Miracle-dz/UniFAR-FTUs huggingface dataset:
Weight Changes, Exercise, and Dietary Patterns During Freshman and Sophomore Years of College

Weight gain and behavioral patterns during college may contribute to overweight and obesity in adulthood. The aims of this study were to assess weight, exercise, and dietary patterns of 764 college students (53% women, 47% men) during freshman and sophomore years. Students had their weight and height measured and completed questionnaires about their recent exercise and dietary patterns. At the beginning of freshman year, 29% of students reported not exercising, 70% ate fewer than 5 fruits and vegetables daily, and more than 50% ate fried or high-fat fast foods at least 3 times during the previous week. By the end of their sophomore year, 70% of the 290 students who were reassessed had gained weight (4.1 ± 3.6 kg, p> .001), but there was no apparent association with exercise or dietary patterns. Future research is needed to assess the contributions of fat, muscle, and bone mass to observed weight gain and to determine the health implications of these findings.
Optimized Design of a Rotary elastoCaloric Heat Pump Using Thermofluid-dynamic Analysis

The most recent information made available to the scientific community claims that a prototype elastocaloric device provides a number of benefits over conventional systems based on vapor compression. Greater energy efficiency is one of the key benefits, as the elastocaloric device method uses less energy than conventional systems. This is accomplished without the use of chemical refrigerants thanks to the elastic deformation of a solid substance utilized as a heat transfer medium. Despite the many benefits, the technology is still in its infancy, and further investigation is required to answer worries about prices, efficiency, and scalability. In general, this technology exhibits potential as a viable and alluring replacement for conventional refrigeration systems. An accurate two-dimensional rotating model of the device's behavior is presented in this paper. The model that can accurately represent the device's dynamic behavior is described. This study presents an optimized geometric configuration of the device that maximizes the energy performance. Along with preliminary performance numbers for cooling power and coefficient of performance, the findings are reported in terms of temperature, velocity, and pressure.
Federated Few-Shot Domain-Adaptive Anomaly Detection for Diagnostic Classification of Schizophrenia

Federated deep learning provides a safe and effective approach for tasks with distributed data, including assisted diagnosis of schizophrenia using brain images. It is utilized to train deep learning models collaboratively with information from multiple sites while protecting subject privacy. However, image data of patients are rare at each single site, which may result in poor classification performance. The incorporation of abundant brain image data of healthy controls from public datasets may help model training while the sample-imbalance issue may still decrease model performance. Constructing a federated anomaly detection model with these data can alleviate the problem. Therefore, we propose our federated few-shot domain-adaptive anomaly detection (Fed-FAAD) model to assist in the screening of patients with schizophrenia, which can utilize abundant public data of healthy subjects and a few labeled data of patients as well as healthy controls to train a reliable anomaly detection model without privacy disclosure. We use fMRI data of healthy controls from the Human Connectome Project and six independent sites of patients with schizophrenia and demographically matched controls for validation. Results at the six sites show that the average area under the curves (AUCs) of the proposed method achieve up to 78.1% and 82.5% in the 10-shot and 20-shot tasks, respectively, which outperform local anomaly detection models with improvements of 9.7% and 7.4%, respectively. The proposed framework can be applied to privacy-protecting distributed screening of patients with schizophrenia.
The performance of simple instruments in detecting geriatric conditions and selecting community-dwelling older people for geriatric assessment.

BACKGROUND comprehensive geriatric assessment (CGA) appears to be less effective when performed in outpatient clinics than in hospital settings. The effectiveness of outpatient CGA might be improved by selectively targeting frailer community-dwelling elderly people. The purpose of this study was to evaluate the clinical performance of rapidly-administered standard screening measures for geriatric syndromes in selecting community-dwelling older people for outpatient CGA. METHODS urban-dwelling older people were screened for CGA at community sites using a self-administered questionnaire containing standardized measures for each of four geriatric target conditions: depression, urinary incontinence, falls and functional impairment. The study sample included all 150 consecutive subjects who were screened, failed on one or more of the four target criteria and completed community-based, academically administered CGA. Diagnostic accuracy of the screening instruments was assessed using CGA diagnoses as the 'gold standard'. In addition, patients' potential for benefiting from CGA was determined by whether they received major medical recommendations for further evaluation or treatment. RESULTS after completing CGA, 60.2% of those failing on functional impairment, 53.5% of those failing on depression, 30.7% of those failing on falls and 92.7% of those on urinary incontinence, were confirmed as having these or highly related conditions as clinical problems. Overall, 81.3% of the subjects completing CGA received the least one major recommendations for further medical intervention; most of these recommendations (79.5%) were for a target-related condition and the further remainder (20.5%) addressed another significant medical condition. CONCLUSION simple screening instruments used in community settings have variable degrees of accuracy, but may be markers for frailty and thus can identify older people likely to benefit from geriatric assessment.
A Novel Methodology Design to Predict Employee Attrition by Using Hybrid Learning Strategy

Numerous businesses often hire new and different personnel and companies invest both time and money into training these reps, and they also provide training for current employees. The goal of such initiatives is to increase the economic security of their staff. Human resource analysis is a subfield of business analytics that aims to boost productivity and profitability by using explanation-based methods to an organization's human resource development (HRD). Human resources evaluation is more than just keeping track of how much information can be gathered on how effective a delegate is. Or, it seeks to shed light on every approach by drawing on cultural and sociological facts, with the goal of making informed choices regarding how to enhance existing methods. The term "Weakening in HR" refers to a gradual decline in a company's human resources department. We will try to disentangle factors that cause less correlation and false prediction by include details including their ages, worker position, average pay, satisfaction with work, decades in firm, duration of present position and so on in the set of data utilized as features. The cost to an organization of staff turnover is a serious issue. Some of the usual expenses of shedding specialists and compensating them include advertising open positions, going through the enlistment process, and relocating to a new location and training zone. To find out is our mission. What causes employee turnover, what variables contribute to it and how does attrition effect businesses? In this lesson, we'll learn how to segment inspections for better preventative care and upkeep. In this study, we provide Hybrid Learning based Employee Attrition Prediction (HLEAP), a novel method for accurately predicting employee turnover. The results of this work compares the suggested method to more traditional learning algorithms like RNN and ANN, providing convincing evidence for the efficacy of the former.
Indoor Localization of User Equipment in a 5G Ecosystem using a Hybrid Approach

This paper investigates the location estimation accuracy of an indoor localization system using both the Received Signal Strength Indicator (RSSI) and Angle of Arrival (AoA) values from the User Equipment (UE). The Quadriga channel model was used to simulate the 5G indoor scenario, operating in the mm-Wave bands and simulations were conducted at two specific frequencies, 2 GHz and 28 GHz. Results showed that the proposed weighted hybrid RSSI/AoA localization system gave a horizontal localization accuracy of 1.76 m. The proposed system showed an improvement of 56 % compared to a TDoA-based approach. However, at 28 GHz, a horizontal localization error of 11.5 m was obtained. Hence, at this frequency, additional localization measurements and more accurate channel modelling are required.
Combined thoracoscopic and laparoscopic robotic-assisted minimally invasive esophagectomy using a four-arm platform: experience, technique and cautions during early procedure development.

OBJECTIVES This study reports an early, single-institution experience of combined thoracoscopic and laparoscopic robotic-assisted minimally invasive esophagectomy (RAMIE) using a four-arm robotic platform, with special attention given to the pitfalls and complications during procedure development. METHODS We conducted a prospective, single-cohort, observational study of patients undergoing RAMIE at a single institution. RESULTS A total of 21 patients (median age, 62 years [range, 37-83 years]) underwent RAMIE with a four-arm robotic platform (17 by Ivor Lewis and 4 by McKeown). Of the patients, 17 (81%) had a complete (R0) resection, and 16 (76%) received induction treatment, the majority (14/21 [67%]) with combined chemoradiation. The median operative time was 556 min (range, 395-807 min), which decreased to 414 min (range, 405-543 min) for the last 5 cases in the series. The median estimated blood loss was 307 cm(3) (range, 200-500 cm(3)), and the median length of hospital stay was 10 days (range, 7-70 days). The median number of lymph nodes resected was 20 (range, 10-49). Five (24%) patients were converted to open procedures. Five patients (24%) had major complications. One (5%) died of complications on postoperative Day 70, and 3 (14%) had clinically significant anastomotic leaks (Grade II or greater, by Common Terminology Criteria for Adverse Events version 3.0). Three patients (14%) in this early experience developed airway fistulas. CONCLUSIONS While four-arm RAMIE may offer advantages over standard minimally invasive esophagectomy approaches, its adoption in a structured program, with critical evaluation of adverse events and subsequent adjustment of technique, is paramount to maximize patient safety, minimize complications and improve the conduct of operation early in the learning curve. Particular technical consideration should be given to prevention of airway complications.
Feline SentiNet: Deciphering Cat Discomfort with Deep Learning and Random Forest for Pain Grading

In a world where cats' silent suffering is often overlooked, scientists set out on a life-changing journey aided by technology's empathetic hand. The adventure goes beyond human comprehension, venturing into the domain of AI-driven empathy and the search for a key to the secret language of cat agony. They found a way to shed light on the intricacies of feline discomfort using Convolutional Neural Networks (CNNs) and the insight of Random Forest. Each layer in the study reflects a new stanza in the song of recognition, and the research itself evolves like a symphony of pixels. Patterns emerge for the authors when others only see pictures, and they pick up on the agony hidden in the stillness of feline emotions. By bringing together scientific rigor and humaneness, we present a new method for identifying pain in feline companions. Inspiringly, the model distinguished pain intensity among five unique categories with an accuracy of 90%, proving its adeptness in realizing the goals. Researchers saw an instrument that leveled the playing field between man and feline experience when accuracy, recall, and F1 scores all came together. The authors introduce a model that expresses the inexpressible and ease the unspoken as pixels dance, filters learn, and forests decide. In this work, authors celebrate the unspoken connection between people and their feline friends by exploring how technology may serve as a conduit for greater human comprehension, empathy, and the goal of a future in which no one's suffering is ignored.
Study of Lysozyme-Loaded Poly-L-Lactide (PLLA) Porous Microparticles in a Compressed CO2 Antisolvent Process

Lysozyme (LSZ)-loaded poly-L-lactide (PLLA) porous microparticles (PMs) were successfully prepared by a compressed CO2 antisolvent process in combination with a water-in-oil emulsion process using LSZ as a drug model and ammonium bicarbonate as a porogen. The effects of different drug loads (5.0%, 7.5% and 10.0%) on the surface morphology, particle size, porosity, tapped density and drug release profile of the harvested PMs were investigated. The results show that an increase in the amount of LSZ added led to an increase in drug load (DL) but a decrease in encapsulation efficiency. The resulting LSZ-loaded PLLA PMs (LSZ-PLLA PMs) exhibited a porous and uneven morphology, with a density less than 0.1 g·cm−3, a geometric mean diameter of 16.9–18.8 μm, an aerodynamic diameter less than 2.8 μm, a fine particle fraction (FPF) of 59.2%–66.8%, and a porosity of 78.2%–86.3%. According to the results of differential scanning calorimetry, the addition of LSZ improved the thermal stability of PLLA. The Fourier transform infrared spectroscopy analysis and circular dichroism spectroscopy measurement reveal that no significant changes occurred in the molecular structures of LSZ during the fabrication process, which was further confirmed by the evaluation of enzyme activity of LSZ. It is demonstrated that the emulsion-combined precipitation with compressed antisolvent (PCA) process could be a promising technology to develop biomacromolecular drug-loaded inhalable carrier for pulmonary drug delivery.
An internet of social things

The Internet of Things is a vision for a world of interconnected smart devices. We present an alternative vision based on a review of literature that emphasizes the importance and role of objects in social relations. We situate this work in relation to a conceptual understanding of objects and sociality, and note some methodological implications of a more object-centred sociality that may suggest design opportunities alongside the emerging Internet of Things.
Efficient Low-Resolution Human Pose Estimation with Knowledge Bridge

In the field of human pose estimation, the problem of knowledge transfer among models with different learning objectives and input resolutions remains unresolved. In this paper, we propose a novel framework specifically designed for low-resolution human pose estimation, aiming to obtain an efficient and powerful model for this task. Specifically, we introduce an intermediate model as a knowledge bridge between the teacher model and the student model. We perform knowledge transfer from the teacher model with different learning objectives and resolutions to the student model through cross-learning-objective pose distillation between the teacher model and the bridge model, as well as cross-resolution pose distillation between the bridge model and the student model. Experimental results on the COCO dataset demonstrate the effectiveness of our model in low-resolution human pose estimation tasks.
FlowDSnet: An End-to-End Scene Flow Based LiDAR De-Skewing Network

The motion distortion of LiDAR is the phenomenon caused by the movement of the LiDAR carrier. Such phenomenon makes the LiDAR point cloud information unreliable and seriously degrades the tasks that rely on LiDAR such as 3D object perception, localization, and mapping. In this work, a novel end-to-end network named FlowDSnet is proposed to compensate for the distortion of LiDAR point clouds in an end-to-end way. The proposed pipeline takes the original adjacent two frames of point clouds as input and recovers the undistorted coordinates of each point by predicting the scene flow in 3D space. Different from the traditional distortion removal algorithm, our method does not rely on the information of other sensors, such as the Inertial measurement unit (IMU), but applies a novel flow embedding algorithm combined with spatiotemporal interpolation to conduct distortion correction. And the input is two adjacent frames of raw LiDAR measurement. Besides, both qualitative and quantitative experiments on automative driving datasets are conducted to prove the effectiveness of the proposed pipeline. The experimental results show that this network can accomplish the LiDAR de-skewing task well, especially in a scene with many dynamic objects.
Leveraging Unannotated Data to Improve Zero-Shot Question Answering in the French Legal Domain

State of the art question answering models have recently shown impressive performance especially in a zero-shot setup. This approach is particularly useful when confronted with a highly diverse domain such as the legal field, in which it is increasingly difficult to have a dataset covering every notion and concept. In this work, we propose a flexible generative question answering approach to contract analysis as well as a weakly supervised procedure to leverage non-annotated data and boost our models’ performance in general, and their zero-shot performance in particular.
Molecular and Functional Bases of Selection against a Mutation Bias in an RNA Virus

The selective pressures acting on viruses that replicate under enhanced mutation rates are largely unknown. Here, we describe resistance of foot-and-mouth disease virus to the mutagen 5-fluorouracil (FU) through a single polymerase substitution that prevents an excess of A to G and U to C transitions evoked by FU on the wild-type foot-and-mouth disease virus, while maintaining the same level of mutant spectrum complexity. The polymerase substitution inflicts upon the virus a fitness loss during replication in absence of FU but confers a fitness gain in presence of FU. The compensation of mutational bias was documented by in vitro nucleotide incorporation assays, and it was associated with structural modifications at the N-terminal region and motif B of the viral polymerase. Predictions of the effect of mutations that increase the frequency of G and C in the viral genome and encoded polymerase suggest multiple points in the virus life cycle where the mutational bias in favor of G and C may be detrimental. Application of predictive algorithms suggests adverse effects of the FU-directed mutational bias on protein stability. The results reinforce modulation of nucleotide incorporation as a lethal mutagenesis-escape mechanism (that permits eluding virus extinction despite replication in the presence of a mutagenic agent) and suggest that mutational bias can be a target of selection during virus replication.
ATM-Mediated Transcriptional and Developmental Responses to γ-rays in Arabidopsis

ATM (Ataxia Telangiectasia Mutated) is an essential checkpoint kinase that signals DNA double-strand breaks in eukaryotes. Its depletion causes meiotic and somatic defects in Arabidopsis and progressive motor impairment accompanied by several cell deficiencies in patients with ataxia telangiectasia (AT). To obtain a comprehensive view of the ATM pathway in plants, we performed a time-course analysis of seedling responses by combining confocal laser scanning microscopy studies of root development and genome-wide expression profiling of wild-type (WT) and homozygous ATM-deficient mutants challenged with a dose of γ-rays (IR) that is sublethal for WT plants. Early morphologic defects in meristematic stem cells indicated that AtATM, an Arabidopsis homolog of the human ATM gene, is essential for maintaining the quiescent center and controlling the differentiation of initial cells after exposure to IR. Results of several microarray experiments performed with whole seedlings and roots up to 5 h post-IR were compiled in a single table, which was used to import gene information and extract gene sets. Sequence and function homology searches; import of spatio-temporal, cell cycling, and mutant-constitutive expression characteristics; and a simplified functional classification system were used to identify novel genes in all functional classes. The hundreds of radiomodulated genes identified were not a random collection, but belonged to functional pathways such as those of the cell cycle; cell death and repair; DNA replication, repair, and recombination; and transcription; translation; and signaling, indicating the strong cell reprogramming and double-strand break abrogation functions of ATM checkpoints. Accordingly, genes in all functional classes were either down or up-regulated concomitantly with downregulation of chromatin deacetylases or upregulation of acetylases and methylases, respectively. Determining the early transcriptional indicators of prolonged S-G2 phases that coincided with cell proliferation delay, or an anticipated subsequent auxin increase, accelerated cell differentiation or death, was used to link IR-regulated hallmark functions and tissue phenotypes after IR. The transcription burst was almost exclusively AtATM-dependent or weakly AtATR-dependent, and followed two major trends of expression in atm: (i)-loss or severe attenuation and delay, and (ii)-inverse and/or stochastic, as well as specific, enabling one to distinguish IR/ATM pathway constituents. Our data provide a large resource for studies on the interaction between plant checkpoints of the cell cycle, development, hormone response, and DNA repair functions, because IR-induced transcriptional changes partially overlap with the response to environmental stress. Putative connections of ATM to stem cell maintenance pathways after IR are also discussed.
Fake News Prediction in Hindi and English Robust Transformer Model

The ever-growing consumption of information from digital platforms such as social media has paved the way for information dissemination like never before, but not necessarily all the information or news delivered to us is authentic or holds true to reality. A huge amount of information is created and shared by social media users, some of which are manipulative and have no influence on reality, and can cause harm in a variety of ways. Implementation of an automated or AI-based system to classify text articles as misinformation is a challenging task as identifying the truth is complicated. An AI-based fake news prediction system is proposed to classify fake news from real ones by understanding the context of news and making decisions accordingly. To identify fake news by examining the context of the news, a Bi-directional Encoder Representations from Transformers (BERT) model, a pretrained model with a more potent feature extractor, has been implemented. BERT is well suited for tasks like predicting fake news because of its extensive contextualizing capabilities. The novelty of this paper is that the proposed system is trained, tested, and evaluated in 2 languages, English and Hindi to develop an overall reliable system to predict fake news in both languages. Two models of BERT namely DeBERTa and XLM-RoBERTa are chosen for the prediction of fake news. Further improvement in the model is achieved by using the additional news data to pre-train this model. Also, the model is fine-tuned to fit the requirements and to get the optimum result possible. The performance comparison of the chosen models is made and it is observed that the XLM-RoBERTa has improved accuracy over DeBERTa while testing for both languages.