Search Results (1,718)

In this pilot study, the crossing behavior of elementary school students commuting on bicycles was investigated with the objective of enhancing safety around pedestrian crossings within school zones. With a noticeable increase in crashes involving young cyclists near schools, this research assessed the effectiveness of visual nudges in the form of red strips displaying “CYCLISTS DISMOUNT” instructions. Initial observations indicated a lack of compliance with dismounting regulations. After the initial observations, a specific elementary school was selected for the implementation of the nudging intervention and additional pre- (N = 91) and post-intervention (N = 71) observations. The pre-intervention observations again revealed poor adherence to the regulations requiring cyclists to dismount at specific points. Following our targeted intervention, the post-intervention observations marked an improvement in compliance. Indeed, the visual nudge effectively communicated the necessity of dismounting at a critical location, leading to a higher rate of adherence among cyclists (52.74% pre-intervention, 97.18% post-intervention). Although it also indirectly affected the behavior of the accompanying adult, who more often held hands with their children while crossing, this effect was weaker than the direct effect on dismounting behavior (20.88% pre-intervention, 39.44% post-intervention). The findings of the current pilot study underscore the possible impact of nudging on behavior and advocate for a combined approach utilizing physical nudges to bolster safety within school zones. Follow-up research, including, for instance, multiple sites, long-term effects, or children traveling alone, is called for. Full article

Background: Ultrasound (U/S) can be used to evaluate skeletal muscle characteristics in clinical and sports settings. Handheld U/S devices have recently emerged as a cheaper and portable alternative to conventional U/S systems. However, further research is warranted on their reliability. We assessed the reliability and inter-device agreement between a handheld U/S device (Clarius L15 HD₃) and a more conventional U/S system (GE LOGIQ e) for measuring the thickness of the rectus femoris (RF) and vastus intermedius (VI). Methods: Cross-sectional images of the RF and VI muscles were obtained in 20 participants by two assessors, and on two separate occasions by one of those assessors, using the Clarius L15 HD₃ and GE LOGIQ e devices. RF and VI thickness measurements were obtained to determine the intra-rater reliability, inter-rater reliability, and inter-device agreement. Results: All intraclass correlation coefficients (ICCs) were above 0.9 for intra-rater reliability (range: 0.94 to 0.97), inter-rater reliability (ICC: 0.97), and inter-device agreement (ICC: 0.98) when comparing the two devices in assessing RF and VI thickness. For the RF, the Bland–Altman plot revealed a mean difference of 0.06 ± 0.07 cm, with limits of agreement ranging from 0.21 to −0.09, whereas for the VI, the Bland–Altman plot showed a mean difference of 0.07 ± 0.10 cm, with limits of agreement ranging from 0.27 to −0.13. Conclusions: The handheld Clarius L15 HD₃ was reliable and demonstrated high agreement with the more conventional GE LOGIQ e for assessing the thickness of the RF and VI in young, healthy adults. Full article

Rising volumes of textile waste necessitate the development of more efficient recycling systems, with a primary focus on the optimization of sorting technologies. Near-infrared (NIR) spectroscopy is a state-of-the-art method for fiber identification; however, its accuracy in quantifying textile blends, particularly common polyester/cotton blend textiles, still requires refinement. This study explores the potential and limitations of NIR spectroscopy for quantifying cotton content in post-consumer textiles. A lab-scale NIR sorter and a handheld NIR spectrometer in complementary wavelength ranges were applied to a diverse range of post-consumer textile samples to test model accuracies. Results show that the commonly assumed 10% accuracy threshold in industrial sorting can be exceeded, especially when excluding textiles with <35% cotton content. Identifying and excluding the range of non-linearity significantly improved the model’s performance. The final models achieved an RMSEP of 6.6% and bias of −0.9% for the NIR sorter and an RMSEP of 3.1% and bias of −0.6% for the handheld NIR spectrometer. This study also assessed how textile characteristics—such as color, structure, product type, and alkaline treatment—affect spectral behavior and model accuracy, highlighting their importance for refining quantification when high-purity inputs are needed. By identifying current limitations and potential sources of errors, this study provides a foundation for improving NIR-based models. Full article

This study investigates how remote multiplayer gameplay, enabled through Augmented Reality (AR), transforms spatial decision-making and enhances player experience in a location-based augmented reality game (LBARG). A remote multiplayer handheld-based AR game was designed and evaluated on how it influences players’ spatial decision-making strategies, engagement, and gameplay experience. In a user study involving 60 participants, we compared remote gameplay in our AR game with traditional hide-and-seek. We found that AR significantly transforms traditional gameplay by introducing different spatial interactions, which enhanced spatial decision-making and collaboration. Our results also highlight the potential of AR to increase player engagement and social interaction, despite the challenges posed by the added navigation complexities. These findings contribute to the engaging design of future AR games and beyond. Full article

This study aimed to compare isometric strength, range of motion (RoM), and strength ratios of shoulder internal and external rotators between female volleyball and hand ball players Twenty-five volleyball players (age = 21.8 ± 4.8 years, height = 178.5 ± 7.1 cm, mass = 69.3 ± 7.7 kg) and twenty-four handball players (age = 19.5 ± 2.9 years, height = 169.7 ± 6.4 cm, mass = 67.6 ± 8.4 kg), all competing in the Slovenian 1st national league, participated. Maximal isometric strength and passive RoM of internal and external rotation were measured bilaterally using a handheld dynamometer and goniometer, respectively. A significant group × side interaction was observed for internal rotation RoM (F = 5.41; p = 0.024; η² = 0.10), with volleyball players showing lower RoM on the dominant side (p = 0.001; d = 0.89), but this was not the case for handball players (p = 0.304). External rotation strength also showed a significant interaction (F = 9.34; p = 0.004; η² = 0.17); volleyball players were stronger in the non-dominant arm (p = 0.033), while handball players were stronger in the dominant arm (p = 0.041). The external-to-internal rotation strength ratio was significantly lower on the dominant side in volleyball players compared to handball players (p = 0.047; d = 0.59). Findings suggest sport-specific adaptations and asymmetries in shoulder function, emphasizing the need for sport-specific and individually tailored injury prevention strategies. Volleyball players, in particular, may benefit from targeted strengthening of external rotators and flexibility training to address imbalances. Full article

This study examined the integraftion of airborne Light Detection and Ranging (LiDAR), Simultaneous Localization and Mapping (SLAM)-based handheld LiDAR, and iPhone LiDAR to inspect forest road networks following forest operations. The goal is to overcome the challenges posed by dense canopy cover and ensure accurate and efficient data collection and mapping. Airborne data were collected using the DJI Matrice 300 RTK UAV equipped with a Zenmuse L2 LiDAR sensor, which achieved a high point density of 285 points/m² at an altitude of 80 m. Ground-level data were collected using the BLK2GO handheld laser scanner (HPLS) with SLAM methods (LiDAR SLAM, Visual SLAM, Inertial Measurement Unit) and the iPhone 13 Pro Max LiDAR. Data processing included generating DEMs, DSMs, and True Digital Orthophotos (TDOMs) via DJI Terra, LiDAR360 V8, and Cyclone REGISTER 360 PLUS, with additional processing and merging using CloudCompare V2 and ArcGIS Pro 3.4.0. The pairwise comparison analysis between ALS data and each alternative method revealed notable differences in elevation, highlighting discrepancies between methods. ALS + iPhone demonstrated the smallest deviation from ALS (MAE = 0.011, RMSE = 0.011, RE = 0.003%) and HPLS the larger deviation from ALS (MAE = 0.507, RMSE = 0.542, RE = 0.123%). The findings highlight the potential of fusing point clouds from diverse platforms to enhance forest road mapping accuracy. However, the selection of technology should consider trade-offs among accuracy, cost, and operational constraints. Mobile LiDAR solutions, particularly the iPhone, offer promising low-cost alternatives for certain applications. Future research should explore real-time fusion workflows and strategies to improve the cost-effectiveness and scalability of multisensor approaches for forest road monitoring. Full article

In dogs with intervertebral disc extrusion (IVDE), the Glasgow Composite Measure Pain Scale—Short Form (GCMPS) and the Sharp and Wheeler Grading Scale (SWGS) are routinely used in the evaluation of pain (GCMPS) and neurological function (SWGS). Additionally, quantitative sensory tests (QSTs) are increasingly being incorporated into veterinary clinical practice for pain characterisation. The aim was to investigate a possible relationship between the GCMPS, the SWGS, and mechanical thresholds (MTs) in 31 client-owned dogs with thoracolumbar IVDEs. Dogs were always assessed in the same order, starting with pain rating using the GCMPS, followed by classifying neurological severity using the SWGS, before determining MTs using a handheld pressure algometer. Dogs were evaluated over a five-day testing period (before surgery and on days one, two, three, and ten after surgery). The GCMPS and the SWGS data remained consistent across all days of testing. No statistically significant correlation or difference was observed between the scores. MTs showed a significant negative correlation with the GCMPS (r = −0.311; p < 0.001) and a positive one with the SWGS (r = 0.282; p = 0.002). The GCMPS and MTs showed a slight divergence in their progression. MTs might be more sensitive than GCMPS in reflecting clinical improvement and should be considered for clinical practice. Full article

(1) Background: Phoridae is a relatively large and understudied family of Diptera. Species within this family occupy diverse ecological niches across a wide range of habitats. (2) Methods: The dataset is based on Phoridae specimens collected in the Republic of Mordovia (European Russia). Sampling was conducted from 2019 to 2024 using six collection methods: hand-held sweep nets, pitfall traps, beer traps, pan traps, Malaise traps, and window traps. (3) Results: The dataset includes 4713 occurrence records from the Republic of Mordovia, comprising a total of 15,701 Phoridae specimens. It provides data on 271 species. The highest species richness was recorded in the Mordovia State Nature Reserve (226 species, 83.4%). Fewer species were documented in the fauna of the National Park “Smolny” (177 species, 65.3%), with comparable diversity observed in other parts of the region. Ten species were dominant in the dataset (Megaselia pusilla, Triphleba opaca, Megaselia angusta agg., Diplonevra funebris, Megaselia brevicostalis, Megaselia plurispinulosa, Megaselia minuta, Megaselia lutea, Megaselia lactipennis, and Megaselia flavicans). A total of 139 species were represented by fewer than ten specimens each. Seasonal dynamics varied across habitats: in the Mordovia State Nature Reserve, both species richness and specimen abundance were already high in April, peaking in June. In contrast, in the National Park “Smolny”, peak values were observed in August. (4) Conclusions: We have listed 151 new recorded species from Russia in this list. Currently, the Phoridae fauna of the Republic of Mordovia is among the best-studied in Russia. The distribution of many species has become clearer, with the Mordovia State Nature Reserve demonstrating the highest biodiversity compared to other areas of the region. Full article

The quality of wine largely depends on the quality of wine grapes, which is determined by their chemical composition. Therefore, measuring parameters related to grape ripeness, such as soluble solids content (SSC), is crucial for harvesting high-quality grapes. Visible–Near-Infrared (Vis/NIR) spectroscopy enables effective, non-destructive detection of SSC in grapes. However, commercial Vis/NIR spectrometers are often expensive, bulky, and power-consuming, making them unsuitable for on-site applications. This article integrated the AS7265X sensor to develop a low-cost handheld IoT multispectral detection device, which can collect 18 variables in the wavelength range of 410–940 nm. The data can be sent in real time to the cloud configuration, where it can be backed up and visualized. After simultaneously removing outliers detected by both Monte Carlo (MC) and principal component analysis (PCA) methods from the raw spectra, the SSC prediction model was established, resulting in an ${\mathrm{R}}_V^2$ of 0.697. Eight preprocessing methods were compared, among which moving average smoothing (MAS) and Savitzky–Golay smoothing (SGS) improved the ${\mathrm{R}}_V^2$ to 0.756 and 0.766, respectively. Subsequently, feature wavelengths were selected using UVE and SPA, reducing the number of variables from 18 to 5 and 6, respectively, further increasing the ${\mathrm{R}}_V^2$ to 0.809 and 0.795. The results indicate that spectral data optimization methods are effective and essential for improving the performance of SSC prediction models. The IoT Vis/NIR Spectroscopic System proposed in this study offers a miniaturized, low-cost, and practical solution for SSC detection in wine grapes. Full article

Soil compaction is a critical challenge in perennial fruit production, limiting root growth, water infiltration, and nutrient uptake—factors essential for climate-resilient and sustainable orchard systems. In subtropical Asian pear (Pyrus pyrifolia Nakai) orchards under the annual top-working system, intensive machinery traffic exacerbates subsurface hardpan formation and tree performance. This study evaluated the effectiveness of pneumatic subsoiling, a minimally invasive method using high-pressure air injection, in alleviating soil compaction without disturbing orchard surface integrity. Four treatments varying in radial distance from the trunk and pneumatic application were tested in a mature orchard in central Taiwan. Pneumatic subsoiling 120 cm away from the trunk significantly reduced soil penetration resistance by 15.4% at 34 days after treatment (2,302,888 Pa) compared to the control (2,724,423 Pa). However, this reduction was not sustained at later assessment dates, and no significant improvements in vegetative growth, fruit yield, and fruit quality were observed within the first season post-treatment. These results suggest that while pneumatic subsoiling can modify subsurface soil physical conditions with minimal surface disturbance, its agronomic benefits may require longer-term evaluation under varying moisture and management regimes. Overall, this study highlights pneumatic subsoiling may be a potential low-disturbance strategy to contribute to longer-term soil physical resilience. Full article

Background: Handheld dynamometry (HHD) is widely utilized for assessing muscle strength, particularly in the wrist. However, variability in measurement reliability due to differences in testing protocols poses a challenge for clinical and research applications. Methods: The design of this study includes a scoping review of the literature, conducted following the PRISMA-ScR checklist methodology developed by the Joanna Briggs Institute. The databases most commonly cited in review articles were consulted: EBSCO, PEDro, PubMed, Scopus, and Cochrane Library. The following MeSH terms were used: “Handheld Dynamometer”, “Wrist”, “Forearm”, “Muscle”, and “Strength”. The search strings were built using combinations of these terms. Article screening was performed by three reviewers independently, blinded to each other’s selections. Results: The review indicates that HHD can provide reliable measurements when standardized protocols are used. Most studies reported high intra-examiner reliability with Intraclass Correlation Coefficients (ICCs) between 0.71 and 0.90. However, inter-examiner reliability showed more variability, particularly when more than two examiners were involved. The review also highlights the importance of precise dynamometer placement and consistent patient positioning in order to reduce measurement variability. Conclusions: While HHD is a valuable tool for wrist strength assessment, the effectiveness of its measurements largely depends on the testing procedure’s standardization. Implementing validated standardized protocols is essential in enhancing measurement reliability and ensuring their consistent application across clinical settings. Further research is needed to firmly implement these protocols and expand their application in clinical practice. Full article

Bats, as key ecological players, interact with a diverse array of organisms and perform essential roles in ecosystems, including pollination, pest control, and seed dispersal. However, their populations face significant threats from habitat contamination, particularly from microplastics (MPs). This study introduces a novel, efficient, and cost-effective method for visualizing transparent microplastics using ultraviolet (UV) light. By employing handheld UV flashlights with a wavelength range of 312 to 400 nm, we enhance the detection of MPs that may otherwise go unnoticed due to color overlap with filtration membranes. All necessary precautions were taken during sampling and analysis to minimize the risk of contamination and ensure the reliability of the results. Our findings demonstrate that the application of UV light significantly improves the visualization and identification of MPs, particularly transparent fibers. This innovative approach contributes to our understanding of plastic contamination in bat habitats and underscores the importance of monitoring environmental pollutants to protect bat populations and maintain ecosystem health. Full article

Beef fat injection technology, used to enhance the perceived quality of lower-grade meat, often results in artificially marbled beef that mimics the visual traits of Wagyu, characterized by dense fat distribution. This practice, driven by the high cost of Wagyu and the affordability of fat-injected beef, has led to the proliferation of mislabeled “Wagyu-grade” products sold at premium prices, posing potential food safety risks such as allergen exposure or consumption of unverified additives, which can adversely affect consumer health. Addressing this, this study introduces a smart sensing system integrated with handheld mobile devices, enabling consumers to capture beef images during purchase for real-time health-focused assessment. The system analyzes surface texture and color, transmitting data to a server for classification to determine if the beef is artificially marbled, thus supporting informed dietary choices and reducing health risks. Images are processed by applying a region of interest (ROI) mask to remove background noise, followed by partitioning into grid blocks. Local binary pattern (LBP) texture features and RGB color features are extracted from these blocks to characterize surface properties of three beef types (Wagyu, regular, and fat-injected). A support vector machine (SVM) model classifies the blocks, with the final image classification determined via majority voting. Experimental results reveal that the system achieves a recall rate of 95.00% for fat-injected beef, a misjudgment rate of 1.67% for non-fat-injected beef, a correct classification rate (CR) of 93.89%, and an F1-score of 95.80%, demonstrating its potential as a human-centered healthcare tool for ensuring food safety and transparency. Full article

The study’s aim was to evaluate electroretinographic (ERG) alterations in Fragile X syndrome (FXS), FMR1 premutation carriers, and controls, and to explore correlations with peripheral blood FMRP expression levels and behavioral outcomes. ERG recordings were obtained using a handheld device across three stimulus protocols in 43 premutation carriers, 39 individuals with FXS, and 23 controls. Peripheral blood FMRP expression levels were quantified using TR-FRET (Time-Resolved Fluorescence Resonance Energy Transfer). Correlations were assessed with cognitive and behavioral measures including IQ (Intelligence Quotient), ABC_FX (Aberrant Behavior Checklist for Fragile X Syndrome), SNAP-IV (Swanson, Nolan, and Pelham Teacher and Parent Rating Scale), SEQ (Sensory Experiences Questionnaire), ADAMS (Anxiety, Depression, and Mood Scale), and the Vineland III Adaptive Behavior Scale standard score. Significant group differences were observed in multiple ERG parameters, particularly in 2 Hz b-wave amplitude (p = 0.0081), 2 Hz b-wave time to peak (p = 0.0164), 28.3 Hz flash combined amplitude (p = 0.0139), 3.4 Hz red/blue flash b-wave amplitude (p = 0.0026), and PhNR amplitude (p = 0.0026), indicating both outer and inner retinal dysfunction in FXS and premutation groups. Despite high test–retest reliability for ERG (ICC range = 0.71–0.92) and FMRP (ICC = 0.70), no correlation was found between ERG metrics and FMRP or behavioral measures. However, FMRP levels strongly correlated with IQ (ρ = 0.69, p < 0.0001) and inversely with behavioral impairment [ABC_FX (ρ = −0.47, p = 0.0041), SNAP-IV (ρ = −0.48, p = 0.0039), SEQ (ρ = −0.43, p = 0.0146), and the Vineland III standard score (ρ = 0.56, p = 0.0019)]. ERG reveals distinct retinal functional abnormalities in FMR1-related conditions but does not correlate with peripheral FMRP expression levels, highlighting the need for multimodal biomarkers integrating radiological, physiological, behavioral, and molecular measures. Full article

With the increasing application of hyperspectral technology, rapid and accurate monitoring of cotton leaf nitrogen concentrations (LNCs) has become an effective tool for large-scale areas. This study used Tahe No. 2 cotton seeds with four nitrogen levels (0, 200, 350, 500 kg ha⁻¹) and four phosphorus levels (0, 100, 200, 300 kg ha⁻¹). Spectral data were acquired using an ASD FieldSpec HandHeld2 portable spectrometer, which measures spectral reflectance covering a band of 325–1075 nm with a spectral resolution of 1 nm. LNCs determination and spectral estimation were conducted at six growth stages: squaring, initial bloom, peak bloom, initial boll, peak boll, and boll opening. Thirty-seven spectral indices (SIs) were selected. First derivative (FD), standard normal variate (SNV), multiplicative scatter correction (MSC), and Savitzky–Golay (SG) were applied to preprocess the spectra. Feature bands were screened using partial least squares discriminant analysis (PLS–DA), and support vector machine (SVM) and random forest (RF) models were used for accuracy validation. The results revealed that (1) LNCs initially increased and then decreased with growth, peaking at the full-flowering stage before gradually declining. (2) The best LNC recognition models were SVM–MSC in the squaring stage, SVM–FD in the initial bloom stage, SVM–FD in the peak bloom stage, SVM–FD in the initial boll stage, RF–SNV in the peak boll Mstage, and SVM–FD in the boll opening stage. FD showed the best performance compared with the other three treatments, with SVM outperforming RF in terms of higher R² and lower RMSE values. The SVM–FD model effectively improved the accuracy and robustness of LNCs prediction using hyperspectral leaf spectra, providing valuable guidance for large-scale information production in high-standard cotton fields. Full article