Miracle-dz/UniFAR-FTUs huggingface dataset:
An adaptive model predictive control for coupled yaw and rollover stability of vehicle during corner maneuvers

This paper presents a control strategy to achieve yaw and roll stability by taking into account the physical interaction between the yaw and roll dynamics to prevent vehicle collisions in hilly or curved terrain. The mathematical model is formulated utilizing a roll dynamic model with a small tyre slip angle and a bicycle model with two degrees of freedom considering coupling of yaw and roll dynamics. An adaptive model predictive controller and a PID controller are included in the proposed control methodology so that a real-time scenario of variation in the longitudinal velocity and friction coefficient is considered. Stability limits are established based on the yaw rate, sideslip, and roll motions of the vehicle, taking into account the effects of the road angle. The friction coefficients of 0.4 and 0.8 are chosen for wet and dry road surfaces to show manoeuvrability and force the vehicle to avoid rollover condition. Using numerical simulations in Matlab R2022a, the effectiveness of the designed controller is assessed. A root mean square error (RMSE) is calculated for the proposed methodology for the evaluation of the performance and the values are obtained as 3.032 and 3.912 for friction coefficient of 0.8 for yaw rate and roll angle respectively. On comparing with the other methodology, it is found that the performance of the proposed method is better based on RMSE. Also, the fluctuations at the corners are removed and the variables are bound inside the stability limit, thus avoiding the vehicle from accidents in hilly areas. The robustness of the controller towards increasing the mass of the vehicle by 5% and 10% is found to be good.
Treatment of clinically severe bovine mastitis – a scoping review

Mastitis is a major health problem for bovines and can be categorized as non-severe or severe, based on clinical symptoms. A severe case of clinical mastitis is usually defined by the cow being affected systemically. It is important to consider how to handle severe cases because these cases can be fatal and cause high production losses. However, there are generally few detailed treatment guidelines. By conducting a scoping review on the topic, we aimed to synthesize the information that is available on treatment and outcomes, as reported from clinical trials and observational studies. This was facilitated by following the PRISMA-guidelines with a stepwise systematic screening of scientific literature on the subject, retrieved via Pubmed and Web of Science, using pre-defined selection criteria. The results yielded a total of 14 reports of treatment and outcomes in cases of naturally occurring severe clinical mastitis. Cross-trial comparison was difficult due to the different exclusion criteria and outcome definitions. Many studies focused on cases caused by gram-negative bacteria treated with intensive antibiotic protocols, often containing antibiotics that are categorized as critical for human health. Few focused on severe cases caused by gram-positive bacteria or on the relative use of non-antibiotic treatment. In general, only a small number of statistically significant differences were found in trials comparing different treatment protocols, with no obvious trends across trials. Our findings emphasize the need for more research into the treatment efficacy of antibiotic and non-antibiotic options for clinically severe mastitis. Furthermore, consideration of how trial conditions relate to the practical circumstances in a field setting could improve the applicability of reported results. This could help to provide practitioners with the information needed to make evidence-based treatment decisions in cases of clinically severe mastitis.
Toward Improving Breast Cancer Classification Using an Adaptive Voting Ensemble Learning Algorithm

Over the past decade, breast cancer has been the most common type of cancer in women. Different methods were proposed for breast cancer detection. These methods mainly classify and categorize malignant and Benign tumors. Machine learning is a practical approach for breast cancer classification. Data mining and classification are effective methods to predict and categorize breast cancer. The optimum classification for detecting Breast Cancer (BC) is ensemble-based. The ensemble approach involves using multiple ways to find the best possible solution. This study used the Wisconsin Breast Cancer Diagnostic (WBCD) dataset. We created a voting ensemble classifier that combines four different machine learning models: Extra Trees Classifier (ETC), Light Gradient Boosting Machine (LightGBM), Ridge Classifier (RC), and Linear Discriminant Analysis (LDA). The proposed ELRL-E approach achieved an accuracy of 97.6%, a precision of 96.4%, a recall of 100%, and an F1 score of 98.1%. Various output evaluations are used to evaluate the performance and efficiency of the proposed model and other classifiers. Overall, the recommended strategy performed better. Results are directly compared with the individual classifier and different recognized state-of-the-art classifiers. The primary objective of this study is to identify the most influential ensemble machine learning classifier for breast cancer detection and diagnosis in terms of accuracy and AUC score.
Transcriptomic analysis of cell envelope inhibition by prodigiosin in methicillin-resistant Staphylococcus aureus

Methicillin-resistant Staphylococcus aureus (MRSA) is a leading threat to public health as it is resistant to most currently available antibiotics. Prodigiosin is a secondary metabolite of microorganisms with broad-spectrum antibacterial activity. This study identified a significant antibacterial effect of prodigiosin against MRSA with a minimum inhibitory concentration as low as 2.5 mg/L. The results of scanning electron microscopy, crystal violet staining, and confocal laser scanning microscopy indicated that prodigiosin inhibited biofilm formation in S. aureus USA300, while also destroying the structure of the cell wall and cell membrane, which was confirmed by transmission electron microscopy. At a prodigiosin concentration of 1.25 mg/L, biofilm formation was inhibited by 76.24%, while 2.5 mg/L prodigiosin significantly reduced the vitality of MRSA cells in the biofilm. Furthermore, the transcriptomic results obtained at 1/8 MIC of prodigiosin indicated that 235and 387 genes of S. aureus USA300 were significantly up- and downregulated, respectively. The downregulated genes were related to two-component systems, including the transcriptional regulator LytS, quorum sensing histidine kinases SrrB, NreA and NreB, peptidoglycan biosynthesis enzymes (MurQ and GlmU), iron-sulfur cluster repair protein ScdA, microbial surface components recognizing adaptive matrix molecules, as well as the key arginine synthesis enzymes ArcC and ArgF. The upregulated genes were mainly related to cell wall biosynthesis, as well as two-component systems including vancomycin resistance-associated regulator, lipoteichoic acid biosynthesis related proteins DltD and DltB, as well as the 9 capsular polysaccharide biosynthesis proteins. This study elucidated the molecular mechanisms through which prodigiosin affects the cell envelope of MRSA from the perspectives of cell wall synthesis, cell membrane and biofilm formation, providing new potential targets for the development of antimicrobials for the treatment of MRSA.
Attachment Orientation and Preferences for Partners’ Emotional Responses in Stressful and Positive Situations

Attachment theory proposes that close relationships help us to regulate our emotions in stressful and positive situations. However, no previous studies have examined preferences for a partner’s emotional response to one’s own stressful and positive situations or tested whether these preferences differ based on attachment orientation. This study examines the association of attachment orientation and preferences for partners’ emotional responses relative to one’s own emotional responses in stressful and positive contexts among 425 United States adults who were currently in a committed relationship of ≥6 months. Data were collected in 2020. Overall, participants preferred their partners to feel and express less distress, less worry, more calm, and more hope than themselves during stressful situations and for their partners to feel and express more excitement, pride, and hope than themselves during positive situations. Higher attachment anxiety predicted preferences for partners to feel and express more distress/worry in stressful situations, whereas higher attachment avoidance predicted preferences for partners to feel and express less hope in stressful situations. Statistical interactions of attachment anxiety × attachment avoidance indicated that the combination of low attachment anxiety and high attachment avoidance (dismissing avoidance) was associated with preferences for partners to feel and express less positive emotions in positive situations, whereas the combination of high attachment anxiety and high attachment avoidance (fearful avoidance) was associated with preferences for partners to feel and express more negative emotions in stressful situations and less positive emotions in positive situations. This investigation provides novel evidence for links between attachment orientation and preferences for partners’ emotional responses in two theoretically important contexts, which has implications for the nature and function of emotion regulation in close relationships. Future research is needed to determine the generalizability of these findings to more collectivist cultural contexts.
The Applications of Augmented Reality (AR) and Virtual Reality (VR) in Teaching Medical and Dentistry Students: A Review on Advantages and Disadvantages

An interactive three-dimensional (3D) experience known as augmented reality (AR) employs computers to superimpose virtual data over the real world. Virtual reality (VR) is the artificial computer reconstruction of a real-life environment or experience. In the last decade, academic medical and dental institutions have witnessed the emergence of teaching technologies created using extended reality technology, such as AR and VR. It has been suggested that these technological advancements might support and further improve the medical and dentistry student teaching process. However, some medical and dentistry educationists are still sceptical about the usage of these technologies. This is because, throughout the centuries, the students in both fields were trained via established conventional methods. The purpose of this paper is to understand the benefit and impediments use of both technologies. We aim to review the advantages and disadvantages of AR and VR applications in the teaching process of medical and dentistry students.
EFFECTS OF PLANTATIONS AND HOME-GARDENS ON TROPICAL FOREST BIRD COMMUNITIES AND MIXED-SPECIES BIRD FLOCKS IN THE SOUTHERN WESTERN GHATS

1Nature Conservation Foundation, 3076/5, 4th Cross, Gokulam Park, Mysore 570 002, Karnataka, India. 2Field Ornithology Group of Sri Lanka, Department of Zoology, University of Colombo, Colombo, Sri Lanka. Current address: Section of Ecology, Behavior and Evolution, Division of Biological Sciences, University of California, San Diego, La Jolla CA 92093, USA. Email: eben.goodale@gmail.com 3Email: swati@ncf-india.org 4Email: trsr@ncf-india.org
Exploring the Relation between Realised Volatility and Trading Volume: Evidence from International Stock Market

Objective: The sequential information theory and mixed distribution hypothesis contends that there exists a bi-directional relation between realised volatility and trading volume. This position has led to the proposition that new information spreads sequentially and reaches market participants at varying times. The purpose of this study was to re-examine these theories. Research Design & Methods: A Granger causality test, Mean Square Error and Mean Average error models were applied to investigate the relationship between realised volatility and trading volume for a sample of five international stock markets from March 5, 2018, to March 5, 2023. Findings: The findings of this study contradict the proposition put forth by the sequential information theory and mixed distribution hypothesis where no meaningful relationship was observed between realised volatility and trading volume except for the CAC 40. Hence, new information rather filters through financial markets at the same time. This finding maybe the explanation for the ever-increasing financial contagion between financial markets. Contribution & Value Added: Traders may need to rely on other indicators and adjust their strategies to incorporate different signals or factors that are more relevant for predicting or identifying market movements. It may become more challenging to gauge the liquidity conditions in the market based solely on volatility. Market participants may need to rely on other liquidity indicators, such as bid-ask spreads, order book depth, or trade size distribution, to assess market liquidity.
Toxicity and Toxicokinetics of a Four-Week Repeated Gavage of Levamisole in Male Beagle Dogs: A Good Laboratory Practice Study

Levamisole (LVM) is considered an immunomodulatory agent that has the potential to treat various cancer and inflammation diseases. However, there is still much debate surrounding the toxicokinetic and toxicological information of LVM. Therefore, it is crucial to assess its toxicity to provide useful data for future human LVM risk assessments. In this study, a barrier environment was established under the guidance of good laboratory practice (GLP) at the Fujian Center for New Drug Safety Evaluation. Male beagle dogs were orally administered with 5, 15, and 30 mg/kg of LVM daily for four weeks. Toxicity assessment was based on various factors such as mortality, clinical signs, food and water consumption, body weight, body temperature, electrocardiogram, ophthalmological examination, hematology, serum biochemistry, organ/body coefficients, histopathological study, and toxicokinetic analysis. The results of this study showed that LVM did not exhibit any significant toxicological effects on beagle dogs at the exposure levels tested. A no observed adverse effect level (NOAEL) of LVM was set at 30 mg/kg/day for male beagle dogs, which is equivalent to a 12-fold clinical dose in humans. Moreover, the repeated exposure to LVM for four weeks did not lead to any bioaccumulation. These findings provide valuable insights for future human LVM risk assessments.
Impact of sleep disruption on cognitive function in patients with postacute sequelae of SARS-CoV-2 infection: initial findings from a Neuro-COVID-19 clinic

Abstract Introduction Fatigue, brain fog, and sleep disturbance are among the most common symptoms of postacute sequelae of SARS-CoV-2 infection (PASC). We sought to determine the impact of sleep disruption on cognition and quality of life in patients with neurologic manifestations of PASC (Neuro-PASC). Methods Thirty-nine patients were recruited from Neuro-COVID-19 clinic. Mean age was 48.1 years, 71.8% were female, and 82% were never hospitalized for COVID-19. Patients were evaluated via clinical assessment, quality-of-life measures in domains of cognitive function, fatigue, sleep disturbance, anxiety, and depression, NIH Toolbox cognitive tests, and 7 days of wrist actigraphy. Results The median number of neurologic symptoms attributed to PASC was 6, with brain fog being the most common in 89.7%. Regarding non-neurologic symptoms, 94.9% complained of fatigue and 74.4% of insomnia. Patients reported significant impairment in all quality-of-life domains and performed worse in a task of attention compared to a normative US population. Actigraphy showed Neuro-PASC patients had lower sleep efficiency, longer sleep latency (both p < 0.001), and later sleep midpoint (p = 0.039) compared to 71 age-matched healthy controls with no PASC history. Self-reported cognitive symptoms correlated with the severity of fatigue (p < 0.001), anxiety (p = 0.05), and depression (p < 0.01). Objective evidence of sleep disruption measured by wakefulness after sleep onset, sleep efficiency, and latency were associated with decreased performance in attention and processing speed. Conclusion Prospective studies including larger populations of patients are needed to fully determine the interplay of sleep disruption on the cognitive function and quality of life of patients with PASC.
The Impact of Innovation and Government Intervention on the Performance of Micro, Small and Medium Enterprises

During the COVID-19 pandemic, Micro, Small and Medium Enterprises (MSMEs) experienced a decline in performance due to low product demand. Therefore, various efforts need to be undertaken to enhance MSMEs' performance in Indonesia. Addressing this issue, this research aims to examine the influence of innovation and government intervention in improving the performance of SMEs in Indonesia. The study employs a quantitative approach with a population of MSME actors in Central Kalimantan, a province of Indonesia. Through proportional random sampling, 356 SME operators were identified as respondents for this study. Data were collected using a survey method and analyzed using simple and multiple linear regression. The results indicate that innovation and government intervention have a positive and significant impact on improving MSMEs' performance both partially and simultaneously. Therefore, this research implies that the more MSMEs engage in innovation and with government involvement in efforts to support MSME development, the greater the opportunity to enhance MSME performance in Indonesia.
Deep Learning-Based Left Ventricular Ejection Fraction Estimation from Echocardiographic Videos

Heart failure (HF) is a prevalent and life-threatening medical condition afflicting millions of people worldwide. A critical parameter in assessing cardiac function and detecting HF is the left ventricular ejection fraction (LVEF), which measures the amount of blood expelled from the left ventricle (LV) during each contraction, indicative of the heart’s ability to efficiently circulate oxygen-rich blood throughout the body. While echocardiography has traditionally served as the primary imaging modality for evaluating LVEF due to its accessibility and cost-effectiveness, recent advancements in cardiac magnetic resonance have positioned it as an invaluable tool, particularly for detecting heart failure with preserved ejection fraction (EF). However, it’s worth noting that CMR is financially more burdensome compared to echocardiography. This study leverages the EchoNet-Dynamic dataset, comprising 10,030 echocardiographic videos with annotations of LV coordinates. We have established a well-structured data preprocessing pipeline to extract frames and associated coordinates, ensuring the dataset’s suitability for deep learning (DL) models. Our proposed model architecture incorporates pretrained transfer learning (TL) models optimized for localizing LV boundaries. Through the application of convolutional neural network (CNN) regression-type models to predict coordinates, we demonstrate a novel volume tracing method that may alleviate the limitations associated with segmentation-based approaches. Our findings underscore the potential of deep learning to enhance the precision and efficiency of cardiac analysis. The methodology we have developed equips healthcare practitioners with timely insights to inform clinical decision-making.
Assessment of Multiple Fiducial Marker Trackers on Hololens 2

Fiducial markers play a fundamental role in various fields in which precise localization and tracking are paramount. In Augmented Reality, they provide a known reference point in the physical world so that AR systems can accurately identify, track, and overlay virtual objects. This accuracy is essential for creating a seamless and immersive AR experience, particularly when prompted to cope with the sub-millimeter requirements of medical and industrial applications. This research article presents a comparative analysis of four fiducial marker tracking algorithms, aiming to assess and benchmark their accuracy and precision. The proposed methodology compares the pose estimated by four algorithms running on Hololens 2 with those provided by a highly accurate ground truth system. Each fiducial marker was positioned in 25 sampling points with different distances and orientations. The proposed evaluation method is not influenced by human error, relying only on a high-frequency and accurate motion tracking system as ground truth. This research shows that it is possible to track the fiducial markers with translation and rotation errors as low as 1.36 mm and 0.015 degrees using ArUco and Vuforia, respectively.
Singular Purpose: Calculating the Degree of Ethno-Religious Over-representation in the US No-Fly List

In January 2023, a partially redacted version of the controversial US No-Fly List was retrieved during a hacking event and made available to journalists and academics. With access to this unique dataset, it is possible to confirm or deny longstanding critiques of whether the No-Fly List features a discriminatory over-representation of certain ethno-religious groups, namely those of Islamic faith and Middle Eastern heritage. As the partially redacted list does not contain ethnic or religious data, the author of this article categorised each name by ethno-linguistic and religious origin to create a proxy with which to analyse claims of discrimination. The research outlined in this article finds that individuals of broadly Islamic and Middle Eastern heritage are vastly overrepresented on the List relative to their proportion of the US population, as well as overrepresented relative to their propensity to engage in terrorism. Only in the narrow analysis of lethality of terrorist attacks committed by this group does the No-Fly List demonstrate fair representation.
Assessment of the Negative Factors for the Clinical Outcome in Patients with SARS-CoV-2 Infection and Type 2 Diabetes Mellitus

Purpose Patients with diabetes mellitus (DM) are more susceptible to viral and bacterial infections, facing a more severe prognosis and higher mortality rates. The study’s main aim was to evaluate the survival and mortality rates of patients with type 2 diabetes (T2DM) and SARS-CoV-2 virus infection alongside the main factors influencing the prognosis. Patients and Methods The present study included 186 patients with T2DM and SARS-CoV-2 virus infection admitted to the COVID-19 Department of the “Pius Brînzeu” Emergency Clinical County University Hospital between November 2020 and March 2021. Patients had investigations performed upon arrival in the emergency room and during hospitalization. We analyzed the risk of negative prognosis based on clinical data (oxygen saturation (SatO2), respiratory rate (RR), lung damage), glycemic control (HbA1c, glycemia at hospital admission), and the duration of T2DM. Results The mortality rate in the studied group was 36.6%. All deceased patients had previously been diagnosed with hypertension; 95.58% had a body mass index (BMI) greater than 25 kg/m2, and 79.41% presented with cardiovascular disease (CVD). Compared to those who recovered, statistically significant differences were observed in BMI, glycemic levels at admission, glycosylated hemoglobin levels (HbA1c), SatO2, RR, and lung damage. Valid statistically significant predictors for death in T2DM patients with COVID-19 were hyperglycemia at admission > 198mg/dl, HbA1c> 8.6%, and SatO2≤ 87%. Conclusion SatO2, glycemia at hospital admission, and HbA1c had the highest sensitivity and specificity to predict the prognosis of T2DM patients with SARS-CoV-2 infection. Glycemic control is essential in the prognosis of patients with DM and COVID-19 infection. The prognosis was worse if other comorbidities were associated, especially hypertension and CVD.