Search Results (15,091)

Background: Freezing of gait (FoG) is one of the most debilitating motor symptoms in Parkinson’s disease (PD). It often leads to falls and reduces quality of life due to the risk of injury and loss of independence. Several types of wearable sensors have emerged as promising tools for the detection of FoG in clinical and real-life settings. Objective: The main objective of this systematic review was to critically evaluate the current usability of wearable sensor technologies for FoG detection in PD patients. The focus of the study is on sensor types, sensor combinations, placement on the body and the applications of such detection systems in a naturalistic environment. Methods: PubMed, IEEE Explore and ACM digital library were searched using a search string of Boolean operators that yielded 328 results, which were screened by title and abstract. After the screening process, 43 articles were included in the review. In addition to the year of publication, authorship and demographic data, sensor types and combinations, sensor locations, ON/OFF medication states of patients, gait tasks, performance metrics and algorithms used to process the data were extracted and analyzed. Results: The number of patients in the reviewed studies ranged from a single PD patient to 205 PD patients, and just over 65% of studies have solely focused on FoG + PD patients. The accelerometer was identified as the most frequently utilized wearable sensor, appearing in more than 90% of studies, often in combination with gyroscopes (25.5%) or gyroscopes and magnetometers (20.9%). The best overall sensor configuration reported was the accelerometer and gyroscope setup, achieving nearly 100% sensitivity and specificity for FoG detection. The most common sensor placement sites on the body were the waist, ankles, shanks and feet, but the current literature lacks the overall standardization of optimum sensor locations. Real-life context for FoG detection was the focus of only nine studies that reported promising results but much less consistent performance due to increased signal noise and unexpected patient activity. Conclusions: Current accelerometer-based FoG detection systems along with adaptive machine learning algorithms can reliably and consistently detect FoG in PD patients in controlled laboratory environments. The transition of detection systems towards a natural environment, however, remains a challenge to be explored. The development of standardized sensor placement guidelines along with robust and adaptive FoG detection systems that can maintain accuracy in a real-life environment would significantly improve the usefulness of these systems. Full article

Background and Objectives: The hypothesis that fascial thickness variability may serve as a biomarker for low back pain (LBP) requires a clear understanding of typical thickness values in both LBP and non-LBP populations—an area still lacking in the literature. This narrative review aims to define reference values and patterns of variability for the superficial fascia, deep fascia, and subcutaneous tissue in individuals with and without LBP. Methods: A literature search was conducted in PubMed and ScienceDirect using keywords such as superficial fascia, deep fascia, thoracolumbar, subcutaneous fat, back pain, lumbar, thorax, and thickness. Inclusion criteria focused on human studies with proper identification of the relevant soft tissue structures. A total of 21 studies, published up to February 2024, met the inclusion criteria and were analyzed. Results: The review revealed notable intra- and inter-study variability in the thickness of the investigated structures. In LBP populations, both deep fascia and subcutaneous tissues were generally equal to or thicker than in controls (non-LBP), whereas consistent data on superficial fascia thickness remain limited. Age, sex, and anatomical location were discussed as potential influencing factors. Conclusions: These findings support the establishment of reference thickness values for subcutaneous and fascial tissues and encourage further investigation into their structural and functional roles in LBP. The observed variability may offer a basis for patient- and site-specific assessment and intervention strategies. Full article

Accurate soil moisture estimation is critical for precision agriculture and water resource management, yet traditional sampling methods are time-consuming, destructive, and provide limited spatial coverage. Ground Penetrating Radar (GPR) offers a promising non-destructive alternative, but optimal machine learning approaches for GPR-based soil moisture prediction remain unclear. This study presents a comparative analysis of regression tree and boosted tree algorithms for predicting soil moisture content from Ground Penetrating Radar (GPR) histogram features across 21 sites in Eastern Thailand. Soil moisture content was measured at multiple depths (0.5, 1.0, 1.5, 2.0, 2.5, and 3.0 m) using samples collected during Standard Penetration Test procedures. Feature extraction was performed using 16-bin histograms from processed GPR radargrams. A single regression tree achieved a cross-validation RMSE of 5.082 and an R² of 0.761, demonstrating superior training accuracy and interpretability. In contrast, the boosted tree ensemble achieved significantly better generalization performance, with a cross-validation RMSE of 4.7915 and an R² of 0.708, representing a 5.7% improvement in predictive performance. Feature importance analysis revealed that specific histogram bins effectively captured moisture-related variations in GPR signal amplitude distributions. A comparative evaluation demonstrates that while single regression trees offer superior interpretability for research applications, boosted tree ensembles provide enhanced predictive performance that is essential for operational deployment in precision agriculture and hydrological monitoring systems. Full article

Measuring-while-drilling (MWD) techniques have great potential for use in geotechnical engineering research. This study first addresses the current use of MWD, which consists of recording data using sensors in a drilling machine operating on site. It then addresses the currently unsolved problems of quality control in drilled piles and assessments of their interaction with the soil under load. Next, an original method of drilling displacement piles using a special EGP auger (Electro-Geo-Probe) is presented. The innovation of this new drilling system lies in the placement of the sensors inside the EGP auger in the soil. These innovative sensors form an integrated measurement system, enabling improved real-time control during pile drilling. The most original idea is the use of a Cone Penetration Test (CPT) probe that can be periodically and remotely inserted at a specific depth below the pile base being drilled. This new MWD-EGP system with cutting-edge sensors to monitor the soil’s impact on piles during drilling revolutionizes pile drilling quality control. Furthermore, implementing this in-auger sensor system is a step towards the development of digital drilling rigs, which will provide better pile quality thanks to solutions based on the results of real-time, on-site soil testing. Finally, examples of measurements taken with the new sensor-equipped auger and a preliminary interpretation of the results in non-cohesive soils are presented. The obtained data confirm the usefulness of the new drilling system for improving the quality of piles and advancing research in geotechnical engineering. Full article

Self-regulatory foraging behavior in honey bees (Apis mellifera) was investigated using the framework of Perceptual Control Theory (PCT). We developed a PCT-based model to describe how bees maintain goal-directed behavior, specifically targeting a sucrose-rich feeding site while overcoming a wind disturbance. In a controlled experiment, we found that 13 of 14 bees could successfully adjust their flight paths to overcome the disturbance and consistently reach the feeding target. While they demonstrated a great deal of individual variability regarding how they overcame the wind across experimental trials, they did so by finally adopting a headwind (i.e., flying into the wind) approach pattern rather than tailwind or crosswind approach patterns. These results support the application of PCT to the study of behavior in honey bees, which can be regarded as self-regulative (i.e., non-linear and dynamic) rather than as linear sequences of inputs and outputs. Given that such dynamic models are concerned with the functions or purposes of behavior, they may also be classified as teleonomic. Full article

Use of sedation for disbudding is common practice in a number European countries, with United Kingdom (UK) practices adopting its use. This study assessed the effects of disbudding with and without xylazine sedation on growth rates and calf health on a UK calf rearing unit. Data was collected from 485 dairy crossed with beef breed calves between April and August 2024 from a single calf rearing unit in England. Calves were purchased from multiple farms across the UK and arrived on site at approximately 21 days of age. Calves were disbudded—and, in the case of male calves, surgically castrated—at approximately three weeks after arrival on farm. Pens of calves were assigned to undergo disbudding with (SED, n = 238) or without (CTL = 234) xylazine sedation at a dose of 0.2 mg/kg administered intramuscularly. Calves from both groups were provided with local anaesthetic (procaine hydrochloride) as a cornual nerve block and a non-steroidal anti-inflammatory drug (meloxicam). While other studies have demonstrated some behavioural and physiological indicators of pain to be reduced with sedation, this study found that calves in the SED group had a reduced daily liveweight gain (DLWG) of 0.14 kg/day in the short term (mean 20 days) following disbudding (p < 0.001), but there was no difference in growth rates in the medium-term (mean 43 days) post-disbudding (p = 0.30). Some of this difference could be explained by the slightly higher DLWG pre-disbudding in the CTR group, and it is likely that the physiological impacts of sedation accounted for the rest of this difference. This initial reduction in DLWG following disbudding with sedation should be considered by vets, especially on farms where growth rates may already be compromised. In the sedated calves, 19.3% exhibited either some movement or entry into sternal recumbency. Specifically, a light plane of sedation with calves entering sternal recumbency was associated with a reduction in DLWG of 0.89 kg/day compared to 0.98 kg/day for those that remained in lateral recumbency throughout (p = 0.008). The light plane of sedation may have created additional calf stress, impacted feeding behaviours, and impinged welfare, with further work needed to establish the reasons for insufficient sedation. There was no difference in the number of post-disbudding treatment outcomes between calves disbudded with and without sedation (p = 0.97). Full article

We investigate the electronic properties of atomic chains placed on group-14 two-dimensional materials, Xenes, by analyzing the local electronic properties. Our results show that the hybridization between the chain and the substrate leads to significant modifications in the local density of states at each chain site, including peak splitting, broadening, and asymmetry. These effects are particularly pronounced for plumbene. Owing to the substrate’s V-shaped-like density of states, the chains exhibit strong localization effects and significant intensity variations in the electronic energy spectrum. In addition the present analysis reveals the emergence of charge density waves in atomic chains, for which the appearance and stability conditions are identified and provided. The charge density waves are more pronounced and stabilized by a specific electronic spectrum of Xenes, allowing them to penetrate deeper into the chain interior. Our findings contribute to the broader understanding of the interaction between one-dimensional chains and two-dimensional Xene materials, which have significant implications for developing advanced hybrid nanostructures and next generation electronic devices. Full article

Meat quality is influenced by genetic, nutritional, and microbial factors, with increasing attention on the role of gut-derived metabolites. In this study, we conducted untargeted metabolomics of 10 gut tract sites and RNA sequencing (RNA-seq) of longissimus dorsi muscles in Meigu goats and Liangshan black sheep raised under standardized conditions. Results showed that goat muscle contained significantly higher levels of essential amino acids (e.g., methionine) and specific fatty acids (e.g., C18:3_N6, C20:4_N6), suggesting improved nutritional quality. Transcriptomic analysis identified 3133 differentially expressed genes (DEGs), among which ADCY1 and SLC38A4 were upregulated in goats and strongly associated with meat traits. Using integrative correlation analysis, we uncovered 17 genes and 19 gut metabolites that were significantly correlated with more than eight meat quality parameters across multiple gut sites. Notably, these metabolites included bioactive compounds such as L-tyrosine ethyl ester and pelargonidin 3-O-glucoside, while genes were enriched in pathways related to amino acid transport, cAMP signaling, and muscle development. Together, these findings highlight a potential gut–muscle axis involving metabolite-mediated modulation of muscle gene expression, contributing to breed-specific differences in meat composition and quality. This study provides a valuable framework for improving ruminant meat quality through integrative multi-omics analysis. Full article

The accurate identification of mosquito species is critical for effective mosquito surveillance and control, especially when presented with morphologically similar species like Aedes aegypti and Aedes sierrensis. Damaged specimens and morphologically similar life stages such as eggs and larvae make it difficult to distinguish Aedes aegypti from Aedes sierrensis using microscopy and taxonomic keys. To address this, the AegySierr.ID-qPCR assay, a multiplex quantitative PCR assay that utilizes single-nucleotide polymorphisms within the mitochondrial cytochrome oxidase subunit I gene, was developed to distinguish between these two species. The assay was tested on DNA extracted from the eggs, larvae, and adults of both species, as well as from environmental DNA (eDNA) collected from natural mosquito reproduction sites. It demonstrated a high diagnostic accuracy across multiple life stages, with a sensitivity exceeding 95% for most groups and specificity exceeding 90%, except for field-collected adult Ae. sierrensis (75%). For eDNA samples, the assay achieved 100% sensitivity and 94% specificity for samples classified as Ae. sierrensis and 91% sensitivity and 86% specificity for Ae. aegypti. A two-graph receiver operating characteristic analysis was also used as an alternate method with which to establish Ct thresholds for interpreting results from unknown samples. The AegySierr.ID-qPCR assay enables the rapid and sensitive identification of Ae. aegypti and Ae. sierrensis from specimens and eDNA, and may be of use in mosquito surveillance programs. Full article

Sea cucumbers, important members of the Echinoderm phylum, play a crucial role in sediment mixing and nutrient cycling on the seafloor. They also hold significant economic value, particularly in Asian food and pharmaceutical markets. In the Mediterranean Sea, the harvesting of sea cucumbers has recently intensified, often without regulation, threatening both species populations and benthic ecosystem health. This study investigated the potential of using fatty acid (FA) profiles as ecological biomarkers to trace the different origin and feeding ecology of two sea cucumber species, Holothuria polii and H. tubulosa, collected from ten coastal sites in Italy. A total of 285 individuals were analyzed by extracting and characterizing lipids from their body walls using gas chromatography (GC-FID and GC-MS). Key fatty acids identified included arachidonic acid, eicosapentaenoic acid, eicosenoic acid, palmitic acid, palmitoleic acid, stearic acid, and nervonic acid. Principal Component Analysis (PCA) revealed patterns consistent with geographic origin, suggesting that FA profiles can reflect site-specific trophic conditions. The analysis also indicated that sea cucumbers primarily feed on diatoms, bacteria, and blue-green algae, with notable regional variation. This study is the first to successfully apply FA-based trophic markers to differentiate Italian populations of these species, providing insights for ecological monitoring and fishery management. Full article

The utilization of ocean wave energy through environmentally sustainable technologies plays a pivotal role in the transition toward renewable energy sources. Among such technologies, the Submerged Horizontal Plate (SHP) stands out as a viable option for clean power production. This study focuses on the system’s application in a region on the southern coast of Brazil, identified as a potential site for future installation. To investigate this system, a three-dimensional numerical wave tank was developed to simulate wave behavior and hydrodynamic loads using the Navier–Stokes framework in the computational fluid dynamics software ANSYS FLUENT 2022 R2. The volume of fluid approach was adopted to track the free surface. The setup for wave generation in the numerical wave tank was verified against analytical solutions to ensure precision and validated under the SHP’s non-oscillating condition. To represent the oscillating condition, boundary conditions constrained motion along the x- and y-axes, allowing movement exclusively along the z-axis. A parametric analysis of 54 cases, with varying geometric configurations, wave characteristics, and submersion depths, indicated that the oscillating SHP configuration elongated perpendicular to wave propagation, combined with specific wave conditions, achieved a theoretical mean efficiency of 76.61%. Full article

A major challenge for Li-O₂ batteries is the slow kinetics of oxygen reduction (ORR) and evolution (OER) reactions. This work presents a high-performance Pt₃Rh/C composite cathode where Pt-Rh nanoalloys are uniformly dispersed on 3D nanoporous carbon. The bimetallic architecture demonstrates significantly enhanced ORR/OER activity compared to conventional catalysts. Super P, with a large specific surface area and omnipresent pores with diverse size distribution, provided sufficient storage space for Li₂O₂ and facilitated transport channels for Li⁺ and O₂, while the highly conductive Pt₃Rh NPs optimized catalytic efficiency. XPS reveals a prominent electron transfer process between Pt and Rh; the Rh sites in Pt₃Rh/C alloy can effectively act as electron donors to improve the oxygen/lithium peroxide (O₂/Li₂O₂) redox chemistry in LOB. Therefore, the Pt₃Rh/C electrode shows the minimum overpotential (0.60 V) for efficient oxygen reduction and evolution under an upper-limit capacity of 2000 mAh g⁻¹. This work introduces a Pt₃Rh/C nanoalloy synthesis method that boosts Li-O₂ battery efficiency by accelerating oxygen reaction kinetics. Full article

Construction sites report the highest rate of industrial accidents, prompting the active development of smart safety management systems based on deep learning-based computer vision technology. To support the digital transformation of construction sites, securing site-specific datasets is essential. In this study, raw data were collected from an actual earthwork site. Key construction equipment and terrain objects primarily operated at the site were identified, and 89,766 images were processed to build a site-specific training dataset. This dataset includes annotated bounding boxes for object detection and polygon masks for instance segmentation. The performance of the dataset was validated using representative models—YOLO v7 for object detection and Mask R-CNN for instance segmentation. Quantitative metrics and visual assessments confirmed the validity and practical applicability of the dataset. The dataset used in this study has been made publicly available for use by researchers in related fields. This dataset is expected to serve as a foundational resource for advancing object detection applications in construction safety. Full article

Stable crop yields require an appropriate supply of essential soil nutrients such as nitrogen (N), phosphorus (P), and potassium (K) based on the accurate diagnosis of soil nutrient status. Traditional laboratory analysis of soil nutrients is often complicated and time-consuming and does not provide real-time nutrient status. Visible–near-infrared (Vis-NIR) spectroscopy has emerged as a non-destructive and rapid method for estimating soil nutrient levels. Vis-NIR spectra reflect sample characteristics as the peak intensities; however, they are often affected by various artifacts and complex variables. Since Vis-NIR spectroscopy does not directly measure nutrient levels in soil, improving estimation accuracy is essential. For spectral preprocessing, the most important aspect is to develop an appropriate preprocessing strategy based on the characteristics of the data and identify artifacts such as noise, baseline drift, and scatter in the spectral data. Machine learning-based modeling techniques such as partial least-squares regression (PLSR) and support vector machine regression (SVMR) enhance estimation accuracy by capturing complex patterns of spectral data. Therefore, this review focuses on the use of Vis-NIR spectroscopy for evaluating soil properties including soil water content, organic carbon (C), and nutrients and explores its potential for real-time field application through spectral preprocessing and machine learning algorithms. Vis-NIR spectroscopy combined with machine learning is expected to enable more efficient and site-specific nutrient management, thereby contributing to sustainable agricultural practices. Full article

Mollusc shells serve as valuable biogeochemical archives of natural or anthropogenic processes occurring in the aquatic environment throughout the life of the molluscs. One such process is trace element pollution, which can be assessed by analyzing the elemental composition of mollusc shells. However, different mollusc species accumulate elements in their shells from the aquatic environment at varying concentrations, and specific patterns of this accumulation remain largely unknown. In the present study, we measured the concentrations of 33 elements in the shells of five commercially important Black Sea molluscs, all collected from the same site, using inductively coupled plasma mass spectrometry. The species were ranked according to the number of elements with the highest concentrations in their shells as follows: Crassostrea gigas (9) = Rapana venosa (9) = Anadara kagoshimensis (9) > Flexopecten glaber ponticus (4) > Mytilus galloprovincialis (2). Cluster analysis of Pearson’s coefficients of correlation of elemental concentrations in the molluscan shells revealed significant separation of C. gigas, F. glaber ponticus, and M. galloprovincialis. Multivariate ordination analyses allowed the accurate classification of >92.3% of shell samples using as few as four elements (Fe, As, Sr, and I). Linear discriminant analysis revealed the probability of separation of all species based on the concentrations of these elements in their shells being not lower than 79%. The applied multivariate approach based on the analysis of four base elements in shells can help not only in the taxonomic identification of molluscs, but also, upon appropriate calibration, in monitoring medium-term dynamics of trace elements in the aquatic environment. Full article