Search Results (140)

The demand for large-scale, heavy-duty autonomous pavement-sweeping robots is rising due to urban growth, hygiene needs, and labor shortages. Ensuring their health and safe operation in dynamic outdoor environments is vital, as terrain unevenness and slope gradients can accelerate wear, increase maintenance costs, and pose safety risks. This study introduces an AI-driven condition monitoring (CM) framework designed to detect terrain unevenness and slope gradients in real time, distinguishing between safe and unsafe conditions. As system vibration levels and energy consumption vary with terrain unevenness and slope gradients, vibration and current data are collected for five CM classes identified: safe, moderately safe terrain, moderately safe slope, unsafe terrain, and unsafe slope. A simple-structured one-dimensional convolutional neural network (1D CNN) model is developed for fast and accurate prediction of the safe to unsafe classes for real-time application. An in-house developed large-scale autonomous pavement-sweeping robot, PANTHERA 2.0, is used for data collection and real-time experiments. The training dataset is generated by extracting representative vibration and heterogeneous slope data using three types of interoceptive sensors mounted in different zones of the robot. These sensors complement each other to enable accurate class prediction. The dataset includes angular velocity data from an IMU, vibration acceleration data from three vibration sensors, and current consumption data from three current sensors attached to the key motors. A CM-map framework is developed for real-time monitoring of the robot by fusing the predicted anomalous classes onto a 3D occupancy map of the workspace. The performance of the trained CM framework is evaluated through offline and real-time field trials using statistical measurement metrics, achieving an average class prediction accuracy of 92% and 90.8%, respectively. This demonstrates that the proposed CM framework enables maintenance teams to take timely and appropriate actions, including the adoption of suitable maintenance strategies. Full article

This study investigates the factors affecting the choice of school travel mode among students in Mersin, Türkiye, focusing on walking, private car, public transit and school bus. A two-step modeling approach was adopted. First, a latent class cluster analysis (LCCA) was applied to identify subgroups of students with similar characteristics. Then, separate multinomial logit (MNL) models were estimated for each cluster. The data come from the 2022 Urban Transport Master Plan household survey and include 2798 students from 2092 households. The results show that trip distance is the most consistent and significant factor across all clusters, as increasing distance makes students more likely to use motorized modes instead of walking. Gender also demonstrates a consistent influence in specific clusters, where male students are less likely to travel by private car. Similarly, residing in a single-family house consistently increases the likelihood of car use in multiple clusters. Conversely, the influence of household structure, parental education, income, and household size differs significantly between clusters, underlining the importance of considering group-level differences in school travel behavior. These findings suggest that policies aiming to promote sustainable school travel should be sensitive to the needs of different student groups. Integrating land use and transportation planning may help to support active and shared modes of travel. Full article

Background/Objectives: Environmental factors, including the differences in genotype-based housing (GbH), can act as confounding variables in studies using transgenic mouse models, potentially influencing experimental outcomes and limiting their reproducibility and translational value. Despite the widespread use of transgenic models in preclinical studies, the extent to which housing conditions can affect the behavioral and molecular parameters of interest remains poorly understood. This study aims to investigate how different GbH conditions influence visuo-spatial memory and gene expression in the A53T mouse model (JAX006823) of Parkinson’s disease (PD) during the pre-motor phase. Methods: A53T+ transgenic male mice and their non-transgenic littermates (A53T−) were housed in either mixed-genotype (MGH) or single-genotype (SGH) environments from postnatal day (PND) 30, with C57BL/6J mice serving as the controls. A behavioral assessment using the Novel Object Recognition and Object Location Tests was conducted at PND 180, followed by a qPCR analysis of Iba1, Gfapα, Bdnf, Tnfα, Il-1β, and Il-6 expression in the medial prefrontal cortex and the hippocampus. Results: The variations in GbH influenced behavior and mRNA expression differently in the A53T+ and A53T− animals. Specifically, the A53T− mice in SGH environments displayed behavioral and molecular profiles similar to the C57BL/6J controls, while the same was not evident in the MGH environments. In the A53T+ mice, the mRNA expression of Iba1, Gfapα, Bdnf, and Tnfα was sensitive to variations in GbH, while memory impairment was not. Conclusions: This study highlights the importance of considering environmental factors in studies using transgenic animal models. The obtained data suggests that GbH can influence the parameters of interest in preclinical research, implicating the need for the optimization of future study designs. Full article

Permanent magnet synchronous motors for electric vehicles (EVs) prioritize high power density and lightweight design, leading to elevated thermal flux density. Consequently, cooling methods and heat conduction in stator windings become critical. This paper proposes a compound cooling structure combining direct oil spray cooling on stator windings and housing oil channel cooling (referred to as the winding–housing composite oil cooling system) for permanent synchronous motors in EVs. A systematic design methodology for oil jet nozzles and housing oil channels is investigated, determining the average convective heat transfer coefficient on end-winding surfaces and the heat dissipation factor of the oil channels. Finite element analysis (FEA) was employed to simulate the thermal field of a 48-slot 8-pole oil-cooled motor, with further analysis on the effects of oil temperature and flow rate on motor temperature. Based on these findings, an optimized oil-cooled structure is proposed, demonstrating enhanced thermal management efficiency. The results provide valuable references for the design of cooling systems in oil-cooled motors for EV applications. Full article

Classical Rett syndrome (RTT) is a neurodevelopmental disorder caused by mutations in the MECP2 gene, resulting in a devastating phenotype associated with a lack of gene expression control. Mouse models lacking Mecp2 expression with an RTT-like phenotype have been developed to advance therapeutic alternatives. Environmental enrichment (EE) attenuates RTT symptoms in patients and mouse models. However, the mechanisms underlying the effects of EE on RTT have not been fully elucidated. We housed male hemizygous Mecp2-null (Mecp2^-/y) and wild-type mice in specially conditioned cages to enhance sensory, cognitive, social, and motor stimulation. EE attenuated the progression of the RTT phenotype by preserving neuronal cytoarchitecture and neural plasticity markers. Furthermore, EE ameliorated defects in neuromuscular junction organization and restored the motor deficit of Mecp2^-/y mice. Treatment with plasma from young WT mice was used to assess whether the increased activity could modify plasma components, mimicking the benefits of EE in Mecp2^-/y. Plasma treatment attenuated the RTT phenotype by improving neurological markers, suggesting that peripheral signals of mice with normal motor function have the potential to reactivate dormant neurodevelopment in RTT mice. These findings demonstrate how EE and treatment with young plasma ameliorate RTT-like phenotype in mice, opening new therapeutical approaches for RTT patients. Full article

A novel temporally resolved settle-plate air sampler was developed using 3D printing technology to improve upon traditional passive air sampling methods. Conventional settle plates provide cumulative measurements of particle or microbial loads over an entire sampling period, lacking the temporal resolution necessary to identify specific contamination events. The described device integrates a petri plate within a 3D-printed housing featuring a narrow slit that exposes only a small portion of the plate to incoming particles. A rotary mechanism, driven by a mechanical clock motor, rotates the petri plate over 12 h, allowing for time-segmented sampling. Validation experiments demonstrated the device’s ability to accurately encode the temporal history of particle deposition using both aerosolized dyes and viable microbial spores. The device effectively correlated bioaerosol deposition with ambient wind conditions during outdoor sampling. The system is inexpensive (under USD 10), requires no specialized skills to assemble, and is compatible with existing settle plate methodologies. This innovation enhances the ability to conduct air quality assessments in critical environments, enabling data-driven decisions to mitigate contamination risks. Full article

Direct liquid cooling technology has the potential to enhance the thermal management performance of electric motors with continuously increasing energy density. However, direct liquid cooling technology has practical limitations for full-scale commercialization. In addition, the conventionally used indirect liquid cooling imposes higher thermal resistance to cope with the increased thermal management performance of high power density electric motors. Therefore, this study proposes a hybrid direct–indirect oil cooling system as a next-generation cooling strategy for the enhanced thermal management of high power density electric motors. The heat transfer characteristics, including maximum winding, stator and motor housing temperatures, heat transfer coefficient, friction factor, pressure drop, and performance evaluation criteria (PEC), are investigated for different spray hole diameters, coolant oil volume flow rates, and motor heat loss levels. The computational model was validated with experimental results within a 5% error developed to evaluate heat transfer characteristics. The results show that spray hole diameter significantly influences cooling performance, with a larger diameter (1.7 mm) reducing hydraulic resistance while causing a slight increase in motor temperatures. The coolant oil volume flow rate has a major impact on heat dissipation, with an increase from 10 to 20 L/minute (LPM) reducing winding, stator, and housing temperatures by 22.05%, 22.70% and 24.02%, respectively. However, higher flow rates also resulted in an increased pressure drop, emphasizing the importance of the selection of a suitable volume flow rate based on the trade-off between cooling performance and energy consumption. Despite the increase in motor heat loss level from 2.6 kW to 8 kW, the hybrid direct–indirect oil cooling system effectively maintained all motor component temperatures below the critical threshold of 180 °C, confirming its suitability for high-performance electric motors. These findings contribute to the development and commercialization of the proposed next-generation cooling strategy for high power density electric motors for ensuring thermal stability and operational efficiency. Full article

Urban sprawl and excessive reliance on motorization have led to many urban problems. The balance of supply and demand in the real estate market, as well as price fluctuations, also face many challenges. Urban rail transit not only alleviates traffic congestion and air pollution, but also significantly reduces residents’ commuting time, broadens urban accessibility, and reshapes the decision-making basis for residents when choosing residential locations. This study takes the 1st, 2nd, 3rd, 4th, 8th, 11th, and 13th metro lines that have been opened in Qingdao City as examples. It selects 12,924 residential samples within a 2 km radius along the rail transit lines. By using GIS spatial analysis tools and the multi-scale geographically weighted regression (MGWR) model, it analyzes the spatial differentiation characteristics of housing prices along the rail transit lines and the reasons and mechanisms behind them. The empirical results show that housing prices decrease to varying degrees with the increase in the distance from the rail transit. For every additional 1 km from the rail transit station, the housing price increases by 0.246%. Through model comparison, it was found that MGWR has a better fitting degree than the traditional ordinary least squares method (OLS) and the previous geographically weighted regression model (GWR), and reveals the spatial heterogeneity of the influence of urban rail transit on housing prices. Different indicator elements have different effects on housing prices along these lines. The urban rail transit factor in the location characteristics has a positive impact on housing prices, and has a significant negative correlation in some areas. The significant influence range of the distance to the nearest metro station on housing prices is concentrated within a radius of 373 m, and the effect decays beyond this range. The total floors, building area, green coverage rate, property management fee, and the distance to hospitals and parks in the neighborhood and structural characteristics have spatial heterogeneity. Analyzing the areas affected by the urban rail transit factor, it was found that the double location superposition effect, the networked transportation system, and the agglomeration of urban functional axes are important reasons for the significant phenomena in some local areas. This research provides a scientific basis for optimizing the sustainable development of rail transit in Qingdao and formulating differentiated housing policies. Meanwhile, it expands the application of the MGWR model in sustainable urban spatial governance and has practical significance for other cities to achieve sustainable urban development. Full article

Effective grinding of residual agricultural materials helps to improve yield in the production of chemical compounds through hydrothermal technology. Milling pretreatment has different types of pre-treatment where ball mills, roller mills, and finally, the knife mill stand out. The knife mill being a mill with continuous processing, its multiple benefits and contributions highlight the knife milling process; however, it is a process that is generally carried out with dry biomass that generates extra processing of the biomass before grinding, implying longer times and wear than other equipment. This work presents the design of a knife mill with an adaptation of free convection drying as a joint process of knife milling and drying. The design is based on lignocellulosic biomass, and the knife milling results are presented for two biomasses: peapods and coffee cherries. The knife mill is designed with a motor, a housing with an integrated drive system, followed by a knife system and a feeding system with a housing and finally the free convection drying system achieving particle sizes in these biomasses smaller than 30 mm, depending on the time processed. The data demonstrate the significant impact of particle size on the yields of various platform chemicals obtained from coffee cherry and peapod waste biomass. For coffee cherry biomass, smaller particle sizes, especially 0.5 mm, result in higher total yields compared to larger sizes while for peapod biomass at the smallest particle size of 0.5 mm, the total yield is the highest, at 45.13%, with notable contributions from sugar (15.63%) and formic acid (19.14%). Full article

A total of 0.3%C-Cr-Mo-V steel, a high-strength alloy steel widely used in rocket motor housings, suspension systems in high-performance vehicles, etc., is noted due to its high strength-to-weight ratio. However, its high carbon equivalent (CE > 1%) makes it challenging to weld, as it is prone to brittle martensitic formation, which increases the risk of cracking and embrittlement. The present paper focuses on enhancing the microstructure and mechanical properties of 0.3% C-Cr-Mo-V steel by gas tungsten arc welded (GTAW) joints, utilizing post-weld heat treatment and cooling strategies (PWHTCS). A systematic experimental approach was employed to ensure a defect-free weld through dye penetrant testing (DPT) and X-ray radiography techniques. Subsequently, test specimens were extracted from the welded sections and subjected to PWHT protocols, including hardening, tempering, and rapid quenching using air and oil cooling (AC and OC, respectively) mediums. Results show that OC has enhanced tensile strength and hardness while simultaneously maintaining and improving ductility, ensuring a well-balanced combination of strength and toughness. Fractography analysis revealed ductile fracture in AC samples, whereas OC weldments exhibited a mixed ductile–brittle fracture mode. Thus, the findings demonstrate the critical role of PWHTCS, with OC, as an effective method for achieving enhanced mechanical performance and microstructural stability in high-integrity applications. Full article

Introduction: Advanced magnetic resonance imaging (MRI) techniques in neuroplasticity evaluations provide important information on stroke disease and the underlying mechanisms of neuronal recovery. It has been observed that gray matter density or volume in brain regions closely related to motor function can be a valuable indicator of the response to treatment. Objective: To compare structural MRI-evaluated gray matter volume changes in patients with post-stroke upper limb spasticity for >1 year between those undergoing surgery and those treated with botulinum toxin A (BoNT-A) and to relate these findings to upper limb function and quality of life outcomes. Materials and Methods: Design. A two-arm controlled and randomized clinical trial in patients with post-stroke upper limb spasticity. Participants. Thirty post-stroke patients with spastic upper limbs. Intervention. Participants were randomly assigned (1:1 allocation ratio) for surgery (experimental group) or treatment with BoNT-A (control group). Main outcome measures. The functional parameters were analyzed with Fugl-Meyer, Zancolli, Keenan, House, Ashworth, pain visual analogue, and hospital anxiety and depression scales. Quality of life was evaluated using SF-36 and Newcastle stroke-specific quality of life scales. The carer burden questionnaire was also applied. Clinical examinations and MRI scans were performed at baseline and at six months post-intervention. Correlations between brain volume/thickness and predictors of interest were examined across evaluations and groups. Results: Five patients were excluded due to the presence of intracranial implants. Eleven patients were excluded from analyses since they were late dropouts. Changes were observed in the experimental group but not in the control group. Between baseline and six months, gray matter volume was augmented at the hippocampus and gyrus rectus and cortical thickness was increased at the frontal pole, occipital gyrus, and insular cortex, indicating anatomical changes in key areas related to motor and behavioral adaptation These changes were significantly related to subjective pain, Ashworth spasticity scale, and Newcastle quality of life scores, and marginally related to the carer burden score. Conclusions: The structural analysis of gray matter by MRI revealed differences in patients with post-stroke sequelae undergoing different therapies. Gray matter volume and cortical thickness measurements showed significant improvements in the surgery group but not in the BoNT-A group. Volume was increased in areas associated with motor and sensory functions, suggesting a neuroprotective or regenerative effect of upper limb surgery. Full article

This article describes the findings of a study that examined the impact of the process of shaping the stator core of a synchronous reluctance motor on its operating parameters. The SynRM motor, with compact geometrical dimensions and a flux barrier rotor, was chosen for this study, for which the technological process of forming the stator and rotor cores may be critical. The numerical results for three types of stator core structures were compared. The first, which is commonly used by academics, has no technological cutouts in the stator. The second type has cutouts for the clamps that hold the core laminates together. The third one has cutouts that allow the core to be positioned inside the motor housing. The research campaign also investigated the effect of partial material structure degradation caused by core laminate shaping on motor operation parameters. As a consequence of the computations, the characteristics and motor parameters were compared, including torque ripple, stator core loss, and motor efficiency. It has been demonstrated that, in the case of SynRM motors with relatively small geometric dimensions, technological cutouts caused by the shaping of stator core laminates can drastically influence the motor’s characteristics. Full article

In this study, the causes of vibration and noise reduction occurring in electric compressors for electric vehicles are analyzed, and a reduction plan is proposed. First, the impact hammer modal of the electric compressor housing was measured and compared with the ANSYS modal analysis. As there was no significant difference between the modal analysis results and the impact hammer modal measurement results, the modal analysis was judged to be reliable. The noise measurement results showed that the main noise sources of the development were analyzed in the 600–800 Hz band, 4000–5000 Hz band, and 10,000 Hz band. From the competitor’s analysis of the housing structure and noise measurement results, the 10,000 Hz band was able to reduce noise caused by changes in the carrier frequency of the motor inverter, and the 4000–5000 Hz band showed a significant effect by adjusting the clearance during discharge, and it was confirmed that noise in the 600–800 Hz band could be reduced to some extent only by changing the housing structure such as rib and mounting reinforcement of the housing and thickness change. Full article

Maintenance strategies such as condition-based maintenance and predictive maintenance of machines have gained importance in industrial automation firms as key concepts in Industry 4.0. As a result, online condition monitoring of electromechanical systems has become a crucial task in many industrial applications. Motor current signature analysis (MCSA) is an interesting noninvasive alternative to vibration analysis for the condition monitoring and fault diagnosis of mechanical systems driven by electric motors. The MCSA approach is based on the premise that faults in the mechanical load driven by the motor manifest as changes in the motor’s current behavior. This paper presents a novel data-driven, MCSA-based CM approach that exploits autoregressive (AR) spectral estimation. A multiresolution analysis of the raw motor currents is first performed using the discrete wavelet transform with Daubechies filters, enabling the separation of noise, disturbances, and variable torque effects from the current signals. AR spectral estimation is then applied to selected wavelet details to extract relevant features for fault diagnosis. In particular, a reference AR power spectral density (PSD) is estimated using data collected under healthy conditions. The AR PSD is then continuously or periodically updated with new data frames and compared to the reference PSD through the Symmetric Itakura–Saito spectral distance (SISSD). The SISSD, which serves as the health indicator, has proven capable of detecting fault occurrences through changes in the AR spectrum. The proposed procedure is tested on real data from two different scenarios: (i) an experimental in-house setup where data are collected during the execution of electric cam motion tasks (imbalance faults are emulated); (ii) the Korea Advanced Institute of Science and Technology testbed, whose data set is publicly available (bearing faults are considered). The results demonstrate the effectiveness of the method in both fault detection and isolation. In particular, the proposed health indicator exhibits strong detection capabilities, as its values under fault conditions exceed those under healthy conditions by one order of magnitude. Full article

This study performed a 3D CFD analysis on a GP3 rotary engine to determine the stroke and flow characteristics and examine the thermal- and flow-related design factors’ validity. The 3D CFD analysis was performed using the CONVERGE program, utilizing the automatic grid generation function based on the 3D engine design drawing, which is suitable for a rotating rotary engine geometry. The target species and error tolerance were selected based on the GRI-Mech 3.0 full reaction mechanism to validate the reaction model and define a reasonable range of target species and error tolerances. The RNG k-ε turbulence and SAGE combustion models were also employed to analyze the four-stroke characteristics for the GP3 engine by visualizing the internal flow. The various outcomes confirmed the rotary engine’s unique characteristics and were reasonably interpreted to validate the engine design factors. In particular, the EGR phenomenon in the intake and exhaust port overlap area and the interference phenomenon in the port overlap area between adjacent cylinders are unique to the engine, and were rationally analyzed to more accurately predict the engine’s performance. The results of this study regarding the flame quenching regions indicated power and efficiency, and the emission characteristics can be used to validate the design parameters. Full article