Search Results (171)

Background/Objectives: The growing population of women aged ≥ 80 years poses a new challenge for urogynecology. Advanced age, comorbidities, and polypharmacy raise concerns regarding the safety of procedures in the management of pelvic floor disorders (PFDs) such as pelvic organ prolapse (POP), stress urinary incontinence (SUI), and overactive bladder (OAB). Individualized, frailty-based assessment is essential in this group. The aim of the study was to evaluate the safety profile of urogynecological surgical procedures among women aged ≥ 80 years at a single tertiary center. Methods: In a retrospective observational single-center study, we analyzed the medical documentation of 774 hospitalizations of women aged ≥ 80 years admitted between 2014 and 2023. The analysis included indications, comorbidities, treatment types, anesthesia, and complications. Comorbidity and surgical risk were evaluated using the Charlson Comorbidity Index (CCI) and Clavien–Dindo classification. Results: A total of 720 admissions with complete medical records were analyzed, of which 65% were for urogynecological conditions. In this group, the mean age was 83.0 years and mean BMI was 27.2 kg/m². Most patients (92.9%) had comorbidities, mainly hypertension (84.2%) and diabetes (21.1%). POP was the leading indication (52%), followed by SUI (35%) and OAB (27%). Surgical management was performed in 95% of POP cases, predominantly via vaginal native tissue repair (80%), especially LeFort colpocleisis (20%). The transobturator sling (TOT) was the most frequent SUI surgery. Intraoperative complications occurred in 1.5% of cases and postoperative ones were mainly minor (Clavien–Dindo I–II). No procedure-related deaths were recorded. Conclusions: In this cohort, surgical treatment of urogynecological problems in women ≥80 years was associated with a low rate of major complications, suggesting that it can be safely offered to elderly patients. Careful preoperative assessment based on frailty and comorbidity rather than chronological age remains essential. Full article

Unmanned Aerial Vehicles (UAVs) are promising nodes for Integrated Sensing and Communication (ISAC), but accurate Direction-of-Arrival (DoA) estimation on a small airframe is challenged by platform loading, motion, attitude, and multipath. Traditionally, DoA algorithms have been developed and evaluated for stationary, ground-based (or otherwise mechanically stable) antenna arrays. Extending them to UAVs violates these assumptions. This work designs a six-element Uniform Circular Array (UCA) at 2.4 GHz (radius $\approx 0.5\lambda$) for a quadrotor and introduces a Pose-Aware MUSIC (MUltiple SIgnal Classification) estimator for DoA. The novelty is a MUSIC formulation that (i) applies pose correction using the drone’s instantaneous roll–pitch–yaw (pose correction) and (ii) applies a Doppler correction that accounts for platform velocity. Performance is assessed using data synthesized from embedded-element patterns obtained by electromagnetic characterization of the installed array, with additional channel/hardware effects modeled in post-processing (Rician LOS/NLOS mixing, mutual coupling, per-element gain/phase errors, and element–position jitter). Results with the six-element UCA show that pose and Doppler compensation preserve high-resolution DoA estimates and reduce bias under realistic flight and platform conditions while also revealing how coupling and jitter set practical error floors. The contribution is a practical PA-MUSIC approach for UAV ISAC, combining UCA design with motion-aware signal processing, and an evaluation that quantifies accuracy and offers clear guidance for calibration and field deployment in GNSS-denied scenarios. The results show that, across 0–25 dB SNR, the proposed hybrid DoA estimator achieves <$0.5^{\circ }$ RMSE in azimuth and elevation for ideal conditions and ≈$5^{\circ }$–$6^{\circ }$ RMSE when full platform coupling is considered, demonstrating robust performance for UAV ISAC tracking. Full article

Accurate indoor floor classification is essential for wireless positioning systems. However, the performance of conventional received signal strength indictor (RSSI)-based fingerprinting approaches is often limited by signal fluctuations and insufficient feature representation. To address these challenges, this paper introduces a heterogeneous graph neural network (GNN) framework that models WiFi signals using two types of nodes: reference points and Media Access Control (MAC) address. The edges between reference points and MAC addresses are weighted by normalized RSSI values, allowing the model to capture signal strength interactions and perform relation-aware message passing. Through this graph-based representation, the model can learn spatial and signal dependencies more effectively than conventional vector-based approaches. The proposed model was extensively evaluated under both benchmark and practical settings. On small-scale datasets, it achieved performance comparable to that of a conventional convolutional neural network trained on large-scale datasets, confirming its effectiveness with limited samples. In addition, the proposed model consistently outperformed other models under noisy conditions, achieving 93.88% accuracy on the widely used UJIIndoorLoc dataset and 97.3% accuracy in real-time experiments conducted at a test site. These values are significantly higher than those achieved using conventional machine learning (ML) baselines, highlighting the ability of the proposed model to handle real-world signal variations. These findings highlight that the heterogeneous GNN effectively captures spatial and signal-level dependencies, offering a robust and scalable solution for accurate indoor floor classification. Overall, this work presents a promising pathway for improving the performance and reliability of future wireless positioning systems. Full article

Airborne LiDAR bathymetry (ALB) provides dense three-dimensional point clouds that enable the detailed mapping of tidal flat environments. However, surface classification using these point clouds remains challenging due to residual noise, water surface reflectivity, and subtle class boundaries that persist even after standard preprocessing. To address these challenges, this study introduces Tidal flat-Attentive Graph Network (TAGNet), a graph-based deep learning framework designed to leverage both local geometric relationships and global contextual cues for the point-wise classification of tidal flat surface classes. The model incorporates multi-scale EdgeConv layers for capturing fine-grained neighborhood structures and employs squeeze-and-excitation channel attention to enhance global feature representation. To validate TAGNet’s effectiveness, classification was conducted on ALB point clouds collected from adjacent tidal flat regions, focusing on four major surface classes: exposed flat, sea surface, sea floor, and vegetation. In benchmarking tests against baseline models, including Dynamic Graph Convolutional Neural Network, PointNeXt with Single-Scale Grouping, and PointNet Transformer, TAGNet consistently achieved higher macro F1-scores. Moreover, ablation studies isolating positional encoding, attention mechanisms, and detrended Z-features confirmed their complementary contributions to TAGNet’s performance. Notably, the full TAGNet outperformed all baselines by a substantial margin, particularly when distinguishing closely related classes, such as sea floor and exposed flat. These findings highlight the potential of graph-based architectures specifically designed for ALB data in enhancing the precision of coastal monitoring and habitat mapping. Full article

Accurate classification of indoor scenes remains a challenging problem in computer vision, particularly when datasets contain diverse room types and varying levels of contamination. We propose a novel method, CLIP-Guided Clustering, which introduces archetype-based similarity as a semantic feature space. Instead of directly using raw image embeddings, we compute similarity scores between each image and predefined textual archetypes (e.g., “clean room,” “cluttered room with dry debris,” “moldy bathroom,” “room with workers”). These scores form low-dimensional semantic vectors that enable interpretable clustering via K-Means. To evaluate clustering robustness, we systematically explored UMAP parameter configurations (n_neighbors, min_dist) and identified the optimal setting (n_neighbors = 5, min_dist = 0.0) with the highest silhouette score (0.631). This objective analysis confirms that archetype-based representations improve separability compared with conventional visual embeddings. In addition, we developed a hybrid segmentation pipeline combining the Segment Anything Model (SAM), DeepLabV3, and pre-processing techniques to accurately extract floor regions even in low-quality or cluttered images. Together, these methods provide a principled framework for semantic classification and segmentation of residential environments. Beyond application-specific domains, our results demonstrate that combining vision–language models with segmentation networks offers a generalizable strategy for interpretable and robust scene understanding. Full article

Acoustic sensing is a passive and cost-effective option for unmanned aerial vehicle detection, where both signal processing and microphone hardware jointly determine field performance. In this study, we focus on the hardware front-end as a foundation for improving the reliability of subsequent DSP- or AI-based detection methods. We present a detection-focused comparison of several microphones in outdoor tests, combining calibrated range measurements with spectral analysis of real unmanned aerial vehicle emissions from three platforms. We report hardware metrics only: signal-to-noise ratio, effective detection range, attenuation slope with distance, and the low-frequency background floor. Across wind conditions and source orientations, the RØDE NTG-2 with WS6 windshield delivered the most balanced performance: in strong wind, it extended the detection range over the bare NTG-2 by approximately 31–131% (depending on azimuth), lowered the low-frequency noise floor by about 2–3 decibels, and matched or increased the wideband signal-to-noise ratio by 1.8–4.4 decibels. A parabolic NTG-2 achieved very low background noise levels at low frequencies and strong on-axis reach but proved vulnerable to gust-induced transients. Based on this evidence, we propose an eight-channel, dual-tier array of NTG-2 + WS6 elements that preserves near-hemispherical coverage and phase coherence, establishing a practical hardware baseline for outdoor acoustic unmanned aerial vehicle detection and a reproducible platform for subsequent localization and classification studies. Full article

Background/Objectives: Urinary incontinence (UI) remains a common and distressing complication following radical prostatectomy (RP). This prospective observational study aimed to assess the effectiveness of structured pelvic floor rehabilitation and to identify clinical and surgical predictors of continence recovery. Methods: A total of 182 patients undergoing RP received standardized physiotherapist-guided pelvic floor muscle training (PFMT), including supervised sessions before and after surgery, as well as individualized home exercise programs. UI severity was evaluated using a 1 h pad test and a four-level UI stage classification at three time points. The primary outcomes were changes in UI stage and the achievement of full continence, defined as a pad test result ≤2 g. Results: Following three rehabilitation sessions, 80.2% of patients regained full continence. Preoperative PFMT (β = −1.27, p = 0.0061) and shorter time to rehabilitation (β = −0.04, p = 0.0026) were associated with greater improvement in continence outcomes. Patients treated with robot-assisted RP showed a higher probability of continence recovery compared to those undergoing laparoscopic RP, particularly in the presence of moderate to severe baseline incontinence. Higher baseline urinary leakage significantly decreased the odds of treatment success (β = −0.01, p = 0.0001). ISUP grade and extraprostatic extension were not independently associated with outcomes. Conclusions: Despite the absence of a control group, this study demonstrates the effectiveness of structured and personalized pelvic floor rehabilitation in improving post-RP continence. Early initiation and preoperative training should be prioritized to optimize recovery in routine clinical practice. Full article

Background/Objectives: Incomplete cut marks produced during dismemberment are often interpreted as indicative of saw class characteristics. However, empirical validation of these associations remains limited, with prior studies examining six or fewer saws. Considering the wide variety of saws available, it is critical to assess the reliability of reported relationships between kerf features and saw classification using a larger sample, particularly in light of the serious legal consequences of erroneous conclusions. This study examines the statistical relationships between five incomplete cut traits—kerf profile shape (KPS), kerf length shape (KLS), floor dip (FD), kerf flare (KF), and floor striae (FS)—and saw class characteristics, including tooth set, tooth shape, teeth-per-inch, power, handle orientation, and cut direction. Methods: Kerf features were scored on a sample of 472 incomplete cuts made with 34 power and hand saws. Results: In reciprocating saws, W-shaped KPS was exclusively associated with crosscut, alternating saws (100%; p < 0.001), with hourglass-shaped KLS also primarily made by alternating sets (95.6%). Necked KLS was linked to wavy sets (76.8%; p < 0.001). FD, though rare, could be correctly assigned to teeth-per-inch groups (86.4%), and was also predominantly associated with alternating saws (90.9%; p < 0.001). Undulating FS were indicative of alternating saws with less than 20 teeth-per-inch (100%, p < 0.001). In contrast, KF showed no strong relationship with saw class characteristics, including handle side. Conclusions: The results of this large-scale analysis support most reported relationships in the saw mark literature but challenge assumptions that KF reliably indicates handle orientation or cut direction, suggesting instead that its location may reflect sawyer technique. Full article

Proper daylighting in educational buildings improves students’ mood and health. However, daylighting can be affected by multiple environmental features, and comprehensive investigations remain limited. This study examined how various classroom environmental features affect students’ mood perception in six classrooms of three middle schools in eastern China. Eighteen environmental features across six dimensions were assessed through field studies and software simulations, and 557 valid mood responses were collected from 243 students through questionnaires. Traditional machine learning and deep learning models were used to predict students’ mood perception with the environmental features, with SHapley Additive exPlanations (SHAP) applied to interpret the contributions of different features. Results showed that Random Forest achieved a relatively high accuracy of 82% in the binary classification of mood perception prediction. Among all features, Exterior view evaluation (EVE) had the largest impact and showed a strong interaction with floor level. Higher floors and EVE ≥ 3 were associated with more positive moods. Beneficial conditions for mood perception also included horizontal desktop illuminance above 300 lx, frontal eye-level illuminance below 400 lx, left-side eye-level illuminance within 300–1000 lx, and proximity to windows below 2.5 m. These findings provide new insights and practical guidance for designing healthier classroom environments to promote adolescent mental health, thereby contributing to sustainable educational environments that integrate human well-being with energy-efficient daylighting design. Full article

Introduction: Multiple sclerosis (MS) is a chronic neurodegenerative disease that entails high costs, progressive disability, and reduced quality of life (QoL). Telerehabilitation (TR), supported by new technologies, is emerging as an alternative or complement to in-person rehabilitation, potentially lowering socioeconomic impact and improving QoL. Aim: The objective of this study was to evaluate the effect of TR on the QoL of people with MS compared with in-person rehabilitation or no intervention. Materials and methods: A systematic review of randomized clinical trials was conducted (March–May 2025) following PRISMA guidelines. Searches were run in the PubMed-Medline, EMBASE, PEDro, Web of Science, and Dialnet databases. Methodological quality was assessed with the CASP scale, risk of bias with the Risk of Bias 2 tool, and evidence level and grade of recommendation with the Oxford Classification. The protocol was registered in PROSPERO (CRD420251110353). Results: Of the 151 articles initially found, 12 RCTs (598 total patients) met the inclusion criteria. Interventions included (a) four studies employing video-controlled exercise (one involving Pilates to improve fitness, another involving exercise to improve fatigue and general health, and two using exercises focused on the pelvic floor muscles); (b) three studies using a monitoring app to improve manual dexterity, symptom control, and increased physical activity; (c) two studies implementing an augmented reality system to treat cognitive deficits and sexual disorders, respectively; (d) one platform with a virtual reality headset for motor and cognitive training; (e) one study focusing on video-controlled motor imagery, along with the use of a pain management app; (f) a final study addressing cognitive training and pain reduction. Studies used eight different scales to assess QoL, finding similar improvements between groups in eight of the trials and statistically significant improvements in favor of TR in four. The included trials were of good methodological quality, with a moderate-to-low risk of bias and good levels of evidence and grades of recommendation. Conclusions: TR was more effective in improving the QoL of people with MS than no intervention, was as effective as in-person treatment in patients with EDSS ≤ 6, and appeared to be more effective than in-person intervention in patients with EDSS between 5.5 and 7.5 in terms of QoL. It may also eliminate some common barriers to accessing such treatments. Full article

Background and Objectives: Pelvic organ prolapse (POP) is a complex condition affecting the pelvic floor, often requiring imaging for accurate diagnosis and treatment planning. Artificial intelligence (AI), particularly deep learning (DL), is emerging as a powerful tool in medical imaging. This scoping review aims to synthesize current evidence on the use of AI in the imaging-based diagnosis and anatomical evaluation of POP. Materials and Methods: Following the PRISMA-ScR guidelines, a comprehensive search was conducted in PubMed, Scopus, and Web of Science for studies published between January 2020 and April 2025. Studies were included if they applied AI methodologies, such as convolutional neural networks (CNNs), vision transformers (ViTs), or hybrid models, to diagnostic imaging modalities such as ultrasound and magnetic resonance imaging (MRI) to women with POP. Results: Eight studies met the inclusion criteria. In these studies, AI technologies were applied to 2D/3D ultrasound and static or stress MRI for segmentation, anatomical landmark localization, and prolapse classification. CNNs were the most commonly used models, often combined with transfer learning. Some studies used hybrid models of ViTs, demonstrating high diagnostic accuracy. However, all studies relied on internal datasets, with limited model interpretability and no external validation. Moreover, clinical deployment and outcome assessments remain underexplored. Conclusions: AI shows promise in enhancing POP diagnosis through improved image analysis, but current applications are largely exploratory. Future work should prioritize external validation, standardization, explainable AI, and real-world implementation to bridge the gap between experimental models and clinical utility. Full article

The applicability of the in-situ pyrolysis of oil-rich coal is highly dependent on regional geological conditions. In this study, six major geological factors and 19 key parameters influencing the in-situ pyrolysis of oil-rich coal were systematically identified. An analytic hierarchy process incorporating index classification and quantification was employed in combination with the geological features of the Tiaohu mining area to establish a feasibility evaluation index system suitable for in-situ development in the study region. Among these factors, coal quality parameters (e.g., coal type, moisture content, volatile matter, ash yield), coal seam occurrence characteristics (e.g., seam thickness, burial depth, interburden frequency), and hydrogeological conditions (e.g., relative water inflow) primarily govern pyrolysis process stability. Surrounding rock properties (e.g., roof/floor lithology) and structural features (e.g., fault proximity) directly impact pyrolysis furnace sealing integrity, while environmental geological factors (e.g., hazardous element content in coal) determine environmental risk control effectiveness. Based on actual geological data from the Tiaohu mining area, the comprehensive weight of each index was determined. After calculation, the southwestern, central, and southeastern subregions of the mining area were identified as favorable zones for pyrolysis development. A constraint condition analysis was then conducted, accompanied by a one-vote veto index system, in which the thresholds were defined for coal seam thickness (≥1.5 m), burial depth (≥500 m), thickness variation coefficient (≤15%), fault proximity (≥200 m), tar yield (≥7%), high-pressure permeability (≥10 mD), and high-pressure porosity (≥15%). Following the exclusion of unqualified boreholes, three target zones for pyrolysis furnace deployment were ultimately selected. Full article

The behavior of floor-raised chickens is closely linked to their health status and environmental comfort. As a type of broiler chicken with special behaviors, understanding the daily actions of yellow-feathered chickens is crucial for accurately checking their health and improving breeding practices. Addressing the challenges of high computational complexity and insufficient detection accuracy in existing floor-raised chicken behavior recognition models, a lightweight behavior recognition model was proposed for floor-raised yellow-feathered chickens, based on a Dual-Backbone Heterogeneous YOLO Network. Firstly, DualHet-YOLO enhances the feature extraction capability of floor-raised chicken images through a dual-path feature map extraction architecture and optimizes the localization and classification of multi-scale targets using a TriAxis Unified Detection Head. Secondly, a Proportional Scale IoU loss function is introduced that improves regression accuracy. Finally, a lightweight structure Eff-HetKConv was designed, significantly reducing model parameters and computational complexity. Experiments on a private floor-raised chicken behavior dataset show that, compared with the baseline YOLOv11 model, the DualHet-YOLO model increases the mAP for recognizing five behaviors—pecking, resting, walking, dead, and inactive—from 77.5% to 84.1%. Meanwhile, it reduces model parameters by 14.6% and computational complexity by 29.2%, achieving a synergistic optimization of accuracy and efficiency. This approach provides an effective solution for lightweight object detection in poultry behavior recognition. Full article

Outdoor cleaning robots must operate reliably across diverse and unstructured surfaces, yet many existing systems lack the adaptability to handle terrain variability. This paper proposes a terrain-aware cleaning framework that dynamically adjusts robot behavior based on real-time surface classification and slope estimation. A 128-channel LiDAR sensor captures signal intensity images, which are processed by a ResNet-18 convolutional neural network to classify floor types as wood, smooth, or rough. Simultaneously, pitch angles from an onboard IMU detect terrain inclination. These inputs are transformed into fuzzy sets and evaluated using a Mamdani-type fuzzy inference system. The controller adjusts brush height, brush speed, and robot velocity through 81 rules derived from 48 structured cleaning experiments across varying terrain and slopes. Validation was conducted in low-light (night-time) conditions, leveraging LiDAR’s lighting-invariant capabilities. Field trials confirm that the robot responds effectively to environmental conditions, such as reducing speed on slopes or increasing brush pressure on rough surfaces. The integration of deep learning and fuzzy control enables safe, energy-efficient, and adaptive cleaning in complex outdoor environments. This work demonstrates the feasibility and real-world applicability for combining perception and inference-based control in terrain-adaptive robotic systems. Full article

Objective: The aim of this study is to investigate uterine prolapse (UP) among women attending a semi-urban health center for routine gynecological examinations. Specifically, the study explores the potential association between UP and various established or suspected risk factors, including age, menopausal status, number and mode of deliveries, birth weight, smoking habits, and body mass index (BMI). Furthermore, it examines the relationship between the presence or severity of UP and the scores of specific questionnaires and their subscales. Finally, the study seeks to develop a predictive model for the likelihood of UP based on questionnaire responses. Methods: A quantitative study was conducted at the gynecological department of a health center in Greece from January 2021 to October 2022. A total of 134 women were recruited using convenience sampling during routine gynecological visits. The degree of prolapse was classified according to the International Continence Society (ICS) Pelvic Organ Prolapse Quantification (POP-Q) classification system. Data collection also included the use of validated instruments: the Australian Pelvic Floor Questionnaire (APFQ), the Urogenital Distress Inventory-6 (UDI-6), the Pelvic Floor Distress Inventory-20 (PFDI-20), and the Pelvic Floor Impact Questionnaire-7 (PFIQ-7). The data were processed with the Statistical Package for the Social Sciences (SPSS) v25. Results: Of the 134 participants, 21 (15.7%) aged 21 to 82 showed signs of UP, while 113 women (84.3%) did not. The average age of the women with UP was 55 years. Fourteen (10.4%) of these women were diagnosed with UP stage I, three of them (2.2%) with stage II, and four of them (3%) with stage III UP. There were no stage IV UP incidents. The risk factors associated with the disease include age, mode of delivery, parity, and duration of menopause. Regarding parity, every subsequent birth after the first one increases the likelihood of a UP incident by approximately 125%. Conclusions: Most women with UP did not exhibit severe symptoms, as UP typically does not manifest symptoms until it reaches a final stage. Considering the population aging and the increase in morbidity, a regular pelvic organ prolapse (POP) checkup should be established to facilitate early recognition, prevention, and treatment of symptoms. This study offers a potential tool for non-invasive screening to facilitate identifying UP in women early, which has not been previously reported. Full article