Miracle-dz/UniFAR-FTUs huggingface dataset:
Selection of Suitable Type of Nozzles for Development of Electrostatic Induction Nozzle

The electrostatic induction mechanism, which superimposes charges on pesticide spray droplets, creates an impact on deposition and wraparound effect on leaf surfaces Smaller droplets have a higher capability to charge accumulation over the surface of the droplet as compared with larger droplets. This paper studied the effect of nozzle type (flat fan, hollow cone, and full cone nozzle), orifice area (1 and 1.5 mm2), and operating pressure (3-5kg cm-2) on spray droplet characteristics on soil bin. Water-sensitive papers were analysed by image analysis software to get the droplet characteristics. The smallest droplets of a hollow cone, flat fan, and full cone were 130, 142, and 279.76 µm respectively produced at 5kg cm-2 and orifice opening 1 mm2. With an increase of pressure droplet size and relative span was decreased for all selective nozzle. From the selected nozzles, the lowest relative span of 0.89 was found with a hollow cone nozzle at 5 kg cm-2 pressure and orifice size of 1 mm2. Among all the selected nozzles hollow cone nozzle produced the smallest droplet sizes and lowest relative span for all selected parameters.
The Effects of Foliar Vermicompost Applications on Yield and Some Yield Components in Triticale Varieties

Vermicompost fertilizers have started to attract attention in agriculture last years. Various liquid vermicompost fertilizers are produced and used in agriculture and some of them known as vermiliquer, vermi leachate, vermi tea or vermicast with little differences. This study is aimed to determine the effects of foliar application of liquid vermicompost fertilizer on yield and some yield components on some Turkey triticale varieties under Central Anatolian conditions in Kırıkkale province during the growing period 2014-15 and 2015-16. Six different (control, 75, 150, 250, 350 and 450 cc da-1) liquid vermicompost fertilizer doses were applied by the foliar spraying method at the beginning and end of March in both years to the five Turkey triticale varieties (Alperbey, Mikham 2002, Tatlıcak 97, Karma 2000 and Ümranhanım). According to the results, plant height, grain number and grain yield in spike, grain yield per decare, harvest index and protein content results were found to be significant in fertilizer × variety × year interaction. There is also a statistically significant difference between thousand-grain weight and the variety x fertilizer doses interaction, and between varieties and fertilizer doses in terms of spike length. The positive effects of liquid vermicompost fertilizer doses, except 450 cc da-1, were determined in varieties. It was observed that a satisfactory yield can be obtained from 250 cc da-1 in the absence of nitrogen fertilizers as the top fertilizer. It could be deduced that 250-350 cc da-1 doses of vermicompost fertilizer can be applied in triticale based on varying ecological conditions and varieties in Central Anatolian ecological conditions.
Predicting home discharge after inpatient rehabilitation of stroke patients with aphasia

ABSTRACT The early identification of the discharge setting from Inpatient Rehabilitation Facilities is a primary goal in stroke-related research because of its clinical and socio-economic relevance. Several features have been identified as significant predictors of the discharge setting. Within cognitive deficits, aphasia is known to be a common and disabling condition that could influence rehabilitation outcome. However, it is often set as an exclusion criterion in stroke research. This study aims to investigate the predictive power of clinical variables, in particular specific language disturbances and nonlinguistic cognitive deficits, for discharge setting in post-acute stroke patients with aphasia after intensive multidisciplinary rehabilitation. In a sample of 158 patients, demographic, motor, language, and nonverbal cognitive data were retrospectively considered for the prediction of the discharge to home vs. another institutional setting. Univariate analysis identified relevant differences between groups and the significant variables were included in a logistic regression model. The results showed that better functional motor status, absence of dysphagia and unimpaired nonlinguistic cognitive profile independently predict the discharge to home. In particular, nonverbal cognitive functioning seemed to be specifically relevant within the aphasic population. The findings could be helpful for setting up the rehabilitation priorities and an adequate discharge arrangement.
Comparative analysis of Tesseract and Google Cloud Vision for Thai vehicle registration certificate

Optical character recognition (OCR) is a technology to digitize a paper-based document to digital form. This research studies the extraction of the characters from a Thai vehicle registration certificate via a Google Cloud Vision API and a Tesseract OCR. The recognition performance of both OCR APIs is also examined. The 84 color image files comprised three image sizes/resolutions and five image characteristics. For suitable image type comparison, the greyscale and binary image are converted from color images. Furthermore, the three pre-processing techniques, sharpening, contrast adjustment, and brightness adjustment, are also applied to enhance the quality of image before applying the two OCR APIs. The recognition performance was evaluated in terms of accuracy and readability. The results showed that the Google Cloud Vision API works well for the Thai vehicle registration certificate with an accuracy of 84.43%, whereas the Tesseract OCR showed an accuracy of 47.02%. The highest accuracy came from the color image with 1024×768 px, 300dpi, and using sharpening and brightness adjustment as pre-processing techniques. In terms of readability, the Google Cloud Vision API has more readability than the Tesseract. The proposed conditions facilitate the possibility of the implementation for Thai vehicle registration certificate recognition system.
Dynamic Measures for Performance Assessment in Complex Environments

The command and control environment is a dynamic and complex setting with complicated technical systems where teams of operators interact to reach shared goals. This paper presents a study where a quasi-dynamic evaluation technique was used to assess performance. Nine operators from the Swedish Air Force participated in a peace support operation scenario in an operational setting. Data was collected by a repeated measurement technique to measure performance, workload, situation awareness, complexity, team coordination and sharing of information. A causal model was derived from the data which shows the relations between mental workload, individual performance and team performance. An efficiency index was calculated showing that the operators performed better over time, even though complexity increased. The use of the repeated measurement technique proved to be successful for measuring complex and dynamic events.
Soft Actor-Critic Deep Reinforcement Learning for Fault Tolerant Flight Control

Fault-tolerant flight control faces challenges, as developing a model-based controller for each unexpected failure is unrealistic, and online learning methods can handle limited system complexity due to their low sample efficiency. In this research, a model-free coupled-dynamics flight controller for a jet aircraft able to withstand multiple failure types is proposed. An offline trained cascaded Soft Actor-Critic Deep Reinforcement Learning controller is successful on highly coupled maneuvers, including a coordinated 40 degree bank climbing turn with a normalized Mean Absolute Error of 2.64%. The controller is robust to six failure cases, including the rudder jammed at -15 deg, the aileron effectiveness reduced by 70%, a structural failure, icing and a backward c.g. shift as the response is stable and the climbing turn is completed successfully. Robustness to biased sensor noise, atmospheric disturbances, and to varying initial flight conditions and reference signal shapes is also demonstrated.
Development of an Electrostatic Generator that Harnesses the Motion of a Living Body. Use of a Honeycomb-Type Variable Capacitor.

We aimed for permanently supplying the electrical energy to an apparatus for in vivo use such as a cardiac pacemaker and proposed a totally implantable electrostatic generator that harnesses the motion of a living body. The generating system consists of a battery for supplying electric charge, a variable capacitor for energy conversion, a capacitor of energy storage and two rectifiers. We made a honeycomb type variable capacitor whose capacitance is varied from 44 to 220 [nF]. It was possible to generate maximum electrical energy of about [μJ/cycle]. We found that the electrical energy for driving a cadinc pacemaker can be generated by using the honeycomb-type capacitor with higher capacitance.
The use of EEG signals for biometric person recognition

This work is devoted to investigating EEG-based biometric recognition systems. One potential advantage of using EEG signals for person recognition is the difficulty in generating artificial signals with biometric characteristics, thus making the spoofing of EEG-based biometric systems a challenging task. However, more works needs to be done to overcome certain drawbacks that currently prevent the adoption of EEG biometrics in real-life scenarios: 1) usually large number of employed sensors, 2) still relatively low recognition rates (compared with some other biometric modalities), 3) the template ageing effect. The existing shortcomings of EEG biometrics and their possible solutions are addressed from three main perspectives in the thesis: pre-processing, feature extraction and pattern classification. In pre-processing, task (stimuli) sensitivity and noise removal are investigated and discussed in separated chapters. For feature extraction, four novel features are proposed; for pattern classification, a new quality filtering method, and a novel instance-based learning algorithm are described in respective chapters. A self-collected database (Mobile Sensor Database) is employed to investigate some important biometric specified effects (e.g. the template ageing effect; using low-cost sensor for recognition). In the research for pre-processing, a training data accumulation scheme is developed, which improves the recognition performance by combining the data of different mental tasks for training; a new wavelet-based de-noising method is developed, its effectiveness in person identification is found to be considerable. Two novel features based on Empirical Mode Decomposition and Hilbert Transform are developed, which provided the best biometric performance amongst all the newly proposed features and other state-of-the-art features reported in the thesis; the other two newly developed wavelet-based features, while having slightly lower recognition accuracies, were computationally more efficient. The quality filtering algorithm is designed to employ the most informative EEG signal segments: experimental results indicate using a small subset of the available data for feature training could receive reasonable improvement in identification rate. The proposed instance-based template reconstruction learning algorithm has shown significant effectiveness when tested using both the publicly available and self-collected databases.
Fire Detection Using CNN & SVM

-Fire detection in outdoor which can't accomplished by sensor. The fire will endanger property and life of humans. An early caution is very important to prevent the damage that could occur to property and life of human. In indoor, there is possibility of false alarm detection due to change that occurs in the environment. So the best alternative solution to avoid the false alarm is the Image processing based fire detection. Using MATLAB software as a tool in image processing, we can identify whether there is fire or not. This paper proposes a classifying algorithm for identification of the input image as fire or non-fire case based on some statistical features obtained from the images. The extracted features from the input image are provided as input to the SVM to classify them. In addition to SVM, we are using CNN for further improvement because CNN is pre-trained model.
Clinical Significance of Gray to White Matter Ratio after Cardiopulmonary Resuscitation in Children

Cardiopulmonary resuscitation (CPR) successfully restores systemic circulation approximately 50% of the time; however, many successfully restored patients have severe neurologic damage. In adults, the gray matter to white matter attenuation ratio (GWR) in brain computed tomography (CT) correlates with the neurologic outcome. However, in children, the clinical significance of GWR still remains unclear. The aim of this study was to evaluate the clinical characteristics of children who underwent CPR for cardiac arrest according to the survival and to demonstrate the differentiation of grey/white matter by Hounsfield units of brain CT and to characterize the attenuations of grey and white matters. Methods: This is a retrospective single-center study. We enrolled those who underwent brain CT within 24 h after return of spontaneous circulation (ROSC) from January 2005 to June 2018. Brain CTs were taken within 24 h of ROSC. We measured the attenuation of grey and white matter in Hounsfield units and calculated GWR. They were compared with healthy controls. Patients were analyzed as follows: survivors vs. non-survivors and better neurologic outcome vs. worse neurologic outcome. Results: Among 100 pediatric patients who had CPR, 56 met inclusion criteria. There were 24 patients who survived and 32 non-survivors. Our study revealed that the incidence of seizure, duration of CPR, and instances of hypothermia were significantly different between survivors and non-survivors. In both survivors and non-survivors, the attenuation of the caudate nucleus, putamen, GWR-basal ganglia, and average GWR were significantly different from controls. In regression analyses, the medial cortex and average GWR were the significant variables to predict survival, and the receiver operating curves revealed areas under curve of 0.733 and 0.666, respectively. Also, the medial cortex 1 was the only variable that predicted the neurologic outcome. Conclusions: There was some predictive survival value of GWR and medial cortex at the centrum semiovale level in early brain CT within 24 h after cardiac arrest. Although we could not find the predictive value of GWR in the neurologic outcome of pediatric patients, we found that the absolute attenuation of the medial cortex was low in patients with worse neurologic outcomes. Further prospective, multicenter studies are needed to determine the predictive value of GWR and the medial cortex.
Examination of the Relationships between Secondary School Students’ Social Media Attitudes, School Climate Perceptions and Levels of Alienation

The aim of this research is to examine the relationships between social media attitudes, school climate perceptions and level of alienation of students studying in secondary schools. In the 2018-2019 academic year, 418 students from various secondary schools in Mardin participated in the study. Descriptive statistics and structural equality model were used in the analysis of the data. As a result of the research, it was determined that the level of alienation variable is positively affected by sharing needs, social isolation and safe learning environment; the safe learning environment variable is negatively affected by the social competence variable while it is affected positively by the social isolation variable. Besides, the safe learning environment as a mediator resulted in negative effects between the need for sharing and alienation, positive ones between social isolation and alienation, negative ones between social competence and alienation, and finally negative effects between relationships with teachers and alienation. According to the results, it can be stated that creating a supportive school climate for students in educational settings will reduce students’ tendency to use social media networks and prevent them from becoming alienated by making more friend. Keywords: alienation, social media, school climate, structural equality model
Determination of Antimicrobial and Antioxidant Activity of Alchemilla alpina L.

Alchemilla genus, which belongs to the Rosaceae family, is a medicinal plant used for various purposes among the people. Species of this genus are known in Turkish folk medicine as lion claw or hazelnut grass. Especially, they are used mainly women’s illnesses, in gastritis, anti-inflammatory, as carminative, and in the treatment of wound. Besides the antimutagenic effect of Alchemilla alpina L. , its above-ground parts are used for antimycotic purposes in the form of tea or oral care water. In this study, it has been aimed to determine the antimicrobial effect of the above-ground parts of Alchemilla alpina extracts obtained from methanol, ethanol and chloroform and the antioxidant activity of different concentrations of the extract obtained from methanol. The antimicrobial activity of methanol, ethanol and chloroform extracts of the above-ground parts of A. alpina has been determined according to disk disc diffusion method. In the results obtained have been showed that these extracts inhibited the growth of some bacteria (Staphylococcus aureus ATCC25923, Escherichia coli ATCC25322, Klebsiella pneumoniae ATCC700603, Bacillus megaterium DSM32) and yeasts (Candida albicans FMC17 and Candida glabrata ATCC66032) at different rates (8-23 mm). The antioxidant activity of different concentrations (1.25, 2.5, 5 and 10 mg/ml) of the above-ground parts of A. alpina extract obtained from methanol has been determined according to the 2,2-diphenyl-1-picrylhydrazyl (DPPH) radical scavenging capacity method. In the results obtained, it has been observed that the effect of removing DPPH radical of A. alpina increased depending on increasing concentrations.
High DC gain self-cascode structure of OTA design with bandwidth enhancement

A high DC gain self-cascode structure of operational transconductance amplifier (OTA) design with bandwidth enhancement is proposed. Based on the concept of self-cascode structure, which provides high output resistance and transconductance, the proposed OTA presents a compensation methodology to obtain better unity-gain bandwidth. The proposed OTA is compared with previous structures, with the same size of input/output transistors and compensation capacitance as well as load capacitance, and simulation results show a DC gain enhancement of >20% and 2.4 times unity-gain bandwidth at the same time, and the performances of the proposed OTA bring a better figure of merit (FOM).
Network analysis of host-pathogen protein interactions in microbe induced cardiovascular diseases

Large-scale visualization and analysis of HPIs involved in microbial CVDs can provide crucial insights into the mechanisms of pathogenicity. The comparison of CVD associated HPIs with the entire set of HPIs can identify the pathways specific to CVDs. Therefore, topological properties of HPI networks in CVDs and all pathogens was studied using Cytoscape3.5. 1. Ontology and pathway analysis were done using KOBAS 3.0. HPIs of Papilloma, Herpes, Influenza A virus as well as Yersinia pestis and Bacillus anthracis among bacteria were predominant in the whole (wHPI) and the CVD specific (cHPI) network. The central viral and secretory bacterial proteins were predicted virulent. The central viral proteins had higher number of interactions with host proteins in comparison with bacteria. Major fraction of central and essential host proteins interacts with central viral proteins. Alpha-synuclein, Ubiquitin ribosomal proteins, TATA-box-binding protein, and Polyubiquitin-C &B proteins were the top interacting proteins specific to CVDs. Signaling by NGF, Fc epsilon receptor, EGFR and ubiquitin mediated proteolysis were among the top enriched CVD specific pathways. DEXDc and HELICc were enriched host mimicry domains that may help in hijacking of cellular machinery by pathogens. This study provides a system level understanding of cardiac damage in microbe induced CVDs.
Experimental and Mathematical Models for Real-Time Monitoring and Auto Watering Using IoT Architecture

Manufacturing industries based on Internet of Things (IoT) technologies play an important role in the economic development of intelligent agriculture and watering. Water availability has become a global problem that afflicts many countries, especially in remote and desert areas. An efficient irrigation system is needed for optimizing the amount of water consumption, agriculture monitoring, and reducing energy costs. This paper proposes a real-time monitoring and auto-watering system based on predicting mathematical models that efficiently control the water rate needed. It gives the plant the optimal amount of required water level, which helps to save water. It also ensures interoperability among heterogeneous sensing data streams to support large-scale agricultural analytics. The mathematical model is embedded in the Arduino Integrated Development Environment (IDE) for sensing the soil moisture level and checking whether it is less than the pre-defined threshold value, then plant watering is performed automatically. The proposed system enhances the watering system’s efficiency by reducing the water consumption by more than 70% and increasing production due to irrigation optimization. It also reduces the water and energy consumption amount and decreases the maintenance costs.
Sustainable Supply Chain Finance and Supply Networks: The Role of Artificial Intelligence

Supply chain finance (SCF) is receiving increasing awareness in research as a result of uncertainties in the global financing for supply chain (SC). There are limited and fragmented studies in the implementations of financial services in SC management. This article builds on recovery from the financial crisis of 2008 and posts COVID-19 pandemic, where uncertainties crippled SCF providers and brokers services. At the same time, cutting-edge technological advancements such as Artificial Intelligence (AI) are revolutionizing the processes of business ecosystem in which SCF is entrenched. This article thus adopts a fuzzy set theoretical approach to unpack the entities relationship validity for sustainable SCF mate-framework, and the originality of AI concepts to sustainable SCF to identify the issues and inefficiencies. The results indicate that AI contributes significant economic opportunities and deliver the most effective utilization of the supply networks. In addition, the article provides a theoretical contribution to financing in SC and broadens the managerial implications in improving performance.