Search Results (10,002)

In modern robotics, actuators are crucial for achieving effective movement and ensuring robustness. Although different applications demand specific actuator qualities, an actuator with built-in compliance and high torque density is generally preferred. Recently, harmonic gearboxes have become widely used in robotics for actuation due to their zero-backlash, lightweight design, flexibility, and high torque density. However, the intricate and precise machining required for these gearboxes makes them economically unviable in some cases. This work presents the RM-Act, a novel Radial Modular Actuator that employs synchronous belts as a harmonic speed reducer. The RM-Act retains the advantages of the harmonic principle, making it a promising candidate for robotic actuation. This paper describes the novel actuation principle and its validation through a prototype, along with a model identification to define its characteristics. The actuator demonstrates a nominal torque density of 10.08 N·m/kg, indicating its potential for efficient robotic applications. Full article

Induction motors piloted by voltage source inverters constitute a major source of acoustic noise in industry. The discrete tonal bands generated by induction motor stator current spectra controlled by the fixed Pulse Width Modulation (PWM) technique have damaging effects on the electronic noise source. Nowadays, the investigation of new advanced control techniques for variable speed drives has developed a potential investigation field. Finite state model predictive control has recently become a very popular research focus for power electronic converter control. The flexibility of this control shows that the switching times are generated using all the information on the drive status. Predictive Torque Control (PTC), space vector PWM and random PWM are investigated in this paper in terms of acoustic noise emitted by an induction machine fed by a three-phase two-level inverter. A comparative study based on electrical and mechanical magnitudes, as well as harmonic analysis of the stator current, is presented and discussed. An experimental test bench is also developed to examine the effect of the proposed PTC and PWM techniques on the acoustic noise of an induction motor fed by a three-phase two-level voltage source converter. Full article

The second virial coefficient (SVC) of the Lennard-Jones fluid is a cornerstone of molecular theory, yet its calculation has traditionally relied on the complex integration of the pair potential. This work introduces a fundamentally different approach by reformulating the problem in terms of ordinary differential equations (ODEs). For the classical component of the SVC, we generalize the confluent hypergeometric and Weber–Hermite equations. For the first quantum correction, we present entirely new ODEs and their corresponding exact-analytical solutions. The most striking result of this framework is the discovery that these ODEs can be transformed into Schrödinger-like equations. The classical term corresponds to a harmonic oscillator, while the quantum correction includes additional inverse-power potential terms. This formulation not only provides a versatile method for expressing the virial coefficient through a linear combination of functions (including Kummer, Weber, and Whittaker functions) but also reveals a profound and previously unknown mathematical structure underlying a classical thermodynamic property. Full article

This study presents an analysis of the condition of street lighting based on a selected typical installation in one of the 1459 rural communes in Poland. The analysis was carried out on the basis of publicly available statistical data, local government reports, and information contained in national and European strategic documents. During the analysis, numerous irregularities and differences in the quality and energy efficiency of the lighting infrastructure were indicated. It was found that outdated sodium luminaires with high energy consumption, low durability, and limited luminous efficacy are used in many cases, which generates significant operating costs and negatively affects the environment. The authors emphasize that a lack of regular and professional lighting audits leads to the suboptimal use of energy resources, an insufficient level of road safety, and failure to adapt lighting to current technical standards and the needs of road users. A lighting audit is a key tool for diagnosing the technical condition, efficiency, and compliance of installations with relevant regulations and recommendations. It also allows for the identification of potential savings and determining the directions of modernization and implementation of energy-saving technologies, such as LED luminaires and intelligent control systems.The presented analysis demonstrates that energy audits are an effective tool for confirming efficiency improvements and enhancing visual safety conditions through better compliance with photometric standards (luminance, lighting uniformity). Direct accident statistics were not within the scope of this study. Full article

Within the context of global climate change and China’s commitment to the “Dual Carbon” goals (carbon peak and carbon neutrality), this study proposes a novel taxonomy of market-based environmental regulations, dividing them into investment-driven and tax-based supervisory mechanisms. Using panel data from 30 Chinese provinces between 2010 and 2023, we empirically investigate their differential effects on carbon emissions. Results indicate that both regulatory approaches significantly curb carbon emissions, each exhibiting distinct nonlinear patterns: an inverted-U curve for investment-oriented measures and a U-shaped trajectory for tax-oriented policies, implying that excessively stringent tax supervision may lead to a rebound in emissions due to effects such as the “resource curse” and “innovation crowding-out.” Industrial structure transformation functions as a common mediating channel, while green innovation efficiency exerts a distinct moderating influence. Both policy types demonstrate adverse spatial spillover effects, with no support found for the “pollution haven” or “race to the bottom” hypotheses. This study offers new empirical insights into how environmental regulations facilitate green and low-carbon transition through market mechanisms, providing valuable implications for designing ecological policy systems that harmonize emission reduction efficiency with sustainability in China and other emerging economies. Full article

Accurate classification between magnetizing inrush currents and internal faults is essential for reliable transformer protection and stable power system operation. Because their transient waveforms are so similar, conventional differential protection and harmonic restraint techniques often fail under dynamic conditions. This study presents a two-stage classification model that combines a self-organizing map (SOM) and a convolutional neural network (CNN) to enhance robustness and accuracy in distinguishing between inrush currents and internal faults in power transformers. In the first stage, an unsupervised SOM identifies topologically structured event clusters without the need for labeled data or predefined thresholds. Seven features are extracted from differential current signals to form fixed-length input vectors. These vectors are projected onto a two-dimensional SOM grid to capture inrush and fault distributions. In the second stage, the SOM’s activation maps are converted to grayscale images and classified by a CNN, thereby merging the interpretability of clustering with the performance of deep learning. Simulation data from a 154 kV MATLAB/Simulink transformer model includes inrush, internal fault, and overlapping events. Results show that after one cycle following fault inception, the proposed method improves accuracy (AC), precision (PR), recall (RC), and F1-score (F1s) by up to 3% compared with a conventional CNN model, demonstrating its suitability for real-time transformer protection. Full article

The reliability of hydraulic valves is critical for the safety and efficiency of industrial systems. While vibration and pressure sensors are widely deployed for condition monitoring, leveraging the heterogeneous data from these multi-sensor systems for accurate remaining useful life (RUL) prediction remains challenging due to noise, outliers, and inconsistent sampling rates. This study proposes a sensor data-driven framework that integrates multi-step signal preprocessing, time–frequency feature fusion, and a machine learning model to address these challenges. Specifically, raw data from vibration and pressure sensors are first harmonized through a multi-step preprocessing pipeline including Hampel filtering for impulse noise, Robust Scaler for outlier mitigation, Butterworth low-pass filtering for effective frequency band retention, and resampling to a unified rate. Subsequently, vibro-acoustic features are extracted from the preprocessed sensor signals, including Fast Fourier Transform (FFT)-based frequency domain features and Wavelet Packet Decomposition (WPD)-based time–frequency features, to comprehensively characterize the valve’s degradation. A health indicator (HI) is constructed by fusing the most sensitive features. Finally, a Kernel Principal Component Analysis (KPCA)-optimized Random Forest model is developed for HI prediction, which strongly correlates with RUL. Validated on the UCI hydraulic condition monitoring dataset through 20-run Monte-Carlo cross-validation, our method achieves a root mean square error (RMSE) of 0.0319 ± 0.0090, a mean absolute error (MAE) of 0.0109 ± 0.0014, and a coefficient of determination (R²) of 0.9828 ± 0.0097, demonstrating consistent performance across different data partitions. These results confirm the framework’s effectiveness in translating multi-sensor data into actionable insights for predictive maintenance, offering a viable solution for industrial health management systems. Full article

Recent trends in research on rotating machinery diagnosis focus on contactless diagnostic technologies. In this paper, novel higher order spectral technologies, based on spectral moduli, are proposed. The proposed technologies estimate statistical dependencies between moduli of harmonics of bearing defect frequencies. Moduli of harmonics of bearing defect frequencies, which appear due to bearing faults, are statistically dependent. The Third Order Modulus (TOM) is a novel higher order spectral signal processing technology developed for rotating machinery diagnostics. The paper presents mathematical expressions for new technologies as well as a detailed description of the signal processing algorithm of motor current for bearings diagnostics. The TOM technology is comprehensively validated via experimental trials for motor bearing diagnosis via motor current signature analysis. Results of experimental trials clearly show that the TOM technology is highly effective for diagnosis of bearing defects. Estimates of the total probabilities of correct diagnosis provided by the TOM technology are 100%. The TOM technology is experimentally compared with the classic bicoherence (CB) technology using eight bearings: four pristine bearings and four damaged bearings with two damage types. Comparison has shown that the TOM technology is more effective than the CB technology. Full article

The air-conditioner noise in a rehabilitation room can seriously affect the mental state of patients. However, the existing single-layer active noise control (ANC) filters may fail to attenuate the complicated harmonic noise, and the deep recursive ANC method may fail to work in real time. To solve the problem, in a bending-pipe model, a new hybrid adaptive self-loading filtered-x least-mean-square (ASL-FxLMS) and convolutional neural network-gate recurrent unit (CNN-GRU) network is proposed. At first, based on the recursive GRU translation core, an improved CNN-GRU network with multi-head attention layers is proposed. Especially for complicated harmonic noises with more or fewer frequencies than harmonic models, the attenuation performance will be improved. In addition, its structure is optimized to decrease the computing load. In addition, an improved time-delay estimator is applied to improve the real-time ANC performance of CNN-GRU. Meanwhile, an adaptive self-loading FxLMS algorithm has been developed to deal with the uncertain components of complicated harmonic noise. Moreover, to achieve balance attenuation, robustness, and tracking performance, the ASL-FxLMS and CNN-GRU are connected by a convex combination structure. Furthermore, theoretical analysis and simulations are also conducted to show the effectiveness of the proposed method. Full article

Biological control with beneficial bacteria offers a sustainable alternative to synthetic agrochemicals for managing plant pathogens and enhancing plant health. However, bacterial biocontrol agents (BCAs) remain underexploited due to regulatory hurdles (such as complex registration timelines and extensive dossier requirements) and limited strain characterization. Recent advances in omics technologies (genomics, transcriptomics, proteomics, and metabolomics) have strengthened the bioprospecting pipeline by uncovering key microbial traits involved in biocontrol. Genomics enables the identification of biosynthetic gene clusters, antimicrobial pathways, and accurate taxonomy, while comparative genomics reveals genes relevant to plant–microbe interactions. Metagenomics uncovers unculturable microbes and their functional roles, especially in the rhizosphere and extreme environments. Transcriptomics (e.g., RNA-Seq) sheds light on gene regulation during plant-pathogen-bacteria interactions, revealing stress-related and biocontrol pathways. Metabolomics, using tools like Liquid Chromatography–Mass Spectrometry (LC-MS) and Nuclear Magnetic Resonance spectroscopy (NMR), identifies bioactive compounds such as lipopeptides, Volatile Organic Compounds (VOCs), and polyketides. Co-culture experiments and synthetic microbial communities (SynComs) have shown enhanced biocontrol through metabolic synergy. This review highlights how integrating omics tools accelerates the discovery and functional validation of new BCAs. Such strategies support the development of effective microbial products, promoting sustainable agriculture by improving crop resilience, reducing chemical inputs, and enhancing soil health. Looking ahead, the successful application of omics-driven bioprospection of BCAs will require addressing challenges of large-scale production, regulatory harmonization, and their integration into real-world agricultural systems to ensure reliable, sustainable solutions. Full article

To address the challenges of low detection accuracy and weak generalization in UAV aerial imagery caused by complex ground environments, significant scale variations among targets, dense small objects, and background interference, this paper proposes an improved lightweight multi-scale small-object detection model, MBD-YOLO (MBFF module, BiMS-FPN, and Dual-Stream Head). Specifically, to enhance multi-scale feature extraction capabilities, we introduce the Multi-Branch Feature Fusion (MBFF) module, which dynamically adjusts receptive fields through parallel branches and adaptive depthwise convolutions, expanding the receptive field while preserving detail perception. We further design a lightweight Bidirectional Multi-Scale Feature Aggregation Pyramid Network (BiMS-FPN), integrating bidirectional propagation paths and a Multi-Scale Feature Aggregation (MSFA) module to mitigate feature spatial misalignment and improve small-target detection. Additionally, the Dual-Stream Head with NMS-free architecture leverages a task-aligned architecture and dynamic matching strategies to boost inference speed without compromising accuracy. Experiments on the VisDrone2019 dataset demonstrate that MBD-YOLO-n surpasses YOLOv8n by 6.3% in mAP50 and 8.2% in mAP50–95, with accuracy gains of 17.96–55.56% for several small-target categories, while increasing parameters by merely 3.1%. Moreover, MBD-YOLO-s achieves superior detection accuracy, efficiency, and generalization with only 12.1 million parameters, outperforming state-of-the-art models and proving suitable for resource-constrained embedded deployment scenarios. The superior performance of MBD-YOLO, which harmonizes high precision with low computational demand, fulfills the critical requirements for real-time deployment on resource-limited UAVs, showing great promise for applications in traffic monitoring, urban security, and agricultural surveying. Full article

This systematic review examines the role of rural tourism in promoting sustainable development, focusing on its interaction with forest ecosystems and the essential ecosystem services they provide. A comprehensive literature search across Scopus, PubMed, and Google Scholar identified 142 peer-reviewed articles, analyzed through qualitative synthesis and bibliometric mapping. The review highlights four thematic clusters in rural tourism research: impacts on rural areas, destination management, resident perspectives and cultural sustainability, and emerging themes like place attachment. It emphasizes the reliance of rural tourism on ecosystem services, including provisioning, regulating, cultural, and supporting, especially those linked to forest ecosystems. Examples from Monteverde, Costa Rica, and Tuscany, Italy, illustrate the role of rural tourism in supporting biodiversity conservation, habitat restoration, and sustainable agriculture. However, uncontrolled tourism in forested regions can lead to deforestation and ecosystem degradation, as seen in the Lake District, Masai Mara, and Rajasthan. The review stresses the need for sustainable practices to mitigate the negative impacts of tourism, advocating for an integrated sustainability framework that balances economic, environmental, and governance aspects. Best practices include eco-friendly infrastructure, community participation, and environmental education. The potential of emerging technologies, such as eco-certification systems and smart tourism, is explored to reduce the environmental footprint of tourism. The review calls for stronger policy integration, equitable benefit-sharing, capacity building, and longitudinal research to ensure resilient rural tourism that harmonizes ecosystem conservation with socio-economic development. In conclusion, the integration of sustainable practices and community involvement is crucial for aligning rural tourism with forest ecosystem conservation. Full article

This study evaluates the environmental impacts of shipbuilding materials through life cycle assessment (LCA) and assesses potential exposure under the EU Carbon Border Adjustment Mechanism (CBAM). Three representative vessel types, a pure car and truck carrier (PCTC), a bulk carrier, and a container ship, were analyzed across scenarios reflecting different steelmaking routes, recycling rates, and regional energy mixes. Results show that structural steel (AH36, EH36, DH36, A/B grades) overwhelmingly dominates embedded emissions, while aluminium and copper contribute secondarily but with high sensitivity to recycling and energy pathways. Coatings, polymers, and yard processes add smaller but non-negligible effects. Scenario-based CBAM cost estimates for 2026–2030 indicate rising liabilities, with container vessels facing the highest exposure, followed by bulk carriers and PCTCs. The findings highlight the strategic importance of steel sourcing, recycling strategies, and verifiable supply chain data for reducing embedded emissions and mitigating financial risks. While operational emissions still dominate the life cycle, the relative importance of construction-phase emissions will grow as shipping decarbonizes. Current EU-level discussions on extending CBAM to maritime services, together with recognition of domestic carbon pricing as a potential pathway to reduce liabilities, underscore regulatory uncertainty and emphasize the need for harmonized methods, transparent datasets, and digital integration to support decarbonization. Full article

This study aimed to forecast monthly PM_2.5 concentrations in Zabol, one of the world’s most dust-prone regions, using four time series models: SARIMA, SARIMAX enhanced with Fourier terms (selected based on spectral peak analysis), TBATS, and a novel hybrid ensemble. Spectral analysis identified a dominant annual cycle (frequency 0.083), which justified the inclusion of two Fourier harmonics in the SARIMAX model. Results demonstrated that the hybrid model, which optimally combined forecasts from the three individual models (with weights ω₂ = 0.628 for SARIMAX, ω₃ = 0.263 for TBATS, and ω₁ = 0.109 for SARIMA), outperformed all others across all evaluation metrics, achieving the lowest AIC (1835.04), BIC (1842.08), RMSE (9.42 μg/m³), and MAE (7.43 μg/m³). It was also the only model exhibiting no significant residual autocorrelation (Ljung–Box p-value = 0.882). Forecast uncertainty bands were constant across the prediction horizon, with widths of approximately ±11.39 μg/m³ for the 80% confidence interval and ±22.25 μg/m³ for the 95% confidence interval, reflecting fixed absolute uncertainty in the multi-step forecasts. The proposed hybrid framework provides a robust foundation for early warning systems and public health management in dust-affected arid regions. Full article

This study examines the forced vibration behavior of a hydroelastic system composed of a functionally graded material (FGM) plate, a barotropic compressible Newtonian viscous fluid, and an adjacent rigid wall. The fluid occupies the gap between the plate and the wall. A time-harmonic force, applied in and along the free surface of the FGM plate, excites vibrations within the system. The plate’s motion is modeled using the exact equations of elastodynamics, while the fluid dynamics are described by the linearized Navier–Stokes equations for compressible viscous flow. The governing equations, which feature variable coefficients, are solved using a discrete analytical approach. Boundary conditions enforce impermeability at the rigid wall and continuity of both forces and velocities at the fluid–plate interface. The investigation focuses on the plane strain state of the plate coupled with the corresponding two-dimensional fluid flow. Numerical analyses are conducted to evaluate normal stresses and velocity distributions along the interface. The primary objective is to assess how the graded material properties of the plate influence the frequency-dependent responses of stresses and velocities at the plate–fluid boundary. Full article