Miracle-dz/UniFAR-FTUs huggingface dataset:
Feeling like the enemy: the emotion management and alienation of hospital doctors

Introduction Globally, an epidemic of psychological distress, burnout, and workforce attrition signify an acute deterioration in hospital doctors' relationship with their work—intensified by COVID-19. This deterioration is more complicated than individual responses to workplace stress, as it is heavily regulated by social, professional, and organizational structures. Moving past burnout as a discrete “outcome,” we draw on theories of emotion management and alienation to analyze the strategies through which hospital doctors continue to provide care in the face of resource-constraints and psychological strain. Methods We used Mobile Instant Messaging Ethnography (MIME), a novel form of remote ethnography comprising a long-term exchange of digital messages to elicit “live” reflections on work-life experiences and feelings. Results The results delineate two primary emotion-management strategies—acquiescence and depersonalization—used by the hospital doctors to suppress negative feelings and emotions (e.g., anger, frustration, and guilt) stemming from the disconnect between professional norms of expertise and self-sacrifice, and organizational realities of impotence and self-preservation. Discussion Illustrating the continued relevant of alienation, extending its application to doctors who disconnect to survive, we show how the socio-cultural ideals of the medical profession (expertise and self-sacrifice) are experienced through the emotion-management and self-estrangement of hospital doctors. Practically, the deterioration of hospital doctors' relationship with work is a threat to health systems and organizations. The paper highlights the importance of understanding the social structures and disconnects that shape this deteriorating relationship and the broad futility of self-care interventions embedded in work contexts of unrealized professional ideals, organizational resource deficits and unhappy doctors, patients, and families.
Random Caching Optimization in Large-Scale Cache-Enabled Internet of Things Networks

The introduction of cache can reduce unnecessary traffic load and improve latency in the wireless access networks, especially for wireless video broadcasting. But how cache impacts video broadcasting with scalable video coding (SVC) is still an open problem. In this paper, we analyze the optimization of random caching in a large-scale cache-enabled Internet of Things networks. Specifically, we propose to optimize the random caching strategy that aims to maximize the successful transmission probability (STP) of the video contents at edge base stations (BSs). To this end, by using the stochastic geometry theory, we derive analytical expression of STP by considering SVC to satisfy different levels of quality of service requirements. We develop a gradient-based iterative algorithm to search the local optimal solution for the general random caching strategy optimization problem. The asymptotical optimal caching strategy is obtained with a lower complexity. The closed-form STP is also obtained in high signal-to-noise ratio and particular cache size. Based on the closed-form STP expressions, the random caching strategy, i.e., caching probability of different video contents, is further optimized to enhance STP performance. Compare to different reference schemes, the proposed caching strategy improves the STP up to $18\%$ and $21.6\%$ with the low density of BSs and the high density of BSs, respectively.
THE RELATIONSHIP BETWEEN TEACHER PEDAGOGICAL COMPETENCE, CAREER EXPECTATIONS, AND SCHOOL INFRASTRUCTURE AND LEARNING OUTCOMES OF GRADE VI STUDENTS

This study aims to find out how much the relationship between teacher pedagogic competence, career expectations, and school infrastructure has to the learning outcomes of class VI elementary school students in Cluster III, Kediri District, Tabanan Regency. This research is ex post facto with a population of 282 students. Determination of the sample through random sampling technique in order to obtain a sample of 162 students. Data is collected through questionnaires and document study. Data analysis uses multiple regression and partial correlation. The results of the study show that: 1) there is a significant relationship between teacher pedagogical competence and student learning outcomes; 2) there is a significant relationship between career expectations and student learning outcomes; 3) there is a significant relationship between school infrastructure and student learning outcomes; and 4) taken together, there is a significant relationship between teacher pedagogical competence, career expectations, and school infrastructure with student learning outcomes.
Memory accuracy, suggestibility and credibility in investigative interviews with native and non-native eyewitnesses

Legal practitioners sometimes obtain eyewitness testimonies from non-native language speakers, yet few studies examine the effects of language in investigative interviews. Here, we investigate how testifying in a non-native vs. native language affects memory accuracy, susceptibility to suggestions, and witnesses perceived credibility. After viewing a mock-crime film, participants in Study 1 (N = 121) testified through (1) free recall, (2) cued recall and (3) the Gudjonsson Suggestibility Scales adapted to the crime scenario used in the present study either in their native (Swedish) or a non-native (English) language. They also rated their confidence in their memories, self-perceived credibility and cognitive effort. Native and non-native witnesses did not differ in memory accuracy, susceptibility to suggestions, self-rated credibility or cognitive effort. Non-native (vs. native) speakers did however report lower confidence in their memories. In Study 2, another group of participants (N = 202) were presented with the testimonies from Study 1, and judged witnesses’ credibility. Non-native witnesses were judged as less credible than native speakers. Thus, while the lower confidence exhibited by non-native eyewitnesses did not correspond to their actual memory accuracy, it influenced observers’ judgments of their performance. The results provide important knowledge for legal practices when evaluating the reliability of testimonies from non-native vs. native speaking eyewitnesses.
Grader grass (Themeda quadrivalvis): changing savannah ecosystems.

Wayne Vogler and Nikki Owen recently published their paper 'Grader grass (Themeda quadrivalvis): changing savannah ecosystems' in Proceedings of the 16th Australian Weeds Conference. Grader grass is an invasive exotic 'high biomass' grass from India that is increasing its distribution in northern Australia. It is unpalatable and can dominate ecosystems, thereby decreasing grazing animal production, degrading conservation areas and increasing fire intensity and hazard. They studied aspects of its biology at a field site in north Queensland where the initial biomass of the grass layer was found to be 70% grader grass. Grader grass also produced 80% of the seed input into this ecosystem during the first growing season. These factors, in combination with a large viable seed bank and rapid germination at the start of the wet season, demonstrate the potential of grader grass to dominate and degrade the savannah ecosystems of northern Australia.
An exposure assessment of a laboratory assistant to hazardous chemical substances

A survey was conducted to measure exposure to hazardous chemical substances of a laboratory assistant working in a university laboratory, who complained of symptoms that could be indicative of chemical absorption. This case study describes the survey to quantify the exposure in order to determine whether it exceeded safety limits and recommend preventative measures if indicated. Exposure was sampled using a direct reading photo-ionization detector and coconut charcoal sorbent tubes. An additive effect between the different hazardous chemicals was identifi ed. The 8-hour TWA exposure to acetone and dichloromethane was well below the occupational exposure limits, but was more than double the OEL-RL for chloroform. It was also likely that the OEL-STEL for dichloromethane (methylene chloride) was exceeded. Immediate removal for medical assessment and improved preventative measures were recommended.
Relationships between internal marketing and job performance mediated by psychological ownership among fitness centers’ employees: an empirical analysis using PLS-SEM techniques

This study examined the relationship between internal marketing and job performance as the mediating variable of psychological ownership by focusing on fitness center trainers. The convenience sampling technique was used in the study. Data were collected from fitness centers engaged in business in Turkey. The research group consists of 177 fitness center trainers. The model test with Partial Least Squares (PLS) was used as the analysis technique in the study. The results support the model proposal that fitness center trainers' internal marketing experiences play a role in job performance both directly and indirectly through psychological ownership. Therefore, fitness centers should give importance to internal marketing to improve the experiences of their employees and obtain corporate ownership. This will increase employees’ job performance, and, consequently, customer satisfaction will be positively affected. Keywords: internal marketing; psychological ownership; job performance; fitness center trainers; PLS-SEM
Calibration and Validation for the Surface Biology and Geology (SBG) Mission Concept: Recommendations for a Multi‐Sensor System for Imaging Spectroscopy and Thermal Imagery

The primary objective of the National Aeronautics and Space Administration (NASA) Surface Biology and Geology (SBG) mission is to measure biological, physical, chemical, and mineralogical features of the Earth's surface, realizing a key conceptual component of the envisioned NASA Earth System Observatory (ESO). SBG is planned to launch as a two‐platform mission in the late 2020s, the first of the ESO satellites. Targeted science and applications objectives based on observations of the Earth's SBG helped to define the mission architecture and instrument capabilities for the SBG mission concept. These objectives further drove the need for enabling change detection and trending of surface biological and geological features. These needs implied fundamental calibration goals to achieve the necessary science data quality characteristics. To meet those goals, calibration and validation pre‐launch and on‐orbit methods formed a basis of the calibration and validation concept, including the combined use of on‐board references, vicarious techniques, and routine lunar imaging. International collaboration with space agencies in other countries, an important feature of the recommended SBG mission architecture, uncovered and emphasized the need for inter‐calibration techniques that underscored the importance of collaborative instrument characterization data sharing and the use of common calibration references that are International System of Units (SI) traceable in pre‐launch and post‐launch on orbit calibration mission phases. International collaboration through the use of terrestrial and aquatic networks on six continents for vicarious calibration and validation activities will further assure necessary science data quality while in orbit.
Reconciliation of Regional Travel Model and Passive Device Tracking Data

The ability to passively track large numbers of mobile devices has generated a lot of excitement in recent years. Traditional travel surveys without Global Positioning Satellite (GPS) tracking, either wholly or in part, seems passe today. However, GPS tracking can only overcome some of the problems associated with travel diary surveys. It can capture missed trips and route choice information, but at increased cost per survey. However, it does nothing to ease the rising cost and difficulty associated with contacting, recruiting, retaining households in the first place, or collecting and processing the data. Passive tracking overcomes these limitations, at the expense of giving up interaction with the device owner. Thus, information about the traveler, trip purpose, and other details must be inferred or lost. The ideal solution is to use both travel diaries and passive tracking together. While techniques for fusing these data are yet to be proven the concept has strong intuitive appeal. Until such techniques emerge the question becomes whether such data can be used on their own, and whether they resemble the output of travel models built using traditional survey data. The opportunity to answer such questions arose recently in the Research Triangle Region of North Carolina, which served as a pilot project for using origin destination data from AirSage as an adjunct to modeling. A detailed analysis of the differences between the AirSage data and the Triangle Regional Model (TRM) has been completed. The differences were subtle in some cases and surprising in others. This paper reports on the findings of this comparison. A brief description of the AirSage data and TRM provides the context for the discussion. Presented in this paper is the methodology for comparing the two data sources along with the results. The results show that the highway assignment using AirSage data is comparable to the highway assignment using model estimated trip tables, supporting the use of passively collected cellular data as a low cost option for travel model validation.
Mobile robot for monitoring park trees: Design and modeling

One of the modern problems in the field of ecology is the creation of environmentally friendly equipment for monitoring and maintaining trees in parks and forests. The traditional use of forest machines and self-propelled tractors with internal combustion engines has a negative impact on the environment as a result of pollutant emissions, i.e., combustion products and fuel residues. An alternative to this tradition can be the use of mobile robots with remote control of their electric drives when performing such technological operations as pruning bacterial growths of trees and diagnosing the state of tree massifs. The article proposes a fundamentally new mobile robot design for monitoring park trees. The main differences between the robot are the new designs of the body and the walking mechanisms of the mobile robot. These design differences provide the robot with high maneuverability when choosing the path of movement along the tree trunk and reliable holding of the robot body on the tree at a sufficient movement speed to perform diagnostics of the state of tree massifs. The article also describes the dynamic models of the movement of a mobile robot along a tree trunk. It presents the simulation results in the form of graph-analytical dependencies of the robot parameters, which constitutes the scientific aspect of the problem. The main motivation of the conducted research is the creation of environmentally friendly equipment in the form of a mobile robot with a reliable system of retention on the surface moving and sufficient performance to perform park tree monitoring operations.
GEOTECHNICAL CHARACTERISTICS OF FLY ASH-SOIL MIXTURES

This paper presents the results of laboratory experimental studies carried out on fly ash-soil mixtures. Two fly ashes, one from Rajghat thermal power station, New Delhi, India, and the other from a chemical manufacturing industry Baumineral near Bochum, Germany, were collected and their physical and chemical characteristics were studied. The fly ashes were mixed with the soils available near their respective areas in different proportions and their geotechnical characteristics were investigated. The laboratory test program included compaction tests, unconfined compression tests, direct shear tests, unconsolidated undrained triaxial shear tests, consolidaiton tests, permeability tests and CBR tests. The results obtained were of similar magnitude and showed comparable characteristics as those reported in the literature on fly ashes from other parts of the world. It might be possible to make a reasonable estimate of the maximum dry unit weight and the optimum moisture content of fly ash-soil mixtures from their specific gravity. (A)
Geodesic Fuzzy Rough Sets for Discriminant Feature Extraction

Feature extraction is a fundamental and challenging task in machine learning, which aims at extracting a subset of significant and discriminant features from raw data for various downstream tasks. The extraction process involves mapping the original data into a space with lower dimensions while preserving the desirable information. However, the data often has hidden manifold structures, which contain neighbor sample information within the same class. Most existing methods that extract data features without considering the potentially significant manifold structures would result in poorly discriminative features. To address these challenges, we propose a novel geodesic fuzzy rough set model (GFRS) to capture those complex manifold structures embed in the data. Given GFRS, we further design a discriminant feature extraction algorithm based on graph embedding to enhance the discriminative performance of the extracted features. Extensive experiments on fourteen real-world datasets and visualization results on modified national institute of standards and technology (MNIST) digits demonstrate the effectiveness of the proposed algorithm and its superiority over baselines methods.
A proposed methodology for detecting the malignant potential of pulmonary nodules in sarcoma using computed tomographic imaging and artificial intelligence-based models

The presence of lung metastases in patients with primary malignancies is an important criterion for treatment management and prognostication. Computed tomography (CT) of the chest is the preferred method to detect lung metastasis. However, CT has limited efficacy in differentiating metastatic nodules from benign nodules (e.g., granulomas due to tuberculosis) especially at early stages (<5 mm). There is also a significant subjectivity associated in making this distinction, leading to frequent CT follow-ups and additional radiation exposure along with financial and emotional burden to the patients and family. Even 18F-fluoro-deoxyglucose positron emission technology-computed tomography (18F-FDG PET-CT) is not always confirmatory for this clinical problem. While pathological biopsy is the gold standard to demonstrate malignancy, invasive sampling of small lung nodules is often not clinically feasible. Currently, there is no non-invasive imaging technique that can reliably characterize lung metastases. The lung is one of the favored sites of metastasis in sarcomas. Hence, patients with sarcomas, especially from tuberculosis prevalent developing countries, can provide an ideal platform to develop a model to differentiate lung metastases from benign nodules. To overcome the lack of optimal specificity of CT scan in detecting pulmonary metastasis, a novel artificial intelligence (AI)-based protocol is proposed utilizing a combination of radiological and clinical biomarkers to identify lung nodules and characterize it as benign or metastasis. This protocol includes a retrospective cohort of nearly 2,000–2,250 sample nodules (from at least 450 patients) for training and testing and an ambispective cohort of nearly 500 nodules (from 100 patients; 50 patients each from the retrospective and prospective cohort) for validation. Ground-truth annotation of lung nodules will be performed using an in-house-built segmentation tool. Ground-truth labeling of lung nodules (metastatic/benign) will be performed based on histopathological results or baseline and/or follow-up radiological findings along with clinical outcome of the patient. Optimal methods for data handling and statistical analysis are included to develop a robust protocol for early detection and classification of pulmonary metastasis at baseline and at follow-up and identification of associated potential clinical and radiological markers.
Patient satisfaction with telemedicine in the Philippines during the COVID-19 pandemic

Introduction: The capacity to deliver essential health services has been negatively impacted by the COVID-19 pandemic particularly due to lockdown restrictions. Telemedicine provides a safe, efficient, and effective solution that addresses the needs of patients and the health system. However, there remain implementation challenges and barriers to patient adoption in resource-limited settings as in the Philippines. This study thus aimed to describe patient perspectives and experiences with telemedicine services, and explore the factors that influence telemedicine use and satisfaction. Methods: This study used a mixed-methods design through online surveys and in-depth interviews. An online survey using Consumer Assessment of Healthcare Providers and Systems (CAHPS) Clinician & Group Adult Visit Survey 4.0 (beta) and Telehealth Usability Questionnaire (TUQ) was accomplished by 200 participants aged 18 to 65 years. A subsample of 16 participants was interviewed to provide insights to the quantitative data. We used descriptive statistics to analyze survey data and grounded theory to analyze data from interviews. Results: Participants were generally satisfied with telemedicine services, with most reporting that this was an efficient and convenient alternative to face-to-face consultations. However, only 2 in 5 perceived telemedicine as affordable. Our quantitative findings suggest that participants preferred telemedicine services rather than in-person consultations, especially in cases where they feel that their condition is not urgent and does not need extensive physical examination. Safety against COVID-19, and the availability of multiple communication platforms contributed to patient satisfaction with telemedicine. Negative perceptions of patients on their telemedicine provider, perceived higher costs, poor connectivity and other technological issues were found to be barriers to patient satisfaction. Discussion: Telemedicine is viewed as a safe and efficient alternative to receiving care. Continued adoption of telemedicine will require improvements in technology and better patient communication related to their telemedicine provider and the associated costs.
Transcriptome and Metabolome Reveal Sugar and Organic Acid Accumulation in Rosa roxburghii Fruit

Sugars and organic acids significantly impact fruit sensory quality, but their accumulation patterns and regulatory mechanisms during the development of Rosa roxburghii fruit are still unclear. We utilized transcriptomics and metabolomics to investigate genes related to sugar and organic acid metabolism in Rosa roxburghii. Metabolomics data revealed that sucrose, glucose and fructose were the primary sugars, whereas citric acid and malic acid were the primary organic acids in Rosa roxburghii fruit. We constructed the metabolic pathways of major sugars and organic acids in Rosa roxburghii and identified five key genes involved in sugar and organic acid synthesis. In addition, we identified a module containing 132 transcription factors that was significantly associated with sucrose, citric acid and malic acid. Based on quantitative polymerase chain reaction (qPCR), we identified 13 transcription factors involved in sugar and organic acid metabolism, including the transcription factor RrANL2 and the sucrose synthase gene RrSUS3. Further yeast one-hybrid and dual luciferase assays showed that RrANL2 could bind to the promoter of RrSUS3 to increase its expression. These results provide new insights into the metabolism of sugars and organic acids in Rosa roxburghii fruit.