Miracle-dz/UniFAR-FTUs huggingface dataset:
DECISION-MAKING SUPPORT TECHNOLOGIES USING THE CAD/CAM/CAE SYSTEM DESIGN AND MANUFACTURE OF BONE SUBSTITUTES WITH FUNCTIONAL PROPERTIES

Currently, the use of additive technologies for the production of bone substitutes determines the effectiveness of the latest methods of treatment and prosthetics in traumatic surgery, oncology, crania-maxillofacial surgery, dentistry, etc. The introduction of additive technologies is the result of the integration of medical visualization, in particular, based on the technologies of computer imaging, and engineering CAD/CAM/CAE systems. In connection with the increase in the number and severity of various types of bone tissue injuries received as a result of wounds during military operations in Ukraine, an important issue in orthopaedics and traumatology is making informed decisions about the possibility of restoring the integrity and functions of bone tissue when using different types of composition, the strength of biopolymer composites. The scientific aim of research is the development of principles and methods for making scientifically based decisions in the design and additive manufacturing of bone substitutes based on biopolymer composites with functional properties depending on the nature of the localization of the cavity bone defect and its size. The new knowledge will become the necessary basis for making optimal decisions in practice for the introduction of the latest methods of treatment and prosthetics in trauma surgery, oncology, crania-maxillofacial surgery, dentistry, taking into account the risks of biocompatibility of biopolymer composites. The results of the research will be used to design bone substitutes with controlled composition, structure, porosity, and mechanical strength for the further selection of additive technology for its production from apatite-polymer composites, which will contribute to increasing the efficiency of treatment and prosthetics in orthopaedics and traumatology.
Intraoperative gaze guidance with mixed reality

Efficient communication and collaboration are essential in the operating room for successful and safe surgery. While many technologies are improving various aspects of surgery, communication between attending surgeons, residents, and surgical teams is still limited to verbal interactions that are prone to misunderstandings. Novel modes of communication can increase speed and accuracy, and transform operating rooms. A mixed reality (MR) based gaze sharing application on Microsoft HoloLens 2 headset that can help expert surgeons indicate specific regions, communicate with decreased verbal effort, and guide novices throughout an operation is presented. The utility of the application is tested with a user study of endoscopic kidney stone localization completed by urology experts and novice surgeons. Improvement is observed in the NASA task load index surveys (up to 25.23%), in the success rate of the task (6.98% increase in localized stone percentage), and in gaze analyses (up to 31.99%). The proposed application shows promise in both operating room applications and surgical training tasks.
Function and dysfunction of the dystonia network: an exploration of neural circuits that underlie the acquired and isolated dystonias

Dystonia is a highly prevalent movement disorder that can manifest at any time across the lifespan. An increasing number of investigations have tied this disorder to dysfunction of a broad “dystonia network” encompassing the cerebellum, thalamus, basal ganglia, and cortex. However, pinpointing how dysfunction of the various anatomic components of the network produces the wide variety of dystonia presentations across etiologies remains a difficult problem. In this review, a discussion of functional network findings in non-mendelian etiologies of dystonia is undertaken. Initially acquired etiologies of dystonia and how lesion location leads to alterations in network function are explored, first through an examination of cerebral palsy, in which early brain injury may lead to dystonic/dyskinetic forms of the movement disorder. The discussion of acquired etiologies then continues with an evaluation of the literature covering dystonia resulting from focal lesions followed by the isolated focal dystonias, both idiopathic and task dependent. Next, how the dystonia network responds to therapeutic interventions, from the “geste antagoniste” or “sensory trick” to botulinum toxin and deep brain stimulation, is covered with an eye towards finding similarities in network responses with effective treatment. Finally, an examination of how focal network disruptions in mouse models has informed our understanding of the circuits involved in dystonia is provided. Together, this article aims to offer a synthesis of the literature examining dystonia from the perspective of brain networks and it provides grounding for the perspective of dystonia as disorder of network function.
Diversity and Advantages of Culturable Endophytic Fungi from Tea (Camellia sinensis)

Sordariomycetes, Dothideomycetes, and Eurotioycetes are three classes of endophytes that colocalize with tea (Camellia sinensis). Overall, the diversity indexes in this study indicated a greater abundance of fungal endophytes in roots and stems. Taking the production system into account, conventional tea plantations exhibit lower diversity compared to organic tea plantations. Notably, the influence of agrochemicals had the largest impact on the fungal endophyte communities within roots and young leaves. Despite the limited fungal diversity in conventional plantations, three fungal endophytes were isolated from tea in this culture system: Diaporthe sp. , YI-005; Diaporthe sp. , SI-007; and Eurotium sp. , RI-008. These isolated endophytes exhibited high antagonistic activity (93.00–97.00% inhibition of hypha growth) against Stagonosporopsis cucurbitacearum, the causal agent of gummy stem blight disease. On the other hand, endophytic fungi isolated from tea in an organic system—Pleosporales sp. , SO-006 and Pleosporales sp. , RO-013—established the ability to produce indole-3-acetic acid (IAA; 0.65 ± 0.06 µg/mL) and assist the solubilizing phosphorus (5.17 ± 1.03 µg/mL) from the soil, respectively. This suggested that the level of diversity, whether at the tissue level or within the farming system, did not directly correlate with the discovery of beneficial fungi. More importantly, these beneficial fungi showed the potential to develop into biological agents to control the devastating diseases in the cucurbit family and the potential for use as biofertilizers with a wide range of applications in plants. Therefore, it can be concluded that there are no restrictions limiting the use of fungal endophytes solely to the plant host from which they were originally isolated.
Positron Emission Tomography-Derived Radiomics and Artificial Intelligence in Multiple Myeloma: State-of-the-Art

Multiple myeloma (MM) is a heterogeneous neoplasm accounting for the second most prevalent hematologic disorder. The identification of noninvasive, valuable biomarkers is of utmost importance for the best patient treatment selection, especially in heterogeneous diseases like MM. Despite molecular imaging with positron emission tomography (PET) has achieved a primary role in the characterization of MM, it is not free from shortcomings. In recent years, radiomics and artificial intelligence (AI), which includes machine learning (ML) and deep learning (DL) algorithms, have played an important role in mining additional information from medical images beyond human eyes’ resolving power. Our review provides a summary of the current status of radiomics and AI in different clinical contexts of MM. A systematic search of PubMed, Web of Science, and Scopus was conducted, including all the articles published in English that explored radiomics and AI analyses of PET/CT images in MM. The initial results have highlighted the potential role of such new features in order to improve the clinical stratification of MM patients, as well as to increase their clinical benefits. However, more studies are warranted before these approaches can be implemented in clinical routines.
Work-related burnout and its associated factors among midwives working at public hospitals in northwest Ethiopia: a multi-centered study

Introduction Work-related burnout (WRB) is defined as the degree of physical and psychological fatigue and exhaustion that is perceived by the person as related to work. Midwives are vulnerable to work-related burnout due to their physically and emotionally demanding nature of their job. It affects the health of professionals and the quality of care provided. However, there is limited evidence on the burden and predictors associated with work-related burnout among midwives in developing countries, including Ethiopia. This study investigated the burden and contributing factors of work-related burnout among midwives in northwest Ethiopia. Methods A facility-based cross-sectional study was conducted from February 7 to April 30, 2022. A simple random sampling method was used to enroll 640 study participants. The Copenhagen burnout inventory tool was used to assess the magnitude of work-related burnout. A self-administered questionnaire was used to collect data, which was then entered into Epi Data 4.6 software and exported to SPSS version 25 for analysis. A multivariable logistic regression analysis model was fitted to identify factors associated with work-related burnout. The Adjusted Odds Ratio (AOR) with 95% confidence interval (CI) was reported to declare the factors that are significantly associated with work-related burnout. Results The prevalence of work-related burnout was found to be 60.47% (95% CI = 56.6–64.2). Workplace violence (AOR = 3.33, CI: 2.02, 5.48), working hours over 60 h a week (AOR = 4.55, CI: 2.78, 7.43), emotional demand of the job (AOR = 8.85, 95% CI: 4.48, 17.47), exposure to blood and body fluids/sharp injuries (AOR = 5.13, CI: 3.12, 7.13), good superior support (AOR = 0.38, CI: 0.23, 0.63), Job rotation of ≤6 months (AOR = 2.30, CI: 1.28, 4.14) and being stressed (AOR = 2.64, CI: 1.63, 4.26) were all found to be strongly linked to work-related burnout. Conclusion and recommendation This study found a significant level of work-related burnout among midwives working in public hospitals. Experiencing workplace violence, a job rotation of less than or equals to six months, working hours over 60 h a week, good superior support, exposure to blood and body fluids or needle stick injuries and experiencing stress were significant factors that influenced work-related burnout. Therefore, reducing prolonged working hours, promoting supportive management, creating a safe working environment, and applying effective stress prevention strategies are some of the interventions to prevent or alleviate work-related burnout.
Prevalence of Fatigue Among Construction Workers in The United States

Within dynamic and complex construction environments, fatal and non-fatal occupational records are alarming all over the world. Fatigue among workers is one of the key causes which affects their ability to operate safely. To improve safety management, the prevalence of fatigue among workers must be investigated to implement effective interventions for fatigue mitigation. However, the prevalence of fatigue among construction workers in the United States has not been studied. Therefore, this research explores this gap by recruiting 120 workers in the U.S. To achieve the research goals, the worker's level of fatigue (i.e., acute, chronic, and inteshift recovery) was measured using a validated OFER scale. Data analysis revealed (1) the prevalence of fatigue among construction workers is alarming (OFER Score=34.23), that acute fatigue, among all aspects of it, has the most profound impact on workers, and (2) there are no statistical correlations between measured fatigue levels and age and experience of construction workers. The findings of this study will be beneficial to practitioners and researchers in construction to implement effective safety measures to prevent accidents in workplaces.
USING MACHINE LEARNING TECHNIQUES TO FILTER VEGETATION IN COLORIZED SFM POINT CLOUDS OF SOIL SURFACES

Abstract. Various soil erosion processes can be quantified using digital elevation models (DEMs) of difference. In this study, cameras were used to capture images of bare soil during artificial rainfall simulations. The photos were then used to generate dense 3D point clouds with millimeter resolution using Structure-from-Motion and Multiview-Stereo (SfM+MVS) techniques. However, the point clouds also captured some vegetation, such as agricultural plants, grass, and weeds, present at the soil surface. It had to be removed to accurately measure soil erosion processes. The removal can be done manually and is hence time-consuming. In this study, several methods have been tested and compared to perform (semi-)automatic vegetation filtering from point clouds of soil surfaces. First, the point clouds were labelled into vegetation and ground data to establish a basis set for the subsequent experiments. Then, three branches of algorithms were tested. The first branch considers knowledge-based thresholding. Thereby, unique features were considered, e.g., point color, height, and roughness within a specified neighborhood. For instance, a threshold was set in the color space to separate green vegetation from brown soil considering the green band. The second branch used a machine learning (ML) algorithm to classify each point as vegetation or ground by automatically finding thresholds. Thereby, again features such as point color were used. In addition, multi-scale features were computed for each point to characterize it in the context of its neighborhood. The calculated features were used afterwards with the random forest (RF). The third branch considered an end-to-end learning approach and thus avoiding the necessity to define features. The deep learning-based architecture PointNet++ was used. For the classification, an adapted model for soil surfaces and vegetation was trained. The performance of the different methods was compared, and an assessment of each method was provided. Overall, the study aims to find a (semi-)automatic method to remove vegetation from time series of soil surface point clouds to achieve an accurate measurement of soil surface changes and thus eventually erosion processes while minimizing manual effort and time consumption.
Using species distribution models to quantify climate change impacts on the rosy-finch superspecies: an alpine obligate

Anthropogenic climate change is forcing plants and animals to respond by shifting their distributions poleward or upward in elevation. An organism’s ability to track climate change is constrained if its habitat cannot shift and is projected to decrease in geographic extent. Geographic distributions were predicted for the Rosy-Finch superspecies (Leucosticte atrata, Leucosticte australis, Leucosticte tephrocotis) for climate change scenarios using correlative species distribution models (SDM). Multiple sources of uncertainty were quantified including choice of SDM, validation statistic, emissions scenario and general circulation model (GCM). Species distribution models are traditionally validated using a threshold-independent statistic called the area under the curve (AUC) of the receiver operating characteristic. However, during k-fold cross-validation, this statistic becomes overinflated due to spatial sorting bias existing between the presence and background points. Therefore, a calibrated area under the curve (cAUC) that removes spatial sorting bias, was used to assess predictive accuracy. Boosted regression trees (BRT) and random forest regression trees (RF) had significantly higher predictive skill than did maximum entropy (MaxEnt) or generalized additive models (GAM; p = 0.05). Predictions in space and time were made using the tree-based algorithms and applied to two representative concentration pathway (RCP) emissions scenarios, RCP2.6 and RCP8.5, for 15 and 17 GCMs, respectively, for 2061-2080. Despite variability in predictions, there is agreement between the projections for many regions suggesting that Rosy-Finches are extremely vulnerable to climate change. The short-term management implications of this study are the need for an immediate assessment of Rosy-Finches by surveying suitable habitats predicted by the species distribution models to enable population estimates to be made and field validation of species distribution model outputs. Such an approach would revisit historical breeding sites lacking coverage in the eBird and Global Biodiversity Information Facility (GBIF) datasets to determine if some populations have already been extirpated due to climate warming. Long-term management implications are to consider the use of large herbivores in alpine and tundra ecosystems in order to mitigate ecosystem response to climate warming and preserve foraging habitat for Rosy-Finches during the breeding season.
D-3He Field Reversed Configuration Fusion Power Plant

The WISC (University of Wisconsin) fusion power plant analysis code serves as the main tool for the analysis of a conceptual D-3He fusion power plant based on a field-reversed configuration (FRC). Advantages of the cylindrical geometry and high β (plasma pressure/magnetic-field pressure) of a FRC fusion core are evident. Comparison with a D-T reactor indicates that a D-3He FRC power plant would be more attractive.
Educational Interventions in Immigrants for Asthma and COPD: A Systematic Review

Objective Asthma and COPD are prevalent respiratory conditions among immigrants, yet many individuals in this population do not effectively utilize available therapies, resulting in exacerbations and limitations in their daily lives. This systematic review seeks to describe asthma/COPD educational interventions specifically tailored for immigrant patients and assess their variability and outcomes, with the ultimate goal of improving self-management and achieving better asthma or COPD control in this population. Design The Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines were followed. A comprehensive literature search was conducted using four electronic databases (CINAHL, PubMed, Embase and PsycInfo). Articles were included if they focused on asthma or COPD interventions conducted in immigrant populations. The Mixed Methods Appraisal Tool was used to assess the quality of included articles. Results Out of the initial 1173 articles identified, 812 were assessed for eligibility. Six articles met the inclusion criteria for educational interventions targeting immigrants with asthma or COPD. These studies explored the effectiveness of interventions on various immigrant populations using different methodologies including group discussion of photographs and classroom-based interventions. The interventions varied in terms of settings, educational materials, and delivery methods. Positive outcomes were observed in areas such as knowledge, understanding of instructions, and inhaler technique. However, the included studies had limitations in assessing the impact on asthma and COPD self-management and sustainability. Conclusion More research is needed on asthma and COPD management in immigrants. The interventions included in this review had positive effects on outcomes like inhaler technique and asthma knowledge. However, due to variability in outcome measures, it is difficult to directly compare the interventions. Future studies should include diverse immigrant populations, consider the specific migration status of the immigrants, long-term sustainability of the intervention and use culturally tailored approaches to improve respiratory health in this population.
Utilizing SrSi2O2N2:Eu2+,Yb2+ phosphor to achieve high hue rendering index and high hue stability

For white-light emitting diode (WLED) applications, a green-to-orange emission nitridosilicate-based phosphor is created. The observed wide-band radiation in the green-orange range is caused by Eu2+ and Yb2+ at the trap point of a doubly doped SrSi2O2N2:Eu2+,Yb2+ (SSON:Eu,Yb) nitridosilicate phosphor. The green-color radiation’s decay duration was measured to validate the energy transfer among activator ions. The co-doping various ratios’ influence of activator ions on luminescence features was investigated. The resulting phosphor’s radiation is a function of the activator ion concentrations and raising the Yb2+ concentration causes red-color radiation to dominate the green radiation. To generate white illumination, the resulting phosphor was coupled with an InGaN blue-LED chip having a pumping wavelength of 450 nm. Two stages were taken to achieve hue balance management. Initially, the green to orange proportion was tuned by varying the Eu2+ and Yb2+ ions’ concentrations. At the second stage, the Commission Internationale de L’Eclairage, International Commission on Illumination (CIE) coordinates were changed from [0.2805; 0.2014] to [0.4071; 03789] by raising the amount of phosphor powder used. White illumination produced under optimal conditions has a hue rendering indicator of 89. The designed single-stage dual-hue-releasing nitridosilicate phosphor and blue-LED chip displayed remarkable hue steadiness over a wideband of forward-bias currents (100 to 500 mA at 3 V).
Circuit Model for Nth-Order Stacked Patch Antenna, Parameter Extraction, and Radiation Prediction

This article introduces a new and generalized approach to designing stacked patch antennas with responses of any order using a generalized circuit model assisted with multiple lump ports. The proposed method incorporates frequency-dependent K-inverters to account for the frequency-dependent radiating characteristics of the radiators. The design process begins with a preliminary configuration developed from a coupling matrix synthesized in accordance with specified design requirements. Subsequently, a circuit model is established based on this configuration, and the circuit parameters linked to this structure are captured through the use of space mapping technology, emphasizing the applicability of the circuit model. Iterative tuning, involving circuit-based “optimization on the fly,” is employed in the electromagnetic model to achieve better matching performance and desired radiation characteristics. It is remarkable that the duality of the circuit model is utilized in an analytical method for predicting the radiating performance of the patch antenna. This application of the dual circuit models, which embodies significant physical properties, represents a novel approach to antenna analysis. A fourth-order antenna prototype is constructed from a synthesized coupling matrix with a new circuit topology, and a new ring-shaped resonator is applied in that realization. The results obtained from the proposed design procedures, utilizing computer-aided techniques, demonstrate the successful design of stacked patch antennas with arbitrary-order frequency responses in a deterministic fashion. This approach offers a versatile and efficient method for designing complex antennas to meet specific design requirements.