Miracle-dz/UniFAR-FTUs huggingface dataset:
Burnout Comparison between Clinical and Basic Sciences Faculty of a Medical School and Evaluation of Related Factors

Objective: It has been shown that clinical practice may be a risk factor for job burnout. On the other hand, annual income may have a protective effect on job burnout. Clinical faculty in contrast to basic sciences faculty members have higher income but are involve in clinical practice. Comparison between these two groups can clarify which factors have greater influence on burnout. As a second aim for this study, reliability and validity of the Persian version of Maslach burnout inventory general survey (MBI-GS) were evaluated as well. Method : This cross-sectional study was conducted at Shiraz Medical School in Iran and a total of 241 faculty members were randomly selected and burnout was measured by the Persian version of the Maslach burnout inventory general survey (MBI-GS). Results: Comparison of burnout between the two groups indicated that clinical faculty showed significantly higher scores in the exhaustion dimension compared to the basic sciences faculty (p value = 0.017) but no significant differences were found between the two groups in other dimensions. Job satisfaction and income satisfaction were negatively correlated with exhaustion and cynicism dimensions, and job satisfaction was positively associated with professional efficacy (p value > 0.05). Internal consistency of the questionnaire was acceptable (α=0.77). Scaling success rate for discrimination and convergent validity were 100% except for convergent validity in the cynicism subscale. Correlation of all questions with their dimensions was equal to or more than 0.4 with the exception of item 13 in the cynicism subscale. Conclusion: Clinical faculty had higher burnout than basic sciences faculty especially in the exhaustion dimension. It has also been shown that income and job satisfaction are the most important factors which can predict professional burnout in medical faculty members. It is important for administrative and organizational decision makers to improve job engagement and decrease job abandonment. This study largely confirmed the 3-dimensional structure of the Persian version of MBI-GS.
Prediction of PM2.5 Concentration Based on the LSTM-TSLightGBM Variable Weight Combination Model

PM2.5 is one of the main pollutants that cause air pollution, and high concentrations of PM2.5 seriously threaten human health. Therefore, an accurate prediction of PM2.5 concentration has great practical significance for air quality detection, air pollution restoration, and human health. This paper uses the historical air quality concentration data and meteorological data of the Beijing Olympic Sports Center as the research object. This paper establishes a long short-term memory (LSTM) model with a time window size of 12, establishes a T-shape light gradient boosting machine (TSLightGBM) model that uses all information in the time window as the next period of prediction input, and establishes a LSTM-TSLightGBM model pair based on an optimal weighted combination method. PM2.5 hourly concentration is predicted. The prediction results on the test set show that the mean squared error (MAE), root mean squared error (RMSE), and symmetric mean absolute percentage error (SMAPE) of the LSTM-TSLightGBM model are 11.873, 22.516, and 19.540%, respectively. Compared with LSTM, TSLightGBM, the recurrent neural network (RNN), and other models, the LSTM-TSLightGBM model has a lower MAE, RMSE, and SMAPE, and higher prediction accuracy for PM2.5 and better goodness-of-fit.
Effect of transformational behavior on millennial job satisfaction

Leaders are responsible for empowering and driving employees to succeed in challenging times or changes and, ultimately, achieve the best results. One of the biggest dilemmas in today’s leaders’ agenda is to understand how to manage a diverse multigenerational workforce in which millennials represent a predominant group by far, being completely different from previous generations due to the technology impact. The aim of the paper is to identify which leadership style and behavior affects most positively millennial job satisfaction in a multinational environment, and to understand the differences between millennials and non-millennials. The Multifactor Leadership Questionnaire, known as the MLQ, was answered by 167 representative employees from various multinational corporations, 125 of whom are millennials. Based on the results, transformational style is strongly correlated with and positively affected by millennial job satisfaction. Moreover, transformational style is a significant predictor of increased millennial satisfaction, and more specifically, idealized attributes and intellectual stimulation are behaviors that have been validated to increase it. On the other hand, individual consideration has been proven to have a productive effect by increasing non-millennial job satisfaction. According to the findings, millennial workforce leaders need to move towards a more transformational style, based on more idealized attributes and an intellectual stimulation approach, if they want to increase their satisfaction and avoid unwanted attrition. Basically, millennials are searching for leaders who trust and embrace innovation, creativity, critical thinking and, most importantly, leaders who also question the status quo.
Algorithm Execution Time and Accuracy of NTC Thermistor-Based Temperature Measurements in Time-Critical Applications

This paper addresses the challenges of selecting a suitable method for negative temperature coefficient (NTC) thermistor-based temperature measurement in electronic devices. Although measurement accuracy is of great importance, the temperature calculation time represents an even greater challenge since it is inherently constrained by the control algorithm executed in the microcontroller (MCU). Firstly, a simple signal conditioning circuit with the NTC thermistor is introduced, resulting in a temperature-dependent voltage UT being connected to the MCU’s analog input. Next, a simulation-based approximation of the actual temperature vs. voltage curve is derived, resulting in four temperature notations: for a look-up table principle, polynomial approximation, B equation and Steinhart–Hart equation. Within the simulation results, the expected temperature error of individual methods is calculated, whereas in the experimental part, performed on a DC/DC converter prototype, required prework and available MCU resources are evaluated. In terms of expected accuracy, the look-up table and the Steinhart–Hart equation offer superior results over the polynomial approximation and B equation, especially in the nominal temperature range of the NTC thermistor. However, in terms of required prework, the look-up table is inferior compared to the Steinhart–Hart equation, despite the latter having far more complex mathematical functions, affecting the overall MCU algorithm execution time significantly.
High-dimensional Nonlinear Profile Monitoring based on Deep Probabilistic Autoencoders

Wide accessibility of imaging and profile sensors in modern industrial systems created an abundance of high-dimensional sensing variables. This led to a a growing interest in the research of high-dimensional process monitoring. However, most of the approaches in the literature assume the in-control population to lie on a linear manifold with a given basis (i.e., spline, wavelet, kernel, etc) or an unknown basis (i.e., principal component analysis and its variants), which cannot be used to efficiently model profiles with a nonlinear manifold which is common in many real-life cases. We propose deep probabilistic autoencoders as a viable unsupervised learning approach to model such manifolds. To do so, we formulate nonlinear and probabilistic extensions of the monitoring statistics from classical approaches as the expected reconstruction error (ERE) and the KL-divergence (KLD) based monitoring statistics. Through extensive simulation study, we provide insights on why latent-space based statistics are unreliable and why residual-space based ones typically perform much better for deep learning based approaches. Finally, we demonstrate the superiority of deep probabilistic models via both simulation study and a real-life case study involving images of defects from a hot steel rolling process.
Uma Análise das Ocorrências de Fogo e Incêndios Florestais no Parque Nacional da Chapada Diamantina entre 2015 e 2020

Incêndios ﬂorestais são considerados catástrofes ambientais e podem ser provocados por ações humanas ou por fatores naturais. A região do Parque Nacional da Chapada Diamantina (Chapada Diamantina Bahia) está entre as Unidades de Conservação Federais que registram grandes números de focos de incêndios. O estudo desta região, e de uma área circundante de 10km do parque, tem como objetivo identificar possíveis relações entre processos atmosféricos e a quantidade de ocorrências de focos de fogo. Para isso, foi utilizada uma série temporal de 6 anos (2015 2020) e coletados dados disponibilizados pelo INPE e INMET, contendo registros de ocorrências de focos de fogo e dados meteorológicos respectivamente.
The Antiviral Potential of Probiotics—A Review on Scientific Outcomes

A rich repertoire of research studies on probiotics has been documented as one of the therapeutic agents or adjuvants for vaccines in treating viral infections. It is well known that the immunomodulatory properties of probiotics reduce the severity of viral infections. The efficacy of probiotics alone and combined boost up the host’s innate immunity, thereby developing a robust antiviral paradigm. As dietary and therapeutic measures, probiotics potentially work as an alternative for those who lack access to vaccines or antiviral drugs. Potential probiotic mechanisms include competing with pathogens for nutrients and colonization sites, producing antimicrobial metabolites and enhancing protective immune responses. The live probiotics can reach and colonize the host animals’ intestines then confer the health benefits by improving the host’s natural defence against viral infections. The research studies on probiotics suggest that they reduce the risk of viral infections, yet the innermost mechanisms are still unknown. The reason for scripting this review is to discuss the current developments in probiotic therapeutic measures and their probable insights into antiviral agents.
Stochastic programming approaches for an energy-aware lot-sizing and sequencing problem with incentive

Motivated by real challenges on energy management faced by industrial firms, we propose a novel way to reduce production costs by including the pricing of electricity in a multi-product lot-sizing problem. In incentive-based programs, when electric utilities face power consumption peaks, they request electricity-consuming firms to curtail their electric load, rewarding the industrial firms with incentives if they comply with the curtailment requests. Otherwise, industrial firms must pay financial penalties for an excessive electricity consumption. A two-stage stochastic formulation is presented to cover the case where a manufacturer wants to satisfy any curtailment request. A chance-constrained formulation is also proposed, and its relevance in practice is discussed. Finally, computational studies are conducted to compare mathematical models and highlight critical parameters and show potential savings when subscribing incentive-based programs. We show that the setup cost ratio, the capacity utilisation rate, the number of products and the timing of curtailment requests are critical parameters for manufacturers.
Seasonal Variation in Nitrate Removal Mechanisms in Coastal Stormwater Ponds

Stormwater wet ponds (SWPs) are engineered structures used to collect and retain stormwater runoff from developed areas. SWPs are generally regarded as important nitrogen (N) sinks, but seasonal variation in SWP N cycling that influences pond nitrogen removal has not been characterized. To inform SWP function across seasons, we sampled the sediments and water columns of three stormwater ponds in the southeastern US coastal plain and measured gas and nutrient fluxes from the sediment‐water interface during ambient conditions and nitrate (NO3)‐enriched “simulated storm” conditions. Dissolved organic nitrogen (DON) was the dominant form of dissolved N in the water column, while nitrate + nitrite (NOx) was typically below detection. SWP sediment organic matter properties varied by study site but had minimal impact on sediment N processes or estimated NO3 fate. SWP sediments generally functioned as TN sinks during NO3‐enriched conditions, but the estimated fate of NO3 varied based on water temperature, DON concentrations, and sediment O2 uptake. These results suggest that permanent N removal (denitrification) by SWPs varies seasonally, with retention of NOx becoming more important during hotter conditions when NOx uptake is largest. Low ambient NOx concentrations and rapid NO3 uptake suggest that coastal stormwater ponds can host reduced conditions that may promote NO3 retention over denitrification. Additional research is needed to determine the fate of retained NO3 in SWP sediments and how variation in NO3 fate might impact downstream water quality.
Sentimen Analisis Pengguna Twitter pada Event Flash Sale Menggunakan Algoritma K-NN, Random Forest, dan Naive Bayes

There is a sales system called Flash Sale in e-commerce. Basically the concept of a Flash Sale is to offer a lower price and a predetermined time and number of products. The sales system is only held at certain moments, by making cheaper product sales but with a limited time and number of products it will make sales increase because buyer interest will be higher. But apart from all the advantages of course there will be pros and cons. Sentiment analysis on Twitter was chosen because Twitter itself is a social media that allows users to be free to comment or write opinions about anything, including opinions about flash sale events that exist in e-commerce today. Thus, this research exists to find out the opinions of existing Twitter users regarding the Flash Sale event held by e-commerce. By using the methodology of three classification algorithms, Naive Bayes, K-Nearest Neighbor and Random Forest in classifying the data to determine the accuracy of the sentiment value of Twitter users in the Flash Sale event. This research takes two data samples from the keywords "flash sale" and "flash sale shopee", the results accuracy of the implementation of the three classification algorithms are 83.53% Naive Bayes, 82.94% K-NN, 80.59% Random Forest for the keyword "flash sale” and 81.48% Naive Bayes, 77.78% K-NN, 74.07% Random Forest for the keyword “flash sale shopee”. With this, the Naive Bayes Algorithm becomes a recommendation for classifying Sentiment Analysis data with greater accuracy and more stability to be used for large and small data.
Calcination Atmosphere Regulated Morphology and Catalytic Performance of Pt/SiO2 in Gas‐phase Oxidative Dehydrogenation of KA‐oil

It is industrially attractive to produce cyclohexanone directly from gas‐phase oxidative dehydrogenation (ODH) of KA‐oil (a mixture of cyclohexanone and cyclohexanol) since extra alcohol‐ketone separation process can be potentially omitted. Herein, by simply tuning the calcination atmosphere of Pt/SiO2, we successfully regulated the morphology of Pt nanoparticles and optimized their catalytic performance in ODH of KA‐oil. Pt/SiO2 calcined in O2 atmosphere showed a cube‐like morphology and achieved a 78.1 % cyclohexanone yield at 170 °C, which surpassed its counterpart calcined in H2 and most of the reported catalysts. Further experimental and theoretical calculation results indicated that cubic Pt nanoparticles possessed stronger adsorption ability to cyclohexanol and therefore performed better in ODH of KA‐oil.
Internet traffic matrix prediction with convolutional LSTM neural network

With the rapid growing trend of Internet, prediction‐based network operation optimization and management has drawn the attention from both the academia and the industry. For predicting Internet traffic, deep learning models have been proven more effective than linear and statistical models. However, some of the previous studies model the Internet traffic prediction as a multivariate time series prediction problem simply, without using the Internet traffic matrix structure. In this letter, the Internet traffic matrix prediction problem is firstly modeled as a video prediction task. Then a ConvLSTM‐based Seq2Seq model named ConvLSTM‐TM is proposed for predicting the traffic matrix in the next time slot. Based on three real‐world traffic matrix datasets, ConvLSTM‐TM outperforms five deep learning baselines with a lower prediction error. For replication, the dataset, code, and results are publicly available in a Github repository.
KSHV Episome Tethering Sites on Host Chromosomes: Regulation of Latency-Lytic Switch by the ChAHP complex

Kaposi’s sarcoma-associated herpesvirus (KSHV) establishes a latent infection in the cell nucleus, but where KSHV episomal genomes are tethered and the mechanisms underlying KSHV lytic reactivation are unclear. Here, we study the nuclear microenvironment of KSHV episomes and show that the KSHV latency-lytic replication switch is regulated via viral long non-coding (lnc)RNA-CHD4 (chromodomain helicase DNA binding protein 4) interaction. KSHV episomes localize with a CHD4 complex, ChAHP, at epigenetically active genomic regions and tethers frequently near centromeric regions of host chromosomes. The ChAHP complex also occupies the 5’-region of a highly-inducible lncRNAs and terminal repeats of KSHV genome with latency-associated nuclear antigen (LANA). Viral lncRNA binding competes with CHD4 DNA binding, and KSHV reactivation is accompanied by the detachment of KSHV episomes from host chromosome docking sites We propose a model in which elevated lncRNA expression determines the KSHV latency-lytic decision by regulating LANA/ChAHP DNA binding at inducible viral enhancers.
Landscape pattern and connectivity importance of protected areas in Kuala Lumpur conurbation for sustainable urban planning

Protected areas in the cities play an important role for nature conservation and sustainable urban planning. In many occasions however development activities and urban planning ignore this ecological aspect. For sustainable urban planning, understanding the landscape pattern and connectivity importance of urban protected areas and its surroundings are important. Therefore, this study examined: i) landscape pattern changes of three protected areas Bukit Sungai Puteh, Bukit Nenas and KL Golf Course and ii) their connectivity importance for biodiversity conservation of Kuala Lumpur conurbation. In this study three satellite images (Landsat TM 30 m resolution) of the study areas in 1988, 1996 and 2005 were processed and analyzed using ERDAS Imagine 9.2 and ArcGIS 9.3 to develop land use/land cover maps of the study areas in the three years. Landscape pattern of the maps was analyzed using landscape metrics calculated by Vector Based Landscape Analysis Tools Extension ( v LATE ) software. Conefor Sensinode 2.2 (CS22) software was used to measure landscape connectivity. Results revealed that over the decades the protected areas experienced highly pressure from anthropogenic activities. Generally, their size is very small and the natural areas within their boundary gradually reduced and fragmented. Analysis also revealed the transformation of natural landscape to the anthropogenic settlements inside all of the three protected areas. This suggests that these protected areas may have lost their capability to support valuable biodiversity if the situation persisted. However, the connectivity analysis showed that some of the large patches of forest outside the protected areas have connectivity importance. Therefore, there is a need for more protected areas in the Kuala Lumpur conurbation to protect valuable biodiversity and also the natural landscapes for sustainable planning of the city.
Índices de eficiencia alimenticia en ovinos de pelo: calidad de la carne y genes asociados. Revisión

Los ovinos de pelo desempeñan un papel importante en la producción de carne en zonas tropicales, donde los estudios de eficiencia alimenticia han sido poco evaluados. El consumo de alimento representa más del 70 % de los costos; por lo tanto, la selección de animales con alta eficiencia alimenticia puede mejorar la rentabilidad del sistema de producción. Se han desarrollado herramientas que permiten seleccionar individuos con mayor eficiencia alimenticia sin comprometer la calidad del producto. Por lo que esta revisión tiene la finalidad de identificar estas herramientas genético-moleculares y estadísticas, como son, el consumo de alimento residual (CAR) y ganancia e ingesta residual (GIR). En la literatura consultada, se reportan diferencias en el consumo de materia seca (CMS) en un rango del 9 al 30 % entre animales eficientes e ineficientes, manteniendo una ganancia diaria de peso (GDP) similar empleando el índice CAR. Por otro lado, utilizando el índice GIR los CMS son similares, aunque la GDP en animales eficientes es mayor hasta en 50 g día-1, reduciendo la conversión alimenticia en un kilo. Esta diferencia se asume a un conjunto de genes asociados a la eficiencia alimenticia (Adra2a, Gfra1, Gh, Glis1, Il1rapl1, Lep, Lepr, Mc4r, Oxsm, Pde8b, Rarb, Ryr2, Sox5 y Sox6, Trdn), que pudieran ser utilizados para la selección de ovinos de razas de pelo con alta eficiencia alimenticia, teniendo en cuenta los genes relacionados con la calidad de carne (Capns1, Cast, Dgat1, Fabp4, Igf-i, Lep, Mstn y Scd).
Light cone dynamics in excitonic states of two-component Bose and Fermi gases

We consider the non-equilibrium dynamics of two-component one dimensional quantum gases in the limit of extreme population imbalance where the minority species has but a single particle. We consider the situation where the gas is prepared in a state with a single spatially localized exciton: the single particle of the minority species is spatially localized while the density of the majority species in the vicinity of the minority particle sees a depression. Remarkably, we are able to consider cases where the gas contains on the order of N = 100 particles, comparable to that studied in experiments on cold atomic gases. We are able to do so by exploiting the integrability of the gas together with the observation that the excitonic state can be constructed through a simple superposition of exact eigenstates of the gas. The number of states in this superposition, rather than being exponentially large in the number of particles, scales linearly with N. We study the evolution of such spatially localized states in both strongly interacting Bose and Fermi gases. The behavior of the light cones when the interaction strength and density of the gas is varied can be understood from exact results for the spin excitation spectrum in these systems. We argue that the light cone in both cases exhibits scaling collapse. However unique to the Bose gas, we show that the presence of gapped finite-momentum roton-like excitations provide the Bose gas dynamics with secondary light cones.