Search Results (726)

This study aimed to develop and validate bioelectrical impedance analysis (BIA)−based prediction equations for estimating the in vivo body composition of reproductive rabbit does across different physiological stages. A total of 87 New Zealand × Californian rabbit does were used to generate calibration models, and 25 additional rabbit does served for independent validation. Animals were categorized according to reproductive status (nulliparous, pregnant–lactating, pregnant–non−lactating, non−pregnant–lactating, and non−pregnant–non−lactating). BIA measurements were obtained using a Quantum II analyzer, and chemical composition was determined by proximate analysis. Multiple linear regression models were developed, and equations were validated through relative mean prediction error (RMPE). Significant effects of physiological status were observed on body composition: pregnant–lactating does showed the highest water content, while non−pregnant–non−lactating females exhibited the greatest protein and fat concentrations. Fat and energy contents decreased markedly (−24% and −32%, respectively) during lactation, indicating intense metabolic mobilization. Regression models revealed strong correlations between impedance parameters and chemical composition. Validation confirmed high predictive accuracy (RMPE 15–25%), with crude protein slightly underestimated (3–4%). These findings confirm that BIA provides a reliable, non−destructive alternative to comparative slaughter for assessing body composition in breeding rabbit does throughout the reproductive cycle. Full article

Efficiently predicting the lifespan of lithium iron phosphate (LFP) batteries early in their operational life is critical to accelerating the development of energy storage systems while reducing testing time, cost, and resource consumption. Traditional degradation models rely on full-cycle testing to estimate long-term performance, which is both time- and resource-intensive. This study proposes a novel semi-empirical degradation model that leverages a small fraction of early-cycle data with just 5% to accurately forecast full-lifetime performance with high accuracy, with less than 1.5% mean absolute percentage error. The model integrates fundamental degradation physics with data-driven calibration, using an improved musical chairs algorithm modified with chaotic map dynamics to optimize model parameters efficiently. Trained and validated on a diverse dataset of 27 LFP cells cycled under varying depths of discharge, current rates, and temperatures, the proposed method demonstrates superior convergence speed, robustness across LFP operating conditions, and predictive accuracy compared to traditional approaches. These results provide a scalable framework for rapid battery evaluation and deployment, supporting advances in electric mobility and grid-scale storage. Full article

In advanced manufacturing, accurate and reliable 3D geometry measurement is vital for the quality control of large-sized components with multiple small key local features. To obtain both the geometric form and spatial position of these local features, a hybrid robot-assisted laser scanning strategy is introduced, combining a laser tracker, a fringe-projection 3D scanner, and a mobile robotic unit that integrates an industrial robot with an Automated Guided Vehicle. As for improving the overall measurement accuracy, we propose an accuracy-enhanced calibration method that incorporates both error control and compensation strategies. Firstly, an accurate extrinsic parameter calibration method is proposed, which integrates robust target sphere center estimation with distance-constrained-based optimization of local common point coordinates. Subsequently, to construct a high-accuracy, large-scale spatial measurement field, an improved global calibration method is proposed, incorporating coordinate optimization and a hierarchical strategy for error control. Finally, a robot-assisted laser scanning hybrid measurement system is developed, followed by calibration and validation experiments to verify its performance. Experiments verify its high precision over 14 m (maximum error: 0.117 mm; mean: 0.112 mm) and its strong applicability in large-scale scanning of key geometric features, providing reliable data for quality manufacturing of large-scale components. Full article

Background/Objectives: Medication non-adherence and incomplete seizure documentation remain major challenges in epilepsy care, particularly in drug-resistant forms. Digital health tools may improve self-management by integrating seizure tracking, adherence support, and patient–clinician communication. This narrative review summarizes current mobile applications for seizure monitoring and adherence and outlines opportunities and gaps in clinical translation. Methods: A narrative synthesis (PubMed, Scopus, Google Scholar; 2019–2025; English) summarized functionality, usability, clinical validation, and limitations of epilepsy-focused mobile/wearable applications; no systematic methods or meta-analysis were applied. Results: Existing tools cluster into seizure diary apps, smartwatch-based monitoring systems, and adherence-focused applications. While they improve documentation and treatment regularity, most lack adaptive personalization, language localization and therapeutically active components. Comprehensive platforms combining tracking, adherence analytics and telehealth remain unevenly validated. Validated wearable detectors for generalized tonic–clonic seizures typically report sensitivity in the 80–95% range in real-world or simulated-real-world studies, alongside variable specificity and false-alarm rates, underscoring the need for individualized deployment and calibration. Conclusions: Mobile and wearable applications are promising adjuncts to routine epilepsy care. The field is gradually shifting from passive monitoring toward integrated, user-centered platforms that blend monitoring, predictive analytics and neuromodulation. This review also briefly outlines a conceptual example of an integrated mobile platform that combines seizure documentation, adherence support and patient-initiated rhythmic cueing; this example is presented at a purely exploratory level and requires further clinical validation. Full article

This paper presents a comprehensive methodology to forecast the daily energy demand associated with recharging private electric vehicles in urban areas. The approach is based on plausible scenarios regarding the penetration of battery-powered vehicles and the availability of charging infrastructure. Accurate space and time forecasting of charging activities and power requirements is a critical issue in supporting the transition from conventional to battery-powered vehicles for urban mobility. This technological shift represents a key milestone toward achieving the zero-emissions target set by the European Green Deal for 2050. The methodology leverages Floating Car Data (FCD) samples. The widespread use of On-Board Units (OBUs) in private vehicles for insurance purposes ensures the methodology’s applicability across diverse geographical contexts. In addition to FCD samples, the estimation of charging demand for private electric vehicles is informed by a large-scale, detailed survey conducted by ENEA in Italy in 2023. Funded by the Ministry of Environment and Energy Security as part of the National Research on the Electric System, the survey explored individual charging behaviors during daily urban trips and was designed to calibrate a discrete choice model. To date, the methodology has been applied to the Metropolitan Area of Rome, demonstrating robustness and reliability in its results on two different scenarios of analysis. Each demand/supply scenario has been evaluated in terms of the hourly distribution of peak charging power demand, at the level of individual urban zones or across broader areas. Results highlight the role of the different components of power demand (at home or at other destinations) in both scenarios. Charging at intermediate destinations exhibits a dual peak pattern—one in the early morning hours and another in the afternoon—whereas home-based charging shows a pronounced peak during evening return hours and a secondary peak in the early afternoon, corresponding to a decline in charging activity at other destinations. Power distributions, as expected, sensibly differ from one scenario to the other, conditional to different assumptions of private and public recharge availability and characteristics. Full article

This paper proposes a fruit-harvesting robot system that improves harvesting efficiency by utilizing a Reachability Map (RM) and an Inverse Reachability Map (IRM). The proposed system accurately detects fruit locations using You Only Look Once version 5 (YOLOv5)–based object detection and camera calibration. Through coordinate transformation and hand–eye calibration, the manipulator is precisely guided to the fruit’s 3D position. During the construction of the reachability map, the reachability index, manipulability isotropy, and harvesting index are jointly considered to quantitatively evaluate manipulator performance. Fruits accessible by the manipulator are prioritized for harvesting. For fruits that cannot be directly reached, the system computes the optimal base pose using the inverse reachability map, enabling the mobile manipulator to reposition itself for harvesting. To further enhance efficiency, multiple fruits are grouped to minimize unnecessary movements. The integrated system is implemented on the Robot Operating System 2 (ROS 2), where fruit detection, autonomous navigation, and harvesting are executed as independent nodes to support scalable and modular operation. Finally, the proposed system is validated in a simulated orchard environment, confirming its effectiveness in improving autonomous fruit-harvesting performance. Full article

Background/Objectives: Muscle strain impairs mobility and quality of life, yet ultrasound diagnosis remains dependent on subjective expert interpretation, which can lead to variability in lesion detection. This study aimed to develop and evaluate MS-detector, a symmetry-aware, two-stage deep learning model that leverages bilateral B-mode ultrasound images to automatically detect muscle strain and provide clinicians with a consistent second-reader decision-support tool in routine practice. Methods: A YOLOv5-based detector proposes candidate regions, and a Siamese convolutional neural network (CNN) compares contralateral regions to filter false positives. The dataset comprised 559 bilateral pairs from 86 patients with consensus labels. All splits were enforced at the patient level. A fixed, independent hold-out test set of 32 pairs was never used for training, tuning, or threshold selection. Five-fold cross-validation (CV) on the remaining development set was used for model selection. The operating point was pre-specified at T1 = 0.01 and T2 = 0.20. Results: The detector achieved mAP = 0.4006 (five-fold CV mean). On the hold-out set at the pre-specified operating point, MS-detector attained recall = 0.826 and precision = 0.486, improving F1/F2 over the YOLOv5 baseline by increasing precision with an acceptable recall trade-off. A representative figure illustrates the reduction in low-confidence false positives after filtering; this example is illustrative rather than aggregate. Conclusions: Leveraging contralateral symmetry in a hierarchical scheme improves detection precision while maintaining clinically acceptable recall, supporting MS-detector as a decision-support tool. Future work will evaluate generalizability across scanners and centers and assess calibrated probabilistic fusion and lesion grading. Full article

Background: Nintedanib (NIN), an intracellular inhibitor of tyrosine kinases that inhibits processes fundamental to the progression of pulmonary fibrosis (PPF), is used in the treatment of patients with PPF associated with systemic sclerosis. During NIN therapy, adverse events lead to a permanent dose reduction and treatment discontinuation. Therapeutic drug monitoring (TDM) can be used to manage and optimize drug administration based on the measurement of drug concentrations. Therefore, TDM can be helpful in minimizing the impact of adverse events and help patients remain in therapy. The aim of this study was to develop and validate a new bioanalytical UPLC-MS/MS method enabling the determination of NIN and its active metabolite in the plasma of patients with PPF associated with systemic sclerosis. Methods: Sample preparation was carried out using protein precipitation with an extraction mixture: acetonitrile neutralized with 2 M sodium carbonate. Analytes and the internal standard (intedanib-d3) were monitored using mass spectrometry (MS) and positive-ion-mode electrospray ionization by MRM. Chromatographic analysis was performed on a Zorbax SB-C18 column kept at 40 °C using isocratic elution. The mobile phase contained 0.1% formic acid in water; acetonitrile (35:65 v/v) was pumped at a flow rate of 0.3 mL/min. The analysis time was 5 min. Results: The method was verified according to the EMA guidelines over a concentration range of 2.00–200.00 ng/mL. The correlation coefficients for the calibration curves were found to be 0.9991 and 0.9957 for NIN and its metabolite BIBF 1202, respectively. The within- and between-run precision and accuracy of LLOQ were evaluated for NIN and BIBF 1202 to be within RSD 2.96%, 4.53%, 5.51%, and 6.72% and in the ranges of 102.2–107.3%, 98.0–101.8%, 104.3–114.2%, and 99.1–104.9, respectively. The stability of the analytes in plasma after 4 h at 30 °C was found to be satisfactory, meeting the assumed bias criteria below 15%. Conclusions: The proposed method was successfully applied to analyze two active compounds—NIN and BIBF 1202—in plasma samples at two time points: trough (pre-dose concentration) and 2–3 h (maximum concentration) after the administration of NIN. Full article

The Timed Up and Go (TUG) test is a widely used clinical tool for assessing mobility and fall risk in older adults and individuals with neurological or musculoskeletal conditions. While it provides a quick measure of functional independence, traditional stopwatch-based timing offers only a single completion time and fails to reveal which movement phases contribute to impairment. This study presents a smartphone-based system that automatically segments the TUG test into distinct phases, delivering objective and low-cost biomarkers of lower-limb performance. This approach enables clinicians to identify phase-specific impairments in populations such as individuals with Parkinson’s disease, and older adults, supporting precise diagnosis, personalized rehabilitation, and continuous monitoring of mobility decline and neuroplastic recovery. Our method combines adaptive preprocessing of accelerometer and gyroscope signals with supervised learning models (Random Forest, Support Vector Machine (SVM), and XGBoost) using statistical features to achieve continuous phase detection and maintain robustness against slow or irregular gait, accommodating individual variability. A threshold-based turn detection strategy captures both sharp and gradual rotations. Validation against video ground truth using group K-fold cross-validation demonstrated strong and consistent performance: start and end points were detected in 100% of trials. The mean absolute error for total time was 0.42 s (95% CI: 0.36–0.48 s). The average error across phases (stand, walk, turn) was less than 0.35 s, and macro F₁ scores exceeded 0.85 for all models, with the SVM achieving the highest score of 0.882. Combining accelerometer and gyroscope features improved macro F1 by up to 12%. Statistical tests (McNemar, Bowker) confirmed significant differences between models, and calibration metrics indicated reliable probabilistic outputs (ROC-AUC > 0.96, Brier score < 0.08). These findings show that a single smartphone can deliver accurate, interpretable, and phase-aware TUG analysis without complex multi-sensor setups, enabling practical and scalable mobility assessment for clinical use. Full article

Robotic arm-based antenna measurement systems offer the flexibility needed for advanced antenna measurement and diagnostics techniques but are typically limited by reach and sampling time. This work integrates two complementary contributions to overcome these constraints. First, a composite-plane range extension is introduced for a medium-size robot mounted on a mobile platform and monitored by an optical tracking system (OTS). Independent planar scans are acquired after manual repositioning of the robot and then accurately aligned and blended into a single, larger measurement plane, with positioning errors mitigated through a calibration process. Second, a localized sparse sampling strategy is proposed to accelerate planar near-field (PNF) measurements when only selected angular regions of the radiation pattern are required. The approach relies on reduced-order modeling and singular value decomposition (SVD) analysis to design non-redundant grids that preserve the degrees of freedom relevant to the truncated angular sector, thereby reducing both the number of samples and the scan area. Numerical examples for a general case and experimental validation in X-band demonstrate that the combined methodology extends the effective measurement aperture while significantly shortening acquisition time for narrow or tilted beams, enabling accurate and portable in situ characterization of complex modern antennas by means of cost-effective acquisition systems. Full article

In autonomous driving and mobile robotics applications, constructing accurate and reliable three-dimensional semantic maps poses significant challenges in resolving conflicts and uncertainties among multi-frame observations in complex environments. Traditional deterministic fusion methods struggle to effectively quantify and process uncertainties in observations, while existing evidential deep learning approaches, despite providing uncertainty modeling frameworks, still exhibit notable limitations when dealing with spatially varying observation quality. This paper proposes a selective learnable discounting method for deep evidential semantic mapping that introduces a lightweight selective $\alpha$-Net network based on the EvSemMap framework proposed by Kim and Seo. The network can adaptively detect noisy regions and predict pixel-level discounting coefficients based on input image features. Unlike traditional global discounting strategies, this work employs a theoretically principled scaling discounting formula, ${\widehat{e}}_k\left(x\right)=\alpha \left(x\right)·e_k\left(x\right)$, that conforms to Dempster–Shafer theory, implementing a selective adjustment mechanism that reduces evidence reliability only in noisy regions while preserving original evidence strength in clean regions. Theoretical proofs verify three core properties of the proposed method: evidence discounting under preservation (ensuring no loss of classification accuracy), valid uncertainty redistribution validity (effectively suppressing overconfidence in noisy regions), and optimality of discount coefficients (achieving the matching of the theoretical optimal solution of ${\alpha }^{*}\left(x\right)=1-N\left(X\right)$). Experimental results demonstrate that the method achieves a 43.1% improvement in Expected Calibration Error (ECE) for noisy regions and a 75.4% improvement overall, with $\alpha$-Net attaining an IoU of 1.0 with noise masks on the constructed synthetic dataset—which includes common real-scenario noise types (e.g., motion blur, abnormal illumination, and sensor noise) and where RGB features correlate with observation quality—thereby fully realizing the selective discounting design objective. Combined with additional optimization via temperature calibration techniques, this method provides an effective uncertainty management solution for deep evidential semantic mapping in complex scenarios. Full article

The periodontal ligament (PDL) is essential for the physiological mobility and load distribution of natural teeth, yet its simulation in mechanical testing remains inconsistent and insufficiently standardized. The absence of a resilient suspension system can alter force transmission, affect failure patterns, and reduce the clinical relevance of in vitro outcomes. This study aimed to develop a reproducible CAD/CAM-based model for PDL simulation that provides elastic suspension of a tooth replica under laboratory conditions. A digitally defined offset was applied around a tooth replica to create a controlled PDL space, which was filled with polyether. To ensure precise seating of the specimens, a 3D-printed positioning device was used. Functional calibration was performed using Periotest measurements to identify the offset that reproduced physiological tooth mobility. A digital offset of 0.85 mm produced a radiographically confirmed polyether layer of 0.86 ± 0.05 mm and yielded Periotest values comparable to natural teeth in the horizontal direction (mean PTV = 2.99 ± 0.92). Vertical measurements demonstrated higher damping (mean PTV = −4.02 ± 0.56), consistent with the anisotropic behavior of natural PDL. The model showed high fabrication accuracy and predictable mechanical behavior, providing a physiologically relevant method for incorporating PDL simulation into laboratory mechanical testing. Full article

Low-cost miniaturized gas sensors are increasingly considered for outdoor air quality monitoring, yet their performance under real-world environmental conditions remains insufficiently characterized. This work evaluates the dynamic gas response of the Bosch BME688 sensor, whose metal oxide sensing layer is based on tin dioxide (SnO₂) material, focusing on its sensitivity, selectivity, and dynamic response to four representative air pollutants: nitrogen dioxide (NO₂), carbon monoxide (CO), sulfur dioxide (SO₂), and isobutylene. This study provides both quantitative performance metrics and a physicochemical interpretation of the sensing mechanism. Controlled experiments were conducted in a custom test chamber to facilitate the precise regulation of temperature, humidity, and gas concentrations in the ppm to sub-ppm range. Despite large variability in the baseline resistance across devices, normalization yields consistent behavior, enabling cross-sensor comparability. The results show that the optimum operating temperatures fall in the range of 360–400 °C, where response and recovery times are reduced to a few minutes, compatible with mobile sensing requirements. Moreover, humidity strongly influences sensor behavior: it generally decreases sensitivity but improves kinetics, and in the case of CO, it enables enhanced responses through additional hydroxyl-mediated pathways. These findings confirm the feasibility of deploying BME688 sensors in distributed outdoor monitoring platforms, provided that humidity and temperature effects are properly addressed through calibration or compensation strategies. In addition, the variability observed in baseline resistance highlights the need for normalization and, consequently, individual calibration steps for each sensor under reference conditions in order to ensure cross-sensor comparability. The findings provided in this study provide support for the design of robust, low-cost air monitoring networks. Full article

Educational equity and access to quality learning opportunities represent fundamental pillars of sustainable societal development, directly aligned with the United Nations Sustainable Development Goal 4 (Quality Education). Student retention remains a critical challenge in higher education, with early disengagement strongly predicting eventual failure and limiting opportunities for social mobility. While machine learning models have demonstrated impressive predictive accuracy for identifying at-risk students, most systems prioritize performance metrics over practical deployment constraints, creating a gap between research demonstrations and real-world impact for social good. We present an accountable and interpretable decision support system that balances three competing objectives essential for responsible AI deployment: ultra-early prediction timing (day 14 of semester), manageable instructor workload (flagging 15% of students), and model transparency (multiple explanation mechanisms). Using the Open University Learning Analytics Dataset (OULAD) containing 22,437 students across seven modules, we develop predictive models from activity patterns, assessment performance, and demographics observable within two weeks. We compare threshold-based rules, logistic regression (interpretable linear modeling), and gradient boosting (ensemble modeling) using temporal validation where early course presentations train models tested on later cohorts. Results show gradient boosting achieves AUC (Area Under the ROC Curve, measuring discrimination ability) of 0.789 and average precision of 0.722, with logistic regression performing nearly identically (AUC 0.783, AP 0.713), revealing that linear modeling captures most predictive signal and makes interpretability essentially free. At our recommended threshold of 0.607, the predictive model flags 15% of students with 84% precision and 35% recall, creating actionable alert lists instructors can manage within normal teaching duties while maintaining accountability for false positives. Calibration analysis confirms that predicted probabilities match observed failure rates, ensuring trustworthy risk estimates. Feature importance modeling reveals that assessment completion and activity patterns dominate demographic factors, providing transparent evidence that behavioral engagement matters more than student background. We implement a complete decision support system generating instructor reports, explainable natural language justifications for each alert, and personalized intervention templates. Our contribution advances responsible AI for social good by demonstrating that interpretable predictive modeling can support equitable educational outcomes when designed with explicit attention to timing, workload, and transparency—core principles of accountable artificial intelligence. Full article

Safe and efficient building evacuation for heterogeneous populations, particularly individuals with disabilities, remains a critical challenge in emergency management. This study proposes a hybrid evacuation framework that integrates Floor Field Cellular Automaton (FFCA) with reinforcement learning, specifically a Deep Q-Network (DQN), to enhance adaptive decision-making in dynamic and complex environments. The model incorporates velocity heterogeneity, friction-based conflict resolution, and real-time path planning to capture diverse mobility capabilities and interactions among evacuees. Simulation experiments were conducted under varying population densities, walking speeds, and exit configurations, considering four types of occupant groups: able-bodied individuals, wheelchair users, and people with visual or hearing impairments. The results demonstrate that the DQN-enhanced model consistently outperforms the conventional SFF + DFF approach, achieving significant reductions in evacuation time, particularly under high-density and reduced-speed scenarios. Notably, the DQN dynamically adapts evacuation paths to mitigate congestion, thereby improving both system efficiency and the safety of vulnerable groups. These findings highlight the potential of combining CA-based environmental modeling with reinforcement learning to develop adaptive and inclusive evacuation strategies. The proposed framework provides practical insights for designing evacuation protocols and intelligent navigation systems in public buildings. Future work will extend the proposed FFCA + DQN framework to more complex and realistic environments, including multi-exit and multi-level buildings, and further integrate multi-agent reinforcement learning (MARL) architectures to enable decentralized adaptation among heterogeneous evacuees. Furthermore, lightweight DQN variants and distributed training schemes will be explored to enhance computational scalability, while empirical data from evacuation drills and real-world case studies will be used for model calibration and validation, thereby improving predictive accuracy and generalizability. Full article