Miracle-dz/UniFAR-FTUs huggingface dataset:
Prediction of Flow Field and Heat Transfer in Channels in Parallel-Mode Rotation at High Rotation and Buoyancy Numbers

This study attempts to understand one of the most fundamental and challenging problems in fluid flow and heat transfer for rotating machines. The study focuses on electric generators for high energy density applications, which employ rotating cooling channels so that materials do not fail under high temperature and high stress environment. Prediction of fluid flow and heat transfer inside internal cooling channels that rotate at high rotation number (Ro) and high wall heat flux similar to those that are existing in generator rotors is the main focus of this study. Rotation, buoyancy and boundary conditions affect the flow inside theses channels. Increasing Ro is one approach to increase heat transfer rate. A fully computational approach is employed in this study. RSM with enhanced near wall treatment is validated against available experimental data (which are primarily at low rotation and buoyancy numbers). The model was then used for cases with high Ro (as much as 0.35) and high wall heat flux. Particular attention is given to how turbulence intensity, Reynolds stresses and transport are affected by Coriolis and buoyancy/centrifugal forces caused by high levels of Ro and wall heat flux. Variations of flow total pressure along the rotating channel are also predicted. The results obtained are explained in view of physical interpretation of Coriolis and centrifugal forces. The results show that the heat transfer rate increases with Ro on three surfaces of the square channel and decreases on the inner surface (that is the one closest to the axis of rotation). However, the four-side average Nu increases with Ro. Increasing wall heat flux at high Ro does not necessarily increase Nu on walls although higher turbulence activity is observed.
Controllable Codoping of Nitrogen and Sulfur in Graphene for Highly Efficient Li-Oxygen Batteries and Direct Methanol Fuel Cells

Lithium–oxygen batteries (LOBs) and direct methanol fuel cells (DMFCs) are both attractive technologies for the development of future energy storage and conversion devices, while the lack of highly active electrocatalysts has largely hampered their large-scale commercial applications. In the present work, we have demonstrated the controllable synthesis of nitrogen and sulfur codoped graphene (NS-G) nanosheets via a simple and cost-effective approach. Owing to their distinctive structural advantages, such as large surface areas, good flexibility, coexistence of N and S atoms with tunable doping contents and positions, and high electrical conductivity, the obtained NS-G sheets exhibit low discharge–recharge voltage gaps and high round-trip efficiencies as well as exceptional rate capability when used as cathode materials for LOBs. Furthermore, the NS-G layers are also identified to be an ideal substrate for the decoration of ultrafine Pt nanoparticles. Benefiting from the synergetic effects, exceptional ele.
Effect of industrial waste with different levels of nitrogen on nutrient uptake and yield of maize, Zea mays

A field experiment was conducted to study the nutrient uptake, quality and yield of maize (Co1) under paper mill sludge with inorganic fertilizers at different rates of nitrogen at Agricultural College and Research Institute, Madurai during Rabi 2008. Based on the field experiment it was found that 75 per cent recommended nitrogen and full dose of phosphorus and potassium with paper mill sludge recorded higher nutrient uptake at different growth stages of maize viz knee-high, tasseling and harvest. Nutrient uptake was higher at harvest stage. Application of paper mill sludge with inorganic nitrogen increased the quality of maize grain significantly over absolute control. Higher yield of grain (4.9 tons/ha) and straw (9.0 tons/ha) was received in the treatment T5 which received 75 per cent inorganic N with paper mill sludge at the rate of 5 tons/ha. The quality characters of the maize grain viz crude protein, total sugars and starch were also increased due to the application of paper mill sludge with inorganic fertilizer.
An Intelligent Monitoring System for Assessing Bee Hive Health

Up to one third of the global food production depends on the pollination of honey bees, making them vital. This study defines a methodology to create a bee hive health monitoring system through image processing techniques. The approach consists of two models, where one performs the detection of bees in an image and the other classifies the detected bee’s health. The main contribution of the defined methodology is the increased efficacy of the models, whilst maintaining the same efficiency found in the state of the art. Two databases were used to create models based on Convolutional Neural Network (CNN). The best results consist of 95% accuracy for health classification of a bee and 82% accuracy in detecting the presence of bees in an image, higher than those found in the state-of-the-art.
Electrical Energy Demand Forecasting Using Artificial Neural Network

The continuous development of technology, population growth, and increased economic comfort enlarge the energy demand. The welfare and economy of both the producer and the consumer need to meet the increasing energy need by making investments and planning with the correct predictions. In this study, the electricity demand forecast is made with the help of Artificial Neural Networks (ANN). In order to increase the accuracy, educational data sets were created with historical electricity consumption data, taking into account social, economic, technological, and demographic factors. The forecasting method developed was applied to Düzce, Turkey's developing provinces. The 15-year electrical energy demand of the region was estimated with the ANN-based method. The results have been evaluated in detail.
Output Feedback Reachability of Controlled–Observable States for Nondeterministic Finite–State Systems

In this letter control design of nondeterministic finite state systems with reachability specifications is addressed. The class of controllers we use is rather general and combines feedforward and output feedback schemes. The proposed controller allows not only the state of the system to reach the desired target set but also the identification of which state of the target set has been reached. Necessary and sufficient conditions are derived for the control problem to admit a solution and a controller is designed. The solution to the investigated problem has important implications in the context of recovery control and symbolic control design of nonlinear and hybrid systems, as discussed also through some examples.
ZnO Nanorods and Their Modification with Au Nanoparticles for UV-light Activated Gas Sensing

Aerosol assisted chemical vapour deposited ZnO nanostructured films integrated into Si-based transducing platforms are modified with preformed Au nanoparticles (NPs) via impregnation. The morphological, structural, and chemical characterization of these films using different characterisation techniques shows the incorporation of well-distributed and stable Au nanoparticles (NPs) at the surface of ZnO. Photoactivated gas sensing tests at room temperature (RT) demonstrate enhanced sensitivity and better speed of response for the Au modified ZnO films (AuZn) providing 3 times higher response to ethanol and acetone as compared to the non-modified ZnO films (Zn).
Practice and Experience in using Parallel and Scalable Machine Learning with Heterogenous Modular Supercomputing Architectures

We observe a continuously increased use of Deep Learning (DL) as a specific type of Machine Learning (ML) for data-intensive problems (i.e., ’big data’) that requires powerful computing resources with equally increasing performance. Consequently, innovative heterogeneous High-Performance Computing (HPC) systems based on multi-core CPUs and many-core GPUs require an architectural design that addresses end user communities’ requirements that take advantage of ML and DL. Still the workloads of end user communities of the simulation sciences (e.g., using numerical methods based on known physical laws) needs to be equally supported in those architectures. This paper offers insights into the Modular Supercomputer Architecture (MSA) developed in the Dynamic Exascale Entry Platform (DEEP) series of projects to address the requirements of both simulation sciences and data-intensive sciences such as High Performance Data Analytics (HPDA). It shares insights into implementing the MSA in the Jülich Supercomputing Centre (JSC) hosting Europe No. 1 Supercomputer Jülich Wizard for European Leadership Science (JUWELS). We augment the technical findings with experience and lessons learned from two application communities case studies (i.e., remote sensing and health sciences) using the MSA with JUWELS and the DEEP systems in practice. Thus, the paper provides details into specific MSA design elements that enable significant performance improvements of ML and DL algorithms. While this paper focuses on MSA-based HPC systems and application experience, we are not losing sight of advances in Cloud Computing (CC) and Quantum Computing (QC) relevant for ML and DL.
Trends on Natural Organic Matter in Drinking Water Sources and its Treatment

Natural organic matter (NOM) can be defined as a mixture of complex organic compounds that universally present in natural waters. High NOM content in water strongly impact the water quality and treatment in several ways (e.g. causing colour and odour, filter fouling and increase coagulant dose). Besides that, NOM also acts as the main precursor to disinfectant by products (DBPs) produce from the reaction of NOM and disinfectant during water treatment. DBPs are known to be carcinogenic to human and animals. The formation of DBPs is depending on NOM characteristics. Generally, NOM characteristics are differ according to the water sources. In order to understand NOM properties, NOM fractionation is required and therefore different approaches have been proposed for its characterization. Meanwhile several methods of treatment have been developed to remove or reduce the amount of NOM in drinking water sources to prevent DBPs formation. The aim of this paper is to review and discuss the properties and available treatment techniques for NOM.
Relaxed Nonquadratic Stabilization of Discrete-Time Takagi-Sugeno Systems via An Efficient Switching Law

This paper investigates the study on further enhancing the stabilization of discrete-time Takagi-Sugeno(T-S) systems by proposing a new fuzzy switching controller. Compared with the recent result reported in the literature, the proposed fuzzy switching controller is with a richer architecture on normalized fuzzy weighting functions(NFWFs) and a much more efficient switching law is developed for on-line activating the best pair of control gain matrices at each sampling instant. As a result, a larger system controllable interval can be obtained and it means that the conservatism of the previous result can be further reduced. In the end, some numerical simulations are provided to verify the effectiveness of the method developed in this paper. Furthermore, the proposed fuzzy switching controller is illustrated to be capable of providing much better control performance than the recent one for the same controllable plant.
Pattern-based Fall Prediction using Hospital Clinical Notes

Falls are considered as the second most leading cause of lethal and non-lethal injuries among the aging population. The fall prediction enables to reduce the health care cost and the negative impacts related to fall such as loss of independence. The objective of this paper is to propose an approach to select discriminative patterns from clinical notes. The selected discriminative patterns are used as features to predict falls among elderly people based on their clinical data. A collection of 12,911 labeled clinical notes of patients aged $\ge 65$ was selected from a medical database, named Medical Information Mart for Intensive Care (MIMIC-III), to create the corpus. The classification algorithms, Logistic Regression, Support Vector Machine and Random Forest are used to evaluate the effectiveness of the selected discriminative patterns for predicting falls. The Support Vector Machine classification algorithm gave the best results. The prediction accuracy of using the proposed discriminative patterns is higher than the baseline approaches when predicting ‘Fall” using the clinical notes. The results of this research suggest that the proposed discriminative pattern mining approach was able to generate a set of interesting patterns that were able to distinguish between fall and not fall clinical notes.
Effect of Dataset Size on Deep Learning in Voice Recognition

Voice recognition systems mostly suffer from environmental effects and accent differences. Therefore, studies on speech recognition have begun to be examined using deep learning which is a method known to be successful in speech recognition and classification. In this study, 12 different voice commands are defined using convolutional neural network, which is a deep learning structure. In this study, the effect of dataset size on test and recognition accuracy was investigated. In addition, a different dataset which was prepared from the records of people whose main language is Turkish to investigate the effect of different accents on both test and recognition accuracy. In the experiments when the test dataset including native-speaker voice records is used, the test accuracy was obtained as 94.64% for large dataset and 64.81% for small dataset. On the other hand when the test dataset including foreigner’s voice records the test accuracy reduced to 63.29% for large and 33.18% for small dataset.
Improving Cryptanalytic Applications with Stochastic Runtimes on GPUs

We investigate cryptanalytic applications comprised of many independent tasks that exhibit a stochastic runtime distribution. We compare four algorithms for executing such applications on GPUs. We demonstrate that for different distributions, problem sizes, and platforms the best strategy varies. We support our analytic results by extensive experiments on two different GPUs, from different sides of the performance spectrum: A high performance GPU (Nvidia Volta) and an energy saving system on chip (Jetson Nano).
Temporal and Spatial Parameters Dynamics of SSVEP During Long-Term Low-Frequency LED-Stimulation: Pilot Study

Brain-computer interface technology is becoming increasingly common. However, there is a problem of stability of such systems during long-term use. The most stable highspeed interfaces include steady-state visual evoked potential interfaces. However, despite the relative stability of the interfaces, they are affected by the habituation effect and, on the other hand, by the steady-state visual evoked potentials amplification. This study proposes to evaluate the steady-state visual evoked potentials amplitude dynamics during prolonged stimulation with a flash pattern of 50 s or 100 s with frequencies of 9, 11, 13, and 15 Hz on healthy subjects. Based on the results obtained, it was concluded that there was no pronounced enhancement or suppression of steady-state visual evoked potentials relative to the alpha-rhythm over time. However, the graphs in the time-frequency area shows the steady-state visual evoked potentials amplitude bursts at short intervals of time. Topographic maps of the steady-state visual evoked potentials distribution on the scalp surface show the activity occurring in the parietal and central regions at different times. The application of the steady-state visual evoked potentials sources spatial separation method using common spatial pattern filtering is also demonstrated.
Sentiment Analysis of Meeting Room

In the last decade, enormous data are being shared throughout the world. In many of today’s big data world, the companies are trying to use some sentiment or emotion analysis techniques to analyze their customer moods and improve their efficiencies according to sentiments. As a different application we focused on the sentiment analysis of closed places in this research. It requires low noise environments obviously. Otherwise, system may be affected by distortion, and it may be contradiction for multiple sentiments. In this regard, an artificial neural network using meaningful voice features are proposed. Ryerson Audio Visual Database of Emotional Speech and Song (RAVDESS) dataset was used in this research. Normalization was applied to data. The artificial neural network was fed by training data and a classifier model was created. Estimation was made using the test data part and it was seen that accuracy of model is about 85%.
Spectroscopic Characterization of TiO2 Polymorphs in Wastewater Treatment and Sediment Samples

The reactivity and toxicity of nano-TiO2 are controlled to a large extent by its phase composition. Although numerous studies have focused on the environmental impacts of nano-TiO2, the phase composition of TiO2 in environmental samples remains unknown. Using Ti K-edge X-ray absorption near-edge structure (XANES) spectroscopy, we reveal the average phase composition of TiO2 present at various stages of a full-scale wastewater treatment plant (WWTP) and in sediments collected both upstream and downstream of the WWTP discharge point. The XANES spectra provide direct evidence of the presence of TiO2 in these samples, in particular the coexistence of both anatase and rutile phases. Spectral decomposition using least-squares linear combination fitting shows that Ti in the WWTP samples contained ∼30% anatase, 60% rutile, and 10% ilmenite. This composition was stable over a two-month sampling period (nine sampling events) and did not change among primary sludge, activated sludge, and the final effluent. In contr.
Characteristic of Noni (Morinda citrifolia Linn) Fruit Juice Produced by Fermentation

Fermentation is one of the methods to get noni juice. In this research, noni is fermented in hanged system and submerged system for 1, 2, 3, and 4 weeks. The result showed the longer fermentation increased pH, alcohol content, turbidity, viscosity, microbial content, and decrease ascorbic acid and acid content, soluble solid content, color and flavor acceptance. Differennt system fermentation affected percent yield, soluble solid content, turbidity of juice, panels opinion to color significantly. Noni juice of submerged system has higher percent yield, higher soluble solid content, turbidity, but provided lighter color than noni juice of hanged system. Juice of submerged system is better than hanged system. Juice from three weeks fermentation indicated changes of metabolism activity which is shown by some characteristic changes. Fruit juice made by submerged fermentation system had more diverse compounds than fruit juice without fermentation. Key words : Noni juice, fermentation.
Impact of malaria co-infection on leukocyte indices of tuberculosis-infected participants at pretreatment, intensive, and continuation phase anti-tuberculosis therapy

BACKGROUND: Hematological changes involving all blood cells are some of the most common complications in both tuberculosis (TB) and malaria infection. The changes induced by malaria infection are diverse, and the first line anti-TB treatment regimen which involves two phases may alter these changes in TB participants co-infected with malaria (TB/MP). OBJECTIVE: In this study, we aimed to ascertain the impact of malaria co-infection on leukocyte indices of TB-infected participants at pre-treatment, intensive and continuation phase therapy. MATERIALS AND METHODS: In this cross-sectional study, 180 participants were recruited comprising; 60 (35 TB and 25 TB malaria) participants before treatment, sixty (36 TB and 24 TB-Malaria) participants after intensive phase treatment and sixty (27 TB and 33 TB-Malaria) participants after continuation phase therapy. Whole blood was used for the measurement of total (total white blood cell [TWBC]) and differential white cell count, Platelet count, and packed cell volume (PCV). RESULTS: Before initiation of treatment, TWBC, neutrophil, lymphocyte, platelet count, and neutrophil-lymphocyte ratio were significantly reduced (P = 0.041, 0.022, 0.046, and 0.026, respectively), whereas eosinophil count was significantly increased in TB/Malaria participants compared to TB participants (P = 0.043). There was no significant change in these parameters after intensive phase treatment (P > 0.05). However, after continuation phase treatment, PCV was significantly reduced, while eosinophil was significantly increased in TB/Malaria participants compared with TB participants (P = 0.046 and 0.045, respectively). CONCLUSION: Malaria co-infection induces the significant reduction in leukocyte indices of TB patients at pretreatment but not at the intensive and continuation phase anti-TB therapy except eosinophils count which was increased before treatment and continuation phase treatment.
Anesthesia for thoracic surgery in infants and children

The management of infants and children presenting for thoracic surgery poses a variety of challenges for anesthesiologists. A thorough understanding of the implications of developmental changes in cardiopulmonary anatomy and physiology, associated comorbid conditions, and the proposed surgical intervention is essential in order to provide safe and effective clinical care. This narrative review discusses the perioperative anesthetic management of pediatric patients undergoing noncardiac thoracic surgery, beginning with the preoperative assessment. The considerations for the implementation and management of one-lung ventilation (OLV) will be reviewed, and as will the anesthetic implications of different surgical procedures including bronchoscopy, mediastinoscopy, thoracotomy, and thoracoscopy. We will also discuss pediatric-specific disease processes presenting in neonates, infants, and children, with an emphasis on those with unique impact on anesthetic management.
Anesthesia for robotic thoracic surgery

The management of the robotic thoracic surgical patient requires the knowledge of minimally invasive surgery techniques involving the chest. Over the past decade, robotic-assisted thoracic surgery has grown, and, in the future, it will take an important place in the treatment of complex thoracic pathologies. The enhanced dexterity and three-dimensional visualization make it possible to do this in the small space of the thoracic cavity. Familiarity with the robotic surgical system by the anesthesiologists is mandatory. Management of a long period of one-lung ventilation with a left-sided double-lumen endotracheal tube or an independent bronchial blocker is required, along with flexible fiberoptic bronchoscopy techniques (best continuous monitoring). Correct patient positioning and prevention of complications such as eye or nerve or crashing injuries while the robotic system is used is mandatory. Recognition of the hemodynamic effects of carbon dioxide during insufflation in the chest is required. Cost is higher and outcome is not yet demonstrated to be better as compared to video-assisted thoracic surgery. The possibility for conversion to open thoracotomy should also be kept in mind. Teamwork is mandatory, as well as good communication between all the actors of the operating theatre.