Search Results (2,692)

Piano fingering estimation remains a complex problem due to the combinatorial nature of hand movements and no best solution for any situation. A recent model-free reinforcement learning framework for piano fingering modeled each monophonic piece as an environment and demonstrated that value-based methods outperform probability-based approaches. Building on their finding, this paper addresses the more complex polyphonic fingering problem by formulating it as an online model-free reinforcement learning task with a novel training strategy. Thus, we introduce a novel Elite Episode Replay (EER) method to improve learning efficiency by prioritizing high-quality episodes during training. This strategy accelerates early reward acquisition and improves convergence without sacrificing fingering quality. The proposed architecture produces multiple-action outputs for polyphonic settings and is trained using both elite-guided and uniform sampling. Experimental results show that the EER strategy reduces training time per step by 21% and speeds up convergence by 18% while preserving the difficulty level and result of the generated fingerings. An empirical study of elite memory size further highlights its impact on training performance in solving piano fingering estimation. Full article

Background and Objectives: Handgrip strength represents a critical indicator of physical fitness and nutritional status in adolescents, yet population-specific reference values remain limited in developing countries. Understanding age- and sex-specific variations is crucial for accurate clinical assessment and effective health monitoring. The objective of this study was to establish comprehensive reference values for handgrip strength in healthy Tunisian adolescents aged 13–19 years and examine sex and age group differences in these measures. Materials and Methods: This cross-sectional study was conducted between September 2024 and June 2025, involving a sample of 950 participants (482 males, 468 females) aged 13–19 years from northwest Tunisia. Handgrip strength was measured using standardized dynamometry protocols for both hands. Anthropometric measurements included height, weight, and body mass index. Percentile curves were generated using the LMS method, and correlations between handgrip strength and anthropometric variables were analyzed using Pearson correlation coefficients. Results: Males demonstrated significantly higher handgrip strength than females from age 13 onward (13 years: p = 0.021; 14–19 years: p ≤ 0.001). Effect sizes for sex differences were consistently large across age groups (Cohen’s d range: 0.53–2.09 for the dominant hand). Mean dominant handgrip strength ranged from 25.60 ± 7.73 kg to 47.60 ± 12.45 kg in males and 21.90 ± 6.13 kg to 28.40 ± 4.74 kg in females across age groups. After adjusting for body mass, sex differences remained significant between groups (13 years: p = 0.014; d= 1.5; 14–19 years: p ≤ 0.001; d: 1.71–3.12). Strong positive correlations emerged between handgrip strength and height (males: r = 0.748, females: r = 0.601), body mass (males: r = 0.659, females: r = 0.601), and body mass index (BMI) (males: r = 0.391, females: r = 0.461). Body mass and height emerged as the strongest predictors of handgrip strength in both sexes, while BMI showed a smaller but still significant contribution. Conclusions: This study provides the first comprehensive age- and sex-specific reference values for handgrip strength in Tunisian adolescents. Healthcare providers can utilize these percentile charts for the clinical assessment and identification of musculoskeletal fitness deficits. The results suggest its use in educational and clinical contexts. Full article

Energy harvesting systems (EHSs) are widely used to power wireless sensors. Piezoelectric harvesters have the advantage of producing an electric signal directly related to the exciting force and can thus be used to power condition monitoring sensors in dynamically loaded structures such as bridges. The need for such monitoring is exemplified by the fact that the condition of close to 25% of public roadway bridges in, e.g., Germany is not satisfactory. Stack-based piezoelectric energy harvesting systems (pEHSs) installed near bridge bearings could provide information about the traffic and dynamic loads on the one hand and condition-dependent changes in the bridge characteristics on the other. This paper presents an approach to co-optimizing the design of the mechanical and electrical components using a nonlinear solver. Such an approach has not been described in the open literature to the best of the authors’ knowledge. The mechanical excitation is estimated through a finite element simulation, and the electric circuitry is modeled in Simulink to account for the nonlinear characteristics of rectifying diodes. We use real traffic data to create statistical randomized scenarios for the optimization and statistical variation. A main result of this work is that it reveals the strong dependence of the energy output on the interaction between bridge, harvester, and traffic details. A second result is that the methodology yields design criteria for the harvester such that the energy output is maximized. Through the case study of an actual middle-sized bridge in Germany, we demonstrate the feasibility of harvesting a time-averaged power of several milliwatts throughout the day. Comparing the total amount of harvested energy for 1000 randomized traffic scenarios, we demonstrate the suitability of pEHS to power wireless sensor nodes. In addition, we show the potential sensory usability for traffic observation (vehicle frequency, vehicle weight, axle load, etc.). Full article

Cancer has remained one of the leading causes of death worldwide throughout history despite significant advancements in drug development, radiation therapy, and surgery. Traditional chemotherapeutic small molecules are often hindered by narrow therapeutic indices and limited specificity, leading to suboptimal clinical outcomes. On the other hand, more advanced approaches, such as antibody–drug conjugates (ADCs), frequently encounter obstacles, including poor tumor penetration and prohibitive production costs. The tumor-forming and metastatic capacity of cancer further challenges currently available cancer therapies by creating a biochemical milieu known as the tumor microenvironment (TME). Although solid tumor development presents significant obstacles, it also opens new avenues for innovative therapeutic approaches. It is well-documented that as tumors grow beyond 1–2 mm³ in size, they undergo profound changes in their microenvironment, including alterations in oxygen levels, pH, enzymatic activity, surface antigen expression, and the cellular composition of the stroma. These changes create unique opportunities that can be exploited to develop novel and innovative therapeutics. Currently, numerous ADCs, small-molecule–drug conjugates (SMDCs), and prodrugs are being developed to target specific aspects of these microenvironmental changes. In this review, we explore five TME parameters in detail, with a focus on their relevance to specific cancer types, phenotypic identifiers, and preferred methods of therapeutic targeting. Additionally, we examine the chemical moieties available to target these changes, providing a framework for design strategies that exploit the dynamics of the tumor microenvironment. Full article

Large-sized components with numerous small key local features are essential in advanced manufacturing. Achieving high-precision quality control necessitates accurate and highly efficient three-dimensional (3D) measurement techniques. A flexible measurement system integrating a fringe-projection-based 3D scanner with an industrial robot is developed to enable the rapid measurement of large object surfaces. To enhance overall measurement accuracy, we propose an enhanced calibration method utilizing a multidimensional ball-based calibrator to simultaneously calibrate for hand-eye transformation and robot kinematic parameters. Firstly, a preliminary hand-eye calibration method is introduced to compensate for measurement errors at observation points, leveraging geometric-constraint-based optimization and a virtual single point derived via the barycentric calculation method. Subsequently, a distance-constrained calibration method is proposed to jointly estimate the hand-eye transformation and robot kinematic parameters, wherein a distance error model is constructed to link parameter errors with the measured deviations of a virtual single point. Finally, calibration and validation experiments were carried out, and the results indicate that the maximum and average measurement errors were reduced from 1.053 mm and 0.814 mm to 0.421 mm and 0.373 mm, respectively, thereby confirming the effectiveness of the proposed method. Full article

Compared to traditional dense asphalt concrete mixtures, stone mastic asphalt (SMA) generally offers superior performance in terms of its mechanical resistance and extended pavement lifespan. Focusing on the Norwegian scenario, this laboratory-based study investigated the durability of SMA considering the influence of the aggregate shape and petrography. The rock aggregates were classified according to three different-shaped refinement stages involving vertical shaft impact crushing. Further, the aggregates were sourced from three distinct locations (Jelsa, Tau and Dirdal) characterized by different petrographic origins: granodiorite, quartz diorite and granite, respectively. Two mixtures with maximum aggregate sizes of 16 mm (SMA 16) and 11 mm (SMA 11) were designed according to Norwegian standards and investigated in terms of their durability performance. In this regard, two main functional tests were performed for the asphalt mixture, namely resistance against permanent deformation and abrasion by studded tyres, and one for the asphalt mortar, namely water sensitivity. Overall, the best test results were related to the aggregates sourced from Jelsa and Tau, thus highlighting that the geological origin exerts a major impact on SMA’s durability performance. On the other hand, the different aggregate shapes related to the crushing refinement treatments seem to play an effective but secondary role. Full article

In sorption cooling systems, an important stage of the thermodynamic cycle is the separation of the refrigerant fluid from the absorbent mixture. This process is called “regeneration” or “desorption,” and it is similar to thermal desalination, where water is separated from an aqueous saline solution. However, since sorption systems utilize high salt concentration solutions, conventional desalination techniques such as reverse osmosis are not suitable. In this regard, membrane devices can enhance heat and mass transfer processes in compact sizes. In the present paper, a membrane device with an air gap membrane distillation configuration was evaluated, operating with the H₂O/LiBr + LiCl solution (with a mass ratio of 2:1, LiBr:LiCl), to assess the produced distilled water flux. Among the operating parameters analyzed (solution temperature, cooling water temperature, salt concentration, and membrane pore size), solution temperature had the highest impact on the distilled water flux, while the membrane pore size had the lowest impact. The maximum distilled water flux was 7.63 kg/h·m² with a solution temperature of 95.3 °C, a cooling water temperature of 25.1 °C, a salt concentration of 44.99% w/w, and a membrane pore size of 0.45 μm. On the other hand, the minimum distilled water flux was 0.28 kg/h·m² with a solution temperature of 80.3 °C, a cooling water temperature of 40.1 °C, a salt concentration of 50.05% w/w, and with a membrane pore size of 0.22 μm. Full article

As populations age and workforces decline, the need for accessible health assessment methods grows. The merging of accessible and affordable sensors such as inertial measurement units (IMUs) and advanced machine learning techniques now enables gait assessment beyond traditional laboratory settings. A total of 52 participants walked at three speeds while carrying a smartphone-sized IMU in natural positions (hand, trouser pocket, or jacket pocket). A previously trained Convolutional Neural Network and Long Short-Term Memory (CNN-LSTM)-based machine learning model predicted gait events, which were then used to calculate stride time, stance time, swing time, and double support time. Stride time predictions were highly accurate (<5% error), while stance and swing times exhibited moderate variability and double support time showed the highest errors (>20%). Despite these variations, moderate-to-strong correlations between the predicted and experimental spatiotemporal gait parameters suggest the feasibility of IMU-based gait tracking in real-world settings. These associations preserved inter-subject patterns that are relevant for detecting gait disorders. Our study demonstrated the feasibility of extracting clinically relevant gait parameters using IMU data mimicking smartphone use, especially parameters with longer durations such as stride time. Robustness across sensor locations and walking speeds supports deep learning on single-IMU data as a viable tool for remote gait monitoring. Full article

Background and Objectives: Three-dimensional (3D) scanning is increasingly utilized in medical practice, from orthotics to surgical planning. However, traditional hand measurement techniques remain inconsistent and prone to human error and are often time-consuming. This research evaluates the practicality of a commercial 3D scanning method by comparing the accuracy of scans conducted by two user groups. Materials and Methods: This study evaluated the following two groups: an experimental group (n = 45) and a control group (n = 42). A total of 261 hand scans were captured using the Structure Sensor Pro 3D scanner for iPad (Structure, Boulder, CO, USA). The scans were then evaluated using Meshmixer software (version 3.5.474), analyzing key parameters, such as surface area, volume, number of vertices, and triangles, etc. Furthermore, a digital literacy test and a user experience survey were conducted to support a more comprehensive evaluation of participant performance within the study. Results: The experimental group outperformed the control group on all measured parameters, including surface area, volume, vertices, triangle, and gap count, with large effect sizes observed. User experience data revealed that participants in the experimental group rated the 3D scanner significantly higher across all dimensions, particularly in ease of use, excitement, supportiveness, and practicality. Conclusions: A short 15 min training session can promote scan reliability, demonstrating that even minimal instruction improves users’ proficiency in 3D scanning, fundamental for supporting clinical accuracy in diagnosis, surgical planning, and personalized device manufacturing Full article

Background/Objectives: This prospective intervention study examined the learning effect of using 3D-printed phantom heads with cleft lip and palate (CLP) and upper jaw models with CLP and maxillary plates during a lecture for dental students in their fourth year at J. W. Goethe Frankfurt University. The primary aim was to evaluate the impact of 3D-printed models on students’ satisfaction levels along with their understanding and knowledge in dental education. Methods: Six life-sized phantom heads with removable mandibles (three with unilateral and three with bilateral CLP) were designed using ZBrush software (Pixologic Inc., Los Angeles, CA, USA) based on MRI images and printed with an Asiga Pro 4K 3D printer (Asiga, Sydney, Australia). Two groups of students (n = 81) participated in this study: the control (CTR) group (n = 39) attended a standard lecture on cleft lip and palate, while the intervention (INT) group (n = 42) participated in a hands-on seminar with the same theoretical content, supplemented by 3D-printed models. Before and after the session, students completed self-assessment questionnaires and a multiple-choice test to evaluate knowledge improvement. Data analysis was conducted using the chi-square test for individual questions and the Wilcoxon rank test for knowledge gain, with the significance level set at 0.05. Results: The study demonstrated a significant knowledge increase in both groups following the lecture (p < 0.001). Similarly, there were significant differences in students’ self-assessments before and after the session (p < 0.001). The knowledge gain in the INT group regarding the anatomical features of unilateral cleft lip and palate was significantly higher compared to that in the CTR group (p < 0.05). Conclusions: The results of this study demonstrate the measurable added value of using 3D-printed models in dental education, particularly in enhancing students’ understanding of the anatomy of cleft lip and palate. Full article

Electromagnetic articulography is a technique developed for recording three-dimensional movements. It is based on magnetic induction, where small currents are induced in miniature receiver coils acting as motion sensors by means of electromagnetic fields generated by transmitter coils. This technology has been applied in dental research to record mandibular movements during mastication, Posselt’s envelope of motion, and micromovements of dental prostheses. The AG501 electromagnetic articulograph (Carstens Medizinelektronik GmbH, Bovenden, Germany) provides a Head Correction (HC) procedure to eliminate head movement, which requires the reference sensors to be firmly attached to the subject’s head. If the sensors shift during the recordings, it becomes necessary to reposition them and repeat the head correction procedure. The aim of this study was to develop a 3D-printed helmet to securely fix the reference sensors to the head of a subject in the context of performing a series of recordings involving the mastication of 36 foods and the execution of Posselt’s envelope of motion. The number of HCs required was recorded for a group using the helmet and for a control group in which the sensors were attached to the subject’s head using tissue adhesive. A total of 29 recordings were conducted with and without the helmet. Without the helmet 44 HCs were required; on the other hand, with the helmet 36 HCs were required. On average, 1.5 HCs were required per session without the helmet and 1.2 HCs with the helmet, showing a non-significant difference (p < 0.05). A reduction in the number of HCs required per session was observed. However, more than one HC was still needed to complete a session. This could be addressed in future research by designing a series of helmets that adapt to different head sizes. Full article

Objectives: With the development of artificial intelligence technology in medicine, an intelligent deep learning-based embryo scoring system (iDAScore) has been developed on full-time lapse sequences of embryos. It automatically ranks embryos according to the likelihood of achieving a fetal heartbeat with no manual input from embryologists. To ensure its performance, external validation studies should be performed at multiple clinics. Methods: A total of 6291 single vitrified–thawed blastocyst transfer cycles from 2018 to 2021 at the Reproductive Medicine Center, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology were retrospectively analyzed by the iDAScore model. Patients with two or more blastocysts transferred and blastocysts that were not cultured in a time-lapse incubator were excluded. Blastocysts were divided into four comparably sized groups by first sorting their iDAScore values in ascending order and then compared with the clinical, perinatal, and neonatal outcomes. Results: Our results showed that clinical pregnancy, miscarriage, and live birth significantly correlated with iDAScore (p < 0.001). For perinatal and neonatal outcomes, no significant difference was shown in four iDAScore groups, except sex ratio. Uni- and multivariable logistic regressions showed that iDAScore was significantly positively correlated with live birth rate (p < 0.05). Conclusions: In conclusion, the objective ranking can prioritize embryos reliably and rapidly for transfer, which could allow embryologists more time for processes requiring hands-on procedures. Full article

Electric vehicles (EVs) have been the most dependable and feasible choice for decarbonizing road transport over the last decade. To ensure the advancement of EVs and establish them as a sustainable alternative to internal combustion engine (ICE) vehicles, the EV sector and technological growth have largely relied on government subsidies. A significant challenge for EVs is their faster depreciation compared to ICE vehicles, primarily owing to swift technological advancements that propel the market while simultaneously rendering older EV models outdated too soon. Another factor that leads to the quicker depreciation of EVs is subsidies. The anticipated cessation of subsidies is expected to provide the required leverage to mitigate the rapid value decline in EVs, given the larger price disparity between new and used EVs. Batteries, which enable EVs to be a viable option, significantly contribute to the depreciation of EVs. In addition to the potential decline in EV battery performance, advancements in technology and reduced prices provide newer models with improved range at a more affordable cost. The used EV market accurately represents the rapid devaluation of EVs; consequently, the two topics are tightly related. Though it might not be immediately apparent, it seems evident that the pace of depreciation of EVs significantly contributes to the small size of the second-hand EV market. Depreciation is a key factor influencing the used EV market. This manuscript outlines the key aspects of depreciation and sustainability in the EV transition, especially those linked to rapid technological advancements, such as batteries, in addition to subsidies and the used EV market. The objective of this manuscript is to expose and analyze the relation between the drivers of the second-hand EV market, such as the cost of ownership, technology, and subsidies, and, on the other hand, present the interplay perspectives and challenges. Full article

Background/Objectives: In the context of facial surgery, particularly reconstructive procedures, sensory recovery is a critical yet often underexplored aspect of functional rehabilitation. Sensory-motor recovery can be considered a key marker of integration following reconstructive surgery. Among sensory modalities, discriminative sensitivity is typically the last to recover, making its evaluation particularly relevant. While established norms for hand sensitivity exist in the literature, there is a paucity of data regarding facial sensitivity. The objective of this study was to evaluate the discriminative sensitivity of the face in a population of healthy women aged 45–60 years. Methods: A total of 20 healthy women were included between January and March 2013. Participants had no history of facial pathologies or trauma. Discriminative sensitivity was measured using the Disk-Criminator™ device across eight facial zones. A detailed mapping of the tested areas was performed. Data obtained were compared with the existing literature. Statistical analyses included Shapiro–Wilk tests for normality, followed by Student’s t-tests for group comparisons. To account for small sample size and verify robustness, non-parametric Mann–Whitney U tests were also performed. Adjustment for multiple comparisons was applied using the Bonferroni correction (adjusted α = 0.0125). Results: The mean age of participants was 52.3 years (±4.0 years). Discrimination threshold values ranged from 2.9 to 14.3 mm. Comparison with existing studies showed no significant age-related differences in zone 2R (cheek) and zone 8 (lower lip), suggesting stable sensitivity in these regions across adulthood. However, a significant decline in sensitivity with age was observed only in zone 1R (forehead), with a p-value < 0.001 after Bonferroni correction. Conclusions: We established a reference framework for cutaneous discriminative sensitivity across eight facial zones. These norms can serve as a baseline for the assessment and monitoring of patients with facial pathologies. Furthermore, our findings contribute to a better understanding of age-related sensory changes. Full article

The present study examines the role of knowledge management practices in developing business intelligence systems (BISMs) and organizational sustainability (OS) in small and medium-sized enterprises (SMEs) in Saudi Arabia. With the underpinning of the knowledge-based view (KBV) in the model of the study, the study employed a deductive approach. Cross-sectional data were gathered from CEOs, senior managers, and business intelligence officers using both offline and online survey tools. Finally, the study utilized 356 usable cases to support its conclusions. The study confirmed a positive effect on knowledge management practices, i.e., knowledge acquisition (KAG) and knowledge dissemination (KDM) on BISMs and OS. On the other hand, the impact of knowledge responsiveness (KRN) on BISMs is negative but positive on OS. Furthermore, BISMs have a positive effect on OS and entrepreneurial leadership (ELP). ELP also positively affects OS. Finally, ELP mediates the relationship between BISMs and OS. The study provides guidelines for SME managers and policymakers on how to invest in knowledge management initiatives to foster a culture of continuous learning and information sharing. The study directly supports Saudi Arabia’s Vision 2030, which requires the development of the sustainability of SMEs. Finally, the study addresses the gaps in the integrated model, providing empirical evidence from a developing context. Full article