Miracle-dz/UniFAR-FTUs huggingface dataset:
HRM PRACTICESON EMPLOYEE PERCEPTION AND ITS EFFECT ON ORGANIZATIONAL COMMITMENT

The corporate environment has changed dramatically since the start of globalization, both in terms of complexity and dynamism. Companies are coping with shifting market conditions, changing workforce demographics and diversity, technological advancements, increased customer and quality focus, a talent shortage, and shifting economic realities (Noe, 2002). In order to attain long-term sustainability and total organizational success, it is crucial for businesses to continuously alter their business strategy, policies, and practices to conform to the shifting needs of the business environment. In other words, firms are compelled to implement and successfully manage change continuously (Bridges & Mitchell, 2000; Kanter, 1985). Despite the fact that organizations need to manage change more effectively, 70% of change initiatives fail (Beer &Nohria, 2000), and this failure rate has remained stable over time. Insufficient attention to issues relating to people is one of the most frequently mentioned causes of change implementation failure (Spiker & Lesser, 1995; Kotter, 1995). During the 1990s, there has been widespread recognition of the significance of employees, their psychological reactions, and their behavior during organizational transformation; yet, in practice, managers do not place enough emphasis on these concerns (Becker & Gerhart, 1996). Others who have conducted empirical studies in this area concur with this viewpoint. For instance, research on the implementation of a customer relationship management (CRM) project at three banks in New Zealand and discovered that firms tend to prioritize technological difficulties while ignoring issues relating to people.
Composition and Explosibility of Gas Emissions from Lithium-Ion Batteries Undergoing Thermal Runaway

Lithium-based batteries have the potential to undergo thermal runaway (TR), during which mixtures of gases are released. The purpose of this study was to assess the explosibility of the gaseous emission from LIBs of an NMC-based cathode during thermal runaway. In the current project, a series of pouch lithium-based battery cells was exposed to abuse conditions (thermal) to study the total amount of gases released and the composition of the gas mixture. First, the battery cells were placed in a closed vessel, and the pressure and temperature rise inside the vessel were measured. In a second step, the composition of gases was analysed using a Fourier transform Infrared (FTIR) spectrometer. We found that the amount of released gases was up to 102 ± 4 L, with a clear dependence on the battery capacity. This study showed that the concentration of gaseous emissions such as carbon monoxide (CO), methane (CH4), ethylene (C2H4), ethane (C2H6), and hydrogen cyanide (HCN) increased with higher cell capacity. Of the five studied flammable gases, the maximum concentrations of carbon monoxide (16.85 vol%), methane (7.6 vol%), and ethylene (7.86 vol%) were identified to be within their explosible range. Applying Le Chatelier’s law, a calculated lower explosion limit (LEL) of 7% in volume fraction was obtained for the gas mixture. The upper explosion limit (UEL) of the gas mixture was also found to be 31% in volume. A filter comprising pyrobubbles was used for the removal of the studied gas components released during the thermal abuse. The investigation revealed that the pyrobubbles filter was highly effect in the removal of HCN (up to 94% removal) and CO2 (up to 100% removal). Herein, we report the dependency of the method of thermal runaway trigger on the measured maximum temperature.
Manipulability in Restricted Separable Domains

We study a problem where a group of voters must decide which candidates are elected from a set of alternatives. The voters’ preferences on the combinations of elected candidates are represented by orderings. We propose a family of restrictions of the domain of separable preferences. These subdomains are generated from a partition that identifies, for each voter, her friends, her enemies and the unbiased candidates. We characterize the family of rules that satisfy strategy-proofness and tops-onliness on each of those subdomains.
Educación médica: ¿Quo vadis?

La educación médica es un campo científico que tiene como objetivo principal conseguir la mejor formación posible de los profesionales de las ciencias de la salud, muy especialmente la de los médicos, en todas las etapas de su continuo educativo, desarrollando las metodologías más apropiadas en cada momento y basadas en las mejores evidencias disponibles
On Designing Good LDPC Codes for

This paper presents a reduced-complexity approxi- mate density evolution (DE) scheme for low-density parity-check (LDPC) codes in channels with memory in the form of a hidden Markov chain. This approximation is used to design degree se- quences representing some of the best known LDPC code ensem- bles for the Gilbert-Elliott channel, and example optimizations are also given for other Markov channels. The problem of approx- imating the channel estimation is addressed by obtaining a spe- cially constructed message-passing schedule in which the channel messages all approach their stable densities. It is shown that this new schedule is much easier to approximate than the standard schedule, but has the same ultimate performance in the limits of long block length and many decoding iterations. This result is ex- tended to show that all message-passing schedules that satisfy mild conditions will have the same threshold under density evolution. Index Terms—Code optimization, density evolution, esti- mation-decoding, Gaussian assumption, irregular low-density parity-check (LDPC) codes, Markov channels, message-passing schedules.
Melting of micro/nanoparticles in an alloy melt with the Soret effect

ABSTRACT The melting of micro/nanoparticles considering the Soret effect in an alloy melt is studied in terms of the asymptotic method. The results reveal that the positive Soret coefficient weakens the dynamic disequilibrium near the interface, and then enhances the melting temperature of the particle and decreases the melting speed of the particle. The negative Soret coefficient strengthens the dynamic disequilibrium near the interface, and then reduces the melting temperature of the particle and increases the melting speed of the particle. The positive Soret coefficient weakens the accumulation of the solute flux near the interface and enhances the solute concentration. The negative Soret coefficient strengthens the accumulation of the solute flux near the interface and reduces the solute concentration. The size dependence of the melting temperature considering the Soret coefficient is quantitatively consistent with the experimental data, especially for the micro/nanoparticles with a radius smaller than 5 nm.
Intrahepatic Cholangiocarcinoma With Lung Metastasis in a 29-Year-Old Male Patient: A Case Report

Cholangiocarcinoma (CCA) is an uncommon biliary neoplasm that is more frequent in male patients. CCA is categorized into intrahepatic cholangiocarcinoma (iCCA) and extrahepatic cholangiocarcinoma (eCCA) associated with the anatomical origin location. The clinical presentation is non-specific and varies depending on the origin, iCCA is generally asymptomatic until advanced disease is present therefore this neoplasm presents a poor prognosis with a survival rate of two years. We present a case of iCCA with lung metastasis in a 29-year-old male patient with no risk factors for this malignancy.
Análisis de la Situación: Enfermedad por el Virus de Marburg en Guinea Ecuatorial y Tanzania

Este informe proporciona una imagen global de los brotes de la enfermedad por el virus de Marburg en Guinea Ecuatorial y Tanzania, así como los factores coyunturales a tener en cuenta para implementar medidas de respuesta en ambos países. Ha sido redactado por Hana Rohan (asesora independiente) con la colaboración de Juliet Bedford (Anthrologica). Fue publicado el 10 de mayo de 2023 bajo la responsabilidad de la SSHAP.
The Potential Role of Epigenetic Modifications on Different Facets in the Periodontal Pathogenesis

Periodontitis is a chronic inflammatory disease that affects the supporting structures of teeth. In the literature, the association between the pathogenicity of bacteria and environmental factors in this regard have been extensively examined. In the present study, we will shed light on the potential role that epigenetic change can play on different facets of its process, more particularly the modifications concerning the genes involved in inflammation, defense, and immune systems. Since the 1960s, the role of genetic variants in the onset and severity of periodontal disease has been widely demonstrated. These make some people more susceptible to developing it than others. It has been documented that the wide variation in its frequency for various racial and ethnic populations is due primarily to the complex interplay among genetic factors with those affecting the environment and the demography. In molecular biology, epigenetic modifications are defined as any change in the promoter for the CpG islands, in the structure of the histone protein, as well as post-translational regulation by microRNAs (miRNAs), being known to contribute to the alteration in gene expression for complex multifactorial diseases such as periodontitis. The key role of epigenetic modification is to understand the mechanism involved in the gene-environment interaction, and the development of periodontitis is now the subject of more and more studies that attempt to identify which factors are stimulating it, but also affect the reduced response to therapy.
Stability Analysis for Steady State Solutions of Huxley Equation

Stability analysis of steady state solutions of Huxley equation using Fourier mode stability analysis in two cases is investigated. Firstly when the amplitude is constant and secondly when the amplitude is variable and the results were found to be: The solutions 0 1 = u and 1 1 = u are always stable
A modified guidance law for ground moving target tracking with a class of the fast adaptive second-order sliding mode

In this paper, a solution for the problem of uncertainty presence in the estimated position of a target is indicated. The dynamic error of ground moving target tracking with special unknown bounded uncertainty has been described. To solve this problem, a class of the second-order sliding mode has been proposed. In order to increase the robustness of the proposed controller against unknown uncertainty, a set of adaptive rules has been added to the guidance law. New adaptive terms have been used to estimate the higher unknown bound of the uncertainty. The offered method, based on the sliding mode strategy, has reduced the chattering phenomena, which is harmful for the production of the command states. Finally, a fast adaptive guidance law has been added and its stability has been proved by the Lyapunov criterion. The designed adaptive fast guidance law improves the classical sliding mode control guidance law. Various simulations and comparison studies have been applied to verify the efficiency of the modified guidance law despite the disturbance and uncertainty. The results show that the uncertain terms have been compensated and the tracking position has been satisfied accurately.
Assessing the association between daily self-reported health symptoms and mental health among respiratory patients during high-pollution period in Thailand

This study aimed to explore the relationship between daily self-reported health symptoms and mental health among respiratory patients during a high-pollution period in Thailand. A cross-sectional study was conducted at the pulmonary clinic of King Chulalongkorn Memorial Hospital in Bangkok, Thailand in February 2022. Patients with respiratory diseases were enrolled and asked to self-evaluate their health via ChulaAir mobile application. Daily respiratory symptoms were assessed using a checklist for a month. The DASS21 questionnaire was used to identify depression, anxiety, and stress at the end of the month. Binary logistic regression was performed to find the associations. A total of 98 patients participated in the study, and the prevalence of depression, anxiety, and stress was 30.6%, 28.6%, and 26.5%, respectively. Cough and Phlegm were the most common health symptoms during the high-pollution period. An increase in the number of days with self-reported sore eyes was associated with depression (AOR=1.307; p=0.001), anxiety (AOR=1.261; p=0.001), and stress (AOR=1.178; p=0.001). Health symptoms are related to the presence of depression, anxiety, and stress during the high-pollution period. For individuals with respiratory symptoms, measures are required to alleviate mental health problems.
Therapeutic Strategies Targeting Respiratory Recovery after Spinal Cord Injury: From Preclinical Development to Clinical Translation

High spinal cord injuries (SCIs) lead to permanent functional deficits, including respiratory dysfunction. Patients living with such conditions often rely on ventilatory assistance to survive, and even those that can be weaned continue to suffer life-threatening impairments. There is currently no treatment for SCI that is capable of providing complete recovery of diaphragm activity and respiratory function. The diaphragm is the main inspiratory muscle, and its activity is controlled by phrenic motoneurons (phMNs) located in the cervical (C3–C5) spinal cord. Preserving and/or restoring phMN activity following a high SCI is essential for achieving voluntary control of breathing. In this review, we will highlight (1) the current knowledge of inflammatory and spontaneous pro-regenerative processes occurring after SCI, (2) key therapeutics developed to date, and (3) how these can be harnessed to drive respiratory recovery following SCIs. These therapeutic approaches are typically first developed and tested in relevant preclinical models, with some of them having been translated into clinical studies. A better understanding of inflammatory and pro-regenerative processes, as well as how they can be therapeutically manipulated, will be the key to achieving optimal functional recovery following SCIs.
Design and Implementation of Intrusion Detection Systems using RPL and AOVD Protocols-based Wireless Sensor Networks

— Wireless Sensor Network (WSN) technology has grown in importance in recent years. All WSN implementations need secure data transmission between sensor nodes and base stations. Sensor node attacks introduce new threats to the WSN. As a result, an appropriate Intrusion Detection System (IDS) is required in WSN for defending against security attacks and detecting attacks on sensor nodes. In this study, we use the Routing Protocol for Low Power and Lossy Networks (RPL) for addressing security services in WSN by identifying IDS with a network size of more or less 20 nodes and introducing 10% malicious nodes. The method described above is used on Cooja in the VMware virtual machine Workstation with the InstantContiki2.7 operating system. To track the movement of nodes, find network attacks, and spot dropped packets during IDS in WSN, an algorithm is implemented in the Network Simulator (NS2) using the Ad-hoc On-Demand Distance Vector (AODV) protocol in the Linux operating system.
Hypotheses for the Dyslexia Signal: Biomarkers in the Left and Right Brain of Developmental Dyslexia

Dyslexia is one of the most common learning disabilities, yet its brain basis and core causes are not yet fully understood. MRI studies commonly demonstrate hypoactivation in left-hemispheric temporo-parietal, occipito-temporal, and inferior frontal networks, along with hyperactivation in corresponding right-hemispheric regions. Based on the recent 15 years of brain imaging researches, this paper proposes three hypotheses about the causes of the activation difference in Dyslexia, analyzes how recent researches support these hypotheses, and draws a conclusion that the three hypotheses have a fusion trend to a certain extent, which can provide possible directions for future studies.
Reduced Voltage-Dependency by Categorical Location Information and Distance Along Street Metric for Meter-Transformer Mapping in Distribution Systems

Deep penetration of distributed energy resources (DERs) and electric vehicles (EVs) introduce benefits but may cause the overloading of service transformers in distribution networks. Such situations require real-time transformer loading information, where an accurate mapping between smart meters and distribution transformers is a prerequisite, e.g., summing the downstream smart meter consumptions. Due to arbitrary curvature in streets, we propose to employ a density-based clustering based on voltage magnitudes (continuous) and street name (categorical) information. However, a density-based approach may only be able to localize a meter to a street segment. Hence, we use a second-stage spectral clustering with distance along the street (DAS), a novel feature, to obtain meter clusters, each with a common parent transformer. For mapping transformers to meter clusters, we use the nearest cluster center approach based on the location since voltage measurements may not be available at transformers. Moreover, we provide a theoretical guarantee for such an approach. Finally, we illustrate the usefulness of the proposed algorithm on long streets, which is a challenging scenario due to many possible incorrect combinations of meter-transformer mapping. The proposed algorithm has been tested on modified IEEE 8-, $6{9}$-, 123-bus test systems and real distribution feeders from a utility in the Southwestern United States, demonstrating outstanding performance.
Changing Uses of Climate Predictions in Agriculture: Implications for Prediction Research, Providers, and Users

Abstract A series of seven studies of the usage of climate predictions by U.S. agribusinesses were conducted during 1981–2001, and their results have been reviewed to identify information to guide future predictive research, providers of predictions, and agribusiness users. Usage fell in two broad classes, for general background information or for making specific business decisions, and both classes revealed sizable increases in usage over the 21-yr period. Business sectors where usage grew rapidly included farm managers/consultants, seed growers, and food-producing firms. The increases in usage over time were attributed to five factors including growing economic pressures in agriculture, improvements in access to predictive information, improved accuracy of predictions, better formats and timeliness of predictions, and increasing employment of atmospheric sciences expertise, either in the firms or as advisors. The value of using predictions in business decisions was estimated as >$100,000 annually by a t.
Native Mitral Valve Endocarditis Caused by a Non-HACEK Gram-Negative Pathogen in a Hemodialysis Patient

Infective endocarditis (IE) due to non-HACEK (species other than Hemophilus, Aggregatibacter, Cardiobacterium, Eikenella, Kingella) bacteremia accounts for less than 2% of all IE cases but is proven to be associated with higher mortality, even more so in hemodialysis (HD) patients. Few data are available in the literature concerning non-HACEK Gram-negative (GN) IE in this immunocompromised population with multiple comorbidities. We report the atypical clinical presentation of an elderly HD patient diagnosed with a non-HACEK GN IE, namely E. coli, successfully treated with intravenous (IV) antibiotics. The objective of this case study and related literature was to highlight the limited applicability of the modified Duke criteria in the HD population, as well as the frailty of HD patients that increases their susceptibility to IE due to unexpected microorganisms that could have fatal consequences. The need for a multidisciplinary approach of an IE in HD patients is therefore imperative.
Irrigation impacts on California's climate with the variable‐resolution CESM

The variable‐resolution capability within the Community Earth System Model (VR‐CESM) is applied to understand the impact of irrigation on the regional climate of California. Irrigation is an important contributor to the regional climate of heavily irrigated regions, and within the U.S. there are few regions that are as heavily irrigated as California's Central Valley, responsible for 25% of domestic agricultural products. A flexible irrigation scheme with relatively realistic estimates of agricultural water use is employed. The impact of irrigation on mean climatology and heat extremes is investigated over the 26 year period 1980–2005 using a relatively fine grid resolution of 0.25° (∼28 km). Three simulations are performed, including an unirrigated control run and two irrigation‐enabled runs, with results compared to gridded observations and weather station data sets. During the summer months (when irrigation peaks), irrigation leads to cooling of the daily maximum near‐surface temperature field (Tmax) by approximately 1.1 K. Under irrigation, latent heat flux increased by ∼61% during the daytime as a result of increased surface evaporation; specific humidity increased by about 12%; heat stress was reduced by 22% and the average soil moisture exhibited a small (∼4.4%) but statistically significant increase. Compared with observations, irrigation improved the frequency distribution of Tmax, and both length and frequency of hot spells were better represented with irrigation enabled. Consequently, we argue that high‐resolution simulations of regional climate in CESM, particularly over heavily irrigated regions, should likely enable the irrigation parameterization to better represent local temperature statistics.
A novel medium- and long-term load forecasting method based on sensitivity analysis of multi meteorological indicators

Traditional power load or electricity forecasting methods have used only limited meteorological information. Specifically, these methods either only select one of maximal, minimal and average temperature as the influencing indicator, or only capture the very basic meteorological information, instead of exploring the nonlinear relationship between temperature and loads in depth. To overcome the shortcomings of the traditional methods, this paper proposes a new medium- and long-term load forecasting method based on sensitivity analysis of multi meteorological indicators. This method constructs the encoder and decoder between weather features and loads. Moreover, this paper combines this method with XGBoost and SVR, which can improve the accuracy of medium- and long-term forecasting algorithm in the power grid field effectively. The simulation results of this synthesized model show that average daily prediction accuracy rate of the year is 95.37% and the average monthly prediction accuracy rate of the year is 98.02%.