Search Results (1,935)

Background and Objectives: Osteoarthritis (OA) is a chronic, degenerative joint disease. The main symptoms include pain that can cause loss of function and stiffness, as well as swelling, reduced range of motion, crepitus, joint deformity, and muscle weakness. It leads to irreversible structural changes, that in advanced stages can require surgical interventions. The aim of this review was to summarize the current literature about the role of virtual reality (VR), exergames and digital technologies in patients with knee osteoarthritis before or after total knee arthroplasty, to understand if it is possible to prevent and reduce the symptoms and if these new technologies are more effective than conventional rehabilitation therapies. Materials and Methods: We conducted a systematic search of PubMed, Cochrane Library, Scopus, and PEDro from inception to November 2024. The review adhered to the PRISMA 2020 guidelines, and the protocol was prospectively registered in PROSPERO (registration number: CRD42024541890). We included randomized controlled trials (RCTs) enrolling participants aged 60 years or older, in which VR or telerehabilitation programs were compared with conventional rehabilitation approaches. Eligible studies had to report at least one of the following outcomes: pain, functionality, stability, or adherence. Two independent reviewers screened titles and abstracts, assessed full-text eligibility, extracted data, and evaluated the risk of bias using the Cochrane Risk of Bias 2 (RoB 2) tool. Results: Fourteen randomized controlled trails (RCTs) (1123 participants; mean age 68.2 years) were included. VR and telerehabilitation generally outperformed conventional rehabilitation for pain (8/13 studies, −0.9 to −2.3 VAS points) and functionality (7/13 studies, WOMAC improvement 8–15%, TUG −1.2 to −2.8 s). Compliance was higher in most technology-assisted programs (6/7 studies, 70–100% adherence). Stability outcomes were less consistent, with only 1/4 studies showing clear benefit. One study favored conventional rehabilitation for functionality. Overall risk of bias was low-to-moderate, with heterogeneity mainly driven by intervention duration, platform type, and supervision level. Conclusions: Structured telerehabilitation, non-immersive VR, and interactive online exercise programs, especially those offering real-time feedback, show comparable or superior benefits to conventional rehabilitation in older adults with knee OA or after TKA, particularly for pain reduction, functional gains, and adherence. These approaches enhance accessibility and home-based care, supporting their integration into clinical practice when in-person therapy is limited. Full article

The increasing integration of renewable energy sources and the widespread adoption of electric vehicles have introduced considerable uncertainty into the operation of large-scale power systems. Traditional deterministic unit commitment models are insufficient for managing such variability in a reliable and cost-effective manner. This study proposes a two-stage stochastic unit commitment model that captures uncertainties in solar photovoltaic generation, electric vehicle charging demand, and load fluctuations using a mixed-integer linear programming framework with recourse. The model is applied to Thailand’s national power system, comprising 171 generators across five regions, to assess its scalability for sustainable large-scale planning. Results indicate that the stochastic model significantly enhances system reliability across most demand profiles. Under the Winter Weekday group, the number of lacking scenarios decreases by 76.92 percent and the number of missing periods decreases by 78.57 percent, while the average and maximum lack percentages are reduced by 56.32 percent and 72.61 percent, respectively. Improvements are even greater under the Rainy Weekday group, where lacking scenarios and periods decline by more than 92 percent and the maximum lack percentage falls by over 98 percent, demonstrating the model’s robustness under volatile solar output and load conditions. Although minor anomalies are observed, such as slight increases in average and maximum lack percentages in the Summer Weekday group, these are minimal and likely attributable to randomness in scenario generation or boundary effects in optimization. Overall, the stochastic model provides substantial advantages in managing uncertainty, achieving notable improvements in reliability with only modest increases in operational cost and computational time. The findings confirm that the proposed approach offers a robust and practical framework for supporting sustainable and resilient power systems in regions with high variability in both generation and demand. Full article

We present a novel encoder-only Transformer model for symbolic music harmony generation, based on a fixed time-grid representation of melody and harmony. Inspired by denoising diffusion processes, our model progressively unmasks harmony tokens over a sequence of discrete stages, learning to reconstruct the full harmonic structure from partial context. Unlike autoregressive models, this formulation enables flexible, non-sequential generation and supports explicit control over harmony placement. The model is stage-aware, receiving timestep embeddings analogous to diffusion timesteps, and is conditioned on both a binary piano roll and a pitch class roll to capture melodic context. We explore two unmasking schedules—random token revealing and midpoint doubling—both requiring a fixed and significantly reduced number of model calls at inference time. While our approach achieves competitive performance with strong autoregressive baselines (GPT-2 and BART) across several harmonic metrics, its key advantages lie in controllability, structured decoding with fixed inference steps, and alignment with musical structure. Ablation studies further highlight the role of stage awareness and pitch class conditioning. Our results position this method as a viable and interpretable alternative for symbolic harmony generation and a foundation for future work on structured, controllable musical modeling. Full article

In the paper, the inverse solution of the heat conduction problem is analysed, which is applied to calculate transient thermal stresses on the internal surface of a thick-walled pipe weakened by a hole. The analysis considered a one-dimensional heat transfer problem when heat is transferred in a radial direction. In the inverse marching method, the measurement of the wall temperature at one point of a thermally insulated pipeline is used. The technique was verified regarding the distance between the point where the wall temperature is measured and the internal surface, the number of finite volumes in the inverse region, and the time step size are selected. The influence of these parameters on the accuracy of the calculated temperature, thermal stresses, heat transfer coefficient on the internal surface of the pipeline and thermal stresses at the hole edge was assessed. The reference values used to verify the technique were those calculated using the analytical method and the direct solution of the heat conduction problem, and the generated ‘measurement data’ were disturbed by random errors. Full article

Underwater vessel localization using passive magnetic anomaly sensing is a challenging problem due to the variability in vessel magnetic signatures and operational conditions. Data-based approaches may fail to generalize even to slightly different conditions. Thus, we propose an Attentive Neural Process (ANP) approach, in order to take advantage of its few-shot capabilities to generalize, for robust localization of underwater vessels based on magnetic anomaly measurements. Our ANP models the mapping from multi-sensor magnetic readings to position as a stochastic function: it cross-attends to a variable-size set of context points and fuses these with a global latent code that captures trajectory-level factors. The decoder outputs a Gaussian over coordinates, providing both point estimates and well-calibrated predictive variance. We validate our approach using a comprehensive dataset of magnetic disturbance fields, covering 64 distinct vessel configurations (combinations of varying hull sizes, submersion depths (water-column height over a seabed array), and total numbers of available sensors). Six magnetometer sensors in a fixed circular arrangement record the magnetic field perturbations as a vessel traverses sinusoidal trajectories. We compare the ANP against baseline multilayer perceptron (MLP) models: (1) base MLPs trained separately on each vessel configuration, and (2) a domain-randomized search (DRS) MLP trained on the aggregate of all configurations to evaluate generalization across domains. The results demonstrate that the ANP achieves superior generalization to new vessel conditions, matching the accuracy of configuration-specific MLPs while providing well-calibrated uncertainty quantification. This uncertainty-aware prediction capability is crucial for real-world deployments, as it can inform adaptive sensing and decision-making. Across various in-distribution scenarios, the ANP halves the mean absolute error versus a domain-randomized MLP (0.43 m vs. 0.84 m). The model is even able to generalize to out-of-distribution data, which means that our approach has the potential to facilitate transferability from offline training to real-world conditions. Full article

The increasing demand for decentralized and user-controlled cryptographic key management in blockchain ecosystems has created interest in alternative entropy sources that do not rely on dedicated hardware. This study investigates whether commercial smartwatches can generate sufficient entropy for secure local key generation by utilizing their onboard sensors. An open-source Wear OS application was developed to harvest sensor data in two acquisition modes: still mode, where the device remains stationary, and shake mode, where data collection is triggered by motion events exceeding a predefined acceleration threshold. A total of 4800 still-mode and 4800 shake-mode samples were collected, each producing 11,400 bits of sensor-generated data. Entropy was evaluated using statistical metrics commonly employed in entropy analysis, including Shannon entropy, min-entropy, Markov dependency analysis, and compression-based redundancy estimation. The shake mode achieved Shannon entropy of 0.997 and min-entropy of 0.918, outperforming the still mode (0.991 and 0.851, respectively) and approaching the entropy levels of software-based random number generators. These results demonstrate that smartwatches can act as practical entropy sources for cryptographic applications, provided that appropriate post-processing, such as cryptographic hashing, is applied. The method offers a low-cost, transparent, and user-friendly alternative to specialized hardware wallets, aligning with the principles of decentralization and self-sovereign identity. Full article

The CPace protocol (Internet-Draft:draft-irtf-cfrg-cpace-14) is a password-authenticated key exchange optimized for simplicity. In particular, it involves only two messages exchanged in an arbitrary order. CPace combines a simple and elegant design with privacy guarantees obtained via strict mathematical proofs. In this paper, we go further and analyze its resilience against malicious cryptography implementations. While the clever design of CPace immediately eliminates many kleptographic techniques applicable to many other protocols of this kind, we point to the remaining risks related to kleptographic setups. We show that such attacks can break the security and privacy features of CPace. Thereby, we point to the necessity of very careful certification of the devices running CPace, focusing in particular on critical threats related to random number generators. Full article

The highly complex memristive chaotic map provides an excellent alternative for engineering applications. To design a memristive chaotic map with high complexity, this paper proposes a new three-dimensional memristive chaotic map (named MLM) by cascading and coupling a non-locally active memristor with a locally active memristor. The dynamical behaviors of MLM are revealed through phase diagrams, Lyapunov exponent spectra, bifurcation diagrams, and dynamic distribution diagrams. Notably, the internal frequency of MLM exhibits unique LE-controlled behavior and shows an extension of the chaotic parameter range. The high complexity of MLM is validated through the use of Spectral entropy (SE) and C0, and Permutation Entropy (PE) complexity algorithms. Subsequently, a pseudorandom number generator (PRNG) based on MLM is designed. NIST test results validate the high randomness of the PRNG. Finally, the STM32 hardware platform is used to implement MLM, and attractors under different parameters are measured by an oscilloscope, verifying the numerical analysis results. Full article

To realize a vacuum-fluctuation-based quantum random number generator (QRNG), various implementations can be explored to improve efficiency and practicality. In this study, we employed a multi-mode (MM) laser as the local oscillator in a vacuum-fluctuation QRNG and compared its performance with that of a conventional single-mode (SM) laser. Despite experiencing frequency-mode hopping, the MM laser successfully interfered with the vacuum state, similar to the SM reference. The common-mode rejection ratio of the balanced homodyne detection setup exceeded 35 dB for all laser sources. The digitized raw data were processed with a cryptographic hash function to generate full-entropy data. These outputs passed both the independent and identically distributed test recommended in NIST SP 800-90B and the statistical test suite under the SP 800-22 guideline, confirming their quality as quantum random numbers. Our results demonstrate that full-entropy data derived from either SM or MM lasers are applicable to systems requiring high-quality randomness, such as quantum key distribution. This study represents the first demonstration of an MM-laser-based vacuum-fluctuation QRNG, achieving a generation rate of 10 Gbps and indicating potential for compact and practical implementation. Full article

Background/Objective: The use of immersive virtual reality (VR) for cognitive training in older adults has shown promising results in recent years. However, the number of well-designed studies remains limited, and variability in methodologies makes it difficult to draw generalizable conclusions. This systematic review aims to examine the effects of VR-based cognitive training in older adults, describe the technological characteristics of these interventions, identify current gaps in the literature, and suggest future research directions. Methods: Following PRISMA guidelines, a search was conducted across major databases (PubMed, PsycINFO, Scopus, ProQuest, ACM, and Web of Science) from 2018 to 2025. The database search identified 156 studies, of which 12 met the inclusion criteria after screening and eligibility assessment. Across these studies, a total of 3202 older adult participants (aged 60 years or older) were included. Interventions varied in duration from 4 to 36 sessions, targeting domains such as memory, executive function, attention, and global cognition. Most interventions were based on cognitive training, with a few employing cognitive stimulation or cognitive rehabilitation approaches. Quality was assessed using the Effective Public Health Practice Project tool. Results: Most studies reported positive effects of VR interventions on cognitive domains such as attention, executive functions, and global cognition. Fewer studies showed improvements in memory. The majority used head-mounted displays connected to computers and custom-built software, often without public access. Sample sizes were generally small, and blinding procedures were often unclear. The average methodological quality was moderate. Conclusions: Immersive VR has potential as an effective tool for cognitive training in older adults. Future research should include larger randomized controlled trials, long-term follow-up, standardized intervention protocols, and the development of accessible software to enable replication and broader application in clinical and community settings. Full article

Sugarcane requires high doses of phosphorus to achieve high productivity. However, not all the phosphorus applied to crops is utilized. Therefore, it is believed that some remaining phosphorus can meet the nutrient demand of the ratoon crop. The objective of this study was to evaluate the effects of mineral fertilization with triple superphosphate (TSP) and organic fertilization with poultry litter (PL), applied to plant cane and the first ratoon, on the quality of second ratoon sugarcane. The experimental design was a randomized complete block design with a 5 × 5 factorial scheme with four replications. The treatments consisted of five TSP doses (0, 60, 120, 180, and 240 kg ha⁻¹) and five PL doses (0, 2, 4, 6, and 8 t ha⁻¹). Fertilization with TSP and PL applied in the two preceding cycles promoted an increase in plant height, stalk diameter, number of tillers, and productivity in the second ratoon. The doses of triple superphosphate and chicken litter applied in cycles preceding the second ratoon were able to increase the agronomic performance of the genotype IACSP95-5094. However, the highest subsequent combined doses of triple superphosphate and chicken litter resulted in a 27% increase in stalk productivity. In general, the preceding doses of chicken litter showed greater potential to enhance the technological attributes. Full article

Boolean chaotic systems solely composed of logic devices have been successfully applied in fields such as random number generation, reservoir computing, and radar detection because of their simple structure and amenability to integration. However, noise in a circuit makes Boolean chaotic systems less robust, which means noise transforms the outputs from chaotic to periodic. In this paper, the characteristics of the process through which logic devices respond to input signals are called device response characteristics. A device’s response characteristic parameters can adjust its response speed and the results it yields to the same input signal. The relationship between logical device response characteristic parameters and the time delay parameter was studied. The results indicate that the distribution range and continuity of chaos in the time delay parameter space can be enhanced by reducing the logical device response characteristic parameters, thereby improving the robustness of a Boolean chaotic system. This research is significant for the hardware design of Boolean chaotic system, as it details the selection of appropriate devices for enhancing chaotic time delay parameter space and robustness. Full article

Road traffic fatalities remain a significant global issue, despite a gradual decline in recent years. Although the number of accidents has decreased—partly due to reduced mobility during the pandemic—the figures remain alarmingly high. To further reduce these numbers, it is crucial to identify regions with the highest accident rates and predict future trends. This study aims to forecast traffic accident occurrences across Poland’s provinces. Using official police data on annual accident statistics, we analyzed historical trends and applied predictive modeling in Statistica to estimate accident rates from 2022 to 2040. Several neural network models were employed to generate these projections. The findings indicate that a significant reduction in road accidents is unlikely in the near future, with rates expected to stabilize rather than decline. The accuracy of predictions was influenced by the random sampling distribution used in model training. Specifically, a 70-15-15 split (70% training, 15% testing, and 15% validation) yielded an average error of 1.75%, and an 80-10-10 split reduced the error to 0.63%, demonstrating the impact of sample allocation on predictive performance. These results highlight the importance of dataset partitioning in accident forecasting models. Full article

Heat stress is one of the main welfare and productivity problems faced by dairy cattle in Mediterranean climates. The main objective of this work is to predict heat stress in livestock from shade-seeking behavior captured by computer vision, combined with some climatic features, in a completely non-invasive way. To this end, we evaluate two soft computing algorithms—Random Forests and Neural Networks—clarifying the trade-off between accuracy and interpretability for real-world farm deployment. Data were gathered at a commercial dairy farm in Titaguas (Valencia, Spain) using overhead cameras that counted cows in the shade every 5–10 min during summer 2023. Each record contains the shaded-cow count, ambient temperature, relative humidity, and an exact timestamp. From here, three thermal indices were derived: the current THI, the previous-night mean THI, and the day-time accumulated THI. The resulting dataset covers 75 days and 6907 day-time observations. To evaluate the models’ performance a 5-fold cross-validation is also used. The results show that both soft computing models outperform a single Decision Tree baseline. The best Neural Network (3 hidden layers, 16 neurons each, learning rate $={10}^{-3}$) reaches an average RMSE of $14.78$, while a Random Forest (10 trees, depth $=5$) achieves $14.97$ and offers the best interpretability. Daily error distributions reveal a median RMSE of $13.84$ and confirm that predictions deviate less than one hour from observed shade-seeking peaks. Although the dataset came from a single farm, the results generalized well within the observed range. However, the models could not accurately predict the exact number of cows in the shade. This suggests the influence of other variables not included in the analysis (such as solar radiation or wind data), which opens the door for future research. Full article

Bypass diode faults are among the most hard-to-detect but impactful anomalies in photovoltaic (PV) systems, especially under partial shading conditions, where their electrical signatures often resemble those caused by non-critical irradiance variations. This study presents a systematic simulation-based investigation into how different bypass diode fault types—short-circuited, open-circuited, and healthy—affect the electrical behavior of PV strings under diverse irradiance profiles. A high-resolution MATLAB/Simulink model is developed to simulate 27 unique diode fault configurations across multiple shading scenarios, enabling the extraction of key features from resulting I–V curves. These features include global and local maximum power point parameters, open-circuit voltage, and short-circuit current. To address the challenge of feature redundancy and classification ambiguity, a preprocessing step is applied to remove near-duplicate instances and improve model generalization. An artificial neural network (ANN) model is then trained to classify the number of faulty bypass diodes based on these features. Comparative evaluations are conducted with support vector machines and random forests. The results indicate that the ANN achieves the highest test accuracy (93.57%) and average AUC (0.9925), outperforming other classifiers in both robustness and discriminative power. These findings highlight the importance of feature-informed, data-driven approaches for fault detection in PV systems and demonstrate the feasibility of diode fault classification without precise fault localization. Full article