Miracle-dz/UniFAR-FTUs huggingface dataset:
Research on Simulation of the PAR Distribution in Tree Canopy

The common methods about research on the distribution of radiation in canopy are introduced.The principle of ray-tracing technology and its application in radiation transmission are described.A radiation transfer model is proposed for tree model based on forward ray tracing.The model hypothesis,processing flow and primary algorithm are expounded.Forward ray tracing is used to simulate transmission course of photosynthetically active radiation in tree canopy.Finally the special conversion method between HSV and RGB color space is adopted to clearly express distribution of radiation in canopy.On the basis of analyzing optical characteristics of leaves,the radiation transfer model is applied in the simulation of distribution of PAR in canopy.At the same time,we can get the reflectance rate,transmission rate and absorption rate.The simulative data have little difference with the actually measured data which indicated that the radiation transfer model based on forward ray tracing is effective.
A Combined Priori and Purity Gaussian OverSampling Algorithm for Imbalanced Data Classification

The imbalanced data classification presents pervasive challenges in real-world data mining scenarios. To tackle these challenges, sampling techniques have emerged as effective approaches. However, the prevailing technique, SMOTE (Synthetic Minority Over-sampling Technique), and its derivatives make the assumption that each minority class observation carries an equal amount of information, neglecting the distribution of minority class observations and their relationship with neighboring majority class observations. Consequently, the synthetic samples generated by these methods deviate from the original data distribution, resulting in an increased overlap with the majority samples. To address this limitation, we introduce a novel sampling technique called Combined Priori and Purity Gaussian OverSampling (PPGO) in this paper. The proposed method incorporates prior probabilities and sample purity to calculate the weight assigned to each minority class sample. This weight is used to determine the quantity of synthetic samples to be generated for each minority class, as well as the level of dispersion during the Gaussian sampling process. This approach aims to restore the original distribution of the observations and minimize the overlap with the majority class regions. The experimental results demonstrate a significant improvement in the G-mean and AUC measures when using the proposed method compared to conventional approaches. These results were obtained through experiments conducted on 32 datasets obtained from the KEEL repository.
Examining the Impact of Organizational Culture and Risk Management and Internal Control on Performance in Healthcare Organizations

Abstract In this volatile and challenging environment, global pandemics, extreme weather events, and the ever-changing global political and economic landscape continue to have a significant impact on the sustainability of organizations. This study presents a framework for measuring or assessing the nature of organizational culture and whether it is effective in influencing the competitive advantage of healthcare organizations in terms of operational performance through risk management and internal control. The results of the study demonstrate that effective consideration of the interaction of risk management and control systems with organizational culture is one of the best ways to prevent operational failure. JEL classification numbers: C10, G32, L25, M14. Keywords: Organizational Culture, Risk Management, Internal Control, Medical Institutions.
EARNINGS MANAGEMENT AND FINANCIAL PERFORMANCE BEFORE AND DURING THE CORONA VIRUS DISEASE-19 PANDEMIC

This research set out to ascertain the impact of profitability as measured by the ratio of return on assets (ROA), return on equity (ROE), and net profit margin (NPM) on earnings management in the tourism, restaurant, and hotel sector firm listed on Indonesian Stock Exchange for the study period of 2017–2020. The research sample was selected by using purposive sampling. Data analysis technique using multiple linear regression analysis and carried out by SPSS version 22 software. According to the findings, net profit margin had an impact on earnings management, while return on equity and return on assets did not affect earnings management.
Molecular Characteristics of Cisplatin-Induced Ototoxicity and Therapeutic Interventions

Cisplatin is a commonly used chemotherapeutic agent with proven efficacy in treating various malignancies, including testicular, ovarian, cervical, breast, bladder, head and neck, and lung cancer. Cisplatin is also used to treat tumors in children, such as neuroblastoma, osteosarcoma, and hepatoblastoma. However, its clinical use is limited by severe side effects, including ototoxicity, nephrotoxicity, neurotoxicity, hepatotoxicity, gastrointestinal toxicity, and retinal toxicity. Cisplatin-induced ototoxicity manifests as irreversible, bilateral, high-frequency sensorineural hearing loss in 40–60% of adults and in up to 60% of children. Hearing loss can lead to social isolation, depression, and cognitive decline in adults, and speech and language developmental delays in children. Cisplatin causes hair cell death by forming DNA adducts, mitochondrial dysfunction, oxidative stress, and inflammation, culminating in programmed cell death by apoptosis, necroptosis, pyroptosis, or ferroptosis. Contemporary medical interventions for cisplatin ototoxicity are limited to prosthetic devices, such as hearing aids, but these have significant limitations because the cochlea remains damaged. Recently, the U.S. Food and Drug Administration (FDA) approved the first therapy, sodium thiosulfate, to prevent cisplatin-induced hearing loss in pediatric patients with localized, non-metastatic solid tumors. Other pharmacological treatments for cisplatin ototoxicity are in various stages of preclinical and clinical development. This narrative review aims to highlight the molecular mechanisms involved in cisplatin-induced ototoxicity, focusing on cochlear inflammation, and shed light on potential antioxidant and anti-inflammatory therapeutic interventions to prevent or mitigate the ototoxic effects of cisplatin. We conducted a comprehensive literature search (Google Scholar, PubMed) focusing on publications in the last five years.
Movement Representation Learning for Pain Level Classification

—Self-supervised learning has shown value for uncovering informative movement features for human activity recognition. However, there has been minimal exploration of this approach for affect recognition where availability of large labelled datasets is particularly limited. In this paper, we propose a P-STEMR (Parallel Space-Time Encoding Movement Representation) architecture with the aim of addressing this gap and specifically leveraging the higher availability of human activity recognition datasets for pain-level classification. We evaluated and analyzed the architecture using three different datasets across four sets of experiments. We found statistically significant increase in average F1 score to 0.84 for pain level classification with two classes based on the architecture compared with the use of hand-crafted features. This suggests that it is capable of learning movement representations and transferring these from activity recognition based on data captured in lab settings to classification of pain levels with messier real-world data. We further found that the efficacy of transfer between datasets can be undermined by dissimilarities in population groups due to impairments that affect movement behaviour and in motion primitives (e.g. rotation versus flexion). Future work should investigate how the effect of these differences could be minimized so that data from healthy people can be more valuable for transfer learning.
Considering both fishing and climate in a model of the Celtic Sea and the Bay of Biscay: what do we learn?

The Celtic Sea and the Bay of Biscay (Figure 1) have been fished intensively for at least a century. Already sizeable between the two World Wars, the fisheries resumed after 1945 with the support of governmental subventions to modernize the fleets and thus causing an unprecedented increase in fishing capacity in the region. Decrease in biomass and landings of several exploited species hint at the impact of fishing while changes in climate and productivity are also suspected. This project examines the impact of fishing on this ecosystem using an Ecosim model and taking into account fisheries, changes in productivity and trophic relationships. The present document presents briefly the methodology used and the preliminary findings for a few commercially important species.
Multiple Sound Source Tracking Based on Generative Modeling and Recursive Bayesian Filtering of Spatial Gradient Spectra

This paper addresses the problem of tracking the positions of multiple sound sources using a compact-sized microphone array. Based on a generative model of the spatial gradient spectra of acoustic point sources, we propose incorporating the temporal continuity of source positions into the sequential posterior inference under a recursive Bayesian filter formulation. Simulation experiments with data generated by the image source model confirmed that the proposed idea had a certain effect on improving the accuracy of tracking moving sources.
Colorado Alpine Dust Deposition and Associated Continental Winds

The winter and early spring of 2008-2009 brought an unusually high number of alpine dust deposition events to the Rocky Mountains of Colorado. The greatest dust accumulations were observed in the San Juan Mountains of southwestern Colorado. Significant dust accumulation was even observed along the Continental Divide in northern Colorado. The primary source for this dust has previously been identified as the Colorado Plateau. Analysis using the HYSPLIT(Hybrid Single Particle Lagrangian Integrated Trajectory) atmospheric trajectory model along with satellite imagery showed that dust from the 2009 events also originated from the Colorado Plateau, especially from areas in and around northeastern Arizona that were experiencing drought conditions this spring. This study utilized data from the USFS/BLM RAWS (Remote Automated Weather Station) network over the southwestern U.S. to identify periods of high winds corresponding to documented Colorado dust events. Wind speeds for the study region were evaluated for the period January through April for the past 20 years in an attempt to quantify and compare both mean wind speed and maximum wind gusts on a seasonal basis. A linear regression analysis showed a significant correlation between the Southern Oscillation Index (SOI) and the frequency of these types of high wind periods, particularly during winter months. The correlation between periods of high winds and the SOI extends through the 20 years of wind data available for these weather stations.
Effects of Heat Wave on ET-1,NO and Body Temperature in ApoE-deficient Mice

The effects of heat wave on ApoE-/-mice by comparing the variations of ET-1,NO and body temperature between C57BL /6 / J mice and ApoE-/-mice are observed.A heat wave weather process during 2001 to 2010 was simulated by climate simulator.The content of ET-1 and NO and the body temperature of mice,which were divided into 2 groups(n = 6) : control group and heat-wave group,were detected during the heat wave.The results indicated that,when suffering the heat wave,the NO level and the body temperature were rising obviously,but the weight,the ET-1 content and the ratio between NO and ET-1 were not changing much.At the meantime,the variations of NO,the ratio and the body temperature of ApoE-/-mice were more significant than C57BL /6 / J mice.ApoE-/-mice were more vulnerable when suffering the heat wave,which might be one of the mechanisms of the phenomenon that coronary artery disease patients are more vulnerable to heat wave weather.
Modelling of green manufacturing barriers using a survey-integrated decision-making approach

Green manufacturing (GM) barriers encompass challenges and obstacles that impede the seamless adoption and effective implementation of environmentally sustainable practices within the manufacturing industry. This paper is dedicated to identifying, analyzing, ranking, and modeling the primary barriers that hinder the integration of GM practices. The study meticulously identifies 11 barriers through an extensive literature review and expert opinions. These barriers are subsequently validated by gathering insights from 90 survey responses. To evaluate these challenges, the study employs a survey-based integrated decision-making model that synergizes Fuzzy Analytic Hierarchy Process (FAHP), Interpretive Structural Modeling (ISM), and Matrice d'Impacts Croisés Multiplication Appliquée à un Classement (MICMAC) methods. This model not only ranks the identified barriers but also delves into their intricate interrelationships. A sensitivity analysis is conducted to fortify the dependability of the findings. Among these obstacles, two stand out as the most prominent including “lack of research and development facilities” and “insufficient in-house knowledge on environmental issues”. This research represents a pioneering endeavor in its domain, shedding light on the vital realm of green manufacturing and its associated challenges.
The Resilient Teacher: Unveiling the Positive Impact of the Collaborative Practicum Model on Novice Teachers

The present quantitative, non-experimental comparative study, delves into the long-term effects of the collaborative practicum model (specifically the “academy-class” model) on novice teachers. The research aims to discern disparities in the professional self-efficacy of novice educators who underwent training within the collaborative practicum model as opposed to those who adhered to the conventional teaching model. This comparative analysis is based on three variables: perception of the teaching profession, professional self-efficacy, and socio-economic security. Furthermore, the study examines whether the collaborative model contributes to cultivating more favorable attitudes toward the teaching profession and a greater inclination to continue teaching for an extended period exceeding three years. The study encompasses a cohort of 436 Bachelor of Education (B.Ed.) graduates from 22 Israeli Higher Education Institutions who completed their degrees within the past five years. The research findings underscore a higher level of teaching efficacy, socio-economic security, and a more positive outlook among those who participated in the collaborative practicum and expressed their intent to persist in the teaching profession. These outcomes underscore the vital role of the collaborative practicum model, hinting at its potential to positively influence the retention rate within the teaching profession. Furthermore, it underscores the crucial connection between comprehensive and meaningful training within a collaborative practicum framework and the sustainable professional growth of educators. This robust training approach can potentially secure the continued presence of dedicated and enthusiastic educators in the field over the long term.
P300 Event-Related Potential in Perception of Multiple Traffic Objects During Vehicle Driving

Visual attention is a function of the brain that allocates its computational resources to enable us to attend to the most important visual information. In actual driving, there are many perceptual objects such as pedestrians, signals, and other vehicles, etc. To selectively recognize these traffic objects for safe driving, the driver’s gaze is appropriately controlled under visual attention. It is known that the P300 event-related potential related to visual attention, and is an essential brain activity to understanding the visual-attention characteristics in visual search. Although the P300 response reflects visual attention, the response characteristics of the P300 especially regarding the multiple object search are largely unexplored. To understand the brain activity on visual attention for multiple objects, we measure the P300 response and examined especially its characteristics for the types of traffic objects in terms of the capacity of induction for visual attention.
The effect of hydrostatic pressure up to 1.61 GPa on the Morin transition of hematite‐bearing rocks: Implications for planetary crustal magnetization

We present new experimental data on the dependence of the Morin transition temperature (TM) on hydrostatic pressure up to 1.61 GPa, obtained on a well‐characterized multidomain hematite‐bearing sample from a banded iron formation. We used a nonmagnetic high‐pressure cell for pressure application and a Superconducting Quantum Interference Device magnetometer to measure the isothermal remanent magnetization (IRM) under pressure on warming from 243 K to room temperature (T0). IRM imparted at T0 under pressure in 270 mT magnetic field (IRM270mT) is not recovered after a cooling‐warming cycle. Memory effect under pressure was quantified as IRM recovery decrease of 10%/GPa. TM, determined on warming, reaches T0 under hydrostatic pressure 1.38–1.61 GPa. The pressure dependence of TM up to 1.61 GPa is positive and essentially linear with a slope dTM/dP = (25 ± 2) K/GPa. This estimate is more precise than previous ones and allows quantifying the effect of a pressure wave on the upper crust magnetization, with special emphasis on Mars.
A light-weighted convolutional neural network for image classification in autopilot system

Autopilot has been a heated technology in recent days, and it is used in plenty of car brands. People are becoming dependent on autopilot technology. However, before this technology becomes mature, there are still a lot of insufficiencies that need to improve. The focus of this essay is on the object detection shortage that exists in the autopilot system. This essay is first going to introduce a lite CNN-based method to improve the object detection and identification module. In the result part of the essay, the essay is going to show the result of the training and some new prediction function will be implemented. Also, some accuracy improving methods including data augmentation and Dropout will be introduced, and the effect of the improving methods will be shown in the essay. After training, the autopilot system is going to distinguish pedestrians from vehicles. Although the road condition is far more complex than just vehicles and pedestrians, for convenience this essay is only going to implement the two datasets. Also, some further improvement intension will be introduced.
A Physical MFIS-FeFET Model with Awareness of Drain-Induced Spatially-Distributed Polarization and Ferroelectric Parametric Fluctuation

The spatially-distributed polarization effect in ferroelectric FET (FeFET) induced by nonzero drain bias is recently found to have a great influence on device and circuit performance, especially for content addressable memory operation, while this physics is ignored in conventional modeling method of FeFET. In this work, we developed a perturbative modeling framework for distributed FeFET, which can accurately capture the spatially-distributed polarization effect induced by the local coupling between polarization and channel charge, especially for large drain bias. Furthermore, considering the ferroelectric (FE) parameter fluctuation with the same statistical distribution but different spatial distribution, the FE parameter close to drain side of FeFET is found to be a crucial variation source for channel conduction, especially for coercive field, providing the guidance of robust FeFET device design strategies.
A Deep Neural Network-Based Optimal Scheduling Decision-Making Method for Microgrids

With the rapid growth in the proportion of renewable energy access and the structural complexity of distributed energy systems, traditional microgrid (MG) scheduling methods that rely on mathematical optimization models and expert experience are facing significant challenges. Therefore, it is essential to present a novel scheduling technique with high intelligence and fast decision-making capacity to realize MGs’ automatic operation and regulation. This paper proposes an optimal scheduling decision-making method for MGs based on deep neural networks (DNN). Firstly, a typical mathematical scheduling model used for MG operation is introduced, and the limitations of current methods are analyzed. Then, a two-stage optimal scheduling framework comprising day-ahead and intra-day stages is presented. The day-ahead part is solved by mixed integer linear programming (MILP), and the intra-day part uses a convolutional neural network (CNN)—bidirectional long short-term memory (Bi LSTM) for high-speed rolling decision making, with the outputs adjusted by a power correction balance algorithm. Finally, the validity of the model and algorithm of this paper are verified by arithmetic case analysis.
UNDERSTANDING NARCISSISTS’ KNOWLEDGE HIDING BEHAVIOUR: A MORAL DISENGAGEMENT MECHANISM PERSPECTIVE

Purpose: Narcissistic people have a strong desire to hold focus only around themselves. Their self-love triggers them to create their monopoly and involve in deviant behavioral outcomes such as knowledge hiding. This study was aimed to examine narcissistic employees’ tendency of knowledge hiding behavior in the presence of underlying mechanisms: moral disengagement as mediator. Methodology: The Data were collected from banking sector professionals using a Likert scale questionnaire based on time-lagged (three rounds, one month apart) survey. Data were analyzed via Partial Least Squares Structural Equation Modeling (PLS-SEM) using an advanced version of Smart PLS 3.9 and SPSS 21. Findings: Results revealed that narcissism has a significant positive relation with knowledge hiding and also established moral disengagement as an underlying mechanism between the positive association of narcissism and knowledge hiding. The results provide important recommendations for managers to handle narcissistic personalities and knowledge hiding issues in the banking sector of Pakistan. Conclusion: Despite the limitations, the present study enhanced the current knowledge on narcissistic personality features through the moral disengagement perspective with knowledge hiding and enriching the validation of the social cognitive theory. The study results conclude that the banking sector of Pakistan must consider the narcissistic personality of employees behind their issue of knowledge hiding.