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The aim of the study was to group the lactation curve (LC) of Holstein cows in several clusters based on their milking characteristics and to investigate physiological differences among the clusters. Milking data of 330 lactations which have a milk yield per day during entire lactation period were used. The data were obtained by refinement from 1332 lactations from 724 cows collected from commercial farms. Based on the similarity measures, clustering was performed using the k-medoids algorithm; the number of clusters was determined to be six, following the elbow method. Significant differences on parity, peak milk yield, DIM at peak milk yield, and average and total milk yield (p < 0.01) were observed among the clusters. Four clusters, which include 82% of data, show typical LC patterns. The other two clusters represent atypical patterns. Comparing to the LCs generated from the previous models, Wood, Wilmink and Dijsktra, it is observed that the prediction errors in the atypical patterns of the two clusters are much larger than those of the other four cases of typical patterns. The presented model can be used as a tool to refine characterization on the typical LC patterns, excluding atypical patterns as exceptional cases.

Machine learning techniques provide efficient data analysis tools without mathematical derivations. Data-centric LC representations are highly demanded to use these tools for LC-related research. A novel data-oriented LC representation model using piecewise linear regression (PWLR) is presented. This representation is intended to be used directly as data for machine learning along with other associated data at an individual base. An LC is represented in vector form as a series of connected line segments and the location and number of segments are determined by the maximum residual. The critical points are determined at the rapid transit point in the LC. The Bayesian information criterion was used to choose the proper number of line segments to avoid the overfitting problem. To demonstrate the validity of the PWLR model as an LC descriptor, its approximation accuracy and representation generality were tested experimentally. The results revealed that the PWLR model is advantageous for representing the LCs of an individual or a large herd that are directly applicable to data-driven approaches.

The violation of data integrity in automotive Cyber-Physical Systems (CPS) may lead to dangerous situations for drivers and pedestrians in terms of safety. In particular, cyber-attacks on the sensor could easily degrade data accuracy and consistency over any other attack, we investigate attack detection and identification based on a deep learning technology on wheel speed sensors of automotive CPS. For faster recovery of a physical system with detection of the cyber-attacks, estimation of a specific value is conducted to substitute false data. To the best of our knowledge, there has not been a case of joining sensor attack detection and vehicle speed estimation in existing literature. In this work, we design a novel method to combine attack detection and identification, vehicle speed estimation of wheel speed sensors to improve the safety of CPS even under the attacks. First, we define states of the sensors based on the cases of attacks that can occur in the sensors. Second, Recurrent Neural Network (RNN) is applied to detect and identify wheel speed sensor attacks. Third, in order to estimate the vehicle speeds accurately, we employ Weighted Average (WA), as one of the fusion algorithms, in order to assign a different weight to each sensor. Since environment uncertainty while driving has an impact on different characteristics of vehicles and causes performance degradation, the recovery mechanism needs the ability adaptive to changing environments. Therefore, we estimate the vehicle speeds after assigning a different weight to each sensor depending on driving situations classified by analyzing driving data. Experiments including training, validation, and test are carried out with actual measurements obtained while driving on the real road. In case of the fault detection and identification, classification accuracy is evaluated. Mean Squared Error (MSE) is calculated to verify that the speed is estimated accurately. The classification accuracy about test additive attack data is 99.4978%. MSE of our proposed speed estimation algorithm is 1.7786. It is about 0.2 lower than MSEs of other algorithms. We demonstrate that our system maintains data integrity well and is safe relatively in comparison with systems which apply other algorithms.

BackgroundCannabis use has been causally linked to violent behaviors in experimental and case studies, but its association with homicide victimization has not been rigorously assessed through epidemiologic research.MethodsWe performed a case-control analysis using two national data systems. Cases were homicide victims from the National Violent Death Reporting System (NVDRS), and controls were participants from the National Survey on Drug Use and Health (NSDUH). While the NVDRS contained toxicological testing data on cannabis use, the NSDUH only collected self-reported data, and thus the potential misclassification in the self-reported data needed to be corrected. We took a data fusion approach by concatenating the NSDUH with a third data system, the National Roadside Survey of Alcohol and Drug Use by Drivers (NRS), which collected toxicological testing and self-reported data on cannabis use for drivers. The data fusion approach provided multiple imputations (MIs) of toxicological testing results on cannabis use for the participants in the NSDUH, which were then used in the case-control analysis. Bootstrap was used to obtain valid statistical inference.ResultsThe analyses revealed that cannabis use was associated with 3.55-fold (95% CI: 2.75–4.35) increased odds of homicide victimization. Alcohol use, being Black, male, aged 21–34 years, and having less than a high school education were also significantly associated with increased odds of homicide victimization.ConclusionsCannabis use is a major risk factor for homicide victimization. The data fusion with MI method is useful in integrative data analysis for harmonizing measures between different data sources.

High‐energy‐density lithium‐ion batteries are crucial for accelerating the widespread adoption of electric vehicles. Silicon monoxide/graphite (SiO/Gr) composite anodes have attracted considerable attention as promising candidates for increasing energy density. However, severe capacity degradation caused by the large volume changes of SiO during charge–discharge cycles remains a major obstacle to commercialization. One effective strategy to address this issue is to limit the charge/discharge operating voltage range (swing range) of the SiO anode. In this study, a cathode design composed of single‐crystalline and polycrystalline LiNi0.8Co0.1Mn0.1O2(NCM811) with a bimodal particle size distribution is proposed to effectively control the charge–discharge operating range of the SiO anode within a full‐cell. This design leverages the reaction heterogeneity of the cathode particles to induce an increase in overpotential at the end of discharge, effectively lowering the discharge endpoint potential of the anode. This design strategy enables stable cycling performance without compromising full‐cell energy density by selectively controlling the discharge depth of SiO in the SiO/Gr anode. The effectiveness of this design is validated through various electrochemical analyses and real‐time operando X‐ray Diffraction (XRD), demonstrating that it is an efficient strategy to enhance the long‐term cycle stability of SiO/Gr anodes without sacrificing energy density. A bimodal cathode composed of single‐ and polycrystalline NCM particles induces end‐of‐discharge overpotential rise via reaction heterogeneity, effectively regulating the depth of discharge of SiO in SiO/graphite anodes. This design suppresses mechanical degradation of SiO and enables long‐term cycling stability without compromising the energy density of full cells.

Hydrovoltaic nanogenerators, which harness small quantities of water to generate power, are gaining considerable attention for applications in next‐generation wearable electronics. Conventional hydrovoltaic nanogenerators are constrained by their limited power density and suboptimal long‐term stability. Therefore, a transpiration‐driven electrokinetic power generator (TEPG) based on silk yarn coated with poly(3,4‐ethylenedioxythiophene):poly(styrene sulfonate) (PEDOT:PSS)/divinyl sulfone (DVS) and designed as a wearable hydrovoltaic nanogenerator offering outstanding power generation efficiency and water stability is presented in this study. Given its hydrophilic surface, mechanical durability, and high aspect ratio, silk yarn is used to design a yarn‐based TEPG system to achieve high spatial‐efficiency and maximize volumetric power density. Furthermore, the covalent crosslinking agent, DVS, is introduced to sustain the long‐term, high‐power production efficiency of PEDOT:PSS. The devised yarn‐type TEPG system generates a maximum power of 112 µW cm−3 with artificial sweat. A system comprising 25 yarn‐type TEPGs arranged in a series–parallel configuration is implemented utilizing the high spatial‐efficiency of the sewable yarn‐type TEPG. The results demonstrate the potential of wearable hydrovoltaic nanogenerators as next‐generation renewable energy systems for wearable applications. A PEDOT:PSS‐based yarn‐type transpiration‐driven electrokinetic power generator with a covalently crosslinking agent, divinyl sulfone, is investigated to address the poor water stability of PEDOT:PSS and achieve an exceptional power generation efficiency. The designed device successfully charges commercial capacitors using a biological aqueous solution, demonstrating the potential of the system for green wearable hydrovoltaic nanogenerator systems.

Key messageWing loading, diaspore type (round-winged vs. single-winged), and aerodynamic motion (autogyro vs. floater) influence the terminal velocity of wind-dispersed diaspores and falling patterns.The dispersal ability of diaspores dispersed by wind can be reflected in the terminal velocity of the diaspore. Therefore, we measured the terminal velocity of wind-dispersed diaspores in 17 major forest and urban tree species in South Korea and tracked falling diaspore patterns up to the achievement of terminal velocity using the video camera recording method. In addition, the morphological characteristics of the diaspores were measured, and their effect on diaspore terminal velocity tested. Chamaecyparis obtusa (2.66 m s−1) had the highest terminal velocity, whereas Picea abies (0.61 m s−1) had the lowest terminal velocity. Falling diaspores achieved terminal velocity through (1) the oscillating falling pattern of floater diaspores, with a constant descent velocity with minor increases and decreases; (2) the decelerating falling pattern of single-winged diaspores, with accelerating descent velocity followed by rapid deceleration to terminal velocity; or (3) the accelerating falling pattern of round-winged autogyro diaspores, with descent velocity increasing steadily up to terminal velocity. The terminal velocities of single-winged diaspores were significantly lower than those of round-winged diaspores. Although there were cases of similar terminal velocity between species in the same genera (e.g., Abies, Pinus), there were large intraspecific differences in terminal velocity within the same genus due to morphological differences (e.g., Acer). The measured terminal velocities could be applied in simulations for diaspore dispersal distances for forestry tree species. The present study explored the relationship between diaspore morphological characteristics and terminal velocity, and is the first to report the dispersal ability of wind-dispersed seeds of major tree species in East Asia. The findings of the present study can be adopted as key input variables in seed dispersal modeling and facilitate the establishment of natural regeneration plans and conservation of endangered species in the wake of climate change.

The quantification of urban tree biomass allocation has primarily relied on estimations using allometric equations (AEs) developed for nondestructive harvest methods. However, the lack of harvest-based AEs that account for belowground biomass, nutrient concentration, and annual growth rates poses challenges in accurately quantifying the greenhouse gas inventory for urban land uses. In this study, we aimed to develop AEs using a log-transformed linear model for eight urban landscaping tree species, taking into account belowground biomass. We purchased 117 urban landscaping trees from tree farms in South Korea and investigated their biomass fractions, carbon and nutrient concentrations, and annual growth rate using a destructive method. We also developed AEs for different tree compartments using diameter at breast height as an independent variable. The AEs obtained exhibited high suitability, as evidenced by their high R2 values (0.853–0.982 and 0.806–0.923 for aboveground and belowground biomass, respectively). The mean belowground biomass fraction across the different species was approximately 30%, suggesting that urban trees could allocate more belowground biomass than forest trees. Conversely, carbon and nitrogen concentrations varied significantly across species and compartments, and the mean annual carbon sequestration rate was 3.96 kg C year−1 tree−1. Therefore, the application of the AEs for urban trees may enhance the accuracy of the national greenhouse gas inventory for the settlement sector.

Natural regeneration in forest management, which relies on artificial planting, is considered a desirable alternative to reforestation. However, there are large uncertainties regarding the natural regeneration processes, such as seed production, seed dispersal, and seedling establishment. Among these processes, seed dispersal by wind must be modeled accurately to minimize the risks of natural regeneration. This study aimed to (1) review the main mechanisms of seed dispersal models, their characteristics, and their applications and (2) suggest prospects for seed dispersal models to increase the predictability of natural regeneration. With improving computing and observation systems, the modeling technique for seed dispersal by wind has continued to progress steadily from a simple empirical model to the Eulerian-Lagrangian model. Mechanistic modeling approaches with a dispersal kernel have been widely used and have attempted to be directly incorporated into spatial models. Despite the rapid development of various wind-dispersal models, only a few studies have considered their application in natural regeneration. We identified the potential attributes of seed dispersal modeling that cause high uncertainties and poor simulation results in natural regeneration scenarios: topography, pre-processing of wind data, and various inherent complexities in seed dispersal processes. We suggest that seed dispersal models can be further improved by incorporating (1) seed abscission mechanisms by wind, (2) spatiotemporally complex wind environments, (3) collisions with the canopy or ground during seed flight, and (4) secondary dispersal, long-distance dispersal, and seed predation. Interdisciplinary research linking climatology, biophysics, and forestry would help improve the prediction of seed dispersal and its impact on natural regeneration.

Underage drinking has been recognized as a public health issue in the United States. Even though participation in sport activities have been recommended as an important tool for youth development and reducing risky behavior, previous research has indicated that sport participation directly increased alcohol consumption among youth. Furthermore, youth with low self-esteem are more likely to engage in delinquent behaviors (including alcohol consumption). There is a lack of empirical research on how self-esteem mediates the relationship between sport participation and underage drinking behavior. Using a Structural Equation Modeling approach, the current study examined the theoretical relationships between sports participation, underage drinking behavior and the mediating effect of self-esteem among a sample of youth (specifically 8th and 10th grades). To have a larger sample size, this study used the data from the annual national surveys, \"Monitoring the Future: A Continuing Study of American Youth\". The results indicated that sport participation significantly increases both drinking behavior and self-esteem. In contrast, a greater level of self-esteem significantly decreases drinking behavior. The findings suggest that although youth who are frequently involved in sport and physical activities have increased drinking behavior, a greater level of self-esteem offset this effect. Since this study provides insight into the under-studied potential mediating factor of self-esteem, people (e.g., parents, teachers, coaches, and administrators) who work closely with youth and adolescents will benefit from these findings and gain a better understanding of underage drinking behavior and the importance of self-esteem among youth. The current study contributes to the literature on positive youth development and public health.