Search Results (3,671)

Muscle–joint flexibility is defined as the ability of a muscle to stretch in a controlled manner, allowing a wide range of movement at the joints. While numerous methodologies exist for improving flexibility, few studies have investigated the role of athletes’ perceptual processes and awareness related to their own body and movement control during such training. In this pilot study, we explored how two different training protocols—static and dynamic stretching (control group, CON) and multi-joint resistance training (experimental group, EXP)—influence both flexibility and psychophysical awareness, understood as a multidimensional construct involving perceived flexibility improvements, self-assessed control over exercise execution, and cognitive-emotional responses such as engagement, motivation, and satisfaction during physical effort. The study involved 24 male amateur track-and-field athletes (mean age 23 ± 2.5 years), randomized into two equal groups. Over 12 weeks, both groups trained three times per week. Flexibility was assessed using the Sit and Reach Test at three time points (pre-, mid-, and post-intervention). A 2 × 3 mixed ANOVA revealed a significant group × time interaction (F = 20.17, p < 0.001), with the EXP group showing greater improvements than the CON group. In the EXP group, Sit and Reach scores increased from pre = 28.55 cm (SD = 4.91) to mid = 29.39 cm (SD = 4.67) and post = 29.48 cm (SD = 4.91), with a significant difference between pre and post (p = 0.01; d = 0.35). The CON group showed minimal changes, with scores of pre = 28.66 cm (SD = 4.92), mid = 28.76 cm (SD = 5.03), and post = 28.84 cm (SD = 5.10), and no significant difference between pre and post (p = 0.20; d = 0.04). Psychophysical awareness was assessed using a custom questionnaire structured on a 5-point Likert scale, with items addressing perception of flexibility, motor control, and exercise-related bodily sensations. The questionnaire showed excellent internal consistency (Cronbach’s α = 0.92). Within the EXP group, psychophysical awareness increased significantly (from 3.50 to 4.17; p = 0.01; d = 0.38), while no significant change occurred in the CON group (p = 0.16). Post-hoc power analysis confirmed small to moderate effect sizes within the EXP group, although between-group differences lacked sufficient statistical power. These results suggest that resistance training may improve flexibility and concurrently enhance athletes’ psychophysical self-awareness more effectively than traditional stretching. Such findings offer practical implications for coaches seeking to optimize flexibility training by integrating alternative methods that promote both physical and perceptual adaptations. Full article

Apple harvesting faces increasing challenges owing to rising labor costs and the limited seasonal workforce availability, highlighting the need for robotic harvesting solutions in precision agriculture. This study presents a 6-DOF robotic arm system designed for harvesting in dwarf hedge-planted orchards, featuring a lightweight perception module, a task-adaptive motion planner, and an adaptive soft gripper. A lightweight approach was introduced by integrating the Faster module within the C2f module of the You Only Look Once (YOLO) v8n architecture to optimize the real-time apple detection efficiency. For motion planning, a Dynamic Temperature Simplified Transition Adaptive Cost Bidirectional Transition-Based Rapidly Exploring Random Tree (DSA-BiTRRT) algorithm was developed, demonstrating significant improvements in the path planning performance. The adaptive soft gripper was evaluated for its detachment and load-bearing capacities. Field experiments revealed that the direct-pull method at 150 mN·m torque outperformed the rotation-pull method at both 100 mN·m and 150 mN·m. A custom control system integrating all components was validated in partially controlled orchards, where obstacle clearance and thinning were conducted to ensure operation safety. Tests conducted on 80 apples showed a 52.5% detachment success rate and a 47.5% overall harvesting success rate, with average detachment and full-cycle times of 7.7 s and 15.3 s per apple, respectively. These results highlight the system’s potential for advancing robotic fruit harvesting and contribute to the ongoing development of autonomous agricultural technologies. Full article

Background: Sarcopenic obesity, characterized by reduced skeletal muscle mass and excess adiposity, is strongly associated with chronic pain and functional decline in older adults. Objective: This prospective controlled trial without randomization investigated the effects of a structured, three-phase rehabilitation program on physical performance, pain, and sarcopenia-specific quality of life in elderly patients with sarcopenic obesity and chronic pain. Methods: In this study, 82 participants were enrolled and allocated to a study group (SG, n = 40), receiving supervised rehabilitation, nutritional counseling, and supplementation, or to a control group (CG, n = 42), which did not receive rehabilitation. The final analysis included 35 patients in SG and 36 in CG. Outcomes were assessed at baseline and six months using the Sarcopenia Quality of Life Questionnaire (SarQoL), Short Physical Performance Battery (SPPB), Numeric Rating Scale (NRS), and Pressure Pain Threshold (PPT). Results: The SG showed significant improvements in all outcomes: SarQoL increased from 57.02 to 63.98, SPPB increased from 7.14 to 8.4, PPT increased from 69.31 to 78.05, and NRS decreased from 6.94 to 4.65 (all p < 0.001). The CG showed no significant changes. Conclusions: The implementation of a structured, three-phase rehabilitation program resulted in clinically and statistically significant improvements in physical performance, pain perception, and sarcopenia-related quality of life in older adults with sarcopenic obesity and chronic pain. Full article

Background/Objectives: Cochlear “injury” is thought to be a significant cause of tinnitus in patients with hearing loss. Interestingly, individuals with normal hearing may also experience tinnitus. This study evaluates otoacoustic distortion product emissions (DPOAEs) in individuals with normal hearing who experience tinnitus perception. Methods: In this prospective study, the tinnitus group (TG) consisted of 34 subjects with tinnitus (four unilaterally) and normal hearing (threshold ≤ 25 dBHL at 0.25–8 kHz). The control group (CG) comprised 10 healthy volunteers (20 ears) without tinnitus and normal hearing. Medical history was recorded, and all participants underwent a complete otolaryngological examination, pure tone audiometry, and DPOAE recording (DP-gram, L1 = 55 dB, L2 = 65 dB, for F2: 619–10,000 Hz). Moreover, participants in the TG completed a detailed tinnitus history (with self-rated loudness scoring) and the Tinnitus Handicap Inventory (Greek-version THI-G) and underwent tinnitus analysis. Results: The recorded mean DPOAE values during the DP-gram of the CG were significantly larger in amplitude at low (t-test, Bonferroni-corrected p < 0.09) and high frequencies (t-test, Bonferroni-corrected p < 0.02) compared with the TG. Tinnitus assessment showed tinnitus pitch matching at the frequency area in the DP-gram, where the acceptance recording criteria were not met. There were no statistically significant differences in tinnitus onset, self-rated loudness scores of >70, and severe disability (THI-G > 58) for TG subjects in whom DPOAEs were not recorded at frequencies of ≤1000 Hz. Participants with abnormal DPOAEs at around 4000 Hz had tinnitus of sudden onset and severe disability (THI-G > 58). Finally, those with pathological recordings of DPOAEs at ≥6000 Hz had gradual onset tinnitus (Pearson Chi-square test, p < 0.05). Conclusions: DPOAEs in normal hearing individuals with tinnitus show lower amplitudes in low and high frequencies compared with normal hearing individuals without tinnitus. The tinnitus matched-frequency coincided with the frequency area where DPOAEs were abnormal. Full article

Background/Objectives: Cochlear implants (CIs) and hearing aids (HAs) have enhanced auditory rehabilitation in elderly individuals, yet limitations in musical perception and psychosocial integration persist. This systematic review aimed to evaluate the effects of music therapy (MT) on the quality of life (QoL), self-esteem, auditory perception, and cognition in older CI and HA users. Methods: A comprehensive search of PubMed was conducted up to March 2022 following PRISMA guidelines. Studies involving participants aged ≥ 60 years with CIs and/or HAs were included. Ten studies (n = 21,632) met eligibility criteria. Data were extracted and assessed using the Newcastle–Ottawa Scale. Results: MT led to improved sound quality, with HISQUI19 scores rising from 60.0 ± 21.8 to 74.2 ± 27.5. Early MT exposure was associated with significantly better MUMU outcomes (p = 0.02). Bilateral CI users showed enhanced stereo detection (52% to 86%), and CI + HA users achieved CNC scores exceeding 95%. Postlingual CI users outperformed prelingual peers in musical discrimination (9.81 vs. 3.48; p < 0.001). Long-term HA use was linked to better a QoL and reduced loneliness. Conclusions: While music therapy appears to support auditory and psychosocial functioning in hearing-impaired older adults, the absence of randomized controlled trials limits causal inference regarding its effects. These results support its integration into hearing rehabilitation strategies for older adults. Full article

This paper presents an interpretable and modular framework for energy management in smart buildings based on fuzzy logic and the IEEE Std 1855-2016. The proposed system builds upon the JFML-IoT library, enabling the integration and execution of fuzzy rule-based systems on resource-constrained IoT devices using a lightweight and extensible architecture. Unlike conventional data-driven controllers, this approach emphasizes semantic transparency, expert-driven control logic, and compliance with fuzzy markup standards. The system is designed to enhance both operational efficiency and user comfort through transparent and explainable decision-making. A four-layer architecture structures the system into Perception, Communication, Processing, and Application layers, supporting real-time decisions based on environmental data. The fuzzy logic rules are defined collaboratively with domain experts and encoded in Fuzzy Markup Language to ensure interoperability and formalization of expert knowledge. While adherence to IEEE Std 1855-2016 facilitates system integration and standardization, the scientific contribution lies in the deployment of an interpretable, IoT-based control system validated in real conditions. A case study is conducted in a realistic indoor environment, using temperature, humidity, illuminance, occupancy, and CO₂ sensors, along with HVAC and lighting actuators. The results demonstrate that the fuzzy inference engine generates context-aware control actions aligned with expert expectations. The proposed framework also opens possibilities for incorporating user-specific preferences and adaptive comfort strategies in future developments. Full article

Background: As intelligent systems increasingly operate in high-risk environments, understanding how they perceive and respond to hazards is critical for ensuring safety. Methods: In this study, we conduct a comparative analysis of 60 real-world accident reports, 30 from process control systems (PCSs) and 30 from autonomous vehicles (AVs), to examine differences in risk triggers, perception paradigms, and interaction failures between humans and artificial intelligence (AI). Results: Our findings reveal that PCS risks are predominantly internal to the system and detectable through deterministic, rule-based mechanisms, whereas AVs’ risks are externally driven and managed via probabilistic, multi-modal sensor fusion. More importantly, despite these architectural differences, both domains exhibit recurring human–AI interaction failures, including over-reliance on automation, mode confusion, and delayed intervention. In the case of PCSs, these failures are historically tied to human–automation interaction; this article extrapolates these patterns to anticipate potential human–AI interaction challenges as AI adaptation increases. Conclusions: This study highlights the need for a hybrid risk perception framework and improved human-centered design to enhance situational awareness and responsiveness. While AI has not yet been implemented in PCS incident studies, this work interprets human–automation failures in these cases as indicative of potential challenges in human–AI interaction that may arise in future AI-integrated process systems. Implications extend to developing safer intelligent systems across industrial and transportation sectors. Full article

In order to meet the needs for the rapid detection of crop growth and support variable management in farmland, an intelligent chlorophyll content in cotton leaves (CCC) detection system based on hyperspectral imaging (HSI) technology was designed and developed. The system includes a near-infrared (NIR) hyperspectral image acquisition module, a spectral extraction module, a main control processor module, a model acceleration module, a display module, and a power module, which are used to achieve rapid and non-destructive detection of chlorophyll content. Firstly, spectral images of cotton canopy leaves during the seedling, budding, and flowering-boll stages were collected, and the dataset was optimized using the first-order differential algorithm (1D) and Savitzky–Golay five-term quadratic smoothing (SG) algorithm. The results showed that SG had better processing performance. Secondly, the sparrow search algorithm optimized backpropagation neural network (SSA-BPNN) and one-dimensional convolutional neural network (1DCNN) algorithms were selected to establish a chlorophyll content detection model. The results showed that the determination coefficients $R_p^2$ of the chlorophyll SG-1DCNN detection model during the seedling, budding, and flowering-boll stages were 0.92, 0.97, and 0.95, respectively, and the model performance was superior to SG-SSA-BPNN. Therefore, the SG-1DCNN model was embedded into the detection system. Finally, a CCC intelligent detection system was developed using Python 3.12.3, MATLAB 2020b, and ENVI, and the system was subjected to application testing. The results showed that the average detection accuracy of the CCC intelligent detection system in the three stages was 98.522%, 99.132%, and 97.449%, respectively. Meanwhile, the average detection time for the samples is only 20.12 s. The research results can effectively solve the problem of detecting the nutritional status of cotton in the field environment, meet the real-time detection needs of the field environment, and provide solutions and technical support for the intelligent perception of crop production. Full article

This work presents the development of a functional real-time SLAM system designed to enhance the perception capabilities of an Automated Guided Vehicle (AGV) using only a 2D LiDAR sensor. The proposal aims to address recurring gaps in the literature, such as the need for low-complexity solutions that are independent of auxiliary sensors and capable of operating on embedded platforms with limited computational resources. The system integrates scan alignment techniques based on the Iterative Closest Point (ICP) algorithm. Experimental validation in a controlled environment indicated better performance using Gauss–Newton optimization and the point-to-plane metric, achieving pose estimation accuracy of 99.42%, 99.6%, and 99.99% in the position ($x$, $y$) and orientation ($\theta$) components, respectively. Subsequently, the system was adapted for operation with data from the onboard sensor, integrating a lightweight graphical interface for real-time visualization of scans, estimated pose, and the evolving map. Despite the moderate update rate, the system proved effective for robotic applications, enabling coherent localization and progressive environment mapping. The modular architecture developed allows for future extensions such as trajectory planning and control. The proposed solution provides a robust and adaptable foundation for mobile platforms, with potential applications in industrial automation, academic research, and education in mobile robotics. Full article

Prejudices, particularly those related to social biases, are shaped by various cognitive and sensory mechanisms. This study investigates the interaction between olfactory perception and propensity and implicit or explicit prejudices through three experimental protocols in a metaverse condition. Experiment 1 examines the impact of five different odors on proxemic behavior when interacting with individuals from stigmatized social groups. Experiment 2 integrates electroencephalography (EEG) to analyze the neural correlates of prejudice-related responses to olfactory stimuli. Experiment 3 explores implicit biases through the Implicit Association Test (IAT) in relation to different fragrances, without employing virtual reality. The proposed protocol is expected to demonstrate a significant relationship between olfactory cues, linked to social relationships, and implicit or explicit prejudices, with variations based on individual differences. These insights will contribute to psychological, neuroscientific, and social interventions, offering new perspectives on the unconscious mechanisms of bias formation. Additionally, this study highlights the potential of virtual reality and olfactory stimuli as innovative tools for studying and addressing social biases in controlled environments. Full article

Background/Objectives: Muscle dysmorphia (MD), a subtype of body dysmorphic disorder, is prevalent among males who engage in the non-medical use of anabolic–androgenic steroids (AASs) and performance-enhancing drugs (PEDs). These individuals often experience severe psychopathology, including mood instability, compulsivity, and a distorted body image. Despite its clinical severity, no randomized controlled trials (RCTs) have evaluated structured psychological treatments in this subgroup. This study aimed to assess the efficacy of a manualized cognitive behavioral therapy (CBT) protocol in reducing MD symptoms and associated psychological distress among male steroid users. Results: Participants in the CBT group showed significant reductions in MD symptoms from the baseline to post-treatment (MDDI: p < 0.001, d = 1.12), with gains sustained at follow-up. Large effect sizes were also observed in secondary outcomes including depressive symptoms (PHQ-9: d = 0.98), psychological distress (K10: d = 0.93), disordered eating (EDE-Q: d = 0.74), and exercise addiction (EAI: d = 1.07). No significant changes were observed in the control group. Significant group × time interactions were found for all outcomes (all p < 0.01), indicating CBT’s specific efficacy. Discussion: This study provides the first RCT evidence that CBT significantly reduces both core MD symptoms and steroid-related psychopathology in men engaged in AAS/PED misuse. Improvements extended to mood, body image perception, and compulsive exercise behaviors. These findings support CBT’s transdiagnostic applicability in addressing both the cognitive–behavioral and affective dimensions of MD. Materials and Methods: In this parallel-group, open-label RCT, 59 male gym-goers with DSM-5-TR diagnoses of MD and a history of AAS/PED use were randomized to either a 12-week CBT intervention (n = 30) or a waitlist control group (n = 29). CBT sessions were delivered weekly online and targeted distorted muscularity beliefs, compulsive behaviors, and emotional dysregulation. Primary and secondary outcomes—Muscle Dysmorphic Disorder Inventory (MDDI), PHQ-9, K10, EDE-Q, EAI, and BIG—were assessed at the baseline, post-treatment, and 3-month follow-up. A repeated-measures ANOVA and paired t-tests were used to analyze time × group interactions. Conclusions: CBT offers an effective, scalable intervention for individuals with muscle dysmorphia complicated by anabolic steroid use. It promotes broad psychological improvement and may serve as a first-line treatment option in high-risk male fitness populations. Future studies should examine long-term outcomes and investigate implementation in diverse clinical and cultural contexts. Full article

Autonomous earthwork machinery is gaining traction as a means to boost productivity and safety on space-constrained urban sites, yet the fast-growing literature has not been fully integrated. To clarify current knowledge, we systematically searched Scopus and screened 597 records, retaining 157 peer-reviewed papers (2015–March 2025) that address autonomy, integrated control, or risk mitigation for excavators, bulldozers, and loaders. Descriptive statistics, VOSviewer mapping, and qualitative synthesis show the output rising rapidly and peaking at 30 papers in 2024, led by China, Korea, and the USA. Four tightly linked themes dominate: perception-driven machine autonomy, IoT-enabled integrated control systems, multi-sensor safety strategies, and the first demonstrations of fleet-level collaboration (e.g., coordinated excavator clusters and unmanned aerial vehicle and unmanned ground vehicle (UAV–UGV) site preparation). Advances include centimeter-scale path tracking, real-time vision-light detection and ranging (LiDAR) fusion and geofenced safety envelopes, but formal validation protocols and robust inter-machine communication remain open challenges. The review distils five research priorities, including adaptive perception and artificial intelligence (AI), digital-twin integration with building information modeling (BIM), cooperative multi-robot planning, rigorous safety assurance, and human–automation partnership that must be addressed to transform isolated prototypes into connected, self-optimizing fleets capable of delivering safer, faster, and more sustainable urban construction. Full article

Blind and visually impaired (BVI) people face significant challenges in perception, navigation, and safety during travel. Existing infrastructure (e.g., blind lanes) and traditional aids (e.g., walking sticks, basic audio feedback) provide limited flexibility and interactivity for complex environments. To solve this problem, we propose a real-time travel assistance system based on deep learning. The hardware comprises an NVIDIA Jetson Nano controller, an Intel D435i depth camera for environmental sensing, and SG90 servo motors for feedback. To address embedded device computational constraints, we developed a lightweight object detection and segmentation algorithm. Key innovations include a multi-scale attention feature extraction backbone, a dual-stream fusion module incorporating the Mamba architecture, and adaptive context-aware detection/segmentation heads. This design ensures high computational efficiency and real-time performance. The system workflow is as follows: (1) the D435i captures real-time environmental data; (2) the processor analyzes this data, converting obstacle distances and path deviations into electrical signals; (3) servo motors deliver vibratory feedback for guidance and alerts. Preliminary tests confirm that the system can effectively detect obstacles and correct path deviations in real time, suggesting its potential to assist BVI users. However, as this is a work in progress, comprehensive field trials with BVI participants are required to fully validate its efficacy. Full article

Urbanization poses mental health risks for urban dwellers, whereas natural environments offer mental health benefits by providing restorative experiences through visual stimuli. While urban waterfront spaces are recognized for their mental restorative potential, the specific environmental features and individual visual behaviors that drive these benefits remain inadequately understood. Grounded in restorative environments theory, this study investigates how these factors jointly influence restoration. Employing a controlled laboratory experiment, subjects viewed real-life images of nine representative spatial locations from the waterfront space of Guangzhou Long Bund. Data collected during the multimodal experiments included subjective scales data (SRRS), physiological measurement data (SCR; LF/HF), and eye-tracking data. Key findings revealed the following: (1) The element visibility rate and visual characteristics of plant and building elements significantly influence restorative benefits. (2) Spatial configuration attributes (degree of enclosure, spatial hierarchy, and depth perception) regulate restorative benefits. (3) Visual behavior patterns (attributes of fixation points, fixation duration, and moderate dispersion of fixations) are significantly associated with restoration benefits. These findings advance the understanding of the mechanisms linking environmental stimuli, visual behavior, and psychological restorative benefits. They translate into evidence-based design principles for urban waterfront spaces. This study provides a refined perspective and empirical foundation for enhancing the restorative benefits of urban waterfront spaces through design. Full article

Image-assisted digital shade guides are increasingly popular for shade matching; however, research on their accuracy remains limited. This study aimed to compare the accuracy and reliability of color coordination in image-assisted digital shade guides constructed using calibrated images of their shade tabs captured by a mirrorless camera (Canon, Tokyo, Japan) (MC-DSG) and a smartphone camera (Samsung, Seoul, Korea) (SC-DSG), using a spectrophotometer as the reference standard. Twenty-nine VITA Linearguide 3D-Master shade tabs were photographed under controlled settings with both cameras equipped with cross-polarizing filters. Images were calibrated using Adobe Photoshop (Adobe Inc., San Jose, CA, USA). The L* (lightness), a* (red-green chromaticity), and b* (yellow-blue chromaticity) values, which represent the color attributes in the CIELAB color space, were computed at the middle third of each shade tab using Adobe Photoshop. Specifically, L* indicates the brightness of a color (ranging from black [0] to white [100]), a* denotes the position between red (+a*) and green (–a*), and b* represents the position between yellow (+b*) and blue (–b*). These values were used to quantify tooth shade and compare them to reference measurements obtained from a spectrophotometer (VITA Easyshade V, VITA Zahnfabrik, Bad Säckingen, Germany). Mean color differences (∆E₀₀) between MC-DSG and SC-DSG, relative to the spectrophotometer, were compared using a independent t-test. The ∆E₀₀ values were also evaluated against perceptibility (PT = 0.8) and acceptability (AT = 1.8) thresholds. Reliability was evaluated using intraclass correlation coefficients (ICC), and group differences were analyzed via one-way ANOVA and Bonferroni post hoc tests (α = 0.05). SC-DSG showed significantly lower ΔE₀₀ deviations than MC-DSG (p < 0.001), falling within acceptable clinical AT. The L* values from MC-DSG were significantly higher than SC-DSG (p = 0.024). All methods showed excellent reliability (ICC > 0.9). The findings support the potential of smartphone image-assisted digital shade guides for accurate and reliable tooth shade assessment. Full article