Miracle-dz/UniFAR-FTUs huggingface dataset:
DETERMINATION OF THE BULK MATERIALS PARTICLES DISTRIBUTION DURING MIXING IN THE CONTINUOUS ACTION CENTRIFUGAL MIXERS ROTOR

The article presents the results of the bulk materials particles movement study in continuous centrifugal mixers with conical rotors. Mathematical models of particle motion in the rotor are developed. The systems of differential equations that describe the particles motion on the rotor bottom and side surface are given. Particle trajectories are shown. The effect of the particle flow initial position in the rotor on the particles distribution at the rotor exit is studied. A relationship is established between the particles distribution at the rotor exit and the mixture homogeneity, which was estimated using the coefficient of variation. The mathematical modelling results of a two-component mixture particles distribution under various initial motion conditions of these components are presented. Four variants of the mixture components movement inside the rotor are considered. The initial conditions for the particles motion differed in the initial components flows position at the rotor bottom, as well as in these flows number. For all cases, histograms of the particles distribution along the rotor outer edge with a 150 step were plotted. In all zones, the particles number of each mixture component was calculated and its percentage composition was determined. Based on the data obtained, the mixture homogeneity was determined. It was found that the mixture homogeneity increases with a decrease in the difference between the particles flows initial motion conditions in the rotor. It was also found that dividing the flows into parts increases the mixture uniformity.
PERILAKU SOSIAL MASYARAKAT BUDDHIS DI JAWA TENGAH

Hubungan erat antar umat Buddha sebagai kelompok minoritas seperti rasa saling memiliki karena manusia pada dasarnya hidup tidak bisa sendiri dan selalu membutuhkan orang lain. Perilaku sosial dalam masyarakat Buddhis dapat mendukung keberlangsungan kelompok umat Buddha di masyarakat terutama di Jawa Tengah. Metode penelitian deskriptif dalam penelitian ini bertujuan untuk memberi gambaran tentang perilaku sosial masyarakat Buddhis di Jawa Tengah khususnya di Kabupaten Jepara dan Kabupaten Pati sebagai lokus penelitian. Hasil dari penelitian ini adalah gambaran perilaku sosial masyarakat Buddhis Jawa Tengah diantaranya mengikuti kegiatan anjangsana (kunjungan ke rumah-rumah), puja bakti, Dhammadesana, kegiatan urun rembug (musyawarah) atau diskusi, saling membantu dalam kegiatan keagamaan, kepedulian terhadap sesama, toleransi antar umat beragama. Beberapa kebiasaan di masyarakat yang juga tergambar sebagai bentuk perilaku sosial masyarakat Buddhis diantaranya adalah menjabat dan mencium tangan yang dilakukan remaja Buddhis ketika bertemu dengan anggota masyarakat Buddhis yang usianya lebih tua sebagai bentuk rasa hormat, mengucap salam Namo Buddhaya jika bertemu dengan sesama umat, kebiasaan menjabat tangan sambil mencium pipi saat perempuan bertemu dengan perempuan, serta para sesama laki-laki yang bertemu sambil berpelukan dan menepuk pundak.
Drag and Reynolds stress distribution within submerged vegetation canopies

In the last few decades extensive studies focusing on both qualitative and quantitative descriptions of the Reynolds stress within aquatic canopies have been carried out. Although these studies have advanced our knowledge of mechanics of flow-vegetation interactions, further research in this area is still required. In particular, there is a need for development of new simple physically-based relationships describing the Reynolds stress profiles within submerged vegetation canopies. This paper addresses this issue and proposes a physically justified formulation for the Reynolds stress profile within the canopy region.
Measurement of optic nerve sheath diameter on computed tomography for the differentiation of transient ischemic attacks

SUMMARY OBJECTIVE: The objective of this study was to investigate whether the measurement of mean optic nerve sheath diameter in patients with transient ischemic attack could be used to distinguish between control groups, the acute ischemic stroke group, and subgroups within the acute ischemic stroke category. METHODS: Retrospectively, the mean optic nerve sheath diameters of patients aged 18 years and older belonging to control, transient ischemic attack, acute ischemic stroke, and subgroups within the acute ischemic stroke category were measured with initial computed tomography conducted in the emergency department. RESULTS: Out of the 773 patients included in the study, 318 (41.1%) were in the control group, 77 (10%) had transient ischemic attack, and 378 (49%) were categorized as stroke patients. The average mean optic nerve sheath diameter was significantly higher in both the stroke and transient ischemic attack groups compared with the control group (p<0.001 for both comparisons). Furthermore, the mean optic nerve sheath diameter in the stroke subgroups was significantly higher than in both the transient ischemic attack and control groups (p<0.001 for all comparisons). In transient ischemic attack patients, the mean optic nerve sheath diameter showed a significant ability to predict transient ischemic attack (AUC=0.913, p<0.001), with a calculated optimal cutoff value of 4.72, sensitivity of 94.8%, and specificity of 73.9%. CONCLUSION: The mean optic nerve sheath diameter of patients in the transient ischemic attack group was lower compared with those in the stroke subgroups but higher compared with the control group.
ROBB: Recurrent Proximal Policy Optimization Reinforcement Learning for Optimal Block Formation in Bitcoin Blockchain Network

Blockchain is a ground-breaking technology that has changed how we manage and store protected data. It is a decentralized ledger that enables safe, open, and unchangeable record-keeping. It relies on a distributed network of nodes rather than a single central authority to check and verify transactions, guaranteeing that each entry is correct and unchangeable. Transactions in a blockchain network are grouped into blocks, which are then linked together in a chronological and immutable chain. Block size is a critical parameter in blockchain technology, which refers to the maximum size of each block in the chain that is not benchmarked yet. However, we cannot just change the block size of the blockchain. It is challenging and will create security issues. The Block size is crucial because it affects the number of transactions processed per second, the confirmation time, and overall network efficiency. The confirmation time should be faster to ensure stable earnings for the miners. Moreover, it needs help with broader applications due to high transaction fees and long verification times. We have proposed a reinforcement learning model named ROBB that can efficiently create a block considering the current network state and previous transactions. At first, the problem was converted into a reinforcement learning environment to solve using multiple reinforcement algorithms. We developed a blockchain simulator to replicate the network environment. To transform it into a reinforcement learning environment, we integrated it with OpenAI Gym. The simulator was trained by generating random transactions. Finally, we designed a reward function that enables the simulator to hold transactions and create blocks with the pending transactions when it determines that the environment is favorable. In the final results, ROBB successfully minimized the waiting time for transactions and utilized the blocks to their full potential. Additionally, it optimized the block space, building upon the findings of previous researchers. From the research, we can see that our proposed models show impressive results with 100% block utilization and 1.8s average waiting time while creating the least number of blocks.
Factors Influencing Nutritional Status in Hospitalized Individuals Aged 70 and Above

Background: Older adults are vulnerable to malnutrition due to physical, psychological, and social factors. Malnutrition, a prevalent and modifiable issue in this population, is associated with an elevated risk of adverse clinical outcomes. The purpose of the study is to assess the nutritional status of older adult individuals admitted to a general hospital and examine its correlation with socio-health and demographic variables. Methods: The study included 239 individuals aged 70 and above, employing a cross-sectional descriptive observational approach with a convenience sampling method. Sociodemographic information was gathered, and variables such as cognitive impairment, functional capacity, comorbidities, medication consumption, and nutritional status were evaluated. Statistical analysis involved descriptive calculations, bivariate analysis, and multivariate analysis, utilizing binary logistic regression. Results: Approximately half of the sample were at risk of malnutrition, with a more notable prevalence among women. Factors such as age (OR = 1.04), cognitive impairment (OR = 1.06), functional dependence (OR = 0.96), and comorbidities (OR = 1.08) were linked to an elevated risk of malnutrition. In our regression model, age, cognitive impairment, and drug consumption emerged as significant predictors of malnutrition risk. Conclusions: Individuals aged 70 and above have a notably high prevalence of malnutrition risk, particularly among those experiencing functional dependence and cognitive impairment. In our sample, cognitive impairment in older adults, coupled with above-median drug consumption, emerges as the primary predictor for malnutrition risk.
Modelling long term effects of cropping and managements systems on soil organic matter, C/N dynamics and crop growth

While simulation of cropping systems over a few years might reflect well the short term effects of management and cultivation, long term effects on soil properties and their consequences for crop growth and matter fluxes are not captured. Especially the effect on soil carbon sequestration/depletion is addressed by this task. Simulations of an ensemble of crop models are performed as transient runs over a period of 120 year using observed weather from three stations in Czech Republic (1961-2010) and transient long time climate change scenarios (2011-2080) from five GCM of the CMIP5 ensemble to assess the effect of different cropping and management systems on carbon sequestration, matter fluxes and crop production in an integrative way. Two cropping systems are regarded comprising two times winter wheat, silage maize, spring barley and oilseed rape. Crop rotations differ regarding their organic input from crop residues, nitrogen fertilization and implementation of catch crops. Models are applied for two soil types with different water holding capacity. Cultivation and nutrient management is adapted using management rules related to weather and soil conditions. Data of phenology and crop yield from the region of the regarded crops were provided to calibrate the models for crops of the rotations. Twelve models were calibrated in this first step. For the transient long term runs results of four models were submitted so far. Outputs are crop yields, nitrogen uptake, soil water and mineral nitrogen contents, as well as water and nitrogen fluxes to the atmosphere and groundwater. Changes in the carbon stocks and the consequences for nitrogen mineralisation, N fertilization and emissions also considered.
The Relevance of Reperfusion Stroke Therapy for miR-9-3p and miR-9-5p Expression in Acute Stroke—A Preliminary Study

Reperfusion stroke therapy is a modern treatment that involves thrombolysis and the mechanical removal of thrombus from the extracranial and/or cerebral arteries, thereby increasing penumbra reperfusion. After reperfusion therapy, 46% of patients are able to live independently 3 months after stroke onset. MicroRNAs (miRNAs) are essential regulators in the development of cerebral ischemia/reperfusion injury and the efficacy of the applied treatment. The first aim of this study was to examine the change in serum miRNA levels via next-generation sequencing (NGS) 10 days after the onset of acute stroke and reperfusion treatment. Next, the predictive values of the bioinformatics analysis of miRNA gene targets for the assessment of brain ischemic response to reperfusion treatment were explored. Human serum samples were collected from patients on days 1 and 10 after stroke onset and reperfusion treatment. The samples were subjected to NGS and then validated using qRT-PCR. Differentially expressed miRNAs (DEmiRNAs) were used for enrichment analysis. Hsa-miR-9-3p and hsa-miR-9-5p expression were downregulated on day 10 compared to reperfusion treatment on day 1 after stroke. The functional analysis of miRNA target genes revealed a strong association between the identified miRNA and stroke-related biological processes related to neuroregeneration signaling pathways. Hsa-miR-9-3p and hsa-miR-9-5p are potential candidates for the further exploration of reperfusion treatment efficacy in stroke patients.
Low Adherence to Mediterranean Diet Characterizes Metabolic Patients with Gastrointestinal Cancer

Background. Gastrointestinal (GI) cancers are one of the most relevant causes of death globally, frequently associated with poor dietary patterns. The Mediterranean Diet (MedDiet) contributes to cancer prevention. To assess adherence to MedDiet, our research group validated a new score, the Chrono Med Diet Score (CMDS), that captures increased visceral adiposity. Methods. We enrolled 401 subjects who underwent an evaluation for metabolic diseases and specific screening procedures according to current guidelines and were asked to answer CMDS. A total of 71 new cancer cases were recorded, including 40 GI and 31 non-gastrointestinal (NON-GI) cancers. Results. We found that CMDS was reduced in subjects who were diagnosed with cancers. Patients who reported a CMDS score of 12 or less had an over three times increased risk of being diagnosed with GI cancers and presented increased waist circumference and triglycerides and reduced HDL cholesterol compared to adherent subjects. Conclusions. Low CMDS values capture the risk for cancer diagnosis, especially for GI cancers. Thus, CMDS, along with waist circumference, can be considered as a bona fide marker for increased risk of cancer, requiring anticipated screening procedures for the detection of premalignant and early stage GI cancers in patients with low adherence to MedDiet.
Evaluation of Radiation Exposure and Influential Factors in Cone-Beam Computed Tomography (CBCT) of the Head and Abdomen during Interventional Procedures

Cone-beam computed tomography (CBCT) is a widely used imaging technique in interventional radiology. Although CBCT offers great advantages in terms of improving comprehension of complex angioarchitectures and guiding therapeutic decisions, its additional degree of radiation exposure has also aroused considerable concern. In this study, we aimed to assess radiation exposure and its influential factors in patients undergoing CBCT scans of the head and abdomen during interventional procedures. A total of 752 patients were included in this retrospective study. Dose area product (DAP) and reference air kerma (RAK) were used as measures of patient dose. The results showed that the median values of DAP were 53.8 (50.5–64.4) Gy⋅cm2 for head CBCT and 47.4 (39.6–54.3) Gy⋅cm2 for that of the abdomen. Male gender and body mass index (BMI) were characterized by increased DAP and RAK values in both head and abdominal CBCT scans. Larger FOV size was associated with a higher DAP but a lower RAK value, especially in head CBCT scans. Exposure parameters under automatic exposure control (AEC) also varied according to patient BMI and gender. In conclusion, the patients received slightly higher radiation doses from head CBCT scans than from those applied to the abdomen. BMI, gender, and FOV size were the key factors that influenced the radiation dose administered to the patients during CBCT scans. Our results may help to define and minimize patients’ exposure to radiation.
Physically Informed Deep Learning Technique for Estimating Blood Flow Parameters in Four-Vessel Junction after the Fontan Procedure

This study introduces an innovative approach leveraging physics-informed neural networks (PINNs) for the efficient computation of blood flows at the boundaries of a four-vessel junction formed by a Fontan procedure. The methodology incorporates a 3D mesh generation technique based on the parameterization of the junction’s geometry, coupled with an advanced physically regularized neural network architecture. Synthetic datasets are generated through stationary 3D Navier–Stokes simulations within immobile boundaries, offering a precise alternative to resource-intensive computations. A comparative analysis of standard grid sampling and Latin hypercube sampling data generation methods is conducted, resulting in datasets comprising 1.1×104 and 5×103 samples, respectively. The following two families of feed-forward neural networks (FFNNs) are then compared: the conventional “black-box” approach using mean squared error (MSE) and a physically informed FFNN employing a physically regularized loss function (PRLF), incorporating mass conservation law. The study demonstrates that combining PRLF with Latin hypercube sampling enables the rapid minimization of relative error (RE) when using a smaller dataset, achieving a relative error value of 6% on the test set. This approach offers a viable alternative to resource-intensive simulations, showcasing potential applications in patient-specific 1D network models of hemodynamics.
Çevrimiçi Tartışma Forumuna Yapılan Katkı ve Katılımlarının Sosyal Buradalığa Etkisinin İncelenmesi

İnsan olmanın doğası gereği birbirleri ile iletişim kurma ve sosyalleşme ihtiyacı, öğrenme ortamına da yansımaktadır. Bu doğrultuda yüz yüze sınıf ortamlarında doğal bir şekilde meydana gelen sosyal ortamların, özellikle asenkron çevrimiçi ortamlarda oluşturulması için fazladan çaba harcanması gerektirmektedir. Bu çalışmada özel bir üniversitede bölüm ayrımı olmaksızın tüm öğrencilerin alabildiği asenkron olarak yürütülen bir kitle dersinde, sosyal buradalığın artırılması ve çevrimiçi tartışma forumuna yapılan katkı ve katılımların sosyal buradalığa etkisi araştırılmıştır. Bu doğrultuda, karma araştırma yöntemi kullanılmıştır. Çalışmanın veri toplama araçlarını sosyal buradalık ölçeği ve çevrimiçi ortamlarda katkı ve katılım ölçeği oluşturmaktadır. Veri analizinden elde edilen bulgular doğrultusunda öğrencilerin cinsiyet ve fakültelerine göre sosyal buradalıklarında bir farklılık olmadığı, sosyal buradalık ile dönem sonu not ortalamaları arasında istatistiksel olarak bir ilişki olmadığı, tartışma forumuna yapılan katkı ve katılım ile sosyal buradalıkları arasında bir istatistiksel olarak ilişki olmadığı sonucuna ulaşılmıştır. Bununla birlikte sosyal buradalığın öğrenme ortamındaki memnuniyeti artırdığı bulgusu sonucu elde edilmiştir.
Efficiency of Identification of Blackcurrant Powders Using Classifier Ensembles

In the modern times of technological development, it is important to select adequate methods to support various food and industrial problems, including innovative techniques with the help of artificial intelligence (AI). Effective analysis and the speed of algorithm implementation are key points in assessing the quality of food products. Non-invasive solutions are being sought to achieve high accuracy in the classification and evaluation of various food products. This paper presents various machine learning algorithm architectures to evaluate the efficiency of identifying blackcurrant powders (i.e., blackcurrant concentrate with a density of 67 °Brix and a color coefficient of 2.352 (E520/E420) in combination with the selected carrier) based on information encoded in microscopic images acquired via scanning electron microscopy (SEM). Recognition of blackcurrant powders was performed using texture feature extraction from images aided by the gray-level co-occurrence matrix (GLCM). It was evaluated for quality using individual single classifiers and a metaclassifier based on metrics such as accuracy, precision, recall, and F1-score. The research showed that the metaclassifier, as well as a single random forest (RF) classifier most effectively identified blackcurrant powders based on image texture features. This indicates that ensembles of classifiers in machine learning is an alternative approach to demonstrate better performance than the existing traditional solutions with single neural models. In the future, such solutions could be an important tool to support the assessment of the quality of food products in real time. Moreover, ensembles of classifiers can be used for faster analysis to determine the selection of an adequate machine learning algorithm for a given problem.
Integrated Strategies for Aedes aegypti Control Applied to Individual Houses: An Approach to Mitigate Vectorial Arbovirus Transmission

Aedes aegypti and Culex quinquefasciatus mosquitoes are vectors of different arboviruses that cause a large burden of disease in humans worldwide. A key step towards reducing the impact of arboviruses on humans can be achieved through integrated mosquito surveillance and control approaches. We carried out an integrated approach of mosquito surveillance and control actions to reduce populations of these insects along with a viral surveillance in a neighborhood of Recife (Northeastern Brazil) with high mosquito densities and arbovirus transmission. The actions were carried out in 40 different houses in the Nova Descoberta neighborhood. The area was divided into two groups, the control group using tools to monitor the mosquito density (1 OVT; 1 Double BR-ovt; monthly capture of alates) and the experimental group with control actions using surveillance tools in an intensified way (2 OVTs; 2 Double BR-ovts; fortnightly capture of alates; toxic baits). We evaluated the study’s impact on the mosquito density via the Egg Density (ED) and Adult Density (AD) over a period of 12 cycles of 28 days each. The collected adult mosquitoes were processed via RT-qPCR for DENV, CHIKV and ZIKV and, subsequently, the Minimum Infection Rate (MIR) was calculated. After 12 cycles, we observed a 91% and 99% reduction in Aedes ED and AD in the monitored properties, as well as a 76% reduction in the AD of Cx. quinquefasciatus in the same properties. Moreover, we detected circulating arboviruses (DENV and ZIKV) in 19.52% of captured adult mosquitoes. We show that enhancing entomological surveillance tools can aid in the early detection of possible risk areas based on vector mosquito population numbers. Additionally, the detection of important arboviruses such as ZIKV and DENV raises awareness and allows for a better selection of risk areas and silent virus spread. It offers supplementary information for guiding emergency mosquito control measures in the target area. The goal is to minimize human–vector interactions and, subsequently, to lower the risk of transmitting circulating arboviruses.