Miracle-dz/UniFAR-FTUs huggingface dataset:
On estimating the reliability in a multicomponent system based on progressively-censored data from Chen distribution

This research deals with classical, Bayesian, and generalized estimation of stress-strength reliability parameter, Rs,k = Pr(at least s of (X1,X2, .,Xk) exceed Y ) = Pr(Xk−s+1:k > Y ) of an s-out-of-k : G multicomponent system, based on progressively type-II right-censored samples with random removals when stress and strength are two independent Chen random variables. Under squared-error and LINEX loss functions, Bayes estimates are developed by using Lindley’s approximation and Markov Chain Monte Carlo method. Generalized estimates are developed using generalized variable method while classical estimates the maximum likelihood estimators, their asymptotic distributions, asymptotic confidence intervals, bootstrap-based confidence intervals are also developed. A simulation study and a real-world data analysis are provided to illustrate the proposed procedures. The size of the test, adjusted and unadjusted power of the test, coverage probability and expected lengths of the confidence intervals, and biases of the estimators are also computed, compared and contrasted.
Dual Exclusive Attentive Transfer for Unsupervised Deep Convolutional Domain Adaptation in Speech Emotion Recognition

Considering different corpora of speech emotions available both publicly and privately with numerous factors that make them different, the premise of having features of both training and testing samples drawn from the same distribution and the parameterization of the same feature space is not applicable in most real world scenarios. Addressing this challenge via a domain adaptation method, we propose a dual exclusive attentive transfer (DEAT) for deep convolutional neural network architecture based on unsupervised domain adaptation setting. The proposed architecture adapts to an unshared attentive transfer procedure for convolutional adaptation of both source and target domain. Correlation alignment loss (CALLoss) is applied to minimize the domain shift through the alignment of the second-order statistics of the convolutional layer’s attention maps in both domains. Then, for the proposed network to effectively model the shift dissimilar domains, we make the weights of the corresponding layers exclusive but related. The proposed model minimizes the classification loss of the source domain with labels and the correlation alignment loss of both convolutional and fully-connected layers collectively. We evaluate our architecture using the Interspeech 2009 Emotion Challenge FAU Aibo Emotion Corpus as target dataset and two publicly available corpora (ABC and Emo-DB) as source dataset. Our experimental results show that our domain adaptation method is superior to other state-of-the-art methods.
Rapid reversed-phase high performance liquid chromatography assay of Tert-butylhydroquinone content in food products

The use of antioxidant has been widely applied in food industry to maintain the product’s condition to the end consumer. Tert-butylhydroquinone (TBHQ) is a synthetic aromatic organic compound which is often used as antioxidant in food, especially oil product and its derivatives. The use of TBHQ in food products has been limited by the government in most countries, including Indonesia. Therefore, the need for a rapid method to assay the concentration of TBHQ in products is quite high in food industry. In this article, we propose a rapid reversed-phase HPLC method to assay TBHQ content in food products. The isocratic system using 1 % acetic acid and acetonitrile in 3:2 ratio with 1 mL min−1 flow rate produces good separation of TBHQ. Target peak was eluted in 5 min and the whole procedure only need maximum of 18 min to elute all the sample component. The LOD of the method was determined at 0.2 mg L−1, while the lower limit of quantification was 0.5 mg L−1. The accuracy of the method has been proved with acceptable recovery ranging from 95 % to 105 %.
Surface kinetics analysis by direct area measurement: Laser assisted dehydration of α-FeOOH

A novel method for surface kinetics analysis based solely on quantitative image analysis technique is proposed, and implemented for evaluating the activation energy, 110 (10) meV, of laser assisted dehydration of goethite. Millisecond radiative heat transfer via 785 nm laser beam, allowed us to elucidate a transient surface dehydration below 200 °C. In addition, the proposed method evaluates the laser threshold Irradiance - 1.2(1) μW/μm2 and its temperature dependence. The complementary contributions of radiative (transient) and conductive (equilibrium) heat transfer to the sample provides viable physical elucidation of the observed phenomenon - dehydrated spot surface area, variations with the laser power and the equilibrium temperature. The method allows effectively to isolate and study surface process and can be efficiently applied for any pulse laser assisted surface transformation, when the initial and final products have visual contrast.A novel method for surface kinetics analysis based solely on quantitative image analysis technique is proposed, and implemented for evaluating the activation energy, 110 (10) meV, of laser assisted dehydration of goethite. Millisecond radiative heat transfer via 785 nm laser beam, allowed us to elucidate a transient surface dehydration below 200 °C. In addition, the proposed method evaluates the laser threshold Irradiance - 1.2(1) μW/μm2 and its temperature dependence. The complementary contributions of radiative (transient) and conductive (equilibrium) heat transfer to the sample provides viable physical elucidation of the observed phenomenon - dehydrated spot surface area, variations with the laser power and the equilibrium temperature. The method allows effectively to isolate and study surface process and can be efficiently applied for any pulse laser assisted surface transformation, when the initial and final products have visual contrast.
Active Battery Equalization Method Based on Redundant Battery for Electric Vehicles

In this paper, an active equalization method based on redundant battery is proposed. A redundant battery is added to a battery pack consisting of several series batteries. According to the capacity and SOC (state-of-charge) of series batteries in the battery pack, one of these batteries is in parallel with the redundant battery in a certain time period. On the one hand, the energy is transferred between the redundant battery and the equalized batteries. On the other hand, during the charging or discharging process, the redundant battery acts as a power source, which reduces the current of the equalized battery. By adjusting the length of time that redundant battery and series batteries are connected in parallel, the purpose of battery equalization will be achieved. The balance current is limited only by the maximum charge–discharge ratio of the batteries. As long as the sum of the difference of series batteries capacity is less than the redundant battery capacity, the balance of all batteries can be achieved. Compared with other active equalization methods, the energy storage element (redundant battery) is used to be the main component of the equalization circuit in this method. It not only has the advantages of simple equalization circuit and large equalization current, but also has higher energy density of battery pack. In this paper, the equalization circuit is given, and the equalization strategy with accurate battery parameters and uncertain battery parameters is proposed. The experimental results also verify the correctness of the theoretical analysis and modeling.
Sparse Deep Tensor Extreme Learning Machine for Pattern Classification

A novel deep architecture, the sparse deep tensor extreme learning machine (SDT-ELM), is presented as a tool for pattern classification. In extending the original ELM, the proposed SDT-ELM gains the theoretical advantage of effectively reducing the number of hidden-layer parameters by using tensor operations, and using a weight tensor to incorporate higher-order statistics of the hidden feature. In addition, the SDT-ELM gains the implementation advantage of enabling the random hidden nodes to be added block by block, with all blocks having the same hidden layer configuration. Moreover, an SDT-ELM without randomness can also achieve better learning accuracy. Extensive experiments with three widely used classification datasets demonstrate that the proposed algorithm achieves better generalization performance.
Validating a Wireless Protocol Implementation at Binary Level through Simulation Using High Level Description of Protocol Properties in Light Esterel

The development and debugging of a wireless protocol are complex tasks that many face in the industry and academia. This paper aims at facilitating those tasks by proposing a simulation framework that is capable of verifying and validating a protocol stack at binary level. This simulation framework is based on the co-simulation of QEMU and SystemC, which are interfaced through TLMu. An observer module was developed to analyze the traffic in the simulated network, which contains protocol properties modeled in Light Esterel to check that the frame exchanges comply with the protocol properties, in order to validate the protocol implementation. We describe the development of the simulation framework and its node models capable of executing the protocol’s binary stack. We then explain the modeling of protocol properties in Light Esterel and their insertion in the simulation framework. Finally, we test the OCARI protocol for wireless sensor networks in the simulation framework.
Simulation-based Retrieval of Adaptation Knowledge

Today’s networking applications have to operate under changing environmental conditions. Web browsers on mobile devices, for example, are facing rapidly changing network conditions. Even though browsers and network stacks provide fine grained configuration options, it is challenging to find configurations which perform well in these changing environments. In this paper, we propose to adapt the configuration to changing network conditions at runtime. Based on monitored network properties, a k-nearest neighbour classifier predicts suitable configurations. For a first evaluation, we trained the classifier for a web browsing scenario with Firefox in Mininet simulations. The predicted configurations outperform all static configurations and reduce the average page load time by 1.5 seconds, realizing 68% of the possible improvement of an optimal prediction.
An Overview on Biofuels and Their Advantages and Disadvantages

Blazing of fossil energy resources generally changes the global climate. Speeding up of global temperate nowadays is an important aspect. Emission of greenhouse gases mainly from blazing of fossil energy resources is one of the most important sources. So, to carry the significant energy and to reduce air pollution, biofuels might be a substitute energy sources. The unnecessary utilization of fossil energy resources or fuels results deficient in the storage in underground earth then people naturally have to depend on biofuels. Consequently increasing demand for the manufacture of biofuels will put a huge burden on agriculture and food prices. Biofuels generally attributed as liquid fuels which are made from the biomass. This consists of mostly wood, vegetable oils, forestry products, agricultural crops, agricultural residues or municipal garbage, residues of domestic animal wastes and aquatic plants. In this review, we summarized the different types of biofuels including biodiesel and their comparative study, production and uses from an ecological perspective.
Synthesis of OMC supported Pt catalysts and the effect of the metal loading technique on their PEM fuel cell performances

Abstract Ordered mesoporous carbon (OMC) supported Pt catalysts were prepared by different loading techniques, in order to be used in the catalysis of oxygen reduction reaction (ORR) in polymer electrolyte membrane fuel cells. OMC was synthesized by an organic-inorganic self assembly route using Pluronic F127 as surface directing agent, resorcinol-formaldehyde as carbon source and tetraethyl ortosilicate (TEOS) as silica source. Pt loading was achieved by three different approaches; one pot in situ synthesis, wet impregnation and surface-modified wet impregnation. Nitrogen adsorption studies showed slight reductions in surface areas, which can be attributed to partial losses of micropore volumes. The XRD and TEM analysis revealed a better metal distribution and smaller particle size in the surface-modified sample with a mean Pt particle size of 3.83 nm. The modified sample also gave the most promising performance among the catalysts with a maximum power density of 73 mW cm−2, which was very close to the commercial Pt/C catalyst.
Design of Interoperable Communication Architecture for TSO-DSO Data Exchange

This paper describes a model driven methodology in order to implement an interoperable communication architecture supporting TSO-DSO information exchange. The model driven methodology goes through Smart Grid Architecture Model interoperability layers and leverage international standards. The Use Case approach is utilized for identification of information exchange requirements, which are materialized through Business Objects gap analysis against existing standardized IEC CIM (Common Information Model) profiles. Determined set of standardized Business Objects can be implemented using several communication technologies. Some of these up-to-date technologies are provided by off the shelf solutions such as ECCo SP, a secure and scalable platform provided by ENTSO-E.
Efficiency and Learnability Comparison of the Gesture-based and the Mouse-based Telerobotic Systems

Telerobotic systems enable operators to interact with remote manipulators. Such systems have often been used in hazardous environments. This paper presents a gesture-based telerobotic system using Kinect. Kinect is an essential component of the Natural User Interface (NUI) capable of translating human body motion into telemanipulation commands. In comparison to traditional mouse-based control, the gesture-based system led to faster completion of the remote object moving task and was also easier to learn. Thus, it is possible to conclude that the gesture-based NUI proved a more intuitive and efficient usage than the traditional user interfaces.
Developing A Fire Monitoring and Control System Based on IoT

An novel fire extinguishing monitoring and control system is proposed in this paper. The system combines online monitoring of fire extinguishing information based on Internet of things (IoT), early fire warning and alarm system with building safety evaluation, which are designed to acquiring the real-time information of fire extinguishing working condition, improving the reliable prediction mechanism in traditional fire monitoring system. A perception terminal based on IoT is developed and the fire real-time monitoring and control system is designed in this paper. The novel system can acquire the real-time working condition information of fire extinguishing facilities, such as pipe flow, pressure, temperature and humidity of the environment, current and voltage of the electric equipment, valve switch, relay action, alarming message, it can upload the collected data to the monitoring center through the C/S(Client /Server) architecture. Based on these data, an electrical early fire warning model based on BP neural network is proposed. A fire safety evaluation model based on ontology with the usage of protege is established, which facilitate the on-line monitoring and control of building fire protection information, the prediction of electrical fire and improve building safety level evaluation. Keywords-internet of things(IoT); fire monitoring and control; ontology; fire safety evaluation
ADAPT: Adaptive distributed optimization approach for uploading data with redundancy in cooperative mobile cloud

With the development of information technology and the ubiquity of mobile devices, increasing amounts of data are generated, processed, and transmitted by mobile devices. To alleviate the tension between the energy poverty of mobile devices and the increasing demand for transmitting data, the energy‐efficient data transmission problem attracts considerable interests. Nonetheless, how to upload data with redundancy efficiently lacks a thorough study despite the wide existence of this problem in many situations like data storage among mobile devices and mobile crowd sensing. Since uploading redundant data brings little value while still consuming precious energy, it is important to design an efficient approach for mobile devices to upload data with redundancy cooperatively. In this work, we formulate the uploading data with redundancy in cooperative mobile cloud as an energy‐constrained utility maximization problem. To solve this problem, we propose an adaptive distributed optimization approach consisting of the correlated upload decision and the online distributed scheduling algorithm. By the correlated upload decision, each mobile device can make adaptive decisions on how much data to upload and which data to upload according to its own observations independently. The online distributed scheduling algorithm enables mobile devices to optimally upload data. A series of simulation experiments are conducted to demonstrate the effectiveness of our approach. Finally, we test our approach on a real demo system to verify its practicability in reality.
A comparative usability study of two touchscreen clinical workstations for use in low resource settings

Cost of implementation is one of the biggest barriers to scale up and sustainability of electronic medical record systems in low resource settings. Several approaches can be used to overcome this barrier. In this comparative usability study, we assess whether a lower cost 10-inch touchscreen clinical workstation (TCW) is a suitable alternative to a 14-inch TCW that has been widely deployed in Malawi. A total of 27 participants performed a patient registration task using three different TCWs, a 14-inch device and two 10-inch devices. One of the 10-inch devices used the same size of interface controls as the 14-inch TCW with reduced space between buttons, and the 10-inch device used a modified interface with controls shrunk down to accommodate the smaller screen size. We measured task completion times and error rates as metrics for assessing performance with each workstation. We also captured user perceptions using a usability survey and an exit survey. We compared the mean task completion time, number of errors, and survey scores between the three devices. In addition, a codebook was created to conduct a thematic analysis of the free-text responses to the exit survey. Our results suggest that the 10inch TCW is a suitable alternative to the 14-inch TCW based on task completion time. Nonetheless, modifications to the user interface are necessary to reduce error rates on the 10-inch TCW before implementation.
CHEMICAL ANALYSIS AND CYTOTOXIC ACTIVITY OF NHEXANE FRACTION OF Zanthoxylum acanthopodium DC. FRUITS

Zanthoxylum acanthopodium DC. the fruit is a potential anticancer agent. This study was evaluated the anticancer activity of n-hexane fraction (nHF) of Zanthoxylum acanthopodium DC. fruit towards T47D cell line. nHFwere analyzed for chemical constituents and tested for cytotoxicity, cell cycle inhibition, apoptosis induction and inhibition of cyclin D1 expression. nHF was found to contain monoterpenes and sesquiterpenes with geranyl acetate (26.34%) as major compound. nHF of Zanthoxylum acanthopodium DC. Fruits were found to have IC50 85.41 ± 0.78 μg/mL, cause accumulation in G0-G1phase, increased apoptosis and decreased cyclin D1 expression. The results reveal that nHF of Zanthoxylum acanthopodium DC. fruits have cytotoxic activities by inhibition cell cycle and induction apoptosis. Our further study is to isolate compounds that responsible for the cytotoxic activity.
Automated Labeling Process for Unknown Images in an open-world Scenario

Most of the recognition systems presume a controlled, well-defined research setting, where all possible classes that can appear during a test are known a priori. This environment is referred to as the ``closed-world'' model, while the ``open-world'' model implies that unknown classes can be incorporated into a recognition algorithm whilst being predicted. Therefore, recognition systems that operate in the real world have to deal with these unknown categories. Our objective was not only to detect data that originate from categories unseen during training, but to identify similarities between pieces of unknown data and then form new classes by automatically labeling them. Our Double Probability Model was extended by an image clustering algorithm, in which Kernel K-means was used. A new procedure, namely the Cluster Classification algorithm for the detection of unknowns and automated labeling, is proposed. These approaches facilitate the transition from open-set recognition to an open-world problem. The Fisher Vector (FV) was used for the mathematical representation of the images and then a Support Vector Machine introduced as a classifier. The measurement of similarity was based on the FV representations. Experiments were conducted on the Caltech101 and Caltech256 datasets of images and the Rand Index was evaluated over the unknown data. The results showed that our proposed Cluster Classification algorithm was able to yield almost the same Rand Index, even though the number of unknown categories increased.
Multi-objective optimization of all-wheel drive electric formula vehicle for performance and energy efficiency using evolutionary algorithms

A new method based on constraint multi-objective optimization using evolutionary algorithms is proposed to optimize the powertrain design of a battery electric formula vehicle with an all-wheel independent motor drive. The electric formula vehicle has a maximum combined motor power of 80 kW, which is a constraint for delivering maximum vehicle performance with minimal energy consumption. The performance of the vehicle will be simulated and measured against different driving events, that is, acceleration event, autocross event, and endurance event. Each event demands a different aspect of performance to be delivered by the motor. The respective event lap time or energy rating will be measured for performance assessment. In this study, a non-dominated sorting genetic algorithm II and constrained multi-objective evolutionary algorithm based on decomposition by using differential evolution are employed to optimize the motor transmission ratio, motor torque scaling, and downforce scale of both front and rear wheels against the acceleration event to minimize energy consumption and event lap time while constraining the combined motor power of all wheels to not exceed 80 kW. The optimization will be performed through software-in-the-loop between MATLAB and VI-Grade, where the high-fidelity vehicle will be modeled in VI-Grade and optimization algorithms will be implemented on the host in MATLAB. Results show that the non-dominated sorting genetic algorithm II outperforms the constrained multi-objective evolutionary algorithm based on decomposition by using differential evolution in obtaining a wider distributed Pareto solution and converges at a relatively shorter time frame. The optimized results show a promising increase in the performance of the electric formula vehicle in completing those events with the highest combined performance scoring, that is, the lap time of acceleration events improves by 9.18%, that of autocross event improves by 6.1%, and that of endurance event improves by 4.97%, with minimum decrease in energy rating of 32.54%.