Search Results (541)

Accurate diagnosis of gastrointestinal (GI) diseases typically requires invasive procedures or imaging studies that pose the risk of various post-procedural complications or involve radiation exposure. Bowel sounds (BSs), though typically described during a GI-focused physical exam, are highly inaccurate and variable, with low clinical value in diagnosis. Interpretation of the acoustic characteristics of BSs, i.e., using a phonoenterogram (PEG), may aid in diagnosing various GI conditions non-invasively. Use of artificial intelligence (AI) and improvements in computational analysis can enhance the use of PEGs in different GI diseases and lead to a non-invasive, cost-effective diagnostic modality that has not been explored before. The purpose of this work was to develop an automated AI model, You Only Listen Once (YOLO), to detect prominent bowel sounds that can enable real-time analysis for future GI disease detection and diagnosis. A total of 110 2-minute PEGs sampled at 44.1 kHz were recorded using the Eko DUO^® stethoscope from eight healthy volunteers at two locations, namely, left upper quadrant (LUQ) and right lower quadrant (RLQ) after IRB approval. The datasets were annotated by trained physicians, categorizing BSs as prominent or obscure using version 1.7 of Label Studio Software^®. Each BS recording was split up into 375 ms segments with 200 ms overlap for real-time BS detection. Each segment was binned based on whether it contained a prominent BS, resulting in a dataset of 36,149 non-prominent segments and 6435 prominent segments. Our dataset was divided into training, validation, and test sets (60/20/20% split). A 1D-CNN augmented transformer was trained to classify these segments via the input of Mel-frequency cepstral coefficients. The developed AI model achieved area under the receiver operating curve (ROC) of 0.92, accuracy of 86.6%, precision of 86.85%, and recall of 86.08%. This shows that the 1D-CNN augmented transformer with Mel-frequency cepstral coefficients achieved creditable performance metrics, signifying the YOLO model’s capability to classify prominent bowel sounds that can be further analyzed for various GI diseases. This proof-of-concept study in healthy volunteers demonstrates that automated BS detection can pave the way for developing more intuitive and efficient AI-PEG devices that can be trained and utilized to diagnose various GI conditions. To ensure the robustness and generalizability of these findings, further investigations encompassing a broader cohort, inclusive of both healthy and disease states are needed. Full article

This study proposes a non-semantic multimodal approach to predict segment access frequency (SAF) in lecture archives. Such archives, widely used as supplementary resources in modern education, often consist of long, unedited recordings that are difficult to navigate and review efficiently. The predicted SAF, an indicator of student viewing behavior, serves as a practical proxy for student engagement. The increasing volume of recorded material renders manual editing and annotation impractical, making the automatic identification of high-SAF segments crucial for improving accessibility and supporting targeted content review. The approach focuses on lecture archives from a real-world blended learning context, characterized by resource constraints such as no specialized hardware and limited student numbers. The model integrates multimodal features from instructor’s actions (via OpenPose and optical flow), audio spectrograms, and slide page progression—a selection of features that makes the approach applicable regardless of lecture language. The model was evaluated on 665 labeled one-minute segments from one such course. Experiments show that the best-performing model achieves a Pearson correlation of 0.5143 in 7-fold cross-validation and 61.05% average accuracy in a downstream three-class classification task. These results demonstrate the system’s capacity to enhance lecture archives by automatically identifying key segments, which aids students in efficient, targeted review and provides instructors with valuable data for pedagogical feedback. Full article

This study focuses on surface wind analysis at the small regional airport in Svidnik, used primarily for pilot training under daytime VFR conditions. Due to the complex local terrain and lack of prior meteorological data, an automatic weather station was installed, collecting over 208,000 wind measurements over a two-year period at ten-minute intervals. The dataset was processed using hierarchical filtering and statistical selection, and visualized via wind rose diagrams. The results confirmed a dominant southeastern wind component, supporting the current runway orientation (01/19). However, a less frequent easterly wind direction was identified as a safety concern, causing turbulence near the runway due to terrain and vegetation. This is particularly critical for trainee pilots during final approach and landing. Statistical analysis showed that easterly winds, though less common, appear year-round with a peak in summer. Pearson correlation and linear regression confirmed a significant relationship between the number of easterly wind days and their measurement frequency. Daytime winds were stronger than nighttime, justifying the focus on daylight data. The study provides practical recommendations for training flight safety and highlights the value of localized wind monitoring at small airports. The presented methodology offers a framework for improving operational awareness and reducing risk in complex environments. Full article

Accurate rainfall quantile estimation is critical for urban flood management, particularly given the escalating climate change impacts. This study comprehensively compared at-site frequency analysis and regional frequency analysis for sub-hourly rainfall quantile estimation, using data from 27 sites across Seoul. The analysis focused on Seoul’s disaster prevention framework (30-year and 100-year return periods). Employing L-moment statistics and Monte Carlo simulations, the rainfall quantiles were estimated, the methodological performance was evaluated, and Seoul’s current disaster prevention standards were assessed. The analysis revealed significant spatio-temporal variability in Seoul’s precipitation, causing considerable uncertainty in individual site estimates. A performance evaluation, including the relative root mean square error and confidence interval, consistently showed regional frequency analysis superiority over at-site frequency analysis. While at-site frequency analysis demonstrated better performance only for short return periods (e.g., 2 years), regional frequency analysis exhibited a substantially lower relative root mean square error and significantly narrower confidence intervals for larger return periods (e.g., 10, 30, 100 years). This methodology reduced the average 95% confidence interval width by a factor of approximately 2.7 (26.98 mm versus 73.99 mm). This enhanced reliability stems from the information-pooling capabilities of regional frequency analysis, mitigating uncertainties due to limited record lengths and localized variabilities. Critically, regionally derived 100-year rainfall estimates consistently exceeded Seoul’s 100 mm disaster prevention threshold across most areas, suggesting that the current infrastructure may be substantially under-designed. The use of minute-scale data underscored its necessity for urban hydrological modeling, highlighting the inadequacy of conventional daily rainfall analyses. Full article

This paper discusses a new method and sensor for the detection of ultra-low masses, such as those of viruses and biomarkers. The sensor contains three microcantilevers with a common substrate that vibrates. The detection method processes phase-shifted signals from Wheatstone bridges from connected piezoresistors formed on the vibrating microcantilevers and passive resistors on the rigid substrate. Each microcantilever has a gold pad that can be either active or passive. When a mass is detected, the shape of the amplitude–frequency response changes. The proposed method has high mass sensitivity and can respond up to one minute, which is an important challenge for nanocantilever sensors. Full article

We have developed an adjustable optical fiber Bragg grating (FBG) sensing unit for monitoring nasal breathing. The FBG sensing unit can accommodate individuals with varying facial dimensions by adjusting the connecting holes of the ear hangers. We employed two FBG configurations: an encapsulated FBG within a silicon tube (FBG₁) and a bare FBG (FBG₂). Calibration experiments show the temperature sensitivities of 6.77 pm/°C and 6.18 pm/°C, respectively, as well as the pressure sensitivities of 2.05 pm/N and 1.18 pm/N, respectively. We conducted breathe monitoring tests on male and female volunteers under the resting and the motion states. For the male volunteer, the breathing frequency is 13.48 breaths per minute during the rest state and increases to 23.91 breaths per minute during the motion state. For the female volunteer, the breathing frequency is 14.12 breaths per minute during rest and rises to 24.59 breaths per minute during motion. Experimental results show that the FBG sensing unit can effectively distinguish breathing rate for the same person in different states. In addition, we employed a random forest algorithm to assess the importance of two sensors in breathing monitoring applications. The findings indicate that FBG₁ outperforms FBG₂ in monitoring performance, highlighting that pressure plays a positive impact in enhancing the accuracy of breathing monitoring. Full article

As climate change alters subarctic ecosystems and human activities in Alaska, ecological baselines are critical for long-term conservation. We applied an ecoacoustic approach to characterize the ecological conditions of a rapidly deglaciating region in Kenai Fjords National Park, Alaska. Using automated recording units deployed at increasing distances from a road, we collected over 120,000 one-minute audio samples during the tourist seasons of 2021 and 2022. Ecoacoustic indices—Sonic Heterogeneity Index (SHI_tf), Spectral Sonic Signature (SSS), Weighted Proportion of Occupied Frequencies (wPOF), and Normalized Difference Sonic Heterogeneity Index (NDSHI)—were used to measure spatio-temporal patterns of the sonoscape. Results revealed higher sonic heterogeneity near the road attributed to technophony (vehicles) and geophony (wind) that spanned across the frequency spectrum, masking mid-high frequency biophony. Seasonal phenology and diel variations reflected ecological and human rhythms, including biophony from the dawn chorus from May–June, technophony from vehicle-based tourism from July–September, and decreased sonic activity in the form of geophonic ambience in October. Low-frequency geophonies were prevalent throughout the sonoscape with more natural sounds at greater distances from the road. Our findings demonstrate the benefits of using ecoacoustic methods to assess ecosystem dynamics for establishing ecological baselines useful for future comparisons in rapidly changing environments. Full article

This study aimed to compare in-match physical and technical requirements of cerebral palsy (CP) football players across different national and international competitive contexts. A total of 79 male outfield players participated in 62 official matches across 3 competitive phases of the Spanish National CP Football League (Regular Phase, Consolation Phase, and Playoffs) and the IFCPF World Cup. Inertial measurement units (IMUs) were used to record locomotor and technical variables during each match. A subset of 10 players was tracked across all phases. Physical demands were normalized per minute of play and analyzed using one-way and repeated-measures ANOVAs. Results revealed that physical requirements during the World Cup were up to three times higher than during national-level matches, with significantly greater maximum velocities, high-intensity distances, and frequencies of accelerations and decelerations (p < 0.001, ηp² > 0.40). Playoffs also imposed significantly greater physical requirements compared to Regular and Consolation Phases. International matches showed a markedly higher number of ball contacts, indicating increased technical involvement. These patterns were consistent in both the full sample and the longitudinal subsample, suggesting that competitive level—rather than player characteristics alone— strongly modulates physical output during the competition. These findings underscore the need for context-specific training and load management strategies to prepare athletes for the elevated demands of high-level CP football competition. Full article

Community-based collaboration has been touted as an effective model for forest governance because it promotes democratized decision-making and stakeholder engagement to address landscape-scale problems. Forest collaboratives are assumed to be heterogeneous, consisting of stakeholders with a diverse range of interests. Few studies have systematically explored variables associated with collaborative composition. We identify six elements of collaborative composition for investigation: size, stakeholder diversity, balance, locality–diversity, core attendance, and cross-participation. This exploratory study examines five forest collaborative groups in eastern Oregon (USA). We analyzed meeting minutes over an 18-month period to track attendance and evaluate who shows up and at what frequency. While forest collaboratives vary in size, larger collaboratives are more heterogeneous, reflecting greater diversity in terms of stakeholders represented, and have a higher proportion of high-frequency (‘core’) attendees. Core attendees and attendees who participated across multiple forest collaboratives regionwide represent a much narrower set of stakeholder interests. Collaboratives’ attendees reflected a mix of local and non-local organizations. The results raise questions about whether collaborative groups represent the array of public interests in planning for forest management. Full article

Forecasting financial time series is challenging due to their intrinsic nonlinearity, high volatility, and complex dependencies across temporal scales. This study introduces MSGformer, a novel hybrid architecture that integrates multi-scale graph neural networks (MSGNet) with Transformer encoders to capture both local temporal fluctuations and long-term global trends in high-frequency financial data. The MSGNet module constructs multi-scale representations using adaptive graph convolutions and intra-sequence attention, while the Transformer component enhances long-range dependency modeling via multi-head self-attention. We evaluate MSGformer on minute-level stock index data from the Chinese A-share market, including CSI 300, SSE 50, CSI 500, and SSE Composite indices. Extensive experiments demonstrate that MSGformer significantly outperforms state-of-the-art baselines (e.g., Transformer, PatchTST, Autoformer) in terms of MAE, RMSE, MAPE, and R². The results confirm that the proposed hybrid model achieves superior prediction accuracy, robustness, and generalization across various forecasting horizons, providing an effective solution for real-world financial decision-making and risk assessment. Full article

This study investigates a novel pulsed electromagnetic field (PEMF) device for dynamic testing and structural health monitoring. The research utilises a PEMF generator CD-1501 with a maximum energy capacity of 0.5 kJ and a flat multifilament coil (IC-1) with a 100 mm diameter. Experiments were conducted on a model steel stand with two joint configurations, using steel plates of 4 mm and 8 mm thickness. The device’s efficacy was evaluated through oscillation pattern analysis and spectral characteristics. Results demonstrate the device’s ability to differentiate between joint states, with the 4 mm plate configuration showing a 15% reduction in high-frequency components compared to the 8 mm plate. Fundamental resonant frequencies of 3D-printed specimens were observed near 5100 Hz, with Q-factors ranging between 200 and 300. The study also found that a 10% increase in volumetric porosity led to a 7% downward shift in resonant frequencies. The developed PEMF device, operating at 50–230 V and delivering 1–5 pulses per minute, shows promise for rapid, non-destructive monitoring of structural joints. When combined with the coaxial correlation method, the system demonstrates enhanced sensitivity in detecting structural changes, utilising an electrodynamic actuator (10 Hz to 2000 Hz range). This integrated approach offers a 30% improvement in early-stage degradation detection compared to traditional methods. Full article

Affective touch (stroking the skin at velocities between 1 and 10 cm/s) is generally perceived as pleasant. However, this pleasant sensation diminishes with continuous stimulation over several minutes, with substantial individual variability in the habituation process. This study aimed to identify individual characteristics associated with the decline in the hedonic value of prolonged affective touch. Eighty-one female participants (mean age = 26 years) received continuous stroking on their forearms for 10 min at two distinct velocities: 3 cm/s (affective touch) and 30 cm/s (nonaffective touch). Every 100 s, participants rated the perceived pleasantness of the stimulation. Regression analyses were conducted to examine whether participants’ age, attitude toward touch by an unfamiliar person, recalled positive touch experiences during childhood, sympathy toward the toucher, reported symptoms of anxiety, depression, or somatization, and order of touch conditions predicted changes in their responses. On average, the perceived pleasantness of touch declined over time. The extent of the decline and individual variability in pleasantness ratings were not significantly associated with the selected predictors. However, higher overall ratings of affective touch pleasantness were linked to greater sympathy toward the toucher, lower levels of depression and somatization, and a lower frequency of recalled positive touch experiences during childhood. Affective touch was perceived as more pleasant when it was preceded by the nonaffective touch condition, compared to when the order was reversed. Order effects, the rapid decline, and substantial individual variability in the perceived pleasantness of prolonged affective touch should be considered in both research and therapeutic applications. Full article

In the context of Doppler radar, studies have examined the changes in the phase shift of the S₂₁ transmission coefficient related to minute movements of the human chest as a response to breathing or heartbeat. Detecting human vital signs remains a challenge, especially when obstacles interfere with the attempt to detect the presence of life. The sensitivity of a measurement system’s perception of vital signs is highly dependent on the monitoring systems and antennas that are used. The current work proposes a computational approach that aims to extract an empirical law of the dependence of the phase shift of the transmission coefficient (S₂₁) on the sensitivity at reception, based upon a set of four parameters. These variables are as follows: (a) the frequency of the continuous wave utilized; (b) the antenna type and its gain/directivity; (c) the electric field strength distribution on the chest surface (and its average value); and (d) the type of material (dielectric properties) impacted by the incident wave. The investigated frequency range is (1–20) GHz, while the simulations are generated using a doublet of dipole or gain-convenient identical Yagi antennas. The chest surface is represented by a planar rectangle that moves along a path of only 3 mm, with a step of 0.3 mm, mimicking respiration movement. The antenna–target system is modeled in the computational space in each new situation considered. The statistics illustrate the multiple regression function, empirically extracted. This enables the subsequent building of a continuous-wave bio-radar Doppler system with controlled and improved sensitivity. Full article

Resistance training (RT) has been shown to produce beneficial effects, including on quality of life, renal function, physical fitness, and survival rates in kidney transplant for 24 recipients. However, the optimal periodization of load components for this population remains unclear, as no consensus has been established. This study aimed to characterize the load components of RT programs in kidney transplant recipients. A scoping review was conducted following the Preferred Reporting Items for Systematic Reviews and Meta-Analyses Extension for Scoping Reviews (PRISMA-ScR). The literature search was performed up to October 2024 in MEDLINE/PubMed, the Web of Science Core Collection, SCOPUS, ScienceDirect, and SPORTDiscus. Only studies that included RT as part of the intervention were considered. The RT variables analyzed included intervention duration, weekly frequency, session duration, number and types of exercises, intensity, number of sets, rest time between sets, progressive overload, and execution velocity. A total of 645 studies were identified, of which 15 met the eligibility criteria and were selected for analysis. The primary strategy for intensity control was based on the percentage of one-repetition maximum (%1RM), with training zones ranging from 30% to 80%. The number of sets varied from two to eight, while repetitions ranged from 10 to 20. The rest intervals between sets lasted between one and five minutes. The most highly implemented type of resistance involved the use of dumbbells, body weight, and elastic bands. A high degree of heterogeneity was identified in load periodization parameters, highlighting a lack of consensus in exercise prescription for this population. However, this review established general criteria that can serve as a reference for exercise professionals to develop more structured and effective training programs. Full article

Background/Objectives: COVID-19 poses significant physiological challenges for athletes, particularly those engaged in high-intensity intermittent sports such as ice hockey. This study aimed to evaluate the impact of SARS-CoV-2 infection—especially symptomatic cases—on aerobic and anaerobic performance in professional ice hockey players. Methods: Fifty athletes from the Polish Hockey League were assigned to three groups: control (CG, n = 13), asymptomatic COVID-19 (NSG, n = 28), and symptomatic COVID-19 with post-infection SpO₂ < 90% (WSG, n = 9). Each underwent assessments at three time points—pre-preparatory period 2020/2021, post-preparatory period 2020/2021, and pre-preparatory period 2021/2022. Aerobic capacity was measured via a progressive cycle ergometer test (VO₂max, VO₂ at lactate threshold [VO_2Lt], minute ventilation [V’E], breathing frequency [BF], and lactate clearance rate [ΔLa]), and anaerobic capacity via a 30 s Wingate test (relative mean power). Results: Compared with CG and NSG, symptomatic athletes exhibited significant post-infection declines in VO₂max (48.2 ± 2.9 vs. 56.2 ± 6.2 and 54.6 ± 3.9 mL/kg/min; p = 0.006, d = 1.56 vs. CG; p < 0.024, d = 1.79 vs. NSG) and VO_2Lt (p < 0.05). Relative mean power also decreased in WSG (p < 0.05). In contrast, CG and NSG improved or maintained these metrics over the same period. Symptomatic players showed elevated BF post infection (p = 0.022, d = 1.72) and reduced V’E (p = 0.035; d = 0.83), while ΔLa was markedly lower (p = 0.0004; d = 2.86). Conclusions: SARS-CoV-2 infection, particularly symptomatic cases, can significantly impair both aerobic and anaerobic capacity in elite hockey players. Targeted recovery protocols are essential for restoring performance in affected athletes. Full article