Search Results (522)

Background: Intervertebral disc degeneration (IVDD) is closely linked to low back pain (LBP), a leading cause of disability worldwide. IVDD is characterized by the loss of proteoglycans (PGs), extracellular matrix (ECM) degradation, and reduced hydration of the nucleus pulposus (NP). Extracellular vesicles (EVs) derived from human umbilical cord mesenchymal stem cells (hUC-MSCs) exhibit tissue repair and immunomodulatory effects and are emerging as promising cell-free therapeutics. Methods: We established a rat IVDD model via fluoroscopy-guided needle puncture of three consecutive coccygeal discs and confirmed degeneration through Alcian Blue and hematoxylin & eosin (H&E) staining. The gene expression of inflammatory and pain markers (ADRβ2, COMP, CXCL1, COX2, PPTA, MMP13, YKL40) was measured by qPCR. Subsequently, we implanted hUC-MSCs or EVs to evaluate their reparative potential. Results: Upregulation of inflammatory and pain genes in IVDD was associated with an immunomodulatory response. Tracking DiI-labelled hUC-MSCs and EVs revealed enhanced survival of hUC-MSCs, retention of EVs, and dispersion within rat tail discs; EVs showed greater retention than hUC-MSCs. Implanted EVs were internalized by NP cells and remained within degenerative IVDs. EVs passively diffused, accumulated at the injury site, interacted with host cells, and enhanced function, as shown by increased expression of human chondrocyte-related markers (SOX9, TGFβ1, TGFβ2, COL2) compared to hUC-MSC treatment. Histological analysis of two weeks post-transplantation showed NP cellular patterns resembling chondromas in treated discs. EVs integrated into and distributed within degenerated NP regions, with greater glycosaminoglycan (GAG) content. Conclusions: Overall, hUC-MSC EVs demonstrated superior regenerative capacity, supporting a safe, cell-free strategy for disc repair. Full article

Ultra-high-energy cosmic rays (UHECRs) are the most energetic particles known—and yet their origin is still an open question. However, with the precision and accumulated statistics of the Pierre Auger Observatory and the Telescope Array, in combination with advancements in theory and modeling—e.g., of the Galactic magnetic field—it is now possible to set solid constraints on the sources of UHECRs. The spectrum and composition measurements above the ankle can be well described by a population of extragalactic, homogeneously distributed sources emitting mostly intermediate-mass nuclei. Additionally, using the observed anisotropy in the arrival directions, namely the large-scale dipole >8 EeV, as well as smaller-scale warm spots at higher energies, even more powerful constraints on the density and distribution of sources can be placed. Yet, open questions remain—like the striking similarity of the sources that is necessary to describe the rather pure mass composition above the ankle, or the origin of the highest energy events whose tracked back directions point toward voids. The current findings and possible interpretation of UHECR data will be presented in this review. Full article

This study presents the development and evaluation of an Augmented Reality (AR)-based training system aimed at improving patient setup accuracy in radiation therapy. Leveraging Microsoft HoloLens 2, the system provides an immersive environment for medical staff to enhance their understanding of patient setup procedures. High-resolution 3D anatomical models were reconstructed from CT scans using 3D Slicer, while Luma AI was employed to rapidly capture complete body surface models. Due to limitations in each method—such as missing extremities or back surfaces—Blender was used to merge the models, improving completeness and anatomical fidelity. The AR application was developed in Unity, employing spatial anchors and 125 × 125 mm² QR code markers to stabilize and align virtual models in real space. System accuracy testing demonstrated that QR code tracking achieved millimeter-level variation, with an expanded uncertainty of ±2.74 mm. Training trials for setup showed larger deviations in the X (left–right), Y (up-down), and Z (front-back) axes at the centimeter scale. This meant that we were able to quantify the user’s patient setup skills. While QR code positioning was relatively stable, manual placement of markers and the absence of real-time verification contributed to these errors. The system offers a radiation-free and interactive platform for training, enhancing spatial awareness and procedural skills. Future work will focus on improving tracking stability, optimizing the workflow, and integrating real-time feedback to move toward clinical applicability. Full article

Meat consumption, a key factor in both environmental sustainability and public health, is strongly influenced by educational characteristics, with higher levels of education often associated with more sustainable dietary patterns. However, research examining the mechanisms through which education influences meat-related behaviours remains limited. This study investigates the mediating role of climate literacy in the relationship between educational characteristics and meat consumption patterns among adults and school-enrolled youth in Slovenia. We used survey data from a sample of 2990 individuals (aged 14–88) to examine how educational stage, track, and level impact meat consumption and reduction. Our focus was on climate literacy as a multidimensional construct, comprising climate knowledge, attitudes, and pro-environmental behaviour. The findings indicate that young people in the tertiary educational track tend to eat less meat, have already reduced their meat consumption in the past, and intend to further reduce it in the future, compared to secondary track students, with climate attitudes playing a mediating role in all three cases. For adults, a tertiary educational level, relative to a secondary level, was linked to lower meat consumption, an association largely explained by more positive climate attitudes. By contrast, adults with only primary education consume meat more often and are less inclined to cut back in the future. Among secondary school students, both vocational and general school groups reported greater past and intended meat reductions than their peers in professional schools. The findings underscore the importance of integrating climate literacy, especially fostering pro-climate attitudes, into educational programmes to promote sustainable dietary choices. Full article

This paper addresses the path tracking problem of underactuated unmanned surface vessels (USVs) in the presence of unknown external disturbances. A fixed-time adaptive integral sliding mode control (AISMC) method, incorporating a nonlinear disturbance observer (NDO), is proposed. Initially, a three-degree-of-freedom dynamic model of the USV is developed, accounting for external disturbances and model uncertainties. Based on the vessel’s longitudinal and transverse dynamic position errors, a virtual control law is designed to ensure fixed-time convergence, thereby enhancing the position error convergence speed. Next, a fixed-time NDO is introduced to estimate real-time external perturbations, such as wind, waves, and currents. The observed disturbances are fed back into the control system for compensation, thereby improving the system’s disturbance rejection capability. Furthermore, a sliding mode surface is designed using a symbolic function to address the issue of sliding mode surface parameter selection, leading to the development of the adaptive integral sliding mode control strategy. Finally, compared with traditional SMC and PID, the proposed AISMC-NDO offers higher accuracy, faster convergence, and improved robustness in complex marine environments. Full article

A physics-based vehicle–track coupled dynamic model embedding a hydraulic electromechanical regenerative damper (HERD) is developed to quantify electrical power recovery and wear depth in high-speed service. The HERD subsystem resolves compressible hydraulics, hydraulic rectification, line losses, a hydraulic motor with a permanent-magnet generator, an accumulator, and a controllable; co-simulation links SIMPACK with MATLAB/Simulink. Wheel–rail contact is computed with Hertz theory and FASTSIM, and wear depth is advanced with the Archard law using a pressure–velocity coefficient map. Both HERD power regeneration and wear depth predictions have been validated against independent measurements of regenerated power and wear degradation in previous studies. Parametric studies over speed, curve radius, mileage and braking show that increasing speed raises input and output power while recovery efficiency remains 49–50%, with instantaneous electrical peaks up to 425 W and weak sensitivity to curvature and mileage. Under braking from 350 to 150 km/h, force transients are bounded and do not change the lateral wear pattern. Installing HERD lowers peak wear in the wheel tread region; combining HERD with flexible wheelsets further reduces wear depth and slows down degradation relative to rigid wheelsets and matches measured wear more closely. The HERD electrical load provides a physically grounded tuning parameter that sets hydraulic back pressure and effective damping, which improves model accuracy and supports calibration and updating of digital twins for maintenance planning. Full article

This feasibility study evaluated a wearable sensor-based haptic feedback system designed to promote ergonomic awareness and influence posture and muscle activation patterns during a standard dental procedure. Inertial measurement units (IMUs) monitored posture by tracking back and neck angles, while four surface electromyography sensors recorded muscle activation in the lower erector spinae (LES) and upper trapezius (UT) muscles. Two IMUs with vibrotactile motors delivered real-time haptic feedback when participants maintained mechanically disadvantageous postures for extended periods during a cast metal crown preparation procedure on a manikin typodont. Data from four dental students participating in a total of 24 trials, half with and half without feedback, were analyzed via a two-way ANOVA to determine the effects of feedback and activity (e.g., inspections or drilling) on posture and muscle activation. Feedback slightly increased neck angles, but back angles remained nominally unchanged. Reduced UT activation and increased right LES activation suggests altered muscle recruitment strategies. Heatmap and RULA analyses indicated a shift toward more varied and potentially safer postural distributions during feedback trials. Postural and muscle activation data were also analyzed across four activity labels, which revealed that Drilling was consistently associated with higher ergonomic risk. Real-time haptic feedback influenced posture and muscle activation in dental students, particularly by reducing UT strain despite increased neck flexion. These findings support the integration of wearable feedback systems into preclinical training to enhance ergonomic awareness and potentially reduce the risk of developing musculoskeletal disorders, to which dentists are particularly prone. Full article

The main objective of this paper is to develop a maximum power point tracker (MPPT) for a photovoltaic module array (PVMA) under conditions of partial shading and sudden changes in solar irradiance. PVMAs exhibit nonlinear characteristics with respect to temperature and solar irradiance conditions. Therefore, when some modules in the array are shaded or when there is a sudden change in solar irradiance, the maximum power point (MPP) of the array will also change, and the power–voltage (P-V) characteristic curve may exhibit multiple peaks. Under such conditions, if the tracking algorithm employs a fixed step size, the time required to reach the MPP may be significantly prolonged, potentially causing the tracker to converge on a local maximum power point (LMPP). To address the issues mentioned above, this paper proposes a novel MPPT technique based on the nature-inspired idols algorithm (IA). The technique allows the promotion value (PM) to be adjusted through the anti-fans weight (afw) in the iteration formula, thereby achieving global maximum power point (GMPP) tracking for PVMAs. To verify the effectiveness of the proposed algorithm, a model of a 4-series–3-parallel PVMA was first established using MATLAB (2024b version) software under both non-shading and partial shading conditions. The voltage and current of the PVMAs were fed back, and the IA was then applied for GMPP tracking. The simulation results demonstrate that the IA proposed in this study outperforms existing MPPT techniques, such as particle swarm optimization (PSO), cat swarm optimization (CSO), and the bat algorithm (BA), in terms of tracking speed, dynamic response, and steady-state performance, especially when the array is subjected to varying shading ratios and sudden changes in solar irradiance. Full article

Photovoltaic (PV) systems are increasingly integral to global energy solutions, but their long-term reliability is challenged by various operational faults. In this article, we propose an advanced hybrid diagnostic framework combining a Deep Belief Network (DBN) for feature pattern extraction and a Light Gradient Boosting Machine (LightGBM) for classification to detect and diagnose PV panel faults. The proposed model is trained and validated on the QASP PV Fault Detection Dataset, a real-time SCADA-based dataset collected from 255 W panels at the Quaid-e-Azam Solar 100 MW Power Plant (QASP), Pakistan’s largest solar facility. The dataset encompasses seven classes: Healthy, Open Circuit, Photovoltaic Ground (PVG), Partial Shading, Busbar, Soiling, and Hotspot Faults. The DBN captures complex non-linear relationships in SCADA parameters such as DC voltage, DC current, irradiance, inverter power, module temperature, and performance ratio, while LightGBM ensures high accuracy in classifying fault types. The proposed model is trained and evaluated on a real-world SCADA-based dataset comprising 139,295 samples, with a 70:30 split for training and testing, ensuring robust generalization across diverse PV fault conditions. Experimental results demonstrate the robustness and generalization capabilities of the proposed hybrid (DBN–LightGBM) model, outperforming conventional machine learning methods and showing an accuracy of 98.21% classification accuracy, 98.0% macro-F1 score, and significantly reduced training time compared to Transformer and CNN-LSTM baselines. This study contributes to a reliable and scalable AI-driven solution for real-time PV fault monitoring, offering practical implications for large-scale solar plant maintenance and operational efficiency. Full article

Previous studies have suggested that the back-reaction of super-Hubble cosmological fluctuations on a symmetric background space-time, with respect to being homogeneous and isotropic, could behave like a dynamical relaxation of the cosmological constant. Moreover, this mechanism appears to be self-regulatory, potentially leading to oscillatory behavior in the effective DE. Such an effect would occur in any cosmological model with super-Hubble matter fluctuations, including the standard $\mathrm{\Lambda }$CDM model. Apart from that, recent DESI data, which indicate that DE may be dynamical, have renewed interest in exploring scenarios leading to such an oscillatory behavior. In this study, we propose a parameterization to account for the impact of super-Hubble fluctuations on the background energy density of the Universe. We model the total effective cosmological constant as the sum of a constant and an oscillating contribution. We performed a preliminary comparison of the background dynamics of this model with recent radial BAO data from DESI. We also discuss the status of the $H_0$ tension problem in this model. Full article

Power loss can influence the accuracy of maximum power point tracking (MPPT) control and the efficiency of a brushless doubly fed generator (BDFG)-based wind turbine (BDFGWT). Because power loss is related to both the active power reference and reactive power reference of BDFG, this article proposes active and reactive power optimal control of BDFGWT by considering power loss. Firstly, the mathematical model of BDFGWT, including the wind turbine, BDFG, and back-to-back converter, is established. Then, an active and reactive power optimal control strategy is proposed. In proposed control, the accurate active power reference of power winding (PW) is calculated by considering the active power loss of BDFG; in this way, proposed MPPT control can capture more wind power compared to traditional MPPT control, ignoring the power losses, thus improving the efficiency of BDFGWT. Furthermore, on the basis of the model of BDFG, the relations between reactive power and total active loss are analyzed, and the optimal reactive power control reference to minimize the active power loss is determined. Finally, in order to verify the validity of the proposed control, 2 MW BDFGWT has been constructed, and the proposed method was studied to make a comparison. The results verify that proposed control can maximize the utilization of wind energy, minimize the power loss of the BDFGWT system, and output maximal active power to the power grid. Full article

This study investigates the risk of developing musculoskeletal disorders (MSDs) in individuals performing standing tasks, with a focus on real-time posture assessment using motion capture technology. Improper body posture and repetitive movements during daily work activities can impose strain on the musculoskeletal system, increasing the likelihood of discomfort and long-term injury. Data were collected from five male and female participants using the Perception Neuron motion capture system, with body-mounted sensors tracking posture and movement. Joint angles were calculated to distinguish between correct and incorrect postures based on ISO 11226:2000 ergonomic guidelines. Key physical risk factors identified included prolonged forward trunk inclination, elevated arm positions, and repetitive actions. The analysis revealed that participants frequently adopted moderate- to high-risk postures, especially when working at non-ergonomic desk heights, suggesting a heightened risk of MSDs such as back and upper limb pain. These findings underscore the importance of real-time ergonomic monitoring and adaptive workstation design to reduce musculoskeletal risks in standing work environments. Full article

This paper explores the feasibility of using a wireless Inductive Resonant Link (IRL) for wearable posture monitoring. The proposed system is based on magnetically coupled textile resonators and is implemented using a Single Input Multiple Output (SIMO) configuration. In particular, the setup consists of four inductively coupled resonators: one transmitting coil integrated into a textile structure and positioned on the back of the neck, and three receiving coils placed on the shoulders. The magnetic coupling between these elements varies as a function of the user’s posture, making it possible to monitor postural changes by analyzing variations in the transmission coefficients of the link. Unlike traditional sensor-based systems that require multiple components and data processing, the proposed method uses the inherent response of the inductive link to detect posture in a simple and efficient way. To validate the concept, experimental measurements of the scattering parameters were carried out using a compact and low-power vector network analyzer. The results show a consistent and measurable relationship between postural changes and variations in the transmission coefficients, demonstrating the effectiveness of the proposed system in distinguishing between different postures. The findings suggest that inductive resonant wireless links, especially when implemented with textile components, represent a promising alternative to traditional wearable sensor technologies for posture tracking. The approach offers significant advantages in terms of wearability, power consumption, and simplicity, making it suitable for applications in ergonomics, rehabilitation, occupational health, and smart clothing. Full article

In this study, a scalable surface modification strategy for M2 high-speed steel was applied using multiple-pass friction stir processing (FSP) with overlapping ratios of 25%, 50%, and 75%. A comprehensive investigation of the microstructure, surface hardness, wear, and corrosion resistance was conducted to elucidate the properties of FSPed M2 as a function of the overlapping ratio. In the single-pass FSPed M2, the major phase was martensite and the minor phases included retained austenite where refined carbides (M₆C, M₂₃C₆, and MC) were detected. However, back-tempering occurred near the overlapped zone (OZ) between consecutive tracks for the multiple-pass FSPed M2. The martensite formed in the first pass was turned into tempered martensite by the thermal cycle from the subsequent pass. This back-tempering resulted in a localized decline in hardness from 900 to 650 HV_0.2. Further wear tests revealed that the wear rates of the tempered zone (TZ) of the multiple-pass FSPed M2 (FSP25%: 1.40 × 10⁻⁵ mm³/N·m, FSP50%: 1.20 × 10⁻⁵ mm³/N·m and FSP75%: 1.00 × 10⁻⁵ mm³/N·m) are all higher than that of SZ of the single-pass FSPed M2 (0.75 × 10⁻⁵ mm³/N·m), indicating lower wear resistance of the TZ. Moreover, increased carbide content in the TZ led to the depletion of passivating elements near proximity of the tempered martensite, acting as the active sites for selective corrosion attack. The corrosion potential (E_corr) and corrosion current density (I_corr) increased significantly, with values of −397.6 ± 5.6 mV and 9.5 ± 0.8 μA·cm⁻² for FSP25%, −424.4 ± 6.0 mV and 14.7 ± 1.7 μA·cm⁻² for FSP50%, and −440.9 ± 2.8 mV and 17.1 ± 1.9 μA·cm⁻² for FSP75%. Full article

Motor insurance can use telematics data not only to understand individual driving style but also to implement innovative coaching strategies that feed back to the drivers, through an app, the aggregated information extracted from the data. The purpose is to encourage an improvement in their driving style. A precondition for this improvement is that drivers are digitally engaged, that is, they interact with the app. This paper proposes a narrow understanding of the term engagement, referring to users’ interactions with the app. This interaction is also a behavior producing specific data that can be tracked and used by insurance companies. Based on the empirical investigation of the dataset of a company selling a telematics motor insurance policy, our research investigates if there is a correlation between engagement with the app and improvement of driving style. The analysis distinguishes different groups of users with different driving abilities, and takes into account time differences. Our findings contribute to clarifying the methodological challenges that must be addressed when exploring engagement and coaching effectiveness in proactive insurance policies. We conclude by discussing the possibility and difficulties of tracking and using second-order behavioral data related to policyholder engagement with the app. Full article