Miracle-dz/UniFAR-FTUs huggingface dataset:
Analysis of Energy-efficient Techniques for SDWSN Energy Usage Optimization

Software-Defined Wireless Sensor Networks (SDWSN) has received significant attention in recent years due to its inherent challenges such as network security, trust management, inefficient energy consumption, and so on. In particular, inefficient energy utilization has remained a core critical challenge as sensor nodes are naturally resource constraint and mostly deployed in unattended environments. This challenge has a direct impact on SDWSN performance and reliability. Albeit, several techniques have been proposed and developed to address the challenge in the traditional WSN and few for SDWSN, optimal efficient energy utilization is yet to be achieved. Therefore, this paper survey some of the energy efficiency techniques reported in the literature. The goal is to gain insights into these techniques, strategies employed, their pros and cons which could be utilized to design an efficient-energy mechanism for SDWSN. The findings obtained show that the existing approach ensures efficient energy utilization and improve network performance. While some have routing protocol and security mechanisms, fault tolerance, and battery fault detector mechanisms are not incorporated.
Synthesis of Activated Carbon Mesoporous from Coffee Waste and Its Application in Adsorption Zinc and Mercury Ions from Aqueous Solution

We obtain activated carbons with high portion of meso pores using coffee residues as precursor for the application of adsorption of large adsorbates. Because of its natural properties, the coffee residue exhibited a large pore size. In this work, the coffee residue were impregnated with ZnCl2 and KOH, and then carbonized under the nitrogen conditions and activated with CO2 respectively. Obtained activated carbons are used in the adsorption of ions Hg(II) and Zn(II). These adsorbents are efficacious to remove these ions from aqueous solution, with monocomponent equilibrium adsorption capacities ranging from from 0.002 to 0.380 mmol∙g-1 for Hg on ACK3 and from 0.002 to 0.330 mmol∙g-1 for ACZ3. For Zn(II) on ACK2 from 0.002 to 0.300 mmol∙g-1, and from 0.001 to 0.274 mmol∙g-1 for ACZ2.
The effects of hydrodistillation and solvent free microwave extraction methods on the chemical composition and toxicity of essential oils from the leaves of Mentha longifolia L. subsp. capensis.

The effects of hydrodistillation (HD) and solvent free microwave extraction (SFME) methods on the chemical composition and toxicity of Mentha longifolia subsp. capensis leaves were studied. Colourless oils of 1.52 and 1.63% (wet/dry weight) were obtained from HD and SFME methods, respectively. Analyses of the two oils revealed a total of 21 components. These accounted for about 80% of the total oil composition. The HD oil was found to be characterized by monoterpenoids with oxygenated monoterpenes, such as menthone (41.9%), a-pinene (5.4%), pulegone (34.2%), β-pinene (2.4%) and 1, 8-cineole (6.5%). Other identified constituents, include cis-sabinene hydrate (0.4%), linalool (1.0%), piperitenone (1.3%), β-caryophyllene (0.4%), germacrene D (0.5%) and isomenthone (0.4%). However, the hydrodistilled oil showed the presence of 3 octanol (0.2%), bicyclogermacrene (0.1%) and caryophyllene oxide (0.1%) which were not found in the oil extracted with solvent free microwave extractor. The SFME oil was characterized by menthone (28.0%), a-pinene (3.6%), pulegone (23.0%), β-pinene (1.0%) and 1, 8-cineole (5.4%). Other identified constituents, include cis-sabinene hydrate (0.1%), linalool (0.5%), piperitenone (0.1%), β-caryophyllene (0.3%), germacrene D (0.2%) and isomenthone (0.3%). In vitro cytotoxicity test was also studied by brine shrimp lethality bioassay. At 40 µg/ml which was the least concentration used, the oil was not toxic; all the brine shrimps survived. While maximum mortalities happened at 200 µg/ml, the least mortalities were observed at 40 µg/ml. Key words: Mentha longifolia, essential oils, hydrodistillation, solvent free microwave extraction, toxicity.
Distributed event-triggered quantized consensus for multi-agent systems under general directed graphs

This paper studies the distributed event-triggered quantized consensus control problem for continuous-time multiagent systems (MASs) under a general directed graph. Taking the constrains of communication bandwidth and storage space into consideration, a logarithmic quantizer is introduced for each channel. To further save communications costs, an event-triggered broadcasting scheme is developed for each channel, without incurring Zeno behavior. Based on the above communication protocol, to achieve consensus, a distributed event-triggered quantized consensus controller is developed for each agent. Compared with the consensus control methods that rely on continuous communication, the proposed method saves the communication costs in both temporal and spatial aspects. The effectiveness of the proposed methodology is illustrated by the results of simulation.
Hit Song Prediction based on Gradient Boosting Decision Tree

Record companies invest billions of dollars in new talent around the globe each year. Gaining insight into what actually makes a hit song would provide tremendous benefits for the music industry. In this research, we tackle this question by focusing on predicting rank of hit songs in the next 6 months. Our dataset is used in ZALO AI CHALLENGE 2019 in Hit Song Prediction problem including not only songs but also its information such as composer, artist name, released date, etc. Because of that, while most previous work formulates hit song prediction as a regression or classification problem, we present in this paper how to apply Gradient Boosting technique to treat it as a ranking problem. The resulting best model has a good performance when predicting whether a song is a top 10 dance hit versus a lower listed position with 1.48815 Root Mean Square Error - our result dominates most of the solution in this competition (better than 3rd ranked solution of 87 in total). Moreover, it is possible to further improve by extracting chords, tones and more information from each song to obtain the highlights of songs and by using linguistics model to offer high-level features of metadata.
Evaluating and Enhancing the Performance of Skin Disease Classification Based on Ensemble Methods

Nowadays, people of all ages are suffering from various diseases. Skin cancer is one of the most common problem and it seriously impacts on human life and health. Fungal infection, bacteria, allergy, harmful UV rays of sunburn, etc. may be the reason for various types of skin disease. It can be recover if the disease can diagnose more quickly and correctly in the novel stage. At present, Machine Learning (ML) plays a significant role in the medical field. So, ML techniques helps to increase the accuracy in diagnosis of several types of skin disease. In our study, for evaluating the performance we have used five Machine Learning techniques like Naïve Bayes (NB), K-Nearest Neighbor (KNN), Support Vector Machine (SVM), Random Forest (RF) and Multilayer Perception (MLP). We have also used three basic ensemble methods as Bagging, Boosting, Stacking to enhance the performance of the trained model and attaching all classifiers in the classification stage. Accuracy, Precision, Sensitivity, f1score are considered as a measure of the usefulness of the proposed model. In this paper, our main motive is to progress the performance of the diagnosis of variant type of skin diseases and compare three ensemble techniques by combining all machine learning approaches.
An Alternative Lightness Control with GAN for Augmenting Camera Data

To build an autonomous car, many technologies have to be taken into application. The most important component of a fully self-driving car is the object detection system. This system is responsible for detecting obstacles on the street. However, these detection models still face many difficulties such as unable to work on extreme conditions (storm, night, chaotic road,…). To tackle one aspect of this problem, in this paper we propose an augmentation method that creates more data by generating night images from day images and vice versa using LCcycleGAN, a Lightness conditional Unpaired Image-to-Image Translation approach, this framework is the fusion of CycleGAN [1] and conditional GAN [2]. To evaluate our method, we measure performance of YoloV3 [3] on our collected dataset (and augmented data) consists of day and night images of Vietnamese streets which are often highly chaotic and extreme. Our method increases AP of base vehicle detection model's performance from 0.5 to 0.56.
Optimal Values of Unknown Parameters of Polymer Electrolyte Membrane Fuel Cells Using Improved Chaotic Electromagnetic Field Optimization

In this paper, the Improved Chaotic Electromagnetic Field optimization (ICEFO) algorithm is adopted to generate the optimal values of unknown parameters of fuel cells (FCs). The mathematical model of polymer electrolyte membrane FC (PEMFC) is considered nonlinear and complex optimization problem with different control variables. The sum of squared error (SSE) between the measured and computed stack voltages is considered as the main objective function. The performance of ICEFO algorithm is tested on three PEMFC stacks. Moreover, sensitivity and statistical measures have been presented to confirm the reliability and accuracy of ICEFO. In addition, the effect of changing the cell temperature and reactants pressures are studied for more validation of ICEFO. Furthermore, the results obtained by ICEFO are competitively compared with other optimization methods. These results confirm the effectiveness of ICEFO in solving the optimization problem of PEMFC parameter estimation.
Software-Defined RFID Readers: Wireless Reader Testbeds Exploiting Software-Defined Radios for Enhancements in UHF RFID Systems

In recent years, RF identification (RFID) technology in the ultrahigh-frequency (UHF) range has become increasingly important in many applications, especially in the context of both the Internet of Things (IoT) and Industry 4.0. RFID is no longer simply a matter of identification; today, it is mainly concerned with recording data from the surrounding environment. As a result, researchers are working on the integration of sensors (or even small actuators) in passive RFID transponders (tags) for a variety of different applications and also on the localization and tracking of RFID tags attached to various objects and machinery. Another key aspect in UHF RFID technologies is security, as the data transmitted may be confidential and should not be shared with third parties. For the fast verification of novel algorithms in localization and novel protocols for security, wireless testbeds allow swift verification of these designs under real-world or real-time conditions [1]. Wireless testbeds are widely used in research and require scalability, modularity, and extendibility of the hardware and software. These needs can be achieved by using software-defined radios (SDRs). In the case of UHF RFID, SDR-based RFID readers have been developed and used as wireless testbeds for enhancements in UHF RFID systems.
Studies on essential oils. Part 35: chemical and biocidal investigations on Tagetes erecta leaf volatile oil

Chemical investigations on the leaf essential oil of Tagetes erecta, performed by HPLC, GC and GC–MS techniques, showed the presence of 26 components, accounting for 89% of the total oil. The major constituents were (Z)-β-ocimene (42.2%), dihydrotagetone (14.3%), (Z)-tagetone (8.3%), limonene (7.3%), (E)-ocimenone (6.1%) and (Z)-ocimenone (5.3%). The biocidal investigations showed that the oil possessed a significant but limited and dose-related antifungal and insecticidal activity. The oil showed 100% mortality of white termite (Odontotermes obesus Rhamb.) at a dose of 6 µl/Petri-plate after 24 h of exposure, while at lower doses and shorter exposures it showed diminished mortality rates. The oil only partially affected the mycelial growth of any of the tested fungi. Thus, the leaf oil of this plant, which is rich in (Z)-β-ocimene, has a statistically significant antifungal and insecticidal activity. Copyright © 2002 John Wiley & Sons, Ltd.
An Empirical Study on the Impact of Social Presence on Customer Citizenship Behavior in E-business

Under the B2C online shopping environment, the lack of social reality inhibits customer behavior, which triggers a need for enhancing social presence. Due to the rapid development of B2C e-business, customer behavior has extended to customer citizenship behavior. It remains unclear whether and how social presence influences customer citizenship behavior. We construct a theoretical model about the impact mechanism, which introduces customer trust and customer attitude as mediating variables. 289 questionnaires are collected from the customers who shop online frequently. The research results indicate that awareness and cognitive social presence have significant effects on customer citizenship behaviors such as recommendation, feedback and helping behavior. The specific mediating effect of customer trust is also significant. These findings point out that e-business platforms are supposed to deepen the degree of social presence to stimulate customer citizenship behavior.
A Wellbore Flow Model of Coiled Tubing Drilling with Supercritical Carbon Dioxide

Abstract Supercritical carbon dioxide (SC-CO2) drilling technology is a new type of drilling technology, which has developed rapidly since the end of the last century. With lower jet erosion threshold pressure, higher jet erosion speed, and excellent hole cleaning capabilities, the SC-CO2 drilling receives much attention. However, for SC-CO2 fluid, the density, viscosity, heat capacity, Joule-Thomson coefficient, and so on are more sensitive to changes of wellbore temperature and pressure, and at the same time the change of parameters will react to the wellbore temperature and pressure, so they should be widely investigated while drilling with SC-CO2. The wellbore temperature and pressure coupled flow model was developed for SC-CO2 drilling, and then the wellbore pressure, temperature, and density profile were analyzed in an example. The results showed that the calculation results have a larger difference between this model and the reference model; the main reason is that the viscosity, isobaric heat capacity, thermal conductivity of SC-CO2, etc. were deemed as a fixed value in the reference model, and the wellbore temperature and pressure were not coupled while calculating.
Adsorption and interfacial electron transfer of Saccharomyces cerevisiae yeast cytochrome c monolayers on Au(111) electrodes

We have studied the adsorption and electron-transfer dynamics of Saccharomyces cerevisiae (yeast) iso-1-cytochrome c adsorbed on Au(111) electrodes in aqueous phosphate buffer media. This cytochrome possesses a thiol group close to the protein surface (Cys102) suitable for linking the protein to gold without drastic protein unfolding. A comprehensive approach, based on linear sweep and differential pulse voltammetry, capacitance measurements, X-ray photoelectron spectroscopy (XPS), in situ scanning tunneling microscopy (STM), and microcantilever sensor (MCS) techniques has been used. The voltammetric data display a thiol reductive desorption signal corresponding to close to monolayer coverage. Reductive desorption is also reflected in a capacitance peak. Voltammetric signals from the heme group in both native and partially denatured states could also be detected. XPS shows clear Au−S bond formation, but this observation is not conclusive for aqueous buffer conditions, as the protein is extensively unfolde.
Understanding Reactions to Natural Disasters: a Text Mining Approach to Analyze Social Media Content

Social media has played a critical role in disaster management. Citizens and officials use social media to access emergency information, send warnings, and communicate with the affected community. On the other hand, the massive data generated by users can be analyzed to extract useful information such as the location and needs of the most affected areas,, as well as social effects of the disaster on the community. In this paper, we study data on two social media, Twitter and Telegram, during the widespread flood in Iran in 2019. The flood affected 80 percent of the country, caused around 2.5 billion dollars worth of damage and resulted in more than 220,000 people being forced into emergency shelters. We focus on the Telegram instant messaging service, as the most popular messaging platform in Iran. We collected all highly viewed flood-related posts from Telegram channels and Twitter posts in a course of 16 days. We analyzed these messages by the area they address, the topic they are assigned to and change in sentiment over time. Our analyses suggest that valuable information can be obtained by looking into data. Such information can guide policy makers in fast and effective decision making during and after a disaster.
A 112-765 GOPS/W FPGA-based CNN Accelerator using Importance Map Guided Adaptive Activation Sparsification for Pix2pix Applications

This paper proposes an algorithm and hardware co-design methodology to accelerate CNNs for pix2pix tasks. An importance map is introduced to train an activation-sparse CNN model, which can effectively reduce the computing cost and external data transmission. Moreover, the model also supports sparse controlling by means of the importance map, making it adaptive for applications with different precision/power requirements. An FPGA-based accelerator with adaptive sparse controlling is designed to support such importance map guided activation sparsity, and demonstrated for super-resolution (SR) application as an example. The accelerator shows advantages in both model accuracy and power consumption. It achieves up to 765 GOPS/W energy efficiency, which is 5.28× batter than the previous FPGA-based SR accelerator.
A Survey of LoRaWAN Adaptive Data Rate Algorithms for Possible Optimization

The Adaptive Data Rate (ADR) is used in LoRa WAnto ensure optimized data rates and transmission powers for static end devices. However, its implementation is still new, and therefore, its functionalities are limited. Multiple proposals have been made to help improve the ADR but an optimum solution is yet to be found. Therefore, this paper surveyed nine most relevant papers with the goal of identifying the ADR challenges and critically analyzing the solutions put forward to solve them. The critique of the solutions was based on the problems they solve, how they solve them, and the improvements they achieved. Findings show that the poorly designed ADR algorithm results in increased collisions and unfairness in collision probabilities amongst network traffic in LoRaWAN. Furthermore, the findings show that the studies surveyed had limitations in terms of the data rates investigated, the transmission parameters considered, and the region in which they were conducted. To help mitigate some of these challenges, few suggestions are presented in this paper.
Deep Learning in Sheet Metal Bending With a Novel Theory-Guided Deep Neural Network

Sheet metal forming technologies have been intensively studied for decades to meet the increasing demand for lightweight metal components. To surmount the springback occurring in sheet metal forming processes, numerous studies have been performed to develop compensation methods. However, for most existing methods, the development cycle is still considerably time-consumptive and demands high computational or capital cost. In this paper, a novel theory-guided regularization method for training of deep neural networks (DNNs), implanted in a learning system, is introduced to learn the intrinsic relationship between the workpiece shape after springback and the required process parameter, e.g., loading stroke, in sheet metal bending processes. By directly bridging the workpiece shape to the process parameter, issues concerning springback in the process design would be circumvented. The novel regularization method utilizes the well-recognized theories in material mechanics, Swift's law, by penalizing divergence from this law throughout the network training process. The regularization is implemented by a multi-task learning network architecture, with the learning of extra tasks regularized during training. The stress-strain curve describing the material properties and the prior knowledge used to guide learning are stored in the database and the knowledge base, respectively. One can obtain the predicted loading stroke for a new workpiece shape by importing the target geometry through the user interface. In this research, the neural models were found to outperform a traditional machine learning model, support vector regression model, in experiments with different amount of training data. Through a series of studies with varying conditions of training data structure and amount, workpiece material and applied bending processes, the theory-guided DNN has been shown to achieve superior generalization and learning consistency than the data-driven DNNs, especially when only scarce and scattered experiment data are available for training which is often the case in practice. The theory-guided DNN could also be applicable to other sheet metal forming processes. It provides an alternative method for compensating springback with significantly shorter development cycle and less capital cost and computational requirement than traditional compensation methods in sheet metal forming industry.
Passive Antenna Systems Embedded into a Load Bearing Wall for Improved Radio Transparency

Providing cellular network services inside residential or office buildings has become challenging, especially for fifth-generation networks that use higher carrier frequencies. Additionally, new energy-efficient buildings contain envelopes such as low-emissivity glass and new multi-layer thermal insulations, all of which – unintendedly but effectively – also block radio signals. As a solution to those problems of indoor coverage, we suggest the use of passive antenna systems embedded into the building walls. We propose a numerical evaluation method for determining the electromagnetic transmission coefficient and the thermal insulation of a typical building wall. Next, we investigate two antenna configurations embedded to the wall, a two-patch and a four-patch design, both operating around 3.5 GHz. We show from numerical simulations that those antenna systems increase the transmission coefficient of the wall. At the same time, we show that the four-patch design does not compromise the thermal insulation of the wall.
Improved Calculation Method for Inductance Value of the Air-Gap Inductor

An improved method for calculating the inductance of the air-gap inductor is introduced. The existing calculation methods mainly focus on the air-gap fringing effect while ignoring the winding reluctance's influence. In some large-capacity occasions, since the winding of the inductor also has a large volume, the influence of the winding reluctance on the inductance value cannot be ignored. In this paper, the winding reluctance is calculated, and the total inductance is solved based on a magnetic reluctance model. The results show that the proposed method is effective.
5G mm-Wave Over-The-Air Measurements of an Agile Multi-Beam Front-End

A millimeter wave agile multi-beam front-end with an integrated 4 by 1 antenna array for 5th generation wireless communications is analyzed by antenna and digital communication measurements. The front-end core comprises a compact low-temperature co-fired ceramic multilayer module with a foot-print area of 74 mm × 74 mm. An over-the-air gain of 50.4 dB and a noise figure of 4.9 dB were measured. Link measurements with broadband single-carrier QAM and OFDM signals result in a data rate of 3.2 Gb/s for 400 MHz bandwidth SC-256QAM modulation with 1.2% error vector magnitude of the front-end determined by a detailed analysis.