Miracle-dz/UniFAR-FTUs huggingface dataset:
Comparative Performance Analysis of Accident Anticipation with Deep Learning Extractors

Accident anticipation has become a major focus to avert accidents or to minimize their impacts. Over the years, several network systems are being developed and applied in self-driving technology. Despite the fact that advancement in the autonomous industry is fast-growing, major efficiency is required in the network systems that are gradually emerging. Recent research has proposed a novel end-to-end dynamic spatial-temporal attention network (DSTA) by combining a Gated Recurrent Unit (GRU) with spatial-temporal attention learning network, to identify an accident video in 4.87 seconds before the occurrence of the accident with 99.6% accuracy when tested on the Car Crash Dataset (CCD). However, DSTA has not been able to provide efficient results on the Dashcam Accident Dataset (DAD) dataset. Moreover, the GRU model integrated in the DSTA network has a weak information processing capability and low update efficiency amid several hidden layers. The decision-making process of the accident anticipation network may be understood using the high quality saliency maps produced by the Grad-CAM and XGradCAM approaches. In this paper, we evaluate that using Wide ResNet network enhances the performance mechanism of feature extraction to increase accident anticipation precision. This change improves the capacity to process information and the learning efficacy. In addition, we suggest employing a Gated Recurrent Unit (GRU) network which will serve as a prominent feature to train the model to recognize data’s sequential properties and apply patterns to forecast the following likely event. Hence, we plan to incorporate Wide ResNet50, a system for extracting features which will identify the vehicles at risk by using wider residual blocks. These neural networks generate labels for identifying hazardous conditions in driving environments in order to anticipate accidents.
Personalized Pain Study Platform using Evidence-Based Continuous Learning Tool

Use of mobile health (mHealth) systems has increased with the advancement and proliferation of mobile computing and related technologies. It has improved efficiency and effectiveness of healthcare services. Non-invasive pain level detection from facial images is one of the promising mHealth applications. Effective pain treatment requires regular and continuous pain assessment. Most of the pain research tools are study or disease specific while some are pain (lumbar pain, cancer pain, etc.) and patient group specific (neonatal, adult, woman, etc.). This results in recurrent but potentially avoidable costs such as time, money, and workforce to develop similar services or software research tools for each research study. In this study, we have proposed, designed, and implemented a customizable personalized pain study platform that offers real-time data collection, research participant management, role-based access control, research data anonymization etc. It is also used to investigate pain level detection accuracy using evidence-based continuous learning from the facial expression data, collected from Bangladesh, Nepal and USA, which yielded about 71% classification accuracy.
Prediction of Mosquito Prevalence in a Warm Semi-Arid Climate using Artificial Neural Network (ANN)

Mosquito is a disease-causing organism that causes harm to humans and animals alike. Over the years, vector control measures such as the use of insecticides, treated mosquito net, and the prediction of mosquito prevalence using statistical tools and Artificial Neural Network models in different weather terrain have not completely eradicated the problems associated with prevalence of mosquito. More so, there is no research available in literature to predict the prevalence of mosquito using artificial neural network in a warm semi-arid climate such as that of Yola, Northeastern Nigeria. This research endeavored to achieve this aim. This study built a prototype artificial neural network model that is capable of predicting mosquito prevalence. The model is a feed forward multi-layer perceptron that was implemented using the supervised learning method and optimized using the back propagation algorithm. The model has four (4) input features, which are weather data (maximum temperature, minimum temperature, relative humidity and rainfall) which were adopted for the research. After compilation, the new model was trained and validated using sourced data by the researcher. To train the model, 80% of the data was used while 20% was used for the validation. The proposed model is a keras sequential classification model that was built in anaconda using the python programming language. The optimal model has three hidden layers of 40 30 and 20 neurons with Sigmoid and ReLu activation function respectively. The simulation of the prototype model recorded 96.67% accuracy with good fit. This research shows that the artificial neural network model is an effective tool in predicting mosquito prevalence in a warm semi-arid climatic region and thus recommends the use of more data and training epochs to increase accuracy and subsequent implementation of the model in real life for prediction of mosquito prevalence.
Leaf pH as a plant trait: species‐driven rather than soil‐driven variation

Summary 1. Interspecific variation in plant functional traits is fast becoming popular as a tool for understanding and predicting ecosystem biogeochemistry as dependent on vegetation composition. Leaf pH has recently been shown to be a promising new candidate trait for this purpose. But how robust is leaf pH as a species trait in the face of environmental variation? We hypothesized that inherent interspecific variation in leaf pH should be greater than phenotypic variation of given species in response to soil environments. 2. We tested this hypothesis in a temperate herbaceous flora by growing 23 species experimentally in three soils of contrasting pH (ranging by almost three pH units) and related chemistry. 3. As predicted, there was large and consistent variation in leaf pH among these species, which was robust to the differences between soil types. Indeed both the species rankings and the absolute species values for leaf pH were remarkably constant in comparisons between soil types. 4. The fact that a given species can maintain a leaf pH very different from that of their soil environment, combined with the great interspecific variation in leaf pH, indicates that leaf pH really is largely a species-specific trait. Linked with recent field evidence we suggest that interspecific variation in leaf pH, while easy and cheap to assess, has important predictive power of biogeochemical properties and processes in ecosystems.
Prevalence of Burnout among Residents at King Abdulaziz Medical City in Riyadh, Saudi Arabia

Background: Working in the medical field is associated with extremely advanced levels of strain. Burnout is a prolonged reciprocation to the continuously persistent emotional and interpersonal stressors on the job and is clarified by the 3 dimensions of emotional exhaustion, depersonalization, and reduced personal accomplishment. Objectives: To evaluate the prevalence of burnout among residents at King Abdulaziz Medical City in Riyadh. Materials and Methods: A cross-sectional study was conducted on medical and surgical residents at King Abdulaziz Medical City in Riyadh. Data were collected through a previously validated questionnaire. Part 1 of the questionnaire was the demographic data collection sheet. Part 2 was the Maslach Burnout Inventory for Health Services Workers (MBIHSS). Results: The 60% of the respondents were male and the mean age was 28 years. Most respondents reported that they work for 51-60 hours per week (33.50%) and 27.50% of them work for 61-70 hours per week. Total 51% of the participants showed high depersonalization, 31.50% exhibited low personal achievements and 12.50% with high emotional exhaustion. Conclusion: The high rates of burnout among residents in KAMC-R were alarming. Efforts to identify at-risk population were warranted and interventions to prevent burnout like counseling and social skills training were encouraged.
A Multi-Scale Analysis of Competition between the House Finch and House Sparrow in the Southeastern United States

Abstract. The decline of House Sparrow (Passer domesticus) populations in eastern North America has been proposed to be a consequence of competition between the House Sparrow and the House Finch (Carpodacus mexicanus), both introduced species. Previous research testing the hypothesis that House Sparrow declines are due to competition with House Finches focused on populations in the northeastern U.S., excluding other regions where the species coexist. We tested for effects of competition between these two species in the southeastern U.S. at two scales of analysis. First, we looked for evidence of competition at a local scale during the breeding season by examining patterns of co-occurrence of House Finches and House Sparrows at count points within a 28.3-km2 study area centered on Auburn, Alabama. Second, we tested for the effects of competition at a regional scale across seven southeastern states by analyzing trends in Christmas Bird Count data. Using null-model analysis, we found no evidence for competitive exclusion in our assessment of local co-occurrence, suggesting that if there is competition between these species it is not important enough to affect their spatial distribution during the breeding season. At the regional scale, contrary to findings in the northeastern U.S., the decline of House Sparrow populations within the southeastern U.S. was not significantly correlated with House Finch abundance. Our results suggest that competition with House Finches is not a major cause of decline of the House Sparrow population in the southeastern U.S.
Atrium Segmentation Using Machine Learning

the segmentation and 3D reconstruction of the human atria are critical for the precise diagnosis and treatment of atrial fibrillation, the most common cardiac arrhythmia, as the manual segmentation of the atria from medical images is a fallible process. Therefore, an automated process is highly desirable. This research aims to create a segmentation pipeline that includes a convolutional neural network (CNN) based on the U-Net architecture. A dataset consisting of 20 MRI scans of the heart with a corresponding ground truth mask is provided by the medical segmentation decathlon. In a 2-dimensional setting, this translates to 4542 MRI scans of the heart and labeled slices. Add more details on the output and the inferences drawn out of our work. The article proposes a machine learning algorithm has been created which can assist in the segmentation of the atrium using machine learning. It explains the process and techniques that have been used in the processing and training stages of the model. Finally, the model result and future directions have been discussed.
Comparison of mean shift algorithms and evaluation of their stability

Mean Shift is an universal and robust segmentation algorithm used also for image segmentation. Implementation corresponding to definition is slow for real-time image processing, thus some faster variants were proposed. Speed and quality of the segmentation can be influenced by the variant of the algorithm as well as by setting its parameters. Modification of a parameter causes change of number of segments and their shape as well. By using segmentation evaluation algorithm, we analyze and present these changes for original and modified Mean Shift algorithms. Influence of noise is also studied
Sequential Multiplex PCR Approach for Determining Capsular Serotypes of Streptococcus pneumoniae Isolates

ABSTRACT Accurate serotyping is essential to monitor the changes in the seroepidemiology of Streptococcus pneumoniae. We devised a simple and schematic sequence-based system of seven multiplex PCRs, in a sequence order based upon Active Bacterial Core surveillance (ABCs) serotype distribution during 2002 to 2003, to reliably deduce specific pneumococcal serotypes. A total of 421 isolates from ABCs were randomly chosen to evaluate this system. Two hundred twenty-nine of the isolates (54.3%) were specifically assigned 1 of 17 serotypes by the multiplex PCR system, with the results in complete concordance with conventional serotyping. One hundred seventy-two additional isolates (40.9%) were assigned to 11 specific sets of 2 to 4 serotypes that with one exception (serotypes 6A and 6B) consisted of the single frequently occurring targeted serotype and 1 to 3 additional rare serotypes primarily within the same serogroup as the targeted serotype. Only 20 isolates (4.8%) could not be assigned specific serotypes or serotype sets, since they were either of rare serotypes not included in the assay design or were nonserotypeable. Overall, we found this system to be highly reliable, with the potential to greatly reduce our reliance upon conventional serotyping. Especially important is the capability of this system to give serotype-determining potential to any facility that lacks the expensive typing sera and expertise needed for conventional serotyping yet has the modest equipment necessary for DNA amplification and electrophoresis.
Microwave LC Oscillators: Multimode Switching Techniques

The rapid development of wireless communications, software-defined radios, and ultrawideband (UWB) engenders an ever-increasing demand for clock generators that fully covers multiple frequency bands <xref ref-type="bibr" rid="ref1">[1]</xref>, <xref ref-type="bibr" rid="ref2">[2]</xref>, <xref ref-type="bibr" rid="ref3">[3]</xref>. As the “heartbeat” that pumps clock generators, such as phase-locked loops (PLLs), microwave oscillators with wideband frequency tuning range (TR) are thus attracting a great deal of research interest.
Salt stress: antioxidant activity as a physiological adaptation of onion cultivars

The germination and development of several plant species can be limited when those plants are grown in soils with high salinity, which reduces seedling viability and vigor, as well as activating the antioxidant defense system. The aim of this study was to evaluate germination, initial growth and activity of antioxidant enzymes (superoxide dismutase, catalase and ascorbate peroxidase) in seedlings of three onion cultivars (Madrugada, Fepagro 27 and Petroline) exposed to different concentrations of NaCl (0, 40, 80, 120 and 160 mM). Seedlings were evaluated for viability, vigor and antioxidant enzyme activity. The experimental procedures were completely randomized in 3 × 5 factorial design, with each treatment performed in triplicate, at a significance level of 5%. For all cultivars, viability and vigor decreased in parallel with increasing NaCl concentrations, whereas antioxidant enzyme activity increased, and one cultivar (Madrugada) showed less salt tolerance than did the others. We conclude that high NaCl concentrations have a negative effect in the physiological quality of onion seeds, resulting in lower seedling growth rates and increased antioxidant enzyme activity, where Fepagro 27 and Petroline cultivars were more tolerant to salt stress than 'Madrugada'.
Effects of the Affordable Care Act on the Receipt of Colonoscopies among the Insured Elderly

Background: The Affordable Care Act (ACA) waived deductibles and eliminated coinsurance for colonoscopies for Medicare beneficiaries beginning in January 1, 2011. This study investigated the effect of the ACA’s directive to remove the financial barriers on the receipt of colonoscopies among the elderly insured, who are predominantly covered by Medicare. Methods: Data from the 2008–2016 Behavioral Risk Factor Surveillance System (BRFSS) were used to examine the receipt of colonoscopies in two years prior to the implementation of the ACA (2008 and 2010) and three years after the change (2012, 2014, and 2016). Multivariate logistic regressions were estimated to examine the change in colonoscopy use before and after the introduction of the ACA, adjusting for patient characteristics and availability of health care providers in the geographic region. Results: Of 349,899 eligible elderly insured in the age group 65 to 75 years, 236,275 (67.2%) had received a colonoscopy in the previous ten years. The receipt of colonoscopies increased from 63.5% in pre-ACA years to 69.2% in the post-ACA years (p < 0.001). Compared with the pre-ACA period, the odds ratio of colonoscopy uptake in post-ACA years was 1.15 (95% CI = 1.08–1.22). Conclusions: A statistically significant increase in colonoscopy use was observed in the post-ACA years. However, achieving the target coverage rate of 80% will require additional interventions to encourage higher levels of screenings.
SSK-Based SWIPT With AF Relay

The performance of space shift keying (SSK) modulation using energy harvesting amplify and forward (AF) relaying is analyzed over the Rayleigh fading channel. Simultaneous wireless information and power transfer technique eliminates the need of power supply at the relay in cooperative communication. However, energy tapped from an information signal for harvesting would degrade receiver performance. At the same time, the distance between the relay and the receiver can be increased due to the power splitting-based relay with the SSK modulation. New closed-form expressions for average bit error probability (ABEP) is derived with a single AF relay and an upper bound expression for multiple relays with a direct link. Furthermore, the ABEP is analyzed for a partial relay selection operation. The numerical and computer simulation results are presented with the discussion.
Pengetahuan tradisional: konsep, dasar hukum, dan praktiknya

Buku ini membahas mengenai pengetahuan tradisional, mulai dari penjabaran konsep-konsep dasarnya, mengenai dasar perlindungan hukum internasional, baik atas dasar hak asasi manusia maupun sebagai sumber daya dan warisan budaya.
Mechanisms and reproductive consequences of breeding dispersal in a specialist predator under temporally varying food conditions

Individual variation in breeding dispersal has extensive ecological and evolutionary consequences, but the factors driving individual dispersal behaviour and their fitness consequences remain poorly understood. Our data on dispersal events of a rodent-specialist predator, the Eurasian kestrel Falco tinnunculus, over 20 years in western Finland offers a unique opportunity to explore the mechanisms underlying breeding dispersal behaviour and its reproductive consequences in a wild bird population. Sex, age, body condition and previous breeding success affected breeding dispersal. Dispersal distances were longer in females than in males as well as longer in yearlings than in older individuals. Body condition was positively correlated to breeding dispersal distances, particularly for females. The lowest dispersal distances were recorded for intermediate brood sizes in the year preceding dispersal. Our results highlight sex- and environment-specific consequences of breeding dispersal on reproductive performance. During increase phases of the three-year vole cycles, males dispersing further had lower reproductive performance after dispersal, whereas in females, long breeding dispersal distances were associated with increased breeding success under all environmental conditions. These results suggest benefits associated to breeding dispersal in females, potentially related to large spatio-temporal variation in main food abundance and intensity of intra-specific competition. Breeding dispersal of males was costly during increasing food abundance, indicating the potential fitness benefits of environmental familiarity in this migratory species. Overall, our results indicate that both individual traits and environmental factors interact to shape breeding dispersal strategies in wide-ranging predator populations under fluctuating food conditions.
Fractal Pattern Identification from Wearable Inertial and Electromyographic Signals Data during Walking

Acceleration, angular velocity and electromyographic (EMG) signal at the lower limb muscles, specially over both leg's Tibialis Anterior muscles are highly non-stationary, even if no perturbing influences can be identified during walking at any speed. This study analyzed the fractal dynamics (i.e., complexity of gait time series) in the walking gait time series of four types of signals obtained from wearable sensors such as IMUs (inertial measurement units), i.e., accelerometer signals which represents the acceleration experienced by the body, gyroscope signals which is the angular velocity, and magnetometer signals which is magnetic field vector, and Electromyographic (EMG) signal from both leg’s Tibialis Anterior muscles. Gait time series from twenty-two healthy participants were analyzed while they performed walking at their comfortable speed. The scaling exponents (i.e., α-values) of the gait dynamics were accomplished by evaluating their fluctuation through detrended fluctuation analysis (DFA), which is most common and widely used non-linear technique for any non-stationary time series. DFA (the scaling exponents α) results established an anti-persistent in EMG and acceleration signal, less persistent pattern in angular velocity and persistent (i.e., long-range or fractal-like correlations) in magnetometer signal. This fractal complexity or noise patterns obtained from the EMG and inertial signals might provide new approaches for assessing and forecasting sudden injury risk during walking.
Guide to mitigating spacecraft charging effects

The only book to blend the theoretical and practical aspects of spacecraft charging, Guide to Mitigating Spacecraft Charging Effects defines the environment that not only creates the aurora, but which also can have significant effects on spacecraft, such as disruption of science measurements and solar arrays from electrostatic discharge (ESD). It describes in detail the physics of the interaction phenomenon as well as how to construct spacecraft to enhance their survivability in the harsh environment of space.
Use of Discrete Cosine-based Stockwell Transform in the Binary Classification of Magnetic Resonance Images of Brain Tumor

biomedical diagnostic tool for the detection of tumors in the brain since it provides detailed and comprehensive information associated with the brain's anatomical structures. The radiologist can detect the existence of malignancies or aberrant cell growths using MRI images. Early-stage brain tumor diagnosis and treatment are greatly aided by MRI image processing. This study inquires about a method for classifying MRI brain images into without tumors and brain tumors to detect tumors using these images. These days, researchers can create reliable Computer-Aided Diagnosis (CAD) systems for identifying tumors and healthy brains thanks to the benefits of machine learning. A crucial stage in any machine-learning model is feature extraction. Time-frequency analysis techniques are more effective for image classification applications since they provide localized information. We suggested using the Discrete Cosine-based Stockwell Transform (DCST) to extract the efficacious features from brain MRI images and create the feature matrix after pre-processing and segmentation. The feature matrix's dimension was decreased using the chi-square test. A Support Vector Machine (SVM) classifies the selected features at the end. We employed a dataset containing 7023 brain MRI images divided into four classes: tumors of the pituitary, glioma, meningioma, and without tumors. For binary classification into brain tumors and no tumors, we attained an accuracy of 97.71%.