Miracle-dz/UniFAR-FTUs huggingface dataset:
Estimation of Cooling Capacity of Reagent-Free Evaporative Cooling Tower

This paper deals with the study of circulating water cooling processes in evaporative cooling towers with various filler units, widely used at TPPs and in chemical, petrochemical, oil & gas processing industries. The process flow diagram and design of cooling tower with closed cooling water are proposed in this paper by the authors that allow to reduce significantly the amount of chemical reagents to be used. The authors studied the design, consisting of developed jet-film contact elements, as a cooling tower filler unit. The proposed checker fillings can provide a large surface area of phase contact with a small number of irrigation points, a minimum percentage of liquid drops entrainment by the gas flow and a low hydraulic resistance. Based on the Lewis analogy, an equation to calculate the volumetric heat transfer coefficient, taking into account the ratio of mass flow rates of air and water, as well as the irrigation density, was obtained. It was found that the volumetric heat transfer coefficient increases with an increase in the irrigation density due to the turbulence of flowing down liquid film. The comparative results of calculations of volumetric heat transfer coefficient and the Merkel number for different cooling tower filler units are shown in this paper. It is proved that the proposed jet-film contact elements have the greatest cooling capacity among the studied checker fillings.
PROFILE OF PHYSICAL INACTIVITY AS A RISK FACTOR FOR NON- COMMUNICABLE DISEASES IN A RURAL POPULATION

BACKGROUND: Eighty-five percent of the global burden of Non Communicable Diseases (NCD) is borne by the low and middle income countries, like India development. Emergence of NCDs in India is identified by WHO, ICMR and Government of India. NCDs share common risk factors like physical inactivity are causing 3.2 million deaths annually in the world (WHO, 2014). AIMS: Aim was to study profile of physical inactivity for non-communicable diseases. METHODS AND MATERIALS: SETTING: Community through home visit. STUDY DESIGN: Community based Cross sectional survey. STUDY DURATION: The study was conducted from July 2008 to September 2010. STUDY POPULATION: Through multistage sampling 3500 persons were studied in 12 villages of Wardha district (MH). STUDY INSTRUMENTS: Step-1(core) of WHOs STEP-wise approach (2002.) was used to study physical activity by home visit using a pretested structured schedule in English/Marathi. Step 1 contains Core or "minimum set", self-report measures for all countries. PHYSICAL ACTIVITY: The total physical activity is the sum of physical activity at work, transport and leisure time. Individuals were divided into physically inactive, moderately active and highly active according to MET minutes of exertion. CONSENT: Informed consent from participants and ethical clearance from Institutional Ethical Committee was obtained. RESULTS: 3500 individuals (Male 54.86% and Female 45.14%) were studied. Prevalence of physical inactivity was 26.88% in all, 18.75% in male and 35.44% in female; more in 15-24 years and 55-64 years, more in female than male in each age group. CONCLUSIONS: Physical inactivity is widely prevalent even in rural population but a preventable and modifiable NCD RF through organized multispectral efforts.
Dynamic Coupled Model of Vibration System With Galfenol Damper Considering Eddy Currents and Hysteresis

Based on the effective magnetic-field expression, the constitutive equations with eddy currents, the hysteresis model, and the electromechanical coupled model, a dynamic mechanical-magnetic-electro bidirectional coupled (MMEBC) model of the vibration system with the Galfenol damper is established, and the system’s transmissibility and key frequency-dependent complex stiffness are derived. Comparisons between the calculated and measured results show that the proposed model can accurately describe the dynamic minor-loop curves of the Galfenol rod around the bias point, can provide reasonable data trends of the frequency-dependent complex Young’s modulus, piezomagnetic coefficients, and relative permeability, and can predict the changing laws of the resonant frequency and the resonant peak with bias point and shunt resistance. It is found that the damper under the shunt resistance circuit can provide the peak attenuation rate of 87.91%, which is higher than 79.58% of the damper under open circuit.
Arousal and Valence Estimation for Visual Non-Intrusive Stress Monitoring

As the capabilities and usefulness of advanced Unmanned Aerial Vehicles (UAVs) increase, communication between the operator and these intelligent systems is becoming a very important factor for mission success. In this context, automatic stress detection is becoming a key research topic in emotion analysis. Stress can be estimated by means of an array of intrusive sensors or via the measurement of some biological markers (e.g. cortisol levels). However, these approaches are not appropriate in many cases of human-machine interactions. In this paper, we propose a deep learning-based psychological stress level estimation approach. The goal is to identify the region where the emotional state of the operator projects in the space defined by the latent dimensional emotions of arousal and valence. The stress region is well defined in this space according to prior works in psychology. The proposed predictive model first extracts and aligns the operator’s face, then generates embeddings from a pre-trained face model. These embeddings are then used to train two different architectures, a hierarchical temporal CNN and a LSTM with an Attention Weighted Average layer. Since we deal with naturalistic behavior in a context of operator-machine interaction, the One-Minute Gradual-Emotion Behavior Challenge (OMG) dataset is used for the validation of continuously estimated arousal/valence levels.
Classification Talent of Employee Using C4.5, KNN, SVM

Employees are one of the important points of driving the company. With the existence of capable human resources, the company has competitive advantage compared to competitors. This research will propose a way to classify employees into 4-box talent management. The classification will use the C4.5 method, K-nearest neighbors (KNN), and Support Vector Machine (SVM). In classification, we use 18 criteria to calculate the performance and potential of the employee. The results of mapping the performance and potential of employees will divide employees into 4 labels, label 1 (needs development or too new to evaluate), label 2 (everyday solid contributor), label 3 (potential to be executive in 3+ years), and label 4 (needs to be an executive in 2 years). The results of the employee classification get the result that the SVM method has the highest accuracy, which is 94.62%, with P-value = 3.20E-11. KNN is a method that has the lowest accuracy of 87.37%.
Outstanding Reliability of Heavy-Ion-Irradiated AlInN/GaN on Silicon HFETs

AlInN/GaN heterostructure field-effect transistors (HFETs) grown on silicon withstand irradiation with 75-MeV sulfur ions up to fluences of $5.5 \times 10 ^{13}$ ions/cm2. The static transistor operation characteristics of the devices exhibit a shift of the threshold voltage and a decrease in the saturation and the OFF-state current. Microphotoluminescence spectroscopy reveals a decrease in the electron carrier density in the channel region. Simulations were performed to model the damage caused to the devices assuming the generation of acceptor-like defects upon irradiation. It turns out that the degradation depends on the thickness of the buffer layer. Therefore, we propose the reduction in the thickness of the buffer layer as a way to increase the radiation tolerance of HFETs.
The Effect of Codoping on Pulse-Shape Discrimination Properties of Gd3Ga3Al2O12:Ce Single Crystals

In this article, we have extended our study on the effect of codopants on the scintillation properties of Ce-doped Gd3Ga3 Al2O12(GGAG:Ce) single crystals by investigating their pulse-shape discrimination (PSD) abilities. The GGAG:Ce crystals were grown with boron and calcium codopants using the Czochralski technique. The codoping was found to have substantial effects on the PSD ability of these scintillators. Observable difference in the pulse shapes for alpha and gamma radiations in the B-codoped GGAG:Ce scintillators exhibited the highest PSD, while those with Ca codoping showed no discrimination among the studied samples despite having a significant effect on the scintillation kinetics and a strong quenching of the light yield by alpha radiations that resulted in a minimum $\alpha / \gamma $ ratio. A correlation of the PSD properties with the defect structure of these single crystals has been made by using optically stimulated luminescence (OSL) for the first time. OSL studies with infrared, blue, and green light provided an insight into the role of defect centers in the relaxation mechanism of the scintillation kinetics of the GGAG:Ce crystals, which subsequently affects their ability to discriminate different kinds of radiation.
A Spectrum Analyzer Based on a Low-Cost Hardware-Software Integration

Instruments are a very important tool for the good comprehension of any science. In the engineering electrical field, most of the practice laboratories are equipped with a large set of devices. One of the major challenges to get a complete laboratory is the economic cost. Professional electronic devices as multi-meters, oscilloscopes or spectrum analyzers are quite expensive on the market. Fortunately, with the aid of software-defined radio (SDR), low-cost equipment can be built. A fundamental device in these projects is the RTL-SDR dongle. This electronic accessory captures wireless signals and puts them on a USB port. Therefore, with digital signal processing it is possible to get access to low-cost instruments like spectrum analyzers. In this work, an integration of hardware and software is presented. An application is developed with Python and implemented on a Raspberry Pi3 B+. This proposal captures, demodulates, and displays AM and FM signals. The signals obtained can be observed on frequency or time or can be recorded as well.
Factors Influencing Plagiarism and Collusion in Programming Assignments

This paper examines student perception toward ethical and unethical behaviors in computer programming. It further explores the impact of gender, major, CGPA, and student level on student perception. Findings indicate that female students and students with CGPA greater than or equal to three appear to have higher ethics than their counterparts. It also shows that there is no significant difference when it comes to major or student level.
Sustainable Engineering Ethics: Teaching Sustainability as a Macroethical Issue

This paper explores how sustainability issues have been integrated into the education of engineering and computing students, as one among many important macroethical issues. Based on surveys of educators focused primarily in the U.S., other Anglo countries, and Western Europe, 673 individuals reported teaching sustainability in their courses. These responses were examined for trends in teaching and assessment methods for ethics and societal impacts (ESI), and compared to those who teach ESI but not including sustainability. Follow-up interviews provided six more detailed examples of teaching practices specific to sustainability in courses where sustainability was a primary focus for integration. The results illustrate sustainability practices that include a macroethics lens, and could inspire educators to increase the integration of sustainability into their teaching.
Electricity Theft Detection to Reduce Non-Technical Loss using Support Vector Machine in Smart Grid

Among the various reason behind Non-technical losses in smart grid, losses due to electricity theft have become major apprehension in power system industries. A significant amount of consumption of electricity in a fraudulent manner decrease the supply quality, increase generation load and the real consumers have to pay excessive electricity bill that finally affects in overall economic condition. The advance metering infrastructure (AMI) has the ability to monitor the consumption detail of every consumer, record the consumption pattern, billing them as well as find any types of abnormalities. The communication capabilities of smart grid have facilitated the utilities to store their consumers’ consumption patterns. With this database it is possible to formulate a theft detection model by machine learning algorithm by analyzing the recorded data of smart meter. In this paper, Support vector machine (SVM), one of the prominent machine learning classifiers applied with principle component analysis to train the data collected from smart meter and calculate the prediction accuracy with the test data. Here the principle component analysis reduces the dimension of the data to make the turtuer processing less complex. After that, by using grid-search method the best-suited meta-parameters for SVM has been selected where the almost 90% accuracy rate is achieved. The classification results with respect to different metaparameters of SVM (regularization parameter C and γ) have presented. Moreover, the obtained result specifies that the applied techniques possesses higher accuracy and less false positive rate for real time consequences.
Comparative Method of Moora and Copras Based on Weighting of the Best Worst Method in Supplier Selection at ABC Mining Companies in Indonesia

Supplier selection is important for the company. The risks caused by suppliers will have a significant impact on the company's performance. To mitigate this, each company has different characteristics regarding suppliers. There have been many previous studies regarding criteria in supplier selection, in this study using criteria that has been previously studied and adjusted to the direction of the company's business objectives. These criteria will be weighted by Best Worst Method. As a sample in this study, used data and information from ABC companies engaged in mining in Indonesia. Supplier assessment will be done by comparing MOORA and COPRAS approaches. The COPRAS method is chosen because the calculation step is simpler and the average final ranking value difference is quite small. This study also produced key vendor selection criteria for ABC companies.
Switched-Capacitor Based Modified Extended High Gain Switched Boost Z-Source Inverters

Two novels switched Z-source inverters (ZSI) based on the active switched capacitor with high voltage conversion ratio are presented. These are active switched capacitor-based capacitor assisted extended boost ZSI and diode assisted extended boost ZSI. High voltage gain is required in various applications such as electric vehicles, grid integration of renewable energy sources, etc. The voltage gain of conventional ZSIs are limited and these conventional ZSI has high voltage conversion ratio at a high duty cycle, which results in poor inversion and poor efficiency. In this work, only one additional diode and one active switch are used with the conventional extended boost ZSI to achieve high voltage conversion ratio without adding any additional passive components. Compared to other ZSI topologies, the proposed inverter also guarantees low voltage across capacitors, a wide range of duty cycle operation and continuous source current in addition to high voltage conversion ratio. The detailed operating principles and steady-state analysis and design details are presented. The performance of the inverters is verified through simulation and also through experiments. Both the simulation and experimental results are presented.
Intelligent Mobile Robot Control in Linguistic Evaluation of Solution Options

The development of modern intelligent control systems and robotics has led to the automation of production processes in completely new applications. In particular, mobile robots are used not only to solve problems where there is a threat to human life and health, but also in normal conditions to perform heavy or long monotonous work, such as picking berries. The article deals with the approach to solving the task of intelligent agricultural mobile robot control in the linguistic evaluation of the usage options for picking berries. The solution of this task is based on the establishment of the order ratio on fuzzy estimates, which is determined through probabilistic estimates for their clear equivalents. As a solution, the variant with the lowest rank from the obtained ranked series of possible solutions is chosen. The article presents the results of applying the developed method for solving the task of agricultural mobile robot control for picking strawberry in the climatic conditions of Central Russia.
Implementing Online Peer-Assisted Learning for Teaching Case Study Coursework

Case study teaching is now frequently appear in many disciplines such as law, medical, education, science, and engineering. It is a very effective technique allows students to develop their creativity and innovative thinking for solving real-life problems that they might face in their career fields after graduation. Many students especially first and second year students at the Department of Civil Engineering, Middle East College (MEC), Sultanate of Oman, are often have unfamiliar experience with research and writing skills, and discussing and analyzing a case in their coursework assessments which negatively affect their performance. Therefore, this study aimed to evaluate the use of Online Peer Assisted Learning (OPAL) in teaching case study coursework in compared with the traditional teaching method as an educational strategy to enhance students' performance, and to develop learners' attributes at MEC. This evaluation is conducted from the students' and faculty perspective. The study is mainly targeted all audience of second year (semester four) Civil Engineering students who have studied Introduction to Environmental Engineering module during 2016/2017 academic year, with a total number of 70 students including those academically at risk and high risk. The results indicated that the use of OPAL improved students' performance by approximately 13.1 % than the traditional method. Another significant indicator of the effectiveness of the OPAL scheme in contrast with the traditional method, is that relatively higher minimum and maximum marks have been achieved by students in the OPAL group as compared with the minimum and maximum marks achieved using the traditional method. Students' and faculty feedback showed that the OPAL enhances learner attributes at MEC particularly communication skills, leadership and teamwork skills, intercultural competence, lifelong learning, digital competence, and professional competence through research and writing skills, the use of library resources, and understanding the subject matter of the coursework clearly.
Carrier Phase Recovery for Array Navigation Receiver: A Fast Phase Retrieval Approach

A fast carrier phase recovery scheme is developed for satellite navigation receiver using an antenna array based on the phase retrieval theory, in which the antenna array provides sufficient measurement information for the phase recovery algorithm. First, the complex satellite signal model after coherent integration is established using the antenna array. Then, considering symbol uncertainty of the navigation data during the coherent integration time, the carrier phase estimation is formulated as a typical phase retrieval problem. At last, using the squared iterative method (SQUAREM), which is capable of superlinear convergence, a fast variant of the Wirtinger Flow (WF) algorithm is derived to solve the phase recovery problem efficiently without compromising the balance between simplicity and stability. As demonstrated by numerical results, the proposed algorithm outperforms the state-of-the-art in terms of the mean squared error (MSE) convergence. Moreover, the adjustment processes of the carrier phase in the proposed method is validated.
An Enhanced Image Encryption Technique using DES Algorithm with Random Image overlapping and Random key Generation

In Present communication environment the protection of Information i.e. (Data) is the biggest challenge in today’s scenario. Image encryption technique should be designed in a way that improve the effectiveness of communication and maintain the data secure from attacks by the unauthorized person. Here in this paper an Enhanced Image Encryption Technique using DES algorithm with Random Image overlapping and random key Generation is proposed. In this we used random key along with random image overlapping for higher variation which improved the security. Our result shows the higher variation in the RGB combination and less information loss after decryption process.
Comparison of Public Key Algorithms for Lightweight and Low Cost System

As resource-constrained devices such as RFID and smart cards are being widely developed and used, there is an increase in the need for an efficient lightweight cryptographic algorithm. Elliptic Curve Cryptography is one type of public key cryptography which provides the same level of security when compared to RSA, with a smaller key size. Hence, ECC has many advantages like less area, less power consumption, and high performance. This paper proposes a processor for ECC over Galois field (2163) for low-cost systems where the main focus is on scalar point multiplication operation. An RSA crypto processor is also implemented to compare the area occupied by the two processors. This is done using Verilog programming in Xilinx Virtex 4 and Virtex 7 series. Simulation is also done for ECC over GF (2233). The simulation and synthesis results show that the ECC crypto processor occupies lesser slices compared to 1024 bit RSA crypto processor in order to achieve the same level of security. It is also proved that the number of slices occupied for ECC 163 bits in Virtex 4 series is much lesser compared to Virtex 7 series. Therefore, it is easy to accomplish compact and faster designs in ECC crypto processor that can be applied to lowcost systems.
Predicting the Efficiency of the Oil Removal From Surfactant and Polymer Produced Water by Using Liquid–Liquid Hydrocyclone: Comparison of Prediction Abilities Between Response Surface Methodology and Adaptive Neuro-Fuzzy Inference System

The present study developed, evaluated and compared the prediction and simulating efficiency of both, the response surface methodology (RSM) and adaptive neuro-fuzzy inference system (ANFIS) approaches for oil removal using a liquid-liquid hydrocyclone (LLHC) from surfactant and polymer (SP) produced water. Six parameters were involved in the process: the surfactant concentration, polymer concentration, salinity, initial oil concentration, feed flowrate and split ratio. For RSM, D-optimal design was used, while the ANFIS model was developed in term of this process with the Gaussian membership function. All models were compared statistically based on the training and testing data set by the coefficient of determination (R2), root-mean-square error (RMSE), average absolute percentage error (AAPE), standard deviation (STD), minimum error, and maximum error. The R2 for RSM and the ANFIS model for the testing set were of 0.972 and 0.999, respectively. Both models made good predictions. Trend analysis has been done to confirm the applicability of the models. From the results, it shows that the ANFIS model was more precise compared to the RSM model, which proves that the ANFIS is a powerful tool for modelling and optimizing the efficiency of the oil removal from the LLHC in the presence of SP.