reliance, and administrative flexibility in regulatory practices worldwide. Full article

The current technical condition of facilities designated for the technical–hygienic maintenance of railway rolling stock is unsatisfactory, as they are neither technologically nor technically equipped to meet the required quality standards. Maintenance is often carried out in open spaces or directly on the tracks of major railway junctions, which prevents year-round execution of these services and causes operational limitations. This article analyses and proposes solutions for the technical–hygienic maintenance center (THU) of railway rolling stock at the Nové Zámky railway station in Slovakia, focusing on improving the efficiency and quality of the provided services. The analysis includes an assessment of technological procedures, identification of operational deficiencies, and a comparison of current maintenance standards with the requirements for contemporary railway systems, such as automated diagnostic platforms, predictive maintenance modules, and modular cleaning infrastructure. The optimization of THU services considers the average time norms for selected technological procedures and the characteristics of train sets passing through the center. The proposed solution involves a more efficient scheduling of operations in line with the valid railway traffic timetable and train set circulation, utilizing a graphical planning method for modelling and optimizing the facility’s service processes. The implementation of optimization measures can lead to increased capacity and efficiency of maintenance, reduced time required for individual procedures, and lower operational costs. The study’s results provide practical recommendations for improving the quality of technical–hygienic maintenance at railway junction stations, contributing to greater railway transport reliability and an overall improvement in passenger comfort. Additionally, the findings offer a transferable framework that may inform the planning and modernization of maintenance facilities at other regional railway stations facing similar infrastructural and operational challenges. Full article

Background/Objectives: Public health activities and the roll-out of new vaccines such as the HPV vaccine in Mali have been disrupted by both the COVID-19 pandemic and by political unrest from March 2020 until recently. The HPV vaccine was introduced into the childhood vaccine program in 2024. Given the persistent threat of ongoing infectious disease epidemics, there is a pressing need to understand the factors influencing vaccine uptake in Mali and other low- and middle-income countries. Methods: To address this need, the GAIA Vaccine Foundation (GAIA VF), a nongovernmental organization (NGO), conducted a study of vaccine confidence in Bamako, the country’s capital and primary population center, between September 2021 and March 2022 at 12 community based primary care clinics and 1 rural primary care clinic. The study was coupled with a vaccine outreach and education campaign at each site. Results: Study staff collected information on vaccine confidence in surveys from 3445 community participants. Healthcare providers were also recruited from the 13 sites for vaccine-related training, and 140 of these participants completed pre- and post-surveys on their vaccine knowledge and confidence. Survey results indicate that community members trust their primary care providers more than they trust the government. However, primary care providers trust government sources of information more than other sources for guidance on vaccines. Conclusions: As new vaccines are introduced, engaging key healthcare leaders to inform healthcare providers and developing primary care provider-led community outreach will be critically important to gaining community confidence prior to and during vaccination campaigns in the future. Full article

High-purity tantalum plates form inhomogeneous microstructures and texture gradients along the thickness through conventional rolling, which seriously affects the sputtering performance of the target. In this work, tantalum plates with a random microstructure were used on different rolling paths, such as those in unidirectional rolling (UR) and cross rolling (CR). The microstructure of the rolled tantalum plates was characterized using electron backscatter diffraction (EBSD), X-ray diffraction (XRD), and Vickers hardness (HV). The results indicated that the UR specimen exhibited the highest hardness values, with a gradual increase in hardness across the entire thickness layer from the surface to the center. Furthermore, specimens with different rolling directions demonstrated distinct texture gradient distributions throughout the thickness. The unidirectional rolling (UR) sample had a {110} (<110>//ND) texture on the surface and a {111} (<111>//ND) texture on the rest of its thickness. Compared with UR, cross rolling introduces more shear deformation, increases the content of the {100} (<100>//ND) texture, and weakens the {111} texture intensity everywhere except the center region. An increase in the rolling direction is beneficial for weakening the inhomogeneity between microstructures. Full article

As global discourse increasingly centers on environmental, social, and governance considerations, ESG investment has become a major trend in financial markets. Artificial intelligence (AI), through its rapid evolution, has exerted a transformative influence that continues to reshape the fundamental structures of this domain. This study investigates the dynamic relationship between AI and ESG investment indices in China, aiming to reveal the bidirectional causal linkages and time-dependent interactions between these two critical areas. In methods, we used four different parameter stability tests to indicate that the Granger causality test based on the full-sample VAR model may produce biased results. Therefore, we employed a bootstrap rolling-window subsample Granger causality test using data from January 2013 to September 2024 in China. The results reveal a significant dynamic relationship between ESG investment and AI. In key findings, we find that AI exerts a negative impact on ESG investment. AI development attracts substantial capital inflows that favor technological advancement and commercialization over long-term ESG investments. Meanwhile, ESG investment shows both positive and negative effects on AI. The positive effect indicates that ESG investment promotes AI research and applications emphasizing energy efficiency, data privacy, and fairness, thereby supporting the sustainable development of AI technologies. However, driven by short-term economic returns, strict ESG standards and compliance requirements may, in the short term, constrain the development of certain energy-intensive or emerging AI technologies. In economic and political implications, our study provides policymakers with scientific evidence to improve the ESG investment environment and to design balanced policies that support both AI development and sustainable investment practices. It underscores the necessity of promoting coordinated development between AI and ESG investment to achieve global sustainability goals and recommends measures to align short-term economic interests with long-term ESG objectives. This study is expected to serve as a scientific basis for ESG goal-setting and contribute to the realization of China’s dual-carbon goals. In particular, it facilitates the convergence of artificial intelligence technologies with sustainable development initiatives and tells the importance of responsible technological progress for global sustainable development. Full article