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Abstract A noninvasive, immediate, and convenient method for assessing muscle tissue status during exercise-induced muscle damage (EIMD) has not been established. This study was designed to assess and determine parameters suitable for measuring EIMD after eccentric exercise, using multi-frequency bioimpedance analysis (BIA). Thirty-five young male participants performed dumbbell exercises with their left arm, and their BIA parameters were measured at various time points up to 168 h post exercise using a multi-frequency BIA device. At all-time points, intra and extracellular water content was greater in the left arm than in the right arm, whereas the impedance, reactance, resistance, and phase angle were lower in the left arm than in the right arm. Established EIMD indices, such as maximal isometric voluntary contraction, were measured and used in correlational analyses. Only reactance was correlated with biomarkers, indicating muscle damage (r = − 0.56 to − 0.49). Furthermore, reactance was found to correlate well with indirect indicators of EIMD, suggesting that it may be a suitable marker for evaluating EIMD. However, the relationship with the limited evaluation indices employed in this study is constrained. Future studies should investigate the correlation between reactance and direct damage indicators, such as structural damage, observed in biopsies.

The purpose of this study was to examine the feasibility of an intervention program focused on career transition psychological support for retired Olympic athletes in Japan. It was expected that the intervention program would significantly enhance knowledge about career transition support for athletes and also enhance attitudes towards seeking career transition support. 24 retired Japanese female athletes who had competed in the Olympics between 1996 and 2016 participated in the study. The content of the support program was delivered in a three-part workshop, focusing on career transitions in sport, post-retirement psychological adjustment difficulties, and coping strategies. Pre-workshop and post-workshop assessment were conducted. Career transition support knowledge for athletes and attitudes about seeking career transition support significantly increased. The current results suggest that increasing knowledge helps retired and retiring athletes seek professional help. Practical applications and future research recommendations are provided.

This study aimed to evaluate the effects of different target distances on various leg joints in the fencing lunge (lunge). Fifteen fencers performed the lunges from three different target distances (normal, short, and long). Joint angle data in the sagittal plane of the hip, knee, and ankle of the front and rear legs were measured using a 3D motion analysis system (Miqus M3). Joint angle variables were compared between each distance using a one-way repeated-measures analysis of variance and Friedman tests (p < 0.05). The results showed significant differences in various extensions, ranges of motion, and flexion angles in the measured joints for all distances. As the distances increased, there was greater flexion of the rear knee joint early during the lunge, followed by greater extension of the rear hip and knee joints, greater plantar flexion of the rear ankle joint, and higher peak velocity of the body center of mass. Furthermore, target distance extension was suggested to significantly affect front hip and knee joint flexion during the braking phase of the lunge. This study provides insight and information valuable to coaches and fencers operating in actual competition settings.

The Noto Peninsula Earthquake that occurred on January 1, 2024, caused significant damage to the Noto Peninsula and surrounding areas. This study aims to contribute to evidence-based disaster response by identifying the information needs of the affected areas through a data-driven approach using web search queries. Specifically, the study focuses on the Oku-Noto region in Ishikawa Prefecture, which includes Wajima City, Suzu City, Anamizu Town, and Noto Town. By assigning anomaly scores to web search queries and applying statistical processing such as standardized scores, kurtosis, and skewness, this study elucidates the patterns of changes in information needs in the affected areas. From January 1, 2024, to June 23, 2024, a total of 15,107 search queries were extracted based on anomaly scoring, and 4790 queries were obtained after filtering out irrelevant data. These search queries were classified into five categories: “Hazard and Situation,” “Transportation,” “Critical Infrastructure,” “Coping and Recovery,” and “Daily Life.” The analysis revealed two distinct periods—around 10 days and one month after the disaster—where significant changes in the nature of information needs were observed. Furthermore, information needs related to “Transportation,” particularly road and traffic information, were relatively high, with a notable emphasis on the Noto Satoyama Kaido and Kanazawa Station.

We investigated the influence of advance lunging in fencing from the perspective of velocity and lower limb joint angles to identify how the joint angles contribute to the peak velocity in a lunge with advance (LWA). Fourteen skilled athletes (age: 19.6 ± 0.9 years, height: 171.2 cm ± 5.2 cm, weight: 63.7 kg ± 5.3 kg, and fencing experience: 9.7 ± 3.1 years) participated by performing two types of attacking movements, and data were collected with a 3D movement analysis system. A correlation between the peak velocity of the body center of mass (CoM) in an advance lunge and several joint angle variables (rear hip peak flexion angle (r = 0.63), rear ankle peak dorsiflexion angle (r = −0.66), rear ankle range of motion (r = −0.59), and front hip peak extension angle (r = 0.54)) was revealed. In addition, the joint angle variables that significantly predicted peak CoM velocity during an LWA were the rear knee peak flexion angle (β = 0.542), rear knee peak extension angle (β = −0.537), and front knee peak extension angle (β = −0.460). Our findings suggest that the rear leg hip joint, rear leg ankle joint, and front leg hip joint may control the acceleration generated by an LWA. Furthermore, more flexion of the rear leg knee joint in the early phase of the lunge and greater extension of the rear and front leg knee joints at the end of the lunge phase may help increase peak velocity.

IntroductionThe fencing lunge (lunge), characterized by minimal body rotation, offers a movement well-suited for 2D video analysis. However, to the best of our knowledge, the validity of 2D video analysis for fencing has not been verified. This study aimed to validate 2D video analysis by comparing lower limb joints (hip, knee, and ankle joints) angles during lunge using both 2D video analysis and 3D motion analysis methods.MethodsTwenty-two male fencers performed lunge trials that were simultaneously recorded using eight motion capture cameras (Qualisys Miqus M1) and two digital video cameras (Sony AX-450 and AX450a).ResultsThe 2D video analysis results exhibited an extremely large correlation in knee joint angles of the front and rear legs in the sagittal with those from 3D motion analysis (r = 0.93–0.99). However, while a robust correlation was found between the ankle joint angles of the front and rear legs (r = 0.82–0.84), a large bias was also observed (−5.23° to −21.31°). Conversely, for the hip joints of the rear leg, a moderate correlation (r = 0.31) and a large bias (−10.89°) were identified.ConclusionsThe results of this study will contribute to the development of coaching using 2D video analysis in competition settings because such analysis can be a useful alternative to 3D motion analysis when measuring the knee joint angle of the front leg and rear leg in the sagittal plane. However, for the ankle joint angle, further research on the optimal shooting position and height of the digital video camera is needed, whereas for the hip joint angle, 3D motion analysis is recommended at this time.

Ice slurry ingestion enhances exercise performance by lowering the core body temperature. However, an operational issue related to this ingestion is the requirement for a high intake of 7.5 g·kg-1 to produce the desired effects. We investigated the effects of the intake of low amounts of ice slurry at −2°C on the tympanic temperature and exercise performance during repeated high-intensity intermittent exercises in a hot environment. This study was a randomized, crossover study, with a 6-day washout period. Twelve university rugby union players performed two 30-min sessions of high-intensity intermittent exercises separated by a 15-min half-time break on a cycle ergometer in a hot environment (28.8°C ± 0.1°C, 49.5% ± 0.6% relative humidity). The participants ingested 450 g of −2°C-ice slurry (ICE), or a 30°C-beverage (CON) having the same composition as ICE, or 30°C-water (WAT) during the half-time break. The tympanic temperature and skin temperature were measured as the physiological data, and the peak power and mean power as the exercise performance data. The tympanic temperature at the half-time break and beginning of the 2nd session was significantly lower in the ICE group as compared with the CON and WAT groups. The skin temperature at the half-time break was significantly lower in the ICE group as compared with the WAT group. While the peak power and mean power during the 2nd session were significantly greater in the ICE group as compared with the CON and WAT groups. Our findings suggest that even the intake of lower amounts, as compared with those used in previous studies, of low-temperature ice slurry can reduce the body temperature and improve the peak power. These results suggest that intake of low-temperature ice slurry as a strategy for internal body cooling is useful for improving endurance exercise performance in hot environments.

Feature selection is crucial to the credit-scoring process, allowing for the removal of irrelevant variables with low predictive power. Conventional credit-scoring techniques treat this as a separate process wherein features are selected based on improving a single statistical measure, such as accuracy; however, recent research has focused on meaningful business parameters such as profit. More than one factor may be important to the selection process, making multi-objective optimization methods a necessity. However, the comparative performance of multi-objective methods has been known to vary depending on the test problem and specific implementation. This research employed a recent hybrid non-dominated sorting binary Grasshopper Optimization Algorithm and compared its performance on multi-objective feature selection for credit scoring to that of two popular benchmark algorithms in this space. Further comparison is made to determine the impact of changing the profit-maximizing base classifiers on algorithm performance. Experiments demonstrate that, of the base classifiers used, the neural network classifier improved the profit-based measure and minimized the mean number of features in the population the most. Additionally, the NSBGOA algorithm gave relatively smaller hypervolumes and increased computational time across all base classifiers, while giving the highest mean objective values for the solutions. It is clear that the base classifier has a significant impact on the results of multi-objective optimization. Therefore, careful consideration should be made of the base classifier to use in the scenarios.

Credit scoring of financially excluded persons is challenging for financial institutions because of a lack of financial data and long physical distances, which hamper data collection. The remote collection of alternative data has the potential to overcome these challenges, enabling credit access for such individuals. Whereas alternative data sources such as mobile phones have been investigated by previous researchers, this research proposes the integration of mobile-phone, satellite, and public geospatial data to improve credit evaluations where financial data are lacking. An approach to integrating these disparate data sources involving both spatial and temporal analysis methods such as spatial aggregation was employed, resulting in various data combinations. The resulting data sets were used to train classifiers of varying complexity, from logistic regression to ensemble learning. Comparisons were based on various performance metrics, including accuracy and the area under the receiver operating-characteristic curve. The combination of all three data sources performed significantly better than mobile-phone data, with the mean classifier accuracy and F1 score improving by 18% and 0.149, respectively. It is shown how these improvements can translate to cost savings for financial institutions through a reduction in misclassification errors. Alternative data combined in this manner could enhance credit provision to financially excluded persons while managing associated risks, leading to greater financial inclusion.

Numerous studies have been conducted on indoor and outdoor seamless positioning and indoor–outdoor detection methods. However, the classification of real space into two types, outdoor space and indoor space, is difficult. One type of space that is difficult to classify is top-bounded space, which can be observed in commercial facilities, logistics facilities, and street-facing sidewalks. In this study, we designed a method for detecting stays in three spatial contexts: Outdoor, top-bounded space, and indoor. This method considers elongated top-bounded spaces covered with a roof and open on one of the sides. Specifically, we selected Global Positioning System (GPS) satellites for stay detection based on the simple extraction of the spatial characteristics of a top-bounded space and designed a decision flow using fuzzy inference based on the signal-to-noise ratio (SNR) of the selected GPS satellites. Moreover, we conducted an evaluation experiment to verify the effectiveness of the proposed method and confirmed that it could correctly detect the stay in three spatial contexts, outdoor, top-bounded space, and indoor, with a high probability of 93.1%.

With the widespread use of indoor positioning technology, various services based on this technology are beginning to be offered to consumers and industrial applications. In the case of logistics facilities, in addition to indoor and outdoor spaces, there are top-bounded spaces (TBSs): elongated areas that are covered with roofs or eaves on the upper parts of buildings. The sides of such spaces are open, and workers and forklifts work in these areas. Only a few studies have been conducted on positioning methods for this unusual environment, and the way by which Signal-to-Noise Ratio (SNR) of Global Positioning System (GPS) changes with the stay in TBSs is unclear. Therefore, we conducted preliminary experiments and confirmed that TBS dwellings are difficult to stably detect with existing methods due to the combination of satellites with variable and unchanged SNRs. In this study, we designed a simple processing flow for selecting satellites with high probabilities of changing SNRs by using the spatial characteristics of TBSs as parameters (height, depth, and side opening orientation). We propose a method to detect the stay in TBSs using the SNR change rates of the selected satellites. As a result of evaluation experiments with three TBSs, we successfully detected the stay in TBSs with about 30% higher probability than those of an existing method.

Due to the associated and substantial efforts of many stakeholders involved in malaria containment, the disease burden of malaria has dramatically decreased in many malaria-endemic countries in recent years. Some decades after the past efforts of the global malaria eradication program, malaria elimination has again featured on the global health agenda. While risk distribution modeling and a mapping approach are effective tools to assist with the efficient allocation of limited health-care resources, these methods need some adjustment and reexamination in accordance with changes occurring in relation to malaria elimination. Limited available data, fine-scale data inaccessibility (for example, household or individual case data), and the lack of reliable data due to inefficiencies within the routine surveillance system, make it difficult to create reliable risk maps for decision-makers or health-care practitioners in the field. Furthermore, the risk of malaria may dynamically change due to various factors such as the progress of containment interventions and environmental changes. To address the complex and dynamic nature of situations in low-to-moderate malaria transmission settings, we built a spatiotemporal model of a standardized morbidity ratio (SMR) of malaria incidence, calculated through annual parasite incidence, using routinely reported surveillance data in combination with environmental indices such as remote sensing data, and the non-environmental regional containment status, to create fine-scale risk maps. A hierarchical Bayesian frame was employed to fit the transitioning malaria risk data onto the map. The model was set to estimate the SMRs of every study location at specific time intervals within its uncertainty range. Using the spatial interpolation of estimated SMRs at village level, we created fine-scale maps of two provinces in western Cambodia at specific time intervals. The maps presented different patterns of malaria risk distribution at specific time intervals. Moreover, the visualized weights estimated using the risk model, and the structure of the routine surveillance network, represent the transitional complexities emerging from ever-changing regional endemic situations.

The disease burden of malaria has decreased as malaria elimination efforts progress. The mapping approach that uses spatial risk distribution modeling needs some adjustment and reinvestigation in accordance with situational changes. Here we applied a mathematical modeling approach for standardized morbidity ratio (SMR) calculated by annual parasite incidence using routinely aggregated surveillance reports, environmental data such as remote sensing data, and non-environmental anthropogenic data to create fine-scale spatial risk distribution maps of western Cambodia. Furthermore, we incorporated a combination of containment status indicators into the model to demonstrate spatial heterogeneities of the relationship between containment status and risks. The explanatory model was fitted to estimate the SMR of each area (adjusted Pearson correlation coefficient R2 = 0.774; Akaike information criterion AIC = 149.423). A Bayesian modeling framework was applied to estimate the uncertainty of the model and cross-scale predictions. Fine-scale maps were created by the spatial interpolation of estimated SMRs at each village. Compared with geocoded case data, corresponding predicted values showed conformity [Spearman's rank correlation r = 0.662 in the inverse distance weighed interpolation and 0.645 in ordinal kriging (95% confidence intervals of 0.414-0.827 and 0.368-0.813, respectively), Welch's t-test; Not significant]. The proposed approach successfully explained regional malaria risks and fine-scale risk maps were created under low-to-moderate malaria transmission settings where reinvestigations of existing risk modeling approaches were needed. Moreover, different representations of simulated outcomes of containment status indicators for respective areas provided useful insights for tailored interventional planning, considering regional malaria endemicity.