Miracle-dz/UniFAR-FTUs huggingface dataset:
NVivo Software Training for Young Researchers

In the scope of engineering research, most of the problems are quantitative, qualitative problems are not so dominant but have a big influence on the results obtained. This qualitative research is conducted on natural objects that develop as they are, are not manipulated by researchers and the presence of researchers does not really affect the dynamics of these objects. Not all young technical researchers understand this method of qualitative data analysis. To answer this, it is necessary to provide qualitative data analysis to young researchers through Nvivo training activities, with implementation stages in the form of training in the theory and application of Nvivo as well as practicing data analysis with NVIVO. This program is used to analyze unstructured or semistructured data such as the results of interviews, surveys, field notes, web and journal articles. The target to be achieved is community empowerment services, scientific reports and articles (journals / proceedings). The method used is learning in class lectures, discussion and question and answer as well as practice / application simulation. The results obtained are partners understand how to use the NVivo application, out of 25 (twenty five) participants with a 'Very Good' score of 12%, a 'Good' score of 60% a 'Medium' score of 28% and a 'Not Appearing' value of 0%. The conclusion obtained is the increased capacity of partners in using the Nvivo application. Abstrak Dalam lingkup penelitian bidang teknik sebagian besar permasalahannya bersifat kuantitatif, permasalahan yang bersifat kualitatif tidak begitu dominan namun memberi pengaruh besar pada hasil yang diperoleh. Penelitian kualitatif ini dilakukan pada objek alamiah yang berkembang apa adanya, tidak dimanipulasi oleh peneliti dan kehadiran peneliti tidak begitu mempengaruhi dinamika pada obyek tersebut. Tidak semua peneliti muda bidang teknik memahami metode analisis data kualitatif ini. Untuk menjawab hal tersebut maka perlu diadakan pembekalan mengenai analisis data kualitatif kepada para peneliti muda melalui kegiatan pelatihan Nvivo, dengan tahapan pelaksanaan berupa pelatihan teori dan aplikasi Nvivo serta praktik analisis data dengan Nvivo. Program ini digunakan untuk menganalisis data tidak terstruktur atau semi terstruktur seperti hasil interview, survey, catatan lapangan, web dan artikel jurnal. Target yang ingin dicapai adalah jasa pemberdayaan masyarakat, laporan dan artikel ilmiah (jurnal/prosiding). Metode yang digunakan adalah pembelajaran di kelas ceramah, diskusi dan tanya jawab serta praktik/simulasi aplikasi. Hasil yang diperoleh adalah mitra memahami cara menggunakan aplikasi NVivo, dari 25 (dua puluh lima) orang peserta dengan nilai ‘Sangat Baik’ sebesar 12%, nilai ‘Baik’ sebesar 60% nilai ‘Sedang’ 28% dan nilai ‘Tidak Tampil’ sebesar 0%. Kesimpulan yang diperoleh adalah meningkatnya kapasitas mitra dalam menggunakan aplikasi Nvivo. © 2021 Author(s).
Pattern Extraction From Industrial Alarm Flood Sequences by a Modified CloFAST Algorithm

Alarm systems are critical for process safety and efficiency of complex industrial facilities. However, the presence of alarm floods severely compromises the performance of alarm systems. To cope with alarm floods, data mining has been applied to discover interesting patterns from historical alarm data, and such patterns can be used for alarm suppression, root cause analysis, and decision supports. However, most existing methods ignored the timestamps in pattern extraction or obtained complete patterns with significant redundancy. In this article, a new method is proposed to extract alarm flood patterns using a modified CloFAST algorithm. The contributions are twofold: first, a closed alarm sequence mining approach is proposed based on the CloFAST algorithm with improvements to incorporate timestamps and tolerate alarm order switchings; second, a pattern distillation strategy is designed to merge similar alarm sequences and export compact alarm sequential patterns. The proposed method is capable of avoiding influences of order ambiguities and also minimizing the redundancy of extracted patterns. The effectiveness of the proposed method is demonstrated by an industrial case study involving alarm data from a large-scale industrial facility.
ATM: An Active-Detection Trust Mechanism for VANETs Based on Blockchain

Existing trust mechanisms in vehicular ad hoc networks (VANETs) suffer from a variety of vulnerabilities, such as trust inconsistency in different regions and fake trust values generated by a set of cooperating malicious nodes. In this paper, we propose a novel local trust management mechanism, ATM, to solve these problems. ATM employs active detection and blockchain techniques. Specifically, the active detection effectively filters the surrounding malicious nodes and prevents their active cooperation, while the blockchain ensures the consistency of trust data across different regions. We conduct numerical analysis to evaluate the performance of ATM. Our experiment results show that ATM performs the best among the three trust mechanisms under test. It can effectively identify malicious behaviors in terms of 95% detection accuracy and 90% deliver ratio, respectively.
Self-Powered SSDCI Array Interface for Multiple Piezoelectric Energy Harvesters

To merge the gap between the low-power output of piezoelectric energy harvesters (PEHs) and the high-power demand of sensors of Internet of Things, researchers recently start to explore PEHs arrays. However, few circuits have been developed to manage the multiple ac inputs from PEHs arrays. This article presents a self-powered PEH array interface circuit based on the synchronized switching and discharging to a storage capacitor through an inductor (SSDCI) technique. The array interface can output a maximum total power greater than the sum of each individual peak power, and achieve a high efficiency when multiple PEHs connect to the SSDCI array circuit. Simulation and experiment are performed to demonstrate the advantages of the SSDCI array interface. The tested results show that the designed SSDCI array circuit achieves an efficiency of 82.3% with three input sources, and allows a gain up to 300% in terms of maximal output power compared to the full-bridge rectifier. In addition, only the peak detection is utilized for the switching control; therefore, the SSDCI array circuit is realized more easily and with fewer components compared to the existing synchronous electric charge extraction array interfaces.
Context-aware Music Recommender System Based on Implicit Feedback

This paper proposes a method for recommending music items without explicit feedback. Context and content features are used as auxiliary information to compensate for implicit feedback. The recent development of communication technology and portable electronic devices has changed the way of consuming music. We can access a vast amount of music items via online music streaming services. As a result, ﬁnding appropriate music items from enormous resources gets to be difﬁcult for users. To help users discover their favorite music items, recommender systems in the music domain have been studied. This paper focuses on two challenges speciﬁc to music recommender systems: the difﬁculty of obtaining explicit feedback such as ratings, and the importance of making use of context information. To handle the context information as auxiliary information to compensate for implicit feedback, this paper employs FMs (Factorization Machines), in which the context information is treated as features. Utilizing the merit of FMs that can easily introduce features, this paper also introduces content features in addition to context features. As it is known that using low-level content features directly is not effective because of the semantic gap, this paper proposes two types of abstract content features: UGP (user genre proﬁle) and UCP (user content proﬁle). The effectiveness of the proposed method and the effect of negative sampling methods are evaluated in terms of MPR with #nowplaying-rs and LFM-1b dataset. The result of the experiment shows that the proposed method outperforms wALS (weighted Alternating Least Squares), which is one of the popular music recommendation algorithms based on matrix factorization. The characteristics of the proposed sampling methods are investigated with different settings of the parameters and the ratio of negative samples. As for the effectiveness of each feature, it is found that a feature is effective when JS (Jensen-Shannon) divergence of popularity distribution among different feature values is large. It is also shown that the UCP and UGP cluster labels are more effective than using content features directly.
Determination of Simazine and Atrazine in River Water by Cloud-Point Extraction and High-Performance Liquid Chromatography

ABSTRACT Atrazine and simazine are endocrine-disrupting herbicides that may be transported to surface water, unbalancing ecosystems. Sensitive and low-cost methods are required for monitoring the residues of these compounds. Although several highly sensitive chromatographic methods coupled to tandem mass spectrometry are available, these methods use high-cost instrumentation. Ultraviolet detection usually does not provide the sensitivity and selectivity for monitoring these herbicide residues at the maximum concentrations levels permitted by regulatory agencies, so that extraction and concentration steps are required. Cloud-point extraction in Triton X-114 micelles was investigated to extract and preconcentrate atrazine and simazine. Treatment of 10 mL of sample solutions with 5 mL of 5% (m v−1) Triton X-114 in the presence of NaCl (0.3 g) with heating at 60°C for 30 min led to phase separation and the transfer of herbicides to the surfactant-rich phase, which was dissolved in 90:10 methanol:water for liquid chromatography analysis with ultraviolet detection. The linear dynamic range was 1–50 µg L−1 for the herbicides. The limits of detection were 0.13 and 0.27 µg L−1 for simazine and atrazine, respectively. The methodology was applied to water samples fortified with 1, 5, 15, and 50 µg L−1 of the analytes, resulting in recoveries between 86 and 132% with relative standard deviations less than 6%. The method is low cost and uses small volumes of toxic solvents with useful application in trial studies.
Comparison of Two Surgical Methods in the Treatment of Acute Achilles Tendon Rupture: a Case-control Study

Background: The Achilles tendon is the strongest tendon in the human body but also prone to injury and rupture, thereby requiring early diagnosis and treatment to prevent the development of lower limb dysfunction. Currently, the best treatment method for acute Achilles tendon rupture remains controversial. As both traditional incision repair and minimally invasive repair have limitations, we propose a new surgical method: the modified Ma-Griffith method combined with minimally invasive small incision(M-G/MISI). This study aimed to compare the efficacy of M-G/MISI and the modified suture technique (MST) for the treatment of acute Achilles tendon rupture.Methods: We conducted a retrospective review of the medical records of all patients who underwent treatment for acute Achilles tendon rupture between January 2012 and January 2020 at our hospital. A total of 67 patients were included and divided into two groups: 34 males in the M-G/MISI treatment group and 33 patients (2 females and 31 males) in the MST group. Demographic characteristics, operative details, and postoperative complications were recorded, and data were statistically analysed using SPSS 24.0 for Windows (IBM Corp. Released 2016. IBM SPSS Statistics for Windows, Version 24.0) to compare the treatment efficacy of the two surgical methods.Results: Although there was no difference in demographic characteristics between the two groups, there were significant differences in the incision length, intra-operative blood loss, post-operative Achilles tendon total rupture score, and post-operative American Orthopedic Foot and Ankle Society score. Post-operatively, there was one case of traumatic Achilles tendon rupture in the M-G/MISI group and one case each of infection and deep vein thrombosis in the modified suture group.Conclusion: After follow-up, the post-operative recovery of acute Achilles tendon rupture treated with M-G/MISI is better than that with the MST.
An In-Flight Radiometric Calibration Method Considering Adjacency Effects for High-Resolution Optical Sensors Over Artificial Targets

When using a field calibration site to perform in-orbit radiometric calibration of a space-borne remote sensor, the measured signal by the sensor may contain radiation from adjacent pixels, due to scattering in the Earth’s atmosphere and the sensor viewing characteristics. If this is not accounted for in the modeling, the accuracy of the radiometric calibration will be reduced. Nonuniformities of the ground target are more significant for the calibration of high-resolution sensors. In addition, if the brightness contrast of neighboring targets is not small, the impact of the adjacency effects will be more significant. It is important to quantitatively analyze and estimate the influence of this kind of adjacency effects, to reduce the uncertainty of in-orbit radiometric calibration. To evaluate the adjacency effects caused by atmospheric multiple scattering, this article constructed a local atmospheric point spread function model using long time-series satellite-ground synchronous observation data and developed an adjacency effects simulation method, which considers background reflectance spectral information. Tests on Sentinel-2A and Worldview-3 imagery overpassing the Baotou calibration and validation site (Baotou C&V site) (China) indicate that the proposed modeling method can effectively account for the influence of the adjacency effects in vicarious calibration. Uncertainties of relevant parameters and their contributions to the calibration result were also analyzed, and uncertainty assessment results show that the vicarious radiometric calibration scheme considering adjacency effects correction can bring about a total uncertainty less than 7%.
Establishment and research of combined forecasting model of carbon

This paper analyzes the influencing factors of carbon dioxide emissions from four aspects: Population, economy, industrial structure and energy, then from the carbon emissions, economic development, industrial structure, energy consumption structure to show the status quo of carbon emissions in Hubei Province. Based on the analysis of the influencing factors, the main influencing factors of carbon emission are population, regional gross product and coal consumption The multivariate linear regression model and the polynomial curve model are established and the error analysis is carried out. The combination weight coefficients of two single models are obtained through the linear programming model and the combination forecasting model is established, finally, the corresponding countermeasures to reduce carbon emissions are put forward.
Challenges and Solutions on Interference Management in D2D System of Cellular Network

A single bidirectional link is used to allow communication between two devices in the device-todevice (D2D) communication system. D2D technology has to implement with the current cellular system. As both users D2D and cellular use the same licensed spectrum for transmission the chances of interferences increases. It is challenging for researchers to find out the proper mechanism to decrease interference and maximize performances. In this paper, we try to survey the challenges and their solutions to enable D2D communication in the cellular network with low interference. Here we describe the peer discover, mode selection process and interference management with power control and resource allocation. Finally, we can say that with proper power control, spectrum slicing and resource allocation we can mitigate co-tier and cross-tier interferences.
On the Detection of the Correct Number of Factors in Two-Facet Models by Means of Parallel Analysis

Methods for optimal factor rotation of two-facet loading matrices have recently been proposed. However, the problem of the correct number of factors to retain for rotation of two-facet loading matrices has rarely been addressed in the context of exploratory factor analysis. Most previous studies were based on the observation that two-facet loading matrices may be rank deficient when the salient loadings of each factor have the same sign. It was shown here that full-rank two-facet loading matrices are, in principle, possible, when some factors have positive and negative salient loadings. Accordingly, the current simulation study on the number of factors to extract for two-facet models was based on rank-deficient and full-rank two-facet population models. The number of factors to extract was estimated from traditional parallel analysis based on the mean of the unreduced eigenvalues as well as from nine other rather traditional versions of parallel analysis (based on the 95th percentile of eigenvalues, based on reduced eigenvalues, based on eigenvalue differences). Parallel analysis based on the mean eigenvalues of the correlation matrix with the squared multiple correlations of each variable with the remaining variables inserted in the main diagonal had the highest detection rates for most of the two-facet factor models. Recommendations for the identification of the correct number of factors are based on the simulation results, on the results of an empirical example data set, and on the conditions for approximately rank-deficient and full-rank two-facet models.
A Prediction Method for the Dose Rate of Fuel Debris Depending on the Constituent Elements

In 2021, fuel debris samplings are planned to start as part of a step-by-step process at the Fukushima Daiichi nuclear power station. The dose rate of the fuel debris for safety treatments of the fuel debris should be predicted. However, various elements are mixed in the fuel debris, and thus predicting the dose rate will be challenging. Therefore, we conducted a large number of Monte Carlo radiation transport simulations for cases where parameters such as fuel debris size, composition, and density were significantly changed. Consequently, we obtained a simple and analytical formula that can predict the dose rate using a minimum number of parameters.
A Personalized Secure Publishing Mechanism of the Sensing Location Data in Crowdsensing Location-Based Services

Crowdsensing enables extensive data collection for Location-Based Services (LBS). However, there is a large amount of sensitive private information within the sensing data of the user’s location. Hence, it will pose a huge threat to user’s privacy if the sensing locations are published without protection. As usual, the user’s location has two typical attributes, i.e., a geographic one and a social semantic one. Hence, the secure publishing of the user’s sensing location data should consider how to protect both of the attributes-related privacy security. However, even for the same location, the same user may have different privacy protection needs along with different sensitive events, let alone that different users have different privacy protection needs due to their differences in age, gender, occupation, etc. Therefore, we propose a personalized secure publishing mechanism of the sensing location data. At first, it makes collaborative protection between semantic and geographic attribute-related privacy from two aspects, i.e., adjusting the user’s location information loss to meet different privacy protection needs of users, and personalizing the privacy-attack tolerance degree to achieve different privacy protection levels. Then, it optimizes the collaborative protection effects with the Stackelberg game to achieve the best balance between user data security and data quality. The experiments based on the real-world dataset demonstrate the effectiveness of our proposed mechanism.
Is corporate governance a good predictor of SMEs financial performance? Evidence from developing countries (the case of Lebanon)

ABSTRACT This paper aims to empirically examine the link between corporate governance and the financial performance of small and medium enterprises (SMEs) in Lebanon. To this end, we use a questionnaire and collect data from a sample of 150 non-listed companies. The Bundles approach following R. Aguilera et al. (2008). ‘An Organizational Approach to Comparative Corporate Governance: Costs, Contingencies, and Complementarities. ’ Organization Science 19: 475–492. ) is used to construct a corporate governance score based on three main components. By applying 2SLS regression to control for endogeneity and a quantile regression, we study the impact of corporate governance (CG) score and each of its components on the financial performance (FP) measured by return on assets (ROA) and return on investment (ROI) while controlling for SMEs age, size, and industry. The study indicates positive interdependency between CG and FP. Effective CG results in increased FP and better performing companies tend to improve their CG). Interestingly, our results show that this relationship depends on the level of SMEs FP These findings provide managers with useful insights and serve as an underpinning for more regulatory efforts aimed at strengthening the CG of SMEs in Lebanon.
Co(III) complex with (E)-2-(2-(pyridine-2-ylmethylene)hydrazinyl)-4-(4-tolyl)-1,3-thiazole: structure and activity against 2-D and 3-D cancer cell models

Abstract Co(III) complex with a 2-hydrazonylthiazole ligand was synthesized and characterized by single-crystal X-ray diffraction. In the inner sphere of the complex, two monoionic ligands are coordinated tridentately forming octahedral geometry around Co(III). Activity of the complex was investigated on MCF-7 breast cancer cell line, with cisplatin (CDDP) as a reference compound. Results showed that after 24-h incubation, Co(III) complex revealed stronger cytotoxic activity compared to CDDP. Treatment of MCF-7 3-D cell model with the complex at 10 μM concentration achieved complete suppression of spheroid growth in almost the same extent as at 100 μM. In combination treatments on MCF-7 spheroids, the complex acted synergistically with CDDP, while additive interaction type was achieved when the complex was applied together with paclitaxel.
Grounding Second Language Vocabulary Instruction in Cognitive Science

Cognitive neuroscience has gained significant insight into the mechanisms underlying the mental lexicon and their impact on second language vocabulary learning. However, relatively little effort has been put into understanding how these mechanisms may impact instructional practices. We attempt to bridge this gap. Towards that end, we first describe three main properties of the mental lexicon: words have distinct form and meaning representations; these representations are organized in multilingual network structures organized by similarity; and the representations differ in strength. Next, we translate the properties into a visual framework that we use to reflect upon how this knowledge affects three important choices in vocabulary instruction: composing word lists, selecting an instruction method, and consolidating newly acquired words. Lastly, we discuss opportunities for both science and practice, highlighting the importance of improving the ecological validity of scientific models and theories, and of developing instructional methods grounded in cognitive science.