Miracle-dz/UniFAR-FTUs huggingface dataset:
Associations between Feeding Behaviors Collected from an Automated Milk Feeder and Neonatal Calf Diarrhea in Group Housed Dairy Calves: A Case-Control Study

Simple Summary Precision technology devices are often integrated on dairies to monitor animal health. One precision technology used to manage calves is an automated milk feeder which can record feeding behaviors such as daily milk intake, drinking speed, and feeder visits. The objective of this study was to determine if calf feeding behaviors collected by an automated milk feeder, changed in the days surrounding diagnosis of neonatal calf diarrhea (NCD; e.g., −3 days to 5 days after diagnosis). Milk intake was lower for the greatest number of days surrounding diagnosis of NCD compared to control calves, but the sensitivity and specificity of detecting NCD using any individual behavior was low. However, parallel testing using cumulative feeding behaviors on the day of diagnosis of NCD increased the sensitivity for detecting disease. This study provides insights into the association of feeding behavior with calves destined for NCD using an automated milk feeder. We suggest feeding behaviors cannot replace visual diagnosis of NCD, but that feeding behaviors might serve as a screening tool for producers. Abstract The objective of this case-control study was to determine if feeding behavior data collected from an automated milk feeder (AMF) could be used to predict neonatal calf diarrhea (NCD) in the days surrounding diagnosis in pre-weaned group housed dairy calves. Data were collected from two research farms in Ontario between 2017 and 2020 where calves fed using an AMF were health scored daily and feeding behavior data (milk intake (mL/d), drinking speed (mL/min), number of rewarded or unrewarded visits) was collected. Calves with NCD were pair matched to healthy controls (31 pairs) by farm, gender, and age at case diagnosis to assess for differences in feeding behavior between case and control calves. Calves were first diagnosed with NCD on day 0, and a NCD case was defined as calves with a fecal score of ≥2 for 2 consecutive days, where control calves remained healthy. Repeated measure mixed linear regression models were used to determine if there were differences between case and control calves in their daily AMF feeding behavior data in the days surrounding diagnosis of NCD (−3 to +5 days). Calves with NCD consumed less milk on day 0, day 1, day 3, day 4 and day 5 following diagnosis compared to control calves. Calves with NCD also had fewer rewarded visits to the AMF on day −1, and day 0 compared to control calves. However, while there was a NCD status x day interaction for unrewarded visits, there was only a tendency for differences between NCD and control calves on day 0. In this study, feeding behaviors were not clinically useful to make diagnosis of NCD due to insufficient diagnostic ability. However, feeding behaviors are a useful screening tool for producers to identify calves requiring further attention.
Towards Predicting Supplier Resilience: A Tree-Based Model Approach

With looming uncertainties and disruptions in today's global supply chains, such as lockdown measures to contain COVID-19, supply chain resilience has gained considerable attention recently. While decision-makers in procurement have emphasized the importance of traditional risk assessment, its shortcomings can be complemented by resilience. However, while most resilience studies are too qualitative in nature and to inform supplier decisions, many quantitative resilience studies frequently rely on complex and impractical operations research models fed with simulated supplier data. Thus there is the need for an integrative, intermediate way for the practical and automated prediction of resilience with real-world data. We therefore propose a random forest-based supervised learning method to predict supplier resilience, outperforming the current human benchmark evaluation by 139 percent. The model is trained on both internal ERP data and publicly available secondary data to help assess suppliers in a pre-screening step, before deciding which supplier to select for a specific product. The results of this study are to be integrated into a software tool developed for measuring and tracking the total cost of supply chain resilience from the perspective of purchasing decisions. © 2022 IEEE Computer Society. All rights reserved.
Ancient DNA Methods Improve Forensic DNA Profiling of Korean War and World War II Unknowns

The integration of massively parallel sequencing (MPS) technology into forensic casework has been of particular benefit to the identification of unknown military service members. However, highly degraded or chemically treated skeletal remains often fail to provide usable DNA profiles, even with sensitive mitochondrial (mt) DNA capture and MPS methods. In parallel, the ancient DNA field has developed workflows specifically for degraded DNA, resulting in the successful recovery of nuclear DNA and mtDNA from skeletal remains as well as sediment over 100,000 years old. In this study we use a set of disinterred skeletal remains from the Korean War and World War II to test if ancient DNA extraction and library preparation methods improve forensic DNA profiling. We identified an ancient DNA extraction protocol that resulted in the recovery of significantly more human mtDNA fragments than protocols previously used in casework. In addition, utilizing single-stranded rather than double-stranded library preparation resulted in increased attainment of reportable mtDNA profiles. This study emphasizes that the combination of ancient DNA extraction and library preparation methods evaluated here increases the success rate of DNA profiling, and likelihood of identifying historical remains.
The Effectiveness of Transcranial Magnetic Stimulation (TMS) Paradigms as Treatment Options for Recovery of Language Deficits in Chronic Poststroke Aphasia

Background In an effort to boost aphasia recovery, modern rehabilitation, in addition to speech and language therapy (SALT), is increasingly incorporating noninvasive methods of brain stimulation. The present study is aimed at investigating the effectiveness of two paradigms of neuronavigated repetitive transcranial magnetic stimulation (rTMS): (i) 1 Hz rTMS and (ii) continuous theta burst stimulation (cTBS) each as a standalone treatment for chronic aphasia poststroke. Methods A single subject experimental design (SSED) trial was carried out in which six people with aphasia (PWA) were recruited, following a single left hemispheric stroke more than six months prior to the study. Three individuals were treated with 1 Hz rTMS, and the remaining three were treated with cTBS. In all cases, TMS was applied over the right pars triangularis (pTr). Language assessment, with standardized and functional measures, and cognitive evaluations were carried out at four time points: twice prior to treatment (baseline), one day immediately posttreatment, and at follow-up two months after treatment was terminated. Quality of life (QoL) was also assessed at baseline and two months posttreatment. In addition, one of the participants with severe global aphasia was followed up again one and two years posttherapy. Results For all participants, both rTMS paradigms (1 Hz rTMS and cTBS) generated trends towards improvement in several language skills (i.e., verbal receptive language, expressive language, and naming and reading) one day after treatment and/or two months after therapy. Rated QoL remained stable in three individuals, but for the other three, the communication scores of the QoL were reduced, while two of them also showed a decline in the psychological scores. The participant that was treated with cTBS and followed for up to two years showed that the significant improvement she had initially exhibited in comprehension and reading skills two months after TMS (1st follow-up) was sustained for at least up to two years. Conclusion From the current findings, it is suggested that inhibitory TMS over the right pTr has the potential to drive neuroplastic changes as a standalone treatment that facilitates language recovery in poststroke aphasia.
Factors influencing perimenopausal syndrome in clinical nurses: a qualitative study.

BACKGROUND The quality of life of women is seriously affected by perimenopausal symptoms and related diseases. The work of female clinical nurses is often stressful and job burnout is not uncommon. Under such conditions, perimenopausal syndrome can be easily induced or aggravated. The health of nurses is positively correlated with nursing quality, residents' health, patients' quality of life and human resource cost in health care institutions. The physical and mental health of perimenopausal nurses is an important issue worth paying attention to. METHODS Clinical nurses were selected from tertiary and secondary hospitals in Chongqing province by purposive sampling from September to November 2020. Front-line nurses diagnosed with perimenopausal syndrome were recruited from different clinical departments and interviewed using a semi-structured method. The interview results were processed using thematic analysis. RESULTS Finally, 16 nurses were included in the present study. Analysis of the interview transcriptions identified four themes: patient factors, work environment factors, personal factors, and family social support factors. CONCLUSIONS The themes influencing perimenopausal syndrome in clinical nurses include aspects of patient factors, working environment factors, personal factors, and family social support factors. Our findings provide an important reference for policy makers to develop management programs that benefit nurses and ensure the safety of patients.
Development of Ag–In Alloy Pastes by Mechanical Alloying for Die Attachment of High-Power Semiconductor Devices

Sintered silver paste is widely used as the die-attachment material for power semiconductors. However, sintered silver joints encounter problems, such as severe coarsening of sintered pores and oxidation issues, in harsh high-temperature environments. These lead to the deterioration of the die-attachment joints. In this paper, a novel method of sintering silver joints is demonstrated, where silver–indium alloy paste is used to improve the reliability of sintered Ag joints. The silver–indium (Ag–In) alloy paste was fabricated through mechanical alloying using the ball-milling technique. The well-bonded sintered Ag–In alloy joints inhibited pore coarsening better than pure sintered Ag joints and significantly enhanced the mechanical properties at high operating temperatures. Lastly, an oxidation mechanism for the sintered joint was proposed, and strategies to prevent such high-temperature oxidation were discussed.
An Optimal Cloud Based Electric Vehicle Charging System

With the evolution of the internet-of-things and the emergence of cloud computing, the charging dynamics of vehicles have changed. This work discusses cloud-based monitoring and management used in charging electric vehicles and their impact on the smart charging system. Charging management plays a key role in assessing the charging infrastructure because of the automakers and charging service providers. As the market evolves, this system looks at the present public and private sectors that provide charging stations and contrasts them with modern cloud-based charging in electric vehicles. The cloud module developed contains layers, with the top layer of the robust calculating ability, which is globally optimized using machine learning technology. The bottom layer counters the real-time issues with the controller. The system also analyzes the current demands in the market and forms strategies to maximize profits through smart charging systems.
Content Spending and Network Quality in Mobile Channels: A Hidden Markov Model of User Engagement and Content Consumption

Nowadays, individuals increasingly depend on mobile devices and apps for every aspect of their daily lives. Even though the number of mobile users and the time that they spend on mobile apps have grown tremendously, app providers are still struggling with low user engagement and a high attrition rate. This study examines how users’ prior content spending and network delay impact consumers’ mobile content consumption decisions and how the impacts vary in different engagement states . We develop a Hidden Markov Model and calibrate it using a large tapstream data set of individual users’ reading activities from a mobile app provider. We identify three engagement states and heterogeneous impacts of the financial and operational factors. This study will generate important implications on content pricing and user engagement in mobile channels.
Integrated Temperature and Position Sensors in a Shape-Memory Driven Soft Actuator for Closed-Loop Control

Soft actuators are a promising option for the advancing fields of human-machine interaction and dexterous robots in complex environments. Shape memory alloy wire actuators can be integrated into fiber rubber composites for highly deformable structures. For autonomous, closed-loop control of such systems, additional integrated sensors are necessary. In this work, a soft actuator is presented that incorporates fiber-based actuators and sensors to monitor both deformation and temperature. The soft actuator showed considerable deformation around two solid body joints, which was then compared to the sensor signals, and their correlation was analyzed. Both, the actuator as well as the sensor materials were processed by braiding and tailored fiber placement before molding with silicone rubber. Finally, the novel fiber-rubber composite material was used to implement closed-loop control of the actuator with a maximum error of 0.5°.
PREDICTION OF DIABETES EMPOWERED WITH FUSED MACHINE LEARNING

In the medical field, it is essential to predict diseases early to prevent them. Diabetes is one of the most dangerous diseases all over the world. In modern lifestyles, sugar and fat are typically present in our dietary habits, which have increased the risk of diabetes. To predict the disease, it is extremely important to understand its symptoms. Currently, machine-learning (ML) algorithms are valuable for disease detection. This article presents a model using a fused machine learning approach for diabetes prediction. The conceptual framework consists of two types of models: Support Vector Machine (SVM) and Artificial Neural Network (ANN) models. These models analyze the dataset to determine whether a diabetes diagnosis is positive or negative. The dataset used in this research is divided into training data and testing data with a ratio of 70:30 respectively. The output of these models becomes the input membership function for the fuzzy model, whereas the fuzzy logic finally determines whether a diabetes diagnosis is positive or negative. A cloud storage system stores the fused models for future use. Based on the patient’s real-time medical record, the fused model predicts whether the patient is diabetic or not. The proposed fused ML model has a prediction accuracy of 94.87, which is higher than the previously published methods.
AN EXPERIENCE OF LOCATING HIGH-IMPEDANCE FAULTS THROUGH SMART METERS' ALARMS IN POWER DISTRIBUTION NETWORKS

In the last decades, multiple methodologies and dedicated devices have been developed to assist in locating faults, especially High-Impedance Faults (HIF). However, if the broken cable remains hanging during an HIF, there is no fault current, impeding the current-based HIF location methodologies in detecting the defect. Locating HIFs is crucial for power distribution utilities, not only for improvement of quality service indices, but also for preventing safety issues posed by energized downed power cables. This research deals with a key Smart Grids' component: Advanced Metering Infrastructure (AMI), through Smart Meters' (SM) capability of emitting Last Gasp (LG) alarms, which indicate power outage. Since SMs are massively deployed throughout the power distribution concession area, a second benefit is provided: detection and location of fuse-cleared faults, a type of fault that the power utility is normally aware only through customers' complaints. Finally, a Brazilian power distribution network is considered for case studies aimed to demonstrate HIFs and fuse-cleared faults location. They successfully demonstrate the proposed methodology suitability to assist utilities engineers in addressing those types of faults.
Volume of surgical interventions for benign colorectal tumors - an analysis of 3510 surgical and endoscopic resections in the single colorectal center in Poland.

INTRODUCTION Colorectal cancer is the most frequent neoplasm of the whole gastrointestinal track. Due to screening colonoscopy program, colorectal lesions are often diagnosed at early stage. The vast majority of them are possible to remove endoscopically. However, a substantial percentage of benign lesion in Western centers are still operated. The aim of this article was to determine the percentage of surgical resections due to benign adenomas in the reference center of endoscopic submucosal dissection (ESD) and colorectal surgery in Poland. MATERIALS AND METHODS Retrospective analysis of 3 510 patients operated from 2015 to 2019 in Center of Bowel Treatment in Brzeziny. RESULTS We have analyzed 3 510 endoscopic and surgical procedures performed in the colon: 601 ESDs; 1 002 endoscopic mucosal resections (EMRs); and 1,907 surgical resections. Out of 601 ESDs, 57 invaded the submucosa, of which 29 (4.8%) were non-therapeutic ESDs. In 5 patients, due to occurrence of post-ESD perforation, an additional surgical intervention was necessary. Out of the 1,002 EMRs, 22 cases (2.2%) were diagnosed with deeply infiltrating cancers, which required a surgery. The overall percentage of the need for surgery in the endoscopically treated patients (ESD + mucosectomy) was 3.5% (56/1 603). Among resection surgeries, 15 of them (0.8%) ended with the diagnosis of a benign lesion in the postoperative histopathological examination. CONCLUSIONS Inclusion advanced endoscopic techniques such as ESD to routine clinical practice in colorectal centers gives clear benefits for the patients. Well defined and standardized process of qualifying for appropriate treatment allows to significantly reduce the percentage of abdominal approach surgery due to benign colorectal lesions.
Withdrawal from repeated nicotine vapor exposure increases somatic signs of physical dependence, anxiety-like behavior, and brain reward thresholds in adult male rats

In recent years, there has been a dramatic increase in nicotine vapor consumption via electronic nicotine delivery systems (i.e., e-cigarettes), particularly in adolescents. While recent work has focused on the health effects of nicotine vapor exposure, its effects on the brain and behavior remain unclear. In this study, we assessed the effects that cessation from repeated nicotine vapor exposure had on behavioral and neuronal measures of withdrawal. For Experiment 1, fifty-six adult male rats were tested for plasma cotinine levels, somatic withdrawal signs, and anxiety-like behavior in the elevated plus maze, immediately following precipitated withdrawal from repeated exposure to 12 or 24 mg/mL nicotine vapor. In Experiment 2, twelve adult male rats were tested for intracranial self-stimulation (ICSS) across 14 days of exposure to 24 mg/mL nicotine vapor and across the 14 days immediately following nicotine exposure. Results revealed that plasma cotinine, somatic signs, anxiety-like behavior, and ICSS stimulation thresholds were all observed to be elevated during withdrawal in the 24 mg/mL nicotine group, when compared to vehicle controls (50/50 vegetable glycerin/propylene glycol). The data suggest that cessation from repeated nicotine vapor exposure using our preclinical model leads to nicotine dependence and withdrawal, and demonstrates that the vapor system described in these experiments is a viable pre-clinical model of e-cigarette use in humans. Further characterization of the mechanisms driving nicotine vapor abuse and dependence is needed to improve policies and educational campaigns related to e-cigarette use. Highlights A rodent model of nicotine e-cigarette vapor use was utilized to assess effects of cessation from repeated nicotine vapor exposure on behavioral and neuronal measures of drug withdrawal. Cessation of repeated nicotine vapor exposure resulted in increased plasma cotinine levels, somatic withdrawal signs, and anxiety-like behavior. Cessation of repeated nicotine vapor exposure resulted in elevations of ICSS reward threshold. Electrode implantations for ICSS were mapped by location and threshold to a standardized reference atlas of the rat brain to facilitate comparisons with the published literature.
WNK kinases sense molecular crowding and rescue cell volume via phase separation

When challenged by hypertonicity, dehydrated cells must defend their volume to survive. This process requires the phosphorylation-dependent regulation of SLC12 cation chloride transporters by WNK kinases, but how these kinases are activated by cell shrinkage remains unknown. Within seconds of cell exposure to hypertonicity, WNK1 concentrates into membraneless droplets, initiating a phosphorylation-dependent signal that drives net ion influx via the SLC12 cotransporters to rescue volume. The formation of WNK1 condensates is driven by its intrinsically disordered C-terminus, whose evolutionarily conserved signatures are necessary for efficient phase separation and volume recovery. This disorder-encoded phase behavior occurs within physiological constraints and is activated in vivo by molecular crowding rather than changes in cell size. This allows WNK1 to bypass a strengthened ionic milieu that favors kinase inactivity and reclaim cell volume through condensate-mediated signal amplification. Thus, WNK kinases are physiological crowding sensors that phase separate to coordinate a cell volume rescue response.
Reconsidering digital labour: Bringing tech workers into the debate

The digital labour debate has produced manifold insights into new forms of work emerging within digital capitalism. So far, though, most research has focused on highly precarious labourers, neglecting the growing ranks of affluent ‘ tech workers ’ . I argue that this analytical oversight can be attributed to a narrow conceptualisation of digital labour. Thus, this article first proposes a broadening of the digital labour concept to encompass all work entangled with the digital economy. In a second step, I demonstrate the heuristic surplus of this theoretical broadening through a discussion of the empirical literature on tech workers. By bringing tech workers into the debate, I point to the cultural, technological and organisational relations between high and low ‐ paid digital labourers. Pursuing twin ‐ aims, the article combines a theoretical re-consideration of digital labour with an analytical discussion of the literature on tech workers to provide a more relational account of work and class in digital capitalism.