Miracle-dz/UniFAR-FTUs huggingface dataset:
Effects of palm-based trimethylolpropane ester/mineral oil blending on dielectric properties and oxidative stability of transformer insulating liquid

Blending of palm-based ester can effectively increase the flash point and thermal-oxidative stability of mineral transformer oil especially in the event of high loading and high temperature. Inversely, owing to the excellent dielectric properties of mineral oil, the dielectric properties of the esters can also be improved. Thus, high oleic palm-based trimethylolpropane (TMP) ester was blended and homogenized with 20 to 50 % of mineral oil, and the physico-chemical and electrical properties of each base fluid was compared with that of pure TMP ester and pure mineral oil. The samples were also subjected to oxidative stability test conducted at 95°C for 500 hours, with the presence of water, oxygen and copper/steel catalyst. During ester and mineral oil degradations, the oxidized oils take on dark brown color with the increase in oxidation products concentration and the sludge formation due to the reaction between the oil and the metal catalyst. XRF measurements performed on the samples showed an increase in iron and sulfur contents, which can be explained by the formation of superficial surface layer of iron (II) sulfide or ferrous sulfide. From the investigations, it was found that the 80/20 of ester/mineral oil blending sample have properties near to the standard values for transformer liquid insulation; thus, it can potentially replace traditional mineral oil as an alternate liquid insulation.
Oral Cavity at Ulcerative Colitis - Preliminary Study

Background. Ulcerative colitis is one of the types of inflammatory bowel diseases. The exact cause of ulcerative colitis still remains unclear. The influence of genetic, environmental, immune and microbial factors had been noticed. The main systemic symptoms of the disease are abdominal pain, diarrhea, rectal bleeding, loss of weight and typical signs of malnutrition. Intraorally ulcerative colitis usually manifests with aphthous stomatitis (both Mikulicz and Sutton aphthae), mucous cobblestoning and pyostomatitis vegetans. Objectives. The purpose of this research was to preliminarily present the most frequent oral cavity changes at patients suffering from ulcerative colitis. Material and Methods. Twelve randomly chosen patients (8 men and 4 women) diagnosed for ulcerative colitis, cured in Clinics of Gastroenterology and Hepatology of Wroclaw Piasts University, had a dental examination. The average age was 45.6 years. Results. Changes in oral cavity were revealed at oral mucosa at 6 patients. One patient had no more than 2 differ− ent changes. Bald tongue was present at two of the examined. So was a coated tongue. Rarely did other changes, such as: buccal cobblestoning, aphthae and tongue angioma of unknown etiology, occur. One of patients manifest− ed oral and skin symptoms of lichen planus. The average DMF was 19.25 and OHI – 0.655 (Dent. Med. Probl. 2008, 45, 4, 382–385).
Recognizing Faces: Pz classification using a research grade system

Objective: Our overall research goal is to use electroencephalography (EEG) to identify recognition of faces by patients with dementia to improve reminiscence therapy. The objective of this paper was to evaluate the performance of a g.tec EEG system using a facial recognition event related potential (ERP) task. Previous work has used the Emotiv off-the-shelf system which resulted in lack of classification. For this work, we sought to use a g.tec system with the same electrode placement as the Emotiv Insight to evaluate whether a reduced electrode set would provide sufficient classification accuracy. Methods: EEG was recorded with the same electrode montage as the Insight while obscure and famous facial stimuli were presented; participants confirmed recognition with a button press and oral confirmation. ERPs were averaged across five presentations of each facial stimuli to improve the signal to noise ratio. Classifiers were trained on one session, tested on another session. Results: Using the same electrode montage as the Insight, the g.tec achieved an average accuracy of 80%. Conclusion and Significance: A new research quality system with only the Pz electrode may provide sufficient classification to develop a system for reminiscence therapy.
Prevalence and Rate of Parasitemia of Haemoproteus columbae in Columba Iiviadomesticain Southwest of Iran

Background Parasites affect the health and productivity of birds. Haemoproteus columbae occurs in pigeons widely in tropical and subtropical regions. The present investigation was concentrated on the prevalence of H. columbae and rate of parasitemia in domestic pigeons in southwest of Iran. Methods Pigeons regimented in three groups, less than six months old, between six and twenty four months old and more than twenty four months old.Then stained blood smears were studied for presence of H. columbae and finally rate of parasitemia in every group calculated. Results Mature and immature stages of H. columbae gametocytes were found in 24% of blood smears prepared from 100 healthy domestic pigeons. Mean of parasitemia in infected pigeons was 9.58%. Mean size of macrogametocytes was 4μm×15μm and mean size of microgametocytes was 3μm×12μm. Mean of parasitemia in infected females was more than males and pipers. Mean of parasitemia in infected old pigeons (pigeons with more than twenty four months old) was more than pigeons with less than six months old and pigeons between six and twenty four months old. Conclusion This study show the prevalence and rate of parasitemia in domestic pigeons in southwest of Iran. We should be care about this parasite in pigeons by knowing the prevalence and high risk groups.
CHEST TRAUMA MANAGEMENT: Good outcomes possible in a general surgical unit

Objective: To determine the predominant pattern of injuries following chest trauma and assess the adequacy of the management strategies employed in a general surgical unit of a trauma care hospital. Methodology: This Case Series study with prospective data collection was conducted in Surgical Unit-II & Unit-VI of Civil Hospital Karachi, from September 2007 to February 2009. One hundred and three consecutive patients with thoracic trauma presenting in emergency department were evaluated. Patients above 12 years of age, who presented with chest trauma either alone or associated with multiple trauma were included. Results: A total of 103 patients were studied for various chest injuries during eighteen months period. As a whole 58% of patients had blunt chest injury as compared to 42% who had penetrating injuries. Thirty patients (29%) had chest wall injuries (rib fracture, mild lung contusion) without haemothorax or pneumothorax, who were managed conservatively. Chest intubation was required in 64 patients (62%) having hemothorax / pneumothorax. Thoracotomy was required in nine patients (9%), in which only two were emergency thoracotomy and seven were elective. Over all mortality rate was 8%. Conclusion: Penetrating injury of chest is rising with time due to gunshot injuries although blunt trauma is still more common. Majority of chest trauma patients can be managed in a general surgical unit satisfactorily and few patients need major operative management. KEY WORD: Chest trauma, Tube thoracostomy, Pneumothorax, Hemothorax, Blunt trauma, Penetrating trauma.
Modified team-based learning (MTBL) and long-term retention in a large classroom setting

To determine the effect of modified team-based learning (MTBL) on students’ learning, long-term retention in a large classroom setting and to assess their perceptions of MTBL and immediate feedback assessment technique (IFAT). Students enrolled into the Pulmonary Disorders pharmacotherapy module were assigned to groups of five or six team members. Student’s individual and team performance on quizzes and exams were compared. Students were asked to complete an anonymous survey evaluating their perceptions of the process. Over a two-year period, the module was offered to 283 students. The mean scores on the team quizzes compared with individual quizzes were 92.6 and 83.3, respectively, (p<0.001). Based on the mean scores of individual quizzes (78.6), team quizzes (97.7), and exams (82.4), students’ retained the course material. Students felt that using both MTBL and IF-AT improved their understanding of the material and should continue in other modules. Overall, MTBL resulted in higher team quiz scores, increased long term retention of material in a pharmacotherapy module, and was wellreceived by the students.
Wearable NFC Reader and Sensor Tag for Health Monitoring

A wearable sensor system based on near field communication (NFC) is proposed. NFC sensor tags are put on human skin and they are read out by a specially designed wearable NFC reader. The whole-body NFC reader suit can be implemented by using a double-sided conductive textile. Multiple flexible NFC antennas with switching circuitry are distributed on a textile. The textile is used as a transmission line that conveys the NFC signals and switch control signals. This paper presents a result of the first implementation trial of the concept.
Detection of Bearing Outer Race Fault in Induction Motors using Motor Current Signature Analysis

Bearing damage is the most common fault type in induction motors. In recent years, bearing fault detection based on motor current signature analysis (MCSA) has been gained extensive attention. However, the changes in the stator current signal which is caused by the bearing fault are usually very weak. In order to detect bearing fault effectively, a method for bearing fault detection based on complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN) analysis of motor current signals is proposed. The CEEMDAN is used to decompose the stator current signal into several independent intrinsic mode functions (IMF), then the most sensitive IMF can be extracted. The experimental results show that the proposed approach is effective for bearing outer race fault detection.
Cluster based Intrusion Detection System for Mobile Ad-hoc Network

Mobile Ad-hoc network is decentralized and composed of various individual devices for communicating with each other. Its distributed nature and infrastructure deficiency are the way for various attacks in the network. On implementing Intrusion detection systems (IDS) in ad-hoc node securities were enhanced by means of auditing and monitoring process. This system is composed with clustering protocols which are highly effective in finding the intrusions with minimal computation cost on power and overhead. The existing protocols were linked with the routes, which are not prominent in detecting intrusions. The poor route structure and route renewal affect the cluster hardly. By which the cluster are unstable and results in maximization processing along with network traffics. Generally, the ad hoc networks are structured with battery and rely on power limitation. It needs an active monitoring node for detecting and responding quickly against the intrusions. It can be attained only if the clusters are strong with extensive sustaining capability. Whenever the cluster changes the routes also change and the prominent processing of achieving intrusion detection will not be possible. This raises the need of enhanced clustering algorithm which solved these drawbacks and ensures the network securities in all manner. We proposed CBIDP (cluster based Intrusion detection planning) an effective clustering algorithm which is ahead of the existing routing protocol. It is persistently irrespective of routes which monitor the intrusion perfectly. This simplified clustering methodology achieves high detecting rates on intrusion with low processing as well as memory overhead. As it is irrespective of the routes, it also overcomes the other drawbacks like traffics, connections and node mobility on the network. The individual nodes in the network are not operative on finding the intrusion or malicious node, it can be achieved by collaborating the clustering with the system.
Zinc oxide (ZnO) induced toxicity and behavioural changes to oligochaete worm Tubifex tubifex (Muller)

Zinc is among the important trace element of the body by participating in several biological processes. But it shows toxicity after a certain concentration. Zinc oxide (ZnO), a hazardous heavy metal, is frequently used in both bulk and nanoparticle format in industrial and household purposes throughout India. In the present study the 24, 48, 72 and 96h LC50 values of ZnO was monitored to benthic oligochaete worm Tubifex tubifex (Muller) through Finney’s probit analysis method. Different abnormal behavioural changes in the worm were also recorded during the exposure period. The 24, 48, 72 and 96h LC50 values of Zinc oxide (ZnO) to Tubifex tubifex was obtained as 30.88, 24.15, 19.95 and 11.15 mg/l respectively. The mortality rate of Tubifex tubifex varied significantly (p<0.05) with a dose and time dependent manner (24, 48, 72 and 96 h). The exposed worm showed several erratic behaviors with the gradually increasing concentrations of the metal and with the progress of time of exposure. The safe concentration of this metal was ranged between 0.0001115 -1.11 mg/l. Based on 96h LC50 value the Maximum allowable concentration (MAC), no observable effect concentration (NOEC) and the lowest observable effect concentration (LOEC) of Zinc oxide to Tubifex tubifex were 1.11, 1.3 and 2.1 mg/l respectively. The present study will be useful in determination of the safe dose of the toxicant before its disposal to the environment.
Detecting Anomalous Events on Distributed Systems Using Convolutional Neural Networks

Detection of anomalous events is very crucial for the maintenance and performance tuning in long-running distributed systems. System logs contain the complete information of system operation that can be used for describing the situations of the computing nodes. However, log messages are unstructured and difficult to utilize. In this work, we propose a novel anomaly detection framework in a Hadoop Distributed File System (HDFS) that transforms the log messages to structured data and automatically monitors the system operation logs using Convolutional Neural Networks (CNN). We evaluate the performance of anomaly detection in terms of precision, recall, and f-measure. The proposed framework can provide with precision = 94.76 ± 0.81%, recall = 99.53 ± 0.23%, and f-measure = 97.09 ± 0.49%. To apply the proposed framework in the practical application, we also concern about the training time and prediction productivity. From our experimental results, our proposed framework outperforms the existing models (i.e., LSTM and Bi-LSTM) with higher recall, lower training time, and higher prediction productivity.
Recognizing hand gestures for controlling home appliances with mobile sensors

Mobile and ambient sensors provide a scalable platform for the integration of computing devices and smart appliances for smart home. In which mobile devices, such as smart watches and smart-phone commonly embedded with actuators and sensors i.e., accelerometers and gyroscopes, have opened up chances for the user to easily control home appliances. This paper proposes an integrated method and system that utilize several deep models and mobile sensors for hand gestures applicable for smart homes. The system consists of three components of actual smart home configurations: (i) smart-watch worn on the user’s wrist for capturing gesture patterns (ii) a recognition application that runs on the smart mobile phone and sends correspond commands to the home automation platform; and (iii) home automation platform with connected smart devices instrumented with ambient sensors. In addition, we define a simple yet easy-to-learn hand-gesture vocabulary composing of 18 gestures to the user. With the F-score of over 75%, our experiment on our self-collected data-set consisting of 18 gestures from 20 subjects, demonstrates that the feasibility of the gesture recognition for controlling home appliances.
Analytical Calculation of Hidden Layer Biases in Extreme Learning Machines with Sinusoidal Activation Function

ELM (Extreme Learning Machine) networks have been gaining attention due to high speed, ease of implementation and minimal human intervention compared to classical feedforward neural networks with iterative training algorithms. Part of the studies that seek improvements in ELM involves the specification of input weights and hidden layer biases, traditionally randomized. In this paper is proposed a variant of ELM network in which the hidden layer biases are analytically calculated simultaneously to the output weights in the solution of the least squares problem. The method is valid for SLFNs (single-hidden layer feedforward neural networks) with sinusoidal activation functions. Experiments have shown that calculating biases with the proposed method may reduce the amount of hidden nodes required to solve a given problem. The proposed variant received the designation CBTELM—Calculated (Hidden Layer) Biases Trigonometric ELM. In addition to standard applications of neural networks, CBTELM may be successfully used for amplitude and phase detection of noisy signals composed by sinusoids with known frequencies, as was verified in experiments.
Automatic Furniture Layout Based on Functional Area Division

We propose an automatic indoor furniture layout scheme based on functional area division and furniture filling. According to the function, we suppose each kind of furniture may be laid out in one or several functional areas, for example, a sofa may be located in the meeting area of a living room and a bed may be located in the sleeping area of a bedroom, etc. Our automatic layout method divides an empty room region into several functional areas by using conditional generative adversarial networks (CGAN). We expound the learning process of the algorithm in the process of functional areas division, including the objective function construct and training process. Moreover, in order to fill furniture into a specific functional area, a learning-based furniture filling algorithm is proposed by training a fully connected network model for different kinds of functional area. Experiments show our automatic furniture layout method has its advantages in performance and effect compared with the existing methods.
Night-Time Brain Inter-Hemispheric Asynchrony in Sleep Apnea Patients Carry Information on Neuropsychological Impairment

Obstructive sleep apnea (OSA) is a serious sleep disorder with diurnal symptoms including neuropsychological impairments such as excessive daytime sleepiness and loss of attention. There are no efficient tools to measure these impairments in current clinical practice. In this paper, we explore the feasibility of measuring neuropsychological impairments using electroencephalography (EEG) data acquired during the standard clinical sleep diagnostic test known as polysomnography (PSG). We hypothesized that left-right hemispheric EEG asynchrony could quantitatively characterize neuropsychological impairment in OSA in a population of sleep laboratory patients. We acquired EEG data from 50 subjects undergoing routine PSG, using symmetric electrode derivations of C4-A2 and C3-A1. Their neuropsychological performance was assessed via a psychomotor vigilance task (PVT). We computed the left-right EEG asynchrony and developed a logistic regression model (LRM) to classify patients according to their PVT performance. Leave-one-out cross-validation studies on a LRM model with two-class PVT performance achieved a sensitivity of 83% (95% CI: 66-100%) and a specificity of 78% (95% CI: 64-92%). These results indicate that EEG asynchrony during sleep carries information on daytime neuropsychological impairments in OSA subjects.
Modelling of Transient Heating and Softening Behaviour of Contact Points During Current Pulses and Short Circuits

In this paper, electrical connectors for high power applications such as gas-insulated switchgear or charging stations are investigated. Recent findings show that the contact points in these connectors are heated up rapidly during current pulses and thus, they soften and their contact resistance drops instantly. The contact points are a major source of power losses in the electrical connector. Therefore, a precise estimation of the drop in contact resistance is needed for an accurate estimation of the maximum temperature during a short circuit. In this paper, a new multiphysics calculation model is presented which aims at a better understanding of these transient processes. For this calculation, the influence of contact temperature, contact hardness and contact force on the contact resistance is evaluated. The contact hardness is highly temperature-sensitive. Therefore, pulse current tests were done to evaluate the contact hardness depending on temperature. The contact force is given by design but can be reduced by high current pulses. For the contact points, this blow-off force is estimated analytically. With these data, the effective contact resistance for every moment of a short circuit is calculated with a transient electric-thermal FEM model. Finally, the calculations are compared to results of short circuit tests.
Plant Location, Inventory Levels and Supply of Products to Areas Affected by a Natural Phenomenon

In order to help organizations in the face of disaster in decision making, in this document was developed a model that combines the problem of the location of facilities for the establishment of a pre-positioned warehouse, the calculation of their inventory levels and the establishment of supply routes. The variability of the model with respect to the others lies in two variables: (1) finding a location that not only meets the minimum distance traveled characteristic, but also that possesses attributes that allow the proper management of the warehouse; and (2) the types of demand to those who focus on the products (food, equipment, water), are divided: early childhood, childhood, adolescence, adulthood and old age. The model is evaluated in one of the ten regions of the state of Veracruz, Mexico, called the Region of the High Mountains, with 57 municipalities, 27% of the total of the municipalities that make up the state and which has been the cause of the issuance of emergency declarations by the National Fund for Natural Disasters (FONDEN). The results show us that the location for the installation of the pre-positioned warehouse is located in the municipality of Fortín de la Flores, the order quantities, reorder point, safety inventory and shortage (in the form of kits) and the routes of delivery derived to the capacity of the vehicles used.
An Improved Heuristic Based Genetic Algorithm for Bin Packing Problem

The NP-complete bin packing problem is a widely studied grouping problem that serves to model several useful and practical problems, e.g., batch-processing machine scheduling, industrial and transportation logistics, etc. Due to the complexity involved to solve this class of problems, usually two main strategies are adopted: sub-optimal building heuristics and optimization models using metaheuristics algorithms. The building heuristics are computationally efficient, however, usually obtaining non-optimal solutions or local minima. Otherwise, adapted metaheuristics to handle this problem allows an effective global search which augments the chance to obtain optimal or quasi-optimal solutions, however with a high computational cost. This work develops a heuristic based genetic algorithm aiming to obtain a hybrid approach constructed to explore the best features of each strategy. Special encoding handling and specific operators are included additionally to the final model to enhance the behavior and performance of the hybrid model. Numerical experimental using well-established benchmarks for one-dimensional bin packing problem are carried out to compare the versions of the presented hybrid methods with high-quality methods presented in the literature. The results indicate the potential for the presented strategy to solve the one-dimensional bin packing problems.