Miracle-dz/UniFAR-FTUs huggingface dataset:
The effects of acute yoga practice on heart rate and heart rate variability responses to mental stress

ABSTRACT Yoga has been shown to reduce stress and to provide health benefits for a number of stress-related disorders. However, the effects and mechanism of an acute bout of yoga practice on relieving stress remain unclear. The aim of this study was to examine the effect of a single bout of yoga on heart rate (HR) and heart rate variability (HRV) response to and recovery from mental stress. This study was conducted with a randomised, counterbalanced, crossover design. A total of 44 participants (mean age 24.58 ± 3.84) completed two conditions (yoga and video watching (VW)) in a randomly assigned order. After each condition, participants underwent a Stroop task and mental arithmetic task. HR and HRV, measured via electrocardiogram, were recorded and analyzed at rest, during each condition, and during the stress and recovery periods. HR was significantly lower during the yoga condition as compared to that during the VW condition. There was no significant difference in HRV observed between the conditions. During both mental stress and recovery periods, there was no significant difference in HR or HRV responses between the yoga and VW conditions. These findings suggested that an acute bout of yoga practice was not superior to quiet sitting in reducing HR and HRV reactivity to and recovery from mental stress. Future studies may consider investigating individuals with exaggerated cardiovascular reactivity to further examine the potential reactivity-lowering effect of yoga.
Young people with acquired brain injury: preventing entrenchment in the criminal justice system

This article explores whether children and young people with an acquired brain injury (ABI) might be over-represented in the criminal justice system in Victoria. It also discusses the strengths and weaknesses of the current system in responding to the needs of young people with cognitive impairment. Interviews were conducted with participants who work closely with young people with brain injury, cognitive disabilities and impairments, and complex trauma. The findings uncover limited identification and diagnosis of ABI; young people’s difficulties comprehending legal and administrative processes; the interaction between ABI, complex trauma and co-occurring conditions; inappropriate or indifferent responses by criminal justice agencies; and that living with an ABI is precarious, in and out of the system. Appropriate, individualised support for young people with ABI should be provided at an early stage to reduce over-representation and entrenchment in criminal justice systems.
Different Formulations Of The Discontinuous Galerkin Method For The Viscous Terms

Discontinuous Galerkin method is a nite element method using completely discontinuous piecewise polynomial space for the numerical solution and the test functions. Until recently it was mainly used for solving convection problems involving only rst spatial derivatives. Recently the method has been extended successfully to solve convection di usion problems involving second derivative viscous terms. In this paper we will use simple examples to illustrate the basic ideas and \pitfalls" for using the discontinuous Galerkin method on the viscous terms.
The methodology and dataset of the coscience eeg-personality project – a large-scale, multi-laboratory project grounded in cooperative forking paths analysis

Despite a plethora of research, associations between individual differences in personality and electroencephalogram (EEG) parameters remain poorly understood due to concerns of low replicability and insufficiently powered data analyses due to relatively small effect sizes. The present article describes how a multi-laboratory team of EEG-personality researchers aims to alleviate this unsatisfactory status quo. In particular, the present article outlines the design and methodology of the project, provides a detailed overview of the resulting large-scale dataset that is available for use by future collaborators, and forms the basis for consistency and depth to the methodology of all resulting empirical articles. Through this article, we aim to inform researchers in the field of Personality Neuroscience of the freely available dataset. Furthermore, we assume that researchers will generally benefit from this detailed example of the implementation of cooperative forking paths analysis.
Privacy-Aware Point-of-Interest Category Recommendation in Internet of Things

In location-based social networks (LBSNs), extensive user check-in data incorporating user preferences for location is collected through Internet of Things devices, including cell phones and other sensing devices. However, directly acquiring the preferences of spars users remains an open challenge. This article offers a point-of-interest (POI) category recommendation model based on group preferences (PPCM). This model is proposed for three reasons: 1) because data influence the training of a deep learning model, the group influence of users is taken into account. To protect the privacy of users’ check-in records and classify similar users into the same group, locality-sensitive hashing (LSH) is used; 2) a successive POI category recommendation model should capture the long- and short-term dependence ability. The attention mechanism and a temporal sliding window are paired with the long short-term memory (LSTM). This paradigm is useful for efficiently mining users’ long-term dependencies and interests; and 3) although the overall users’ check-in data are vast, check-in location options are also massive for a single user. There is a scarcity of data that may be utilized to mine user interests. Thus, instead of using POI, we leverage the POI category to better mine the user’s interests. On real check-in data sets from New York City and Tokyo, the PPCM is compared to other models. The comparison results indicate that the PPCM has improved recommendation performance.
IMPLEMENTASI FILTER GRAY LEVEL CO-OCCURANCE MATRIKS TERHADAP SISTEM KLASIFIKASI KANKER PAYUDARA DENGAN METODE CONVOLUTIONAL NEURAL NETWORK

Kanker payudara merupakan salah satu penyakit dengan proyeksi kematian terbesar selama 10 tahun terakhir dengan indeks kematian mencapai rata-rata 5 juta per-tahun, dan diprediksi akan terus naik hingga 60% di seluruh dunia. Umumnya, banyak metode yang digunakan untuk mendeteksi penyakit ini, salah satunya dengan mengamati jaringan histopatology. Banyak dari para ilmuwan, yang menggunakan jaringan histopatology untuk menganalisa, mengamati dan membuat sistem klasifikasi kanker payudara dengan berbagai metode, seperti: convolutional neural network, deep learning, support vector machine. Penggunaan metode convolutional neural network terbukti paling unggul pada sistem klasifikasi kanker payudara, namun akurasi rata-rata yang dihasilkan relatif cukup rendah. Selain itu, penggunaan metode convolutional neural network, membutuhkan waktu komputasi yang relatif lama untuk mengklasifikasikan 7909 dataset ukuran 4 GB. Berdasarkan alasan tersebut, desain sebuah sistem klasifikasi dengan mengimplementasikan Gray Level Co-occurance Matrix pada saat prapengolahan data CNN di butuhkan. Hasil penelitian menunjukkan bahwa, penggunaan metode CNN menghasilkan waktu komputasi lebih lama, yaitu: 3300 detik dibandingkan dengan kombinasi metode GLCM Entropy dan CNN 2040 detik. Sedangkan rata-rata akurasi latih dan uji yang dihasilkan oleh metode kombinasi GLCM entropy dan CNN adalah 92,26% dan 94,16%, lebih unggul dibandingkan dengan metode CNN, yaitu: 88,41% untuk data latih, dan 87,68% untuk data uji.
Forecasting green roof detention performance by temporal downscaling of precipitation time-series projections

Abstract. A strategy to evaluate the suitability of different multiplicative random cascades to produce rainfall time series, taking into account climate change, inputs for green infrastructures models. The multiplicative random cascades reproduce a (multi)fractal distribution of precipitation through an iterative and multiplicative random process. In the current study, the initial model, a flexible cascade that deviates from multifractal scale invariance, was improved with (i) a temperature dependency and (ii) an additional function to reproduce the temporal structure of rainfall. The structure of the models with depth and temperature dependency was found to be applicable in eight locations studied across Norway and France. The resulting time series from both reference period and projection based on RCP 8.5 were applied to two green roofs with different properties. The different models led to a slight change in the performance of green roofs, but this was not significant compared to the range of outcomes due to ensemble uncertainty in climate modelling and the stochastic uncertainty due to the nature of the process. The hydrological dampening effect of the green infrastructure was found to decrease in most of the Norwegian cities due to an increase in precipitation, especially Bergen (Norway), while slightly increasing in Marseille (France) due to decrease in rainfall event frequency.
WHISTLE: CPU Abstractions for Hardware and Software Memory Safety Invariants

Memory safety invariants extracted from a program can help defend and detect against both software and hardware memory violations. For instance, by allowing only specific instructions to access certain memory locations, system can detect out-of-bound or illegal pointer dereferences that lead to correctness and security issues. In this paper, we propose CPU abstractions, called WHISTLE, to specify and check program invariants to provide defense mechanism against both software and hardware memory violations at runtime. WHISTLE ensures that the invariants must be satisfied at every memory access. We present a fast invariant address translation and retrieval scheme using a specialized cache. It stores and checks invariants related to global, stack and heap objects. The invariant checks can be performed synchronously or asynchronously. WHISTLE uses synchronous checking for high security-critical programs, while others are protected by asynchronous checking. A fast exception is proposed to alert any violations as soon as possible in order to close the gap for transient attacks. Our evaluation shows that WHISTLE can detect both software and hardware, spatial and temporal memory violations. WHISTLE incurs 53% overhead when checking synchronously, or 15% overhead when checking asynchronously.
EVALUATION OF PARASITIC GASTROINTESTINAL PARASITES IN CAPTIVE MAMMALS AT OGBA ZOOLOGICAL GARDEN AND NATURE PARK, BENIN CITY

This study investigated the gastro-intestinal helminthes present in the capture-held mammals at Ogba Zoological garden comprising a total of twenty-six(26) resident mammals representing thirteen (13) wildlife species, between the period of March and June, 2019. The objectives of this study were to identify the common helminthes present in the zoo mammals, estimate number of positive animal species and investigate the prevalence, abundance and diversity of the gastro intestinal helminthes among the animals. Fecal samples of the animals were collected from the study site and subjected to direct microscopic examination and concentration techniques. Overall, a total of four (4) helminthes parasites’ eggs and cyst were identified; Strongyle spp. (Threadworm), Trichuris trichuria (Whipworm) Ancyclostoma duodenale (Hookworm) and Nematodes’ eggs. A protozoan species (Entamoeba spp) was also detected in the Grey Monkeys. Fourteen (14) (53.84%) of the sampled animals were infected with the gastrointestinal helminthes parasites and eggs. Strongyle spp (30.8%), was the most prevalent gastro intestinal helminth found among the animals used for the study, while 19.2% prevalence of Hookworm (15.4%) of whipworm, (15.4%) of Entamoeba spp and (3.8%) of Nematode eggs were recorded. The gastro-intestinal helminthes were highly distributed among the Mona monkeys and grey monkeys. The Nematode eggs and larvae were isolated in the Donkey. Hookworm was highly spread among the Baboons and also found in the Civet Cat. 46.16% of the examined animals were free from gastrointestinal helminthes infection. No record of parasitic helminthes were found in the Chimpanzee, Patas monkeys, White-throated monkeys, Crested porcupine, horses, Maxwell duiker, lion and Hyena in the study site
Molecular Characterization of Basella alba L. and Basella rubra L. using Random Amplified Polymorphic DNA Profiling

The limitation of morphological characterization in revealing genetic variation in species has been the predisposition of most quantitative characters and some qualitative characters to environmental influences. This has necessitated the use of molecular methods in the characterization of species in the recent time. This study was designed to examine and characterize Basella alba and Basella rubra using Random Amplified Polymorphic DNAs (RAPDs) profiling in order to provide useful information that could enhance the elucidation of their taxonomic status. Genomic DNA of fresh leaf samples of the two species were isolated and subsequently subjected to RAPD analysis by scoring for presence (1) or absence (0) of bands. Monomorphic and polymorphic bands were identified, number of amplified alleles determined, percentage polymorphism established, genetic diversity and genetic distance were calculated. The study revealed 123 individual fragments obtained from the 11 primers used, with 112 (91.06%) of the fragments being monomorphic, with 11 being polymorphic. The genetic variation between the species studied was 8.94%, while the genetic distance was 0.21. The results obtained indicated that the Basella alba and Basella rubra studied were quite close genetically, thus suggesting that there could be a possibility of gene flow between them.
Potential Biotherapeutic Effect of Lactobacillus reuteri NA1 on The Treatment of Induced Peptic Ulcer in Rats: Histopathological Study

Peptic ulcer disease (PUD) is still a global public health problem. Development of novel therapies became necessary to overcome the limitations of traditional therapies. In this study we intend to treat experimentally induced peptic ulcer by using a new probiotic bacterial strain. Lactobacillus reuteri NA1 was isolated from peptic ulcer human-biopsy on MRS medium, molecularly identified, and registered in GenBank with accession number MH714546. This study was conducted on 2 experiments, the first experiment included 48 rats divided into 4 groups. Saline group, negative control group (saline); H.p. 1 group, H. pylori -ve stool administrated group; H.p. 2 group, H. pylori +ve stool administrated group; H.p. 3 group, H. pylori induced ulcer and treated with L. reuteri NA1. The second experiment included 36 rats divided into 3 groups. Saline group, negative control group (saline); As. 1 group, aspirin induced ulcer group; As. 2 group, aspirin induced ulcer and treated with L. reuteri NA1. Histopathological investigations showed a significant recovery of the experimentally induced gastric ulcers by both H. pylori and Aspirin after 6 weeks of the treatment with L. reuteri NA1. Complete healing of gastric mucosa was observed after 12 weeks of daily administration of L. reuteri NA1. L. reuteri NA1 has shown a promising solution for treatment of peptic ulcer in H. pyloriantibiotic resistance patients, and aspirin users.
Multi-Objective Deep Reinforcement Learning for Recommendation Systems

Most existing recommendation systems (RSs) are primarily concerned about the accuracy of rating prediction and only recommending popular items. However, other non-accuracy metrics such as novelty and diversity should not be overlooked. Existing multi-objective (MO) RSs employed collaborative filtering and combined with evolutionary algorithms to handle bi-objective optimization. Besides cold-start problem from collaborative filtering, it also vulnerable to highly sparse environment, while the evolutionary algorithm suffers from premature convergence and curse of dimensionality. These limitations have prompted this work to propose deep reinforcement learning (DRL) approaches for MO optimization in RSs. Several works in DRL are available but none has addressed MO RS problems. In this study, the performances of proposed DRL approaches that based on Deep Q-Network in MO recommendation problem were investigated. The approaches were evaluated with movie recommendation dataset by using three conflicting metrics, namely precision, novelty, and diversity. The results demonstrated that deep reinforcement learning approaches has superiority performance in MO optimization, and its capability of recommending precise item along with achieving high novelty and diversity against the benchmark that using probabilistic based multi-objective approach based on evolutionary algorithm (PMOEA). Although PMOEA algorithm secured higher average value in precision, it has lower values of novelty and diversity than the proposed DRL approaches. The DRL approaches surpassed the benchmark results in average of maximum novelty and the average of mean diversity metrics, the optimization between accuracy and non-accuracy metrics is inevitable. In addition, the experiments revealed that incorporation of user latent features enhanced the recommendation quality.
Explosive Weapons and ARms Detection with sIngular Classification (WARDIC) on novel Weapon Dataset using Deep Learning : Enhanced OODA Loop

Rising armed conflicts and violent agitations in populated areas have upstretched stark trepidations worldwide. Especially, the armed conflicts are increasingly being fought using explosive weapons, rifles, and lethal arms, endangering civilian lives and infrastructure. Concerns raised by the state actors accentuate the importance of identifying the miscreants in possession of explosive weapons to limit unlawful activities. Armed forces, law enforcement agencies, while carrying out operations in populated areas, demand the high availability of meaningful information promptly to shorten the OODA loop for effective decision-making by the military commanders. Existing research is limited to classifying revolvers, pistols, and knives and has privations to detect explosive weapons and firearms due to severe void of relevant datasets, computationally optimal weapon detection methodology, which imposes severe restrictions in instrumenting an effective system. Hence, we introduce two customized, high-balanced Novel Operational Weapon Arms Datasets - named NOWAD post detailed Exploratory Data analysis. We propose a state-of-the-Art methodology to implement a novel weapons and arms detection singular classifier -named (WARDIC) to identify explosive weapons and arms. The WARDIC - an augmented architecture of the ConvNets and Singular Classifiers showed promising performance in detecting weapons in surveillance feeds. The evaluation metrics show promising performance of the tuned WARDIC classifier (fusion of DenseNet-121 with the Isolation Forest) over traditional baselines models with the perfect scores of 100\%. Cross validations of the classifier employing 5-Fold and 10-Fold CV showed accuracy scores of 99.27\% and 99.46\%, respectively, with linear complexity. Our experiments propose the State-of-the-Art Classifier formulation, which shortens the OODA loop for effective decision-making by significant improvement in computational complexity to instrument a quick response system. The WARDIC classifier distinctively supersedes the performance of the existing classifiers
Why do galactic spins flip in the cosmic web? A Theory of Tidal Torques near saddles

Abstract Filaments of the cosmic web drive spin acquisition of disc galaxies. The point process of filament-type saddle represent best this environment and can be used to revisit the Tidal Torque Theory in the context of an anisotropic peak (saddle) background split. The constrained misalignment between the tidal tensor and the Hessian of the density field generated in the vicinity of filament saddle points simply explains the corresponding transverse and longitudinal point-reflection symmetric geometry of spin distribution. It predicts in particular an azimuthal orientation of the spins of more massive galaxies and spin alignment with the filament for less massive galaxies. Its scale dependence also allows us to relate the transition mass corresponding to the alignment of dark matter halos' spin relative to the direction of their neighboring filament to this geometry, and to predict accordingly it's scaling with the mass of non linearity, as was measured in simulations.
Factors predicting postoperative morbidity after cytoreductive surgery for ovarian cancer: a systematic review and meta-analysis

Objective Advances in ovarian cancer cytoreductive surgery have enabled more extensive procedures to achieve maximal cytoreduction but with a consequent increase in postoperative morbidity and mortality. The aim of this study was to evaluate factors for postoperative morbidity after extensive cytoreductive surgery for primary epithelial ovarian cancer (EOC), particularly those which may be modifiable. Methods Electronic databases were searched. Meta-analysis was conducted using random-effects models. Results Fifteen relevant studies, involving 15,325 ovarian cancer patients, were included in this review. Severe 30-day postoperative complications occurred in 2,357 (15.4%) patients. The postoperative mortality rate was 1.92%. Meta-analysis demonstrated that patient with following risk factors; age (p<0.001), Eastern Cooperative Oncology Group score >0 (p=0.001), albumin level <3.5 g/dL (p<0.001), presence of ascites on CT scan (p=0.013), stage IV disease (p<0.001) and extensive surgical procedure (p<0.001) has a significantly increase risk of developing postoperative complications. Surgical procedures including peritonectomy (p=0.012), splenectomy (p<0.001) and colon surgery (p<0.001) were significant predictors for postoperative complications. Moreover, we found that patients who received neoadjuvant chemotherapy followed by interval debulking surgery (NACT-IDS) had a lower risk of developing severe complications compared to those who underwent primary debulking surgery (PDS) (p<0.001). Conclusion Our study demonstrated that patient performance status and hypoalbuminemia were the only significant adjustable preoperative risk factors associated with postoperative complications. Patients who underwent NACT-IDS had a lower risk of developing severe complications compared to PDS. Trial Registration International Prospective Register of Systematic Reviews (PROSPERO) Identifier: CRD42021282770