Miracle-dz/UniFAR-FTUs huggingface dataset:
Impact of heat stress on dairy cow rumination, milking frequency, milk yield and quality in a pasture-based automatic milking system

Context . Heat stress is an increasing concern for the Australian dairy industry. Aims . This study aimed to evaluate the effect of temperature – humidity index (THI) on rumination time (RT), milk yield and quality, and milking frequency in a pasture-based voluntary-movement automatic milking system (AMS). Methods . Data were collected from the University of Melbourne Dookie College AMS farm for 3 years (June 2016 to March 2019). Daily RT was collected through the transponder collar (Qwes-HR, Lely). Climatic data (maximum ambient temperature and relative humidity) were obtained from the Dookie Meteorological station to calculate daily maximum THI (THI max ). Key results . Daily milk yield increased with a rising THI max to 65, then declined after THI max 65. Milking frequency was highest at THI max 90, followed by a steady decline afterwards. Rumination time was maximum at mid-range THI max and declined for high and low values. Conclusions . The ﬁ ndings of this study clearly indicated that under pasture-based voluntary-movement AMS, high THI resulted in a drop in the milk yield, milking frequency and RT. Implications . With the provision of automation of data collection from AMS, further study with mathematical modelling describing the daily patterns and thresholds in conjunction with the different heat stress levels can be useful for assessing animal welfare and to mitigate heat stress and seek alternative management strategies.
Market and welfare valuation of the economic burden of diseases attributable to air pollution exposure in the Western Balkans

Aim: The population in the Western Balkans is exposed to high air pollution concentrations, among the highest in Europe, causing death and disability. Research, however, on the resulting economic cost in the region is still limited. We estimate the economic cost of the adverse health effects from air pollution exposure, including fine particulate matter (ambient and household) and ambient ozone air pollution in the region. Methods: We employ both market and welfare-oriented methods. According to the Cost-of-Illness (COI) approach, we estimate both the direct (healthcare expenditure) and indirect cost (mortality and morbidity cost). Against the shortcomings of a market-based valuation, the Willingness to Pay (WTP) approach is also used. The most recent data from the Global Burden of Disease Study 2019 are used. Results: Under the COI approach, total economic cost is estimated at PPP$ 6.3 billion. Equivalently, it ranges from 0.8% of GDP in Croatia to 2.39% of GDP in Bosnia and Herzegovina. The WTP methodology yields a significantly higher estimate, equal to PPP$ 76.7 billion. The monetary amount associated with the disease burden of air pollution is significant. Conclusion: Public health policies should include monitoring of the adverse health effects of air pollution. Abatement policies should aim at reducing ambient air pollution as well as the dependence on polluting household energy usage. The reduced economic cost can be accompanied by benefits associated with climate change mitigation and an overall improvement in population’s health status, an important aspect given the current COVID-19 pandemic.
Studies on Object Recognition Based on Combination of Image Compacting and Zernike Moments

This paper proposed a meth od of recognition of planar shapes.It first uses the compacting algorithm bring the image to its most compact form,thus settled the problem of the translation ,scaling,skewing of the image resulting from the change of our viewpoint to the objects,then by computing the Zernike moments of the contour of the compac ting image,it can detect the different characteristics of different kinds of objects and recognize them.This method is based on a simple therom and shows a less computation task,but has a good recognition result.
Merging the gap between physical and virtual realities: A pilot study on the role designed props play in creating a more immersive virtual experience

Abstract Virtual Reality (VR) technologies have gained momentum in various fields, including art and design, where it plays a significant role in modelling, prototyping, and testing. However, most VR headsets offer only visual and auditory inputs, leaving other senses unintrigued. This study has two objectives: First, to investigate whether a physical prop can create a more immersive experience. Second, to establish the relationship between the physical prop and memory retention of the virtual experience. Two VR rollercoaster conditions were created: one where participants simply sat on a chair, and another where they were secured in with a harness. Various qualitative and quantitative data collection methods were used, including physiological, observational, and self-reporting techniques. Biosensors were used to measure heart rate, breathing rate, skin conductance and brain activity, in addition to a survey. Early results confirm a positive correlation, with varying degrees, between the use of props in immersion and memory retention.
Clustering Different Learning Behavior with Model of Culture

Culturally based habits and expectations influence learning. Identifying these cultural expectations is a prerequisite for designing learning environments that best serve students. This research studied the behavior of university students in the greater Jakarta area using the model of culture. The model of culture was an extension from Hall’s model, and consists of eight dimensions, i.e., time focus, time orientation, power, competition, action, space, communication, and structure. 1207 responses from different universities in the greater Jakarta area were collected and analyzed using two-stage cluster analysis (combination of Ward method and K-means clustering). The result shows three clusters, namely: the millennial engineers, the diffused social studies students, and the silent achiever. Different teaching tools were then suggested for each cluster to address their weaknesses and strengths.
SVM-based infrared small target detection

Because of the influence of nature meteorological condition,background environment and the structure of target,the detection of weak and small targets in infrared image is one of the difficulties among image targets detection and identification.A new small target detection method is presented,in which the infrared image intensity surface is well fitted by the least square support vector machine(LS-SVM),and then the maximum extremum points are detected based on the fitted intensity surface.It is decided whether the extremum point is a target position or not by comparing the gray level of the point with the average gray intensity of its local area.The second order directional derivative operators are de-duced from the LS-SVM with the radial basis kernel function,as an example.The computer small target detection experiments are carried out for the real and simulated images.Experimental results demonstrate that the proposed algorithm is robust and efficient.
A Psychometric Validation of the Claremont Purpose Scale with an Emerging Adult University Student Population

Purpose is a key protective factor for well-being, resilience, and satisfaction with life. The Claremont Purpose Scale (CPS) was developed by Bronk and colleagues with adolescents and emerging adults to measure three dimensions of purpose: Meaning, Goal-Directedness, and Beyond-the-Self. The psychometric properties examined in the original scale development paper were replicated with our independent, emerging adult college student sample: confirmatory factor analysis (CFA) of a 3-factor model, good-to-excellent internal consistency, convergent validity with a measure of general perceived meaning in life (MLQ-P), and discriminant validity with a measure of self-reported depression (DASS-21-D). Additional psychometrics examined in this study were test–retest reliability, a 1-factor CFA model, correlations with stress and anxiety, and descriptive statistics that had not yet been reported in the literature. Beyond-the-self items did not load well onto the overall factor in the 1-factor model, and their correlations with other scales (MLQ-S, DASS-21 subscales) differed significantly from the correlations between these other scales and the CPS total, General Meaning, and Goal-Directedness scores. Implications of these data include strong support for the use of each subscale as a distinct, psychometric index, and we recommend the total scale score not be used as a measure of overall purpose. Clearly, there are separate, but related, facets to this measure that, while positive, undermine the utility of a global purpose score. A slightly changed naming of the CPS to the “Claremont Purpose Scales” could promote their use as three separate scales instead of a single, total scale score.
Analysis of Traffic Using the Snort Tool for the Detection of Malware Traffic

The increasing prevalence of malware threats necessitates the development of robust methods for detecting and mitigating malicious network traffic. This paper presents an analysis of traffic using the Snort tool for the detection of malware traffic. The study focuses on understanding traffic patterns, evaluating Snort's performance, and comparing it with other tools or methods for malware detection. The methodology involves data collection, preprocessing, Snort configuration, and traffic analysis. The results reveal valuable insights into traffic patterns associated with malware activities, demonstrate Snort's effectiveness in detecting known malware signatures, and assess its efficiency and scalability. The comparison with other tools provides a comprehensive understanding of Snort's strengths and limitations. This research contributes to the field of network security by providing practical insights for network administrators and suggesting future research directions.
Efficient bearing fault diagnosis with neural network search and parameter quantization based on vibration and temperature

In this work, we propose a deep-learning method to diagnose bearing faults of electric motors based on vibration and bearing housing temperature. Our methods can accurately diagnose faults related to bearing cracking and lubricant shortages. The proposed method is effective in terms of computational complexity and model capacity thanks to the advantages of neural architecture search (NAS) and parameter quantization in the model establishment. The experimental results found that the information on bearing temperature improved the diagnostic accuracy for the bearing fault diagnosis task. The proposed method has explored the most optimal model in terms of computational resources and model capacity with a pre-defined accuracy target. The searched model has a relatively high diagnostic accuracy of 98.7% and a size of about 27.3 kB. After quantization, the obtained model maintained 96.9% accuracy and reduced 4 times in size. All experiments are executed elaborately on our custom bearing fault dataset.
A Long COVID Risk Predictor Focused on Clinical Workflow Integration

For the NIH Long COVID Computational Challenge (L3C) in the Fall of 2022, we developed a machine learning model to predict who is at high risk for developing Long COVID, optimized for clinical deployment. Our submission won second prize in the competition. We present lessons learned, with details on the features, model selection and performance, fairness analysis, limitations, and deployment implications.
Multimodal Risk Prediction with Physiological Signals, Medical Images and Clinical Notes

The broad adoption of electronic health records (EHRs) provides great opportunities to conduct healthcare research and solve various clinical problems in medicine. With recent advances and success, methods based on machine learning and deep learning have become increasingly popular in medical informatics. Combining data from multiple modalities may help in predictive tasks. To assess the expectations of multimodal data, we introduce a comprehensive fusion framework designed to integrate temporal variables, medical images, and clinical notes in Electronic Health Record (EHR) for enhanced performance in downstream predictive tasks. Early, joint, and late fusion strategies were employed to effectively combine data from various modalities. Model performance and contribution scores show that multimodal models outperform uni-modal models in various tasks. Additionally, temporal signs contain more information than CXR images and clinical notes in three explored predictive tasks. Therefore, models integrating different data modalities can work better in predictive tasks.
Preventing thrombotic events in a case of postpartum ovarian artery aneurysm rupture: clinical challenges and management approaches

Abstract Ovarian artery aneurysm is a rare asymptomatic condition usually diagnosed when it ruptures. It causes massive bleeding, often in the peripartum period of multiparous women, who are already at an increased risk for thromboembolic events. Balancing the bleeding risk against the thrombotic complications remains unexplored in such cases. A 35-year-old woman presented with hemorrhagic shock 3 days after delivering her seventh healthy child. During the emergent exploratory laparotomy, she responded well to the blood transfusion; the stable retroperitoneal hematoma indicated no need to explore it. A subsequent episode of hemodynamic instability necessitated another laparotomy, during which the hematoma was evacuated and both ovarian arteries were ligated. Shortly thereafter, the patient suffered a pulmonary embolism (PE). In multiparous patients presenting with peripartum retroperitoneal hematoma and hemorrhagic shock, exploring the hematoma and ligating the ovarian and uterine arteries may reduce the risk of PE or the need for reoperation.
Simplifying screening for Trypanosoma cruzi in pregnant persons and their infants

1 Department of Epidemiology, School of Public Health and Tropical Medicine, Tulane University, New Orleans, Louisiana, United States of America, 2 Departamento de Laboratorio Clı́nico, Hospital Escuela; Instituto de Enfermedades Infecciosas y Parasitologı́a Antonio Vidal; Tegucigalpa, Honduras, 3 Instituto de Efectividad Clı́nica y Sanitaria, Buenos Aires, Argentina, 4 Department of Tropical Medicine, School of Public Health and Tropical Medicine, Tulane University, New Orleans, Louisiana, United States of America, 5 Servicio de Infectologı́a, Departamento de Pediatrı́a, Hospital Escuela; Servicio de Infectologı́a, Instituto Hondureño de Seguridad Social; Tegucigalpa, Honduras, 6 Laboratoire de Parasitologie, Faculté de Médecine, Université Libre de Bruxelles (ULB), Brussels, Belgium
Impact of Intraoperative Findings on Hearing in Revision Ear Surgery

Introduction: Hearing results after chronic ear surgery encompass recurrence, localization and extent of cholesteatoma, type of surgery, ossiculoplasty methods, but rarely interpret intraoperative findings. This study aimed to analyze the impact of intraoperative findings in revision tympanomastoidectomy in predicting postoperative hearing. Materials and Methods: This was a retrospective non-randomized cohort of 101 patients treated for recurrent chronic otitis media by tympanomastoidectomy. The patients’ demographics, localizations of disease recurrence and perioperative hearing results were analyzed. Results: Logistic regression showed that presence of tympanic perforation (p=0.036), ossicular chain damage (p=0.006), were negatively associated with improved hearing postoperatively. Attic cholesteatoma was associated with better postoperative hearing (p=0.045). Presence of tympanic perforation (p=0.050), alongside perifacial localization of imflammation (p=0.021) and ossicle destruction (p=0.013) were associated with worse postoperative hearing results. Multivariate analysis confirmed that tympanic perforation (p=0.040, F=4.401), and ossicular chain involvement (p=0.025, F=5.249), were consistent negative predictors of hearing improvement, while postoperative deterioration of hearing was associated with tympanic perforation (p=0.038, F=4.465) and facial nerve dehiscence (p=0.045, F=4.160). Conclusions: Comparison of postoperative revision tympanomastoidectomy hearing outcomes revealed significant positive reductions in air-bone gap values, primarily at low and mid frequencies. Postoperative hearing results at high frequencies are not affected by revision surgery.
Drought is threatening plant growth and soil nutrients of grassland ecosystems: A meta‐analysis

Abstract As a widespread direct effect of global warming, drought is currently wreaking havoc on terrestrial ecosystems' structure and function, however, the synthesized analysis is lacked to explore the general rules between drought changes and main functional factors of grassland ecosystems. In this work, meta‐analysis was used to examine the impacts of drought on grassland ecosystems in recent decades. According to the results, drought greatly reduced aboveground biomass (AGB), aboveground net primary production (ANPP), height, belowground biomass (BGB), belowground net primary production (BNPP), microbial biomass nitrogen (MBN), microbial biomass carbon (MBC) and soil respiration (SR), and increased dissolved organic carbon (DOC), total nitrogen (TN), total phosphorus (TP), nitrate nitrogen (NO3−‐N), and the ratio of microbial biomass carbon and nitrogen (MBC/MBN). The drought‐related environmental factor mean annual temperature (MAT) was negatively correlated with AGB, height, ANPP, BNPP, MBC, and MBN, however, mean annual precipitation (MAP) had positive effect on these variables. These findings indicate that drought is threatening the biotic environment of grassland ecosystem, and the positive steps should be taken to address the negative effects of drought on grassland ecosystems due to climate change.
Stability and Active Control of Low Altitude Aerostats

Low altitude aerostats are usually uncontrolled and dependent on passive stability solutions sometimes they do not have any. However, their low speed, the high turbulence and the gusts present in the lower atmospheric boundary layer, as well as their large surface area bring the stability solution’s theme into a foreground. This paper aims to survey common approaches to the stability problem when the aerostat is subjected to turbulent winds and gusts and compare them with new active control techniques, especially for low altitude aerostats. Addressed topics are: the dynamic modeling of tether and aerostat; comparison between different positions of the tether confluence point (the point through the the aerostat is attached to the tether) and analyses of elevator control demands. Conclusions are given to make the use of aerostats more reliable for operations with diverse electronic equipment which have tight requirements for oscillation and altitude changes.
Evaluation of predictive models of aneurysm focal growth and bleb development using machine learning techniques

Background The presence of blebs increases the rupture risk of intracranial aneurysms (IAs). Objective To evaluate whether cross-sectional bleb formation models can identify aneurysms with focalized enlargement in longitudinal series. Methods Hemodynamic, geometric, and anatomical variables derived from computational fluid dynamics models of 2265 IAs from a cross-sectional dataset were used to train machine learning (ML) models for bleb development. ML algorithms, including logistic regression, random forest, bagging method, support vector machine, and K-nearest neighbors, were validated using an independent cross-sectional dataset of 266 IAs. The models' ability to identify aneurysms with focalized enlargement was evaluated using a separate longitudinal dataset of 174 IAs. Model performance was quantified by the area under the receiving operating characteristic curve (AUC), the sensitivity and specificity, positive predictive value, negative predictive value, F1 score, balanced accuracy, and misclassification error. Results The final model, with three hemodynamic and four geometrical variables, along with aneurysm location and morphology, identified strong inflow jets, non-uniform wall shear stress with high peaks, larger sizes, and elongated shapes as indicators of a higher risk of focal growth over time. The logistic regression model demonstrated the best performance on the longitudinal series, achieving an AUC of 0.9, sensitivity of 85%, specificity of 75%, balanced accuracy of 80%, and a misclassification error of 21%. Conclusions Models trained with cross-sectional data can identify aneurysms prone to future focalized growth with good accuracy. These models could potentially be used as early indicators of future risk in clinical practice.
In-silico identification of linear B-cell epitopes in specific proteins of Bartonella bacilliformis for the serological diagnosis of Carrion’s disease

Carrion´s disease is caused by Bartonella bacilliformis, it is a Gram-negative pleomorphic bacterium. B. bacilliformis is transmitted by Lutzomyia verrucarum in endemic areas of the Peruvian Inter-Andean valleys. Additionally, the pathogenicity of B. bacilliformis involves an initial infection of erythrocytes and the further infection of endothelial cells, which mainly affects children and expectant women from extreme poverty rural areas. Therefore, the implementation of serological diagnostic methods and the development of candidate vaccines for the control of CD could be facilitated by the prediction of linear b-cell epitopes in specific proteins of B. bacilliformis by bioinformatics analysis. In this study, We used an in-silico analysis employing six web servers for the identification of epitopes in proteins of B. bacilliformis. The selection of B. bacilliformis-specific proteins and their analysis to identify epitopes allowed the selection of seven protein candidates that are expected to have high antigenic activity.
Radiofrequency Ablation for Colorectal Cancer Liver Metastases: Outcomes and Prognostic Factors Associated with Survival

Background: To determine the long-term outcomes of radiofrequency ablation with respect to overall survival, disease-free survival, and complications in patients with colorectal cancer liver metastases. Additionally, we sought to examine whether various patient- and treatment-related characteristics were associated with prognosis. Methods: Fifty-nine patients with colorectal cancer liver metastases who had undergone percutaneous radiofrequency ablation treatment were included in this study. A total of 138 lesions were treated with radiofrequency ablation in the first and second sessions. Tumor diameters ranged from 10 to 60 mm (mean, 2.45 mm). Treatment efficacy, complications, and overall survival and disease-free survival were analyzed. Results: The primary success rate of radiofrequency ablation was 94.4%. At the end of the first month, the residual disease was detected in 12 lesions, 10 of which underwent secondary radiofrequency ablation treatment, resulting in a cumulative secondary success rate of 98.4%. The 1-, 3-, and 5-year overall survival rates in 59 patients with colorectal cancer liver metastases were 94.9%, 52.5%, and 40.6%, respectively. The median survival was 42 months in patients with metastasis size of ≤3 cm, while it was 25 months in patients with metastasis size of >3 cm (P = .001). The 1-, 3-, and 5-year disease-free survival rates were 44%, 10.2%, and 6.7%, respectively. Metastatic tumor status (solitary or multiple) was a significant prognostic factor in determining overall survival and disease-free survival; furthermore, extrahepatic recurrence during follow-up was a prognostic factor affecting overall survival. Minor complications developed in four radiofrequency ablation procedures (6.7%). Conclusion: Radiofrequency ablation remains a safe and effective treatment option improving survival in select cases of colorectal cancer liver metastases.