Miracle-dz/UniFAR-FTUs huggingface dataset:
Phase-Based Quantification of Sports Performance Metrics Using a Smart IoT Sensor

Sports performance is often judged based on the results of a series of motions rather than observing and analyzing the detailed sequential motions that lead to the results. Hence, subjective feedback from the coaches is often ineffective in improving player performance. In this work, we custom-built a smart Internet of Things wristband motion sensor to implement data-based sports performance evaluation. A phase-based feature selection method is also proposed to assess the athletes’ sequential detailed motion for selected sport activities. To demonstrate the merits of this technology, we quantified the quality of the sequential motions of a specific type of volleyball serve by analyzing 183 samples of motion data obtained from a total of 18 players. The general skill levels (i.e., elite, subelite, and amateur) of the players were identified by machine learning algorithms, with accuracies of up to 95%. Moreover, we adopted biomechanical principles to extract 11 motion-related performance metrics from various phases of the players’ serve motion. We identified the distributions of these metrics across different skill levels and found eight key metrics that were highly correlated to the skill level of a players. We suggest that these metric distributions can be used as a reference for providing feedback to the coaches and players, to improve a player’s skill in the future. This phased-based analysis method can potentially be applied across many sports to increase the effectiveness of athletes’ training.
Greenhouse gas emissions and removals from forest land, woodlands, urban trees, and harvested wood products in the
United States, 1990–2021

As a signatory to the United Nations Framework Convention on Climate Change (UNFCCC), the United States has reported an inventory of greenhouse gas (GHG) emissions and removals by sector, as defined by the Intergovernmental Panel on Climate Change (IPCC), since the mid-1990s (U.S. EPA 2023). In 2021, United States net GHG emissions increased by more than 6.8 percent relative to 2020 net emissions, which had decreased substantially from previous years and was due, in large part, to the global pandemic. Forest land, harvested wood products (HWP), woodlands, and urban trees within the land sector collectively continue to represent the largest net carbon sink in the United States, offsetting the equivalent of more than 12.4 percent of total (i.e., gross) GHG emissions in 2021 (U.S. EPA 2023). Estimates of GHG emissions and removals are compiled by U.S. Department of Agriculture (USDA), Forest Service researchers and partners and are based primarily on National Forest Inventory (NFI) data collected and maintained by the Forest Inventory and Analysis (FIA) Program within the Forest Service. This resource bulletin provides an overview of the status and trends of GHG emissions and removals from forest land, woodlands in the grassland category, HWP, and urban trees in settlements in the United States from 1990 to 2021. The estimates for the United States summarized here are based on the compilation reported in the “Land Use, Land-Use Change, and Forestry” chapter of the U.S. EPA (2023) submission to the UNFCCC. Most of the national scale estimates are also developed and reported at the individual State level (fig. 1) for the entire 1990–2021 time series and are available in a published research dataset (Walters et al. 2023). This report also includes regional carbon stock and stock change estimates by broad ownership category (i.e., private or public land) and National Forest System region.
Geophysical sediment properties of a late Pleistocene loess–paleosol sequence, Chenarli, northeastern Iran

Abstract We present stratigraphic, magnetic, diffuse reflectance spectrophotometric analyses, and chronologic data for the Chenarli loess–paleosol sequence in northeastern Iran. Eight loess units (LU) are identified, described, and constrained in time based on relative stratigraphy and luminescence dating from >130 ± 9.1 ka to ~9.8 ± 0.7 ka. Our data indicate that pedogenic magnetite/maghemite formation gives rise to magnetic enhancement in modern soil and paleosols. The top of LU 7 is demarcated by the well-developed last interglacial soil in which magnetic depletion occurred. We infer that magnetic depletion in this paleosol was produced by reducing conditions in a seasonally waterlogged soil during a warm and wet phase within Marine Isotope Stage (MIS) 5e. Units LU 6 to 1 record several episodes of dust accumulation and soil formation during the last glacial and Holocene. Increased dust accumulation rates occurred during middle-late MIS 3 and lasted into the late MIS 2, with a peak during the last glacial maximum (LU 2). These findings could be applicable to understanding magnetic enhancement/dissolution mechanism in the loess–paleosol sequences in study area. We infer paleoenvironmental changes in northeastern Iran relative to northern Iran, Eurasia, and China.
Wavelet denoising based on watershed and generalized non-local means

Non-local means filter de-noising method and wavelet de-noising method which is based on generalized non-local means filter are both ineffective in reserving image's detailed information when using them for image denoising.For this shortcoming,this paper puts forward a wavelet de-noising method based on watershed segmentation and the generalized non-local means filter.Firstly,in order to segment the noised image and keep the ridge,we applied the watershed segmentation based on gradient method;then the noised image was multidimensionally wavelet decomposed,and we evaluated scale coefficient and shape coefficient for every layer of coefficients decomposed,after which the generalized Gaussian model was constructed for each layer of wavelet sub-coefficients,and then apply non-local means filter based on generalized Gaussian model respectively from horizontal,vertical and diagonal directions for sub-band information of every layer details.Finally,the noised image pixels which correspond to the watershed ridge replaced the corresponding pixels in the wavelet reconstructed image.Simulation results showed that this method can obtain better vision effect and denoising effect than the method based on the wavelet analysis of generalized non-local means.
An Airplane Target Recognition Method Based on Singular Value and PNN

An active exploration is made aiming at low recognition rate and processing speed of the current methods,and an airplane target recognition method by combining the singular value of the contour of image with probabilistic neural network(PNN) is proposed.Firstly,the target data is compressed,and then the contour of binary image is extracted and discretized.Secondly,the distance of discrete points to the mass center of the target is calculated,and then the singular value of the loop matrix constructed by the distance feature vector of the target is extracted.Thirdly,the PNN is built and the singular values are regarded as the feature input,and then the PNN is trained.Finally,the trained PNN classifier is used to recognize the test target.In the simulation experiments,the recognition rate and the processing speed of the proposed method in this paper are greatly improved,which shows the effectiveness of this method.
Adaptive space-time domain décomposition methods for Euler and Navier-Stockes equations

Numerical simulations of more and more complex fluid dynamics phenomena, especially unsteady phenomena, require solving systems of equations with high degrees of freedom. Under their original form, these aerodynamic multi-scale problems are difficult to solve, costly in CPU time and do not allow simulations of large time scales. An implicit formulation, similar to the Schwarz method, with a simple block parallelisation and explicit coupling is no longer sufficient. More robust domain decomposition methods must be conceived so as to make use and adapt to the most of existent hardware.The main aim of this study was to build a parallel in space and in time CFD Finite Volumes code for steady/unsteady problems modelled by Euler and Navier-Stokes equations based on Schwarz method that improves consistency, accelerates convergence and decreases computational cost. First, a study of discretisation and numerical schemes to solve steady and unsteady Euler and Navier–Stokes problems has been conducted. Secondly, an adaptive timespace domain decomposition method has been proposed, as it allows local time stepping in each sub-domain. Thirdly, we have focused our study on the implementation of different parallel computing strategies (OpenMP, MPI, GPU). Numerical results illustrate the efficiency of the method.
Prevalence and Potential Risk Factors for the Acquisition of Antibiotic-Resistant Staphylococcus spp. Bacteria Among Pastoralist Farmers in Kajiado Central Subcounty, Kenya

Antimicrobial resistance (AMR) is a growing health problem globally. To address this challenge, there is a need to generate baseline data on the prevalence and AMR profile of the main disease-causing bacteria. Here, we interrogated the prevalence of bacteria in the nasal cavity of healthy pastoralists in Kajiado Central Subcounty, Kenya, and the occurrence of AMR in Staphylococcus isolates among the study subjects. Nasal swabs from 176 pastoralists were cultured, and the bacteria isolates identified using standard phenotypic and biochemical bacteriological methods. Among the obtained 195 isolates, the most prevalent isolates were coagulase-negative Staphylococcus (CoNS) (44.9%), followed by Enterococci spp. (43.2%) while Staphylococcus aureus prevalence was 8%. Antimicrobial sensitivity of the Staphylococcus spp. isolates to 14 antibiotics representing six antibiotic groups was undertaken using the Kirby-Bauer disk diffusion method. Among the CoNS, the highest resistance was reported in amoxicillin (78.7%) and ceftazidime (76%), while the most resistance for S. aureus was reported in ceftazidime (100%), amoxicillin (71.4%), and streptomycin (71.4%). From an administered questionnaire looking at gender, animal contact frequency, history of hospital visitation and antibiotic usage, and habitual intake of raw milk, the study showed that male participants had a higher risk of carrying multiple drug resistant (MDR) bacteria than females (p = 0.02, OR = 1.3). Likewise, habitual intake of raw milk was significantly associated MDR acquisition (p = 0.02, OR = 1.82). This study reveals a high prevalence of AMR Staphylococcus isolates in the study area laying a foundation for further analysis of molecular characterization of the observed resistance as well as the development of interventions that can reduce the occurrence of AMR in the study area.
A Novel Adaptive STFT-SFA Based Fault Detection Method for Nonstationary Processes

In the field of fault detection, the nonstationary characteristics caused by external disturbances of wind turbine (WT) and other reasons can mask the fault signals, while the inconsistent data distribution between training data and test data due to equipment loss and other reasons can lead to model mismatch problems, both of which can lead to the degradation of fault detection performance. In order to solve the above problems, a novel adaptive fault detection framework is proposed in this work. First, the stationary features of nonstationary variables are extracted based on short-time Fourier analysis, after which the features are combined with the stationary variables. Second, isolation-based anomaly detection using nearest-neighbor ensembles (iNNE) is introduced as monitoring metrics for designing the statistic for the slow feature analysis (SFA) method. Then, the differences between online normal data and training data are calculated, the model update factor and update strategy are designed, and an adaptive fault detection framework based on short-time Fourier transform-SFA (STFT-SFA) is proposed. Finally, the effectiveness of the proposed fault detection framework is verified using the Tennessee Eastman process (TEP) and actual WT data. The results show that the proposed STFT-SFA method has a 94.0% correct monitoring rate (CMR) for TEP failures, and the proposed adaptive STFT-SFA method has a 95.6% CMR for WT failures, which are better than other comparative fault detection methods.
Stationarity Testing Under Endogenous Smooth Deterministic Components: Some Asymptotic Results

Stationarity testing for time series which include a smooth trend with unknown parameters is considered in this paper. A pseudo-Lagrange Multiplier stationarity test is proposed and its asymptotic null distribution is obtained. The distribution depends on the unknown parameters of the model. Bootstrap techniques permit this problem to be circumvented, and the limiting percentiles of the statistic may be estimated consistently. The proposed procedure can be implemented without computer simulations. The theoretical proposal is complemented with a simulation study which allows us to check the performance of the test in finite samples. The paper ends with an empirical application.
Integrating biodiversity and ecosystem services to identify ecological priorities for an ecologically fragile city in China

A high rate of urbanization comes with high environmental costs, leading to reductions in biodiversity and ecosystem services (BES). How to maximize the efficiency and representation of BES in cities is of utmost urgency. However, in the process of spatial prioritization identification, it remains unclear whether a preference for ecosystem services (ES) promotes or detracts from biodiversity. In this study, a Marxan‐based spatial conservation prioritization framework is provided to achieve a win–win situation for ES provision and biodiversity protection. Using Hohhot city, China as a study case, it sets up different weighting scenarios with species and five ES to determine the optimal protected area network for each possible combination. At the same time, it tests the conservation costs, protected features, and spatial overlap of different scenarios with existing protected areas to quantify their conservation efficiency. We found that (1) closed deciduous broadleaved forests, closed evergreen needle‐leaved forests, and deciduous shrublands could support both high biodiversity and abundant ES at altitudes of 1600–2000 m. (2) Although a positive association is found between ES and biodiversity, there is some spatial variation. The geographical overlap rate with biodiversity prioritization was only 29.72% when only ES were considered. (3) Conservation of ecological hotspots by increasing the weight of ES can reduce conservation expenses by 0.69%–20.32% compared to meeting solely biodiversity targets. Our analyses highlight the need for an appropriate weighting of ES in decisions seeking to identify protected areas. This study provides methodological support for the integration of ES and biodiversity, facilitating more comprehensive conservation planning decisions.
Energy regimes help tackle limitations with the prehistoric cultural‐phases approach to learn about sustainable transitions: Archaeological evidence from northern Spain

Human societies face challenges in transitioning towards low‐carbon economies and sustainable management of land use and natural resources. Documenting and learning from past transitions helps policy‐makers cope with such challenges. The agricultural revolution in Cantabrian Spain (ca. 7000 cal a bp) was one major adaptation of hunter‐gatherers to a changing environment that started with the Last Glacial Maximum (ca. 24 000 cal a bp) and lasted until the Mid‐Holocene (ca. 5300 cal a bp). Classic approaches to documenting prehistoric cultural timelines are based on manufacturing and technology, thus limited in their ability to describe the sustainability of past societies. Energy regimes, a functional societal approach independent from time, investigate and consider patterns of resource and energy use in various cohabiting and cooperating cultural phases. To examine past energy regimes, a database of archaeological remains was compiled to document four indicators: mobility, economy, overexploitation and societal complexity. Statistical analyses were conducted to elucidate trends, changes and continuity in subsistence strategies by hunter‐gatherers and sedentary societies. Results show that energy regimes act as a complement to cultural phases, adding novel functional analyses of past societies to cultural stratigraphy units common in archaeology, shedding light on the sustainability of past societal transitions.
SON-Related Zhu-Tokita-Takenouchi-Kim Syndrome With Recurrent Hemiplegic Migraine

Background and Objectives Zhu-Tokita-Takenouchi-Kim (ZTTK) syndrome (OMIM 617140) is a recently identified neurodevelopmental disorder caused by heterozygous loss-of-function (LoF) variants in SON. Because the SON protein functions as an RNA-splicing regulator, it has been shown that some clinical features of ZTTK syndrome can be attributed to abnormal RNA splicing. Several neurologic features have been observed in patients with ZTTK syndrome, including seizure/epilepsy and other EEG abnormalities. However, a relationship between SON LoF in ZTTK syndrome and hemiplegic migraine remains unknown. Methods We identified a patient with a pathogenic variant in SON who shows typical clinical features of ZTTK syndrome and experienced recurrent episodes of hemiplegic migraine. To define clinical features, brain MRI and EEG during and after episodes of hemiplegic migraine were characterized. To identify molecular mechanisms for this clinical presentation, we investigated the impact of small interfering RNA (siRNA)-mediated SON knockdown on mRNA expression of the CACNA1A, ATP1A2, SCN1A, and PRRT2 genes, known to be associated with hemiplegic migraine, by quantitative RT-PCR. Pre-mRNA splicing of PRRT2 on SON knockdown was further examined by RT-PCR using primers targeting specific exons. Results Recurrent episodes of hemiplegic migraine in our patient typically followed modest closed head injuries, and recurrent seizures occurred during the most severe of these episodes. Transient hemispheric cortical interstitial edema and asymmetric EEG slowing were identified during episodes. Our siRNA experiments revealed that SON knockdown significantly reduces PRRT2 mRNA levels in U87MG and SH-SY5Y cell lines, although a reduction in CACNA1A, ATP1A2, and SCN1A mRNA expression was not observed. We further identified that SON knockdown leads to failure in intron 2 removal from PRRT2 pre-mRNA, resulting in a premature termination codon that blocks the generation of functionally intact full-length PRRT2. Discussion This report identifies recurrent hemiplegic migraine as a novel clinical manifestation of ZTTK syndrome, further characterizes this clinical feature, and provides evidence for downregulation of PRRT2 caused by SON LoF as a mechanism causing hemiplegic migraine. Examination of the SON gene may be indicated in individuals with recurrent hemiplegic migraine.
A kind of new type controller for multi-agent leader-follower formation

In this paper,the algorithm of formation control concerning the Leader-Follower Multi-Agent System( MAS) based on the discrete-time model is researched. By introducing the neighbor-based local control law and the neighbor-based state estimation rules,a kind of new type controller for multi-agent leader-follower formation is designed. Arbitrary shape formations can be easily achieved by simply setting the follower and leader relative coordinates. At the same time,the coordinate rotating formula is introduced to realize the entire formation rotating accordingly with changes of the direction of the movement of the leader. This paper gives two sufficient conditions for the system's stable formation of fixed topology and switching topology,respectively. The correctness and the practical feasibility of this algorithm were verified through the MATLAB simulation and experiments were conducted using the Amigobot robot platform,respectively.
Focal-plane electric field sensing with pupil-plane holograms

The direct detection and spectral characterization of exoplanets requires a coronagraph to suppress the diffracted star light. Amplitude and phase aberrations in the optical train fill the dark zone of the coronagraph with quasi-static speckles that limit the achievable contrast. Focal-plane electric field sensing, such as phase diversity introduced by a deformable mirror (DM), is a powerful tool to minimize this residual star light. The residual electric field can be estimated by sequentially applying phase probes on the DM to inject star light with a well-known amplitude and phase into the dark zone and analyzing the resulting intensity images. The DM can then be used to add light with the same amplitude but opposite phase to destructively interfere with this residual star light. Using a static phase-only pupil-plane element we create holographic copies of the point spread function (PSF), each superimposed with a certain pupil-plane phase probe. We therefore obtain all intensity images simultaneously while still retaining a central, unaltered science PSF. The electric field sensing method only makes use of the holographic copies, allowing for correction of the residual electric field while retaining the central PSF for uninterrupted science data collection. In this paper we demonstrate the feasibility of this method with numerical simulations.
Efficiency Maximization of a Direct Internal Reforming Solid Oxide Fuel Cell in a Two-Layer Self-Optimizing Control Structure

Control system configuration is essential for the efficiency performance of a solid oxide fuel cell (SOFC). In this paper, we aim to report a novel two-layer self-optimizing control (SOC) system for the efficiency maximization of a direct internal reforming SOFC, where the efficiency index is defined as the profit of generated electricity penalized by carbon (CO2) emission. Based on the lumped-parameter model of the SOFC, comprehensive evaluations are carried out to identify the optimal controlled variables (CVs), the control of which at constant set-points can optimize the efficiency, in spite of operating condition changes. In the lower SOC layer, we configure single variables as the CVs. The results show that the stack temperature is the active constraint which should be controlled to maintain the cell performance. In addition, the outlet hydrogen composition is identified as the optimal CV. This result differs from several previous proposals, such as methane composition. In the presence of operating condition changes, the set-point of hydrogen composition is further automatically adjusted by the upper SOC layer, where a linear combination of the SOFC measurements is configured as the CV, giving negligible efficiency losses. The cascaded two-layer SOC structure is able to maximize the SOFC efficiency and reduce carbon emission without using online optimization techniques; meanwhile, it allows for smooth and safe operations. The validity of the new scheme is verified through both static and dynamic evaluations.
The effects of vitamin E on T cell subsets and immunoglobulin-containing plasma cells in the spleen of heat-stressed broiler chickens

iven a bird’s underdeveloped lymph vessels and lymph nodes, the spleen is of the utmost importance in the avian immune system. Broiler chickens can be subjected to heat stress (HS), which causes an increase in mortality, a decrease in feed intake, body weight gain, and hatchability. Vitamin E is an important antioxidant for cells, but there is limited information about how heat stress affects spleen tissue in chickens and how vitamin E can change this effect. Therefore, the aim of this study was to investigate the effects of vitamin E based on the location of subpopulations of T cells and immunoglobulin-producing plasma cells in the spleens of broiler chickens subjected to HS. The results indicated that vitamin E supplementation in the broiler chicken diet could increase the population of immune system cells in heat-stressed chickens. The findings of this study could be used as a reference for the development of management strategies for broiler chickens, especially in countries with a hot climate.