Miracle-dz/UniFAR-FTUs huggingface dataset:
DETECTION OF COHESIVE SUBGROUPS IN SOCIAL NETWORKS USING INVASIVE WEED OPTIMIZATION ALGORITHM

Social network analysis ( SNA ) is a very popular research area that helps to analyze social structures through graph theory. Objects in social structures are represented by nodes and are modeled according to the relations (edges) they establish with each other. The determination of community structures on social networks is very important in terms of computer science. In this study, the Invasive Weed Optimization ( IWO ) algorithm is proposed for the detection of meaningful communities from social networks. This algorithm is proposed for the first time in community detection ( CD ). In addition, since the algorithm works in continuous space, it is made suitable for solving the CD problems by being discretized. The experimental studies are conducted on human-social networks such as Dutch College, Highland Tribes, Jazz Musicians and Physicians. The results obtained from experimental results are compared and analyzed in detail with the results of the Bat Algorithm and Gravitational Search Algorithm. The comparative results indicate that IWO algorithm is an alternative technique in solving CD problem in terms of solution quality.
Broadband Circularly Polarized Magnetoelectric Dipole Antenna by Loading Parasitic Loop

A broadband circularly polarized (CP) magnetoelectric (ME) dipole antenna loaded with parasitic loop is presented in this communication. The proposed ME dipole antenna is fed through the slot-coupled feeding method, in which a microstrip line is used to couple the energy to the antenna by the slot etched on the ground plane. By introducing a parasitic loop coplanar with the horizontal patch, extra CP modes are generated due to the coupling with the radiation patch. Combining with the original CP mode, the simulated 3 dB axial-ratio (AR) bandwidth is enhanced from 12.2% to 45.8% with an increment of 33.6%. The measured results show that the 10 dB impedance bandwidth is 55.3% (3.4–6.0 GHz) and the 3 dB AR bandwidth is 42.1% (3.62–5.55 GHz). Moreover, the proposed antenna owns flat gain and stable unidirectional radiation patterns within the designated frequency band.
On the control and automation of a novel membrane electro-bioreactor (MEBR)

The membrane electro-bioreactor (MEBR) has demonstrated to be effective in the treatment of wastewater, where superior quality of the effluent was achieved. The MEBR is a compact hybrid unit that uses several processes, such as activated sludge, membrane filtration, and electrokinetic phenomena. The objective of this study was to improve the treatment of wastewater by monitoring and controlling MEBR processes on-line, which was accomplished by implementing an automation system. As the complexity of the processes increase in the treatment wastewater, it is difficult to their guarantee performance; the automation system maintained the wastewater treatment to satisfactory performance. Automation of the system was accomplished through control algorithms using on-line instrumentation of critical parameters such as: dissolved oxygen, aeration, and water levels. The MEBR system demonstrated removal of carbon and nutrients (phosphorus, and nitrogen) for water recovery. Automated aeration ensured biological treatment without excessive aeration, fluctuating low dissolved oxygen concentrations allowed for simultaneous aerobic and anoxic conditions without inhibiting biological treatment. Automated electrokinetic improved nutrient removal with reduced energy consumption, also biological treatment was not inhibited. Electrokinetic demonstrated even lower than previously observed energy consumption. A user interface was implemented to allow on-site monitoring of the processes as well as allow adjustment of process parameters. Having a completely automated MEBR allowed this novel wastewater treatment system to be implemented in a remote location, as a decentralized system, in order to simulate an effective wastewater treatment system which may be applied to improve the quality of life for the secluded population of northern Canada and Quebec.
Recent Progress on Graphene Flexible Photodetectors

In recent years, optoelectronics and related industries have developed rapidly. As typical optoelectronics devices, photodetectors (PDs) are widely applied in various fields. The functional materials in traditional PDs exhibit high hardness, and the performance of these rigid detectors is thus greatly reduced upon their stretching or bending. Therefore, the development of new flexible PDs with bendable and foldable functions is of great significance and has much interest in wearable, implantable optoelectronic devices. Graphene with excellent electrical and optical performance constructed on various flexible and rigid substrates has great potential in PDs. In this review, recent research progress on graphene-based flexible PDs is outlined. The research states of graphene conductive films are summarized, focusing on PDs based on single-component graphene and mixed-structure graphene, with a systematic analysis of their optical and mechanical performance, and the techniques for optimizing the PDs are also discussed. Finally, a summary of the current applications of graphene flexible PDs and perspectives is provided, and the remaining challenges are discussed.
Does Executive Compensation Affect Accounting Irregularities? Evidence From Listed Firms in Indonesia

This research examined the association between executive compensation and accounting irregularities in Indonesia. Employing 1,100 firm-year observations during 2014 to 2017 period, we found that executive compensation is positively associated with accounting irregularities. This result is robust after we mitigated a serial order correlation of accounting irregularities and a self-selection bias using propensity score matching. We also discovered a positive relationship after considering alternative measures of accounting irregularities. Furthermore, we documented that the positive association between executive compensation and accounting irregularities is stronger for firms with higher board independence. Our findings provide important insights for regulators and Asian emerging market regulators who are considering promoting equity incentives for listed firms.
EEG Motor Imagery Classification Based on Multi-spatial Convolutional Neural Network

EEG motor imagery classification has important implications for the development of brain-computer interfaces. Unfortunately, how to accurately and comprehensively utilize the feature information contained in EEG motor imagery signals to further improve the classification performance is still a challenge. To solve this problem, this paper proposes an EEG motion imagery classification model based on multiple spatial convolution kernels. The model consists of spatial convolution and temporal convolution to simultaneously extract the feature expressions of EEG signals in different spaces. The experimental results show that the algorithm proposed in this paper achieves better classification accuracy than most existing algorithms in multiple data sets, which reflects the superiority of the algorithm. The work in this paper will advance the field of EEG motor imagery.
Discovery of pH-Selective Marine and Plant Natural Product Inhibitors of Cathepsin B Revealed by Screening at Acidic and Neutral pH Conditions

Dysregulation of cathepsin B, which involves the translocation of the enzyme from acidic pH lysosomes to the neutral pH cytosol, followed by the initiation of cell death and inflammation, occurs in numerous brain disorders. The wide difference in the acidic pH (4.6) of lysosomes compared to the neutral pH (7.2) of the cytosol suggests that screening at different pH conditions may identify pH-selective modulators of cathepsin B. Therefore, a collection of pure marine and plant natural product (NP) compounds, with synthetic compounds, was screened at pH 4.6 and pH 7.2 in cathepsin B assays, which led to the identification of GER-12 (Crossbyanol B) and GER-24 ((7Z,9Z,12Z)-octadeca-7,9,12-trien-5-ynoic acid) marine NP inhibitors at acidic pH but not at neutral pH. GER-12 was effective for the reversible inhibition of cathepsin B, with an IC50 of 3 μM. GER-24 had an IC50 of 16 μM and was found to be an irreversible inhibitor. These results show that NP screening at distinct biological pH conditions can lead to the identification of pH-selective cathepsin B modulators. These findings suggest that screening efforts for molecular probes and drug discovery may consider the biological pH environment of the target in the disease process.
Choosing a Mobile Wallet: Motives and Attitudes of Saudi Consumers toward the Adoption of Apple Pay

Purpose: The mobile payment system is widely used globally. However, this notion is not shared by all consumers in Saudi Arabia, and there is still prevailed prejudice or lack of trust among consumers towards using this unorthodox method of paying. Which raises the question of: What are the reasons that are hindering towards usage of mobile wallet method such as ‘Apple pay’ in Saudi Consumers. This study aims to find motives and attitudes of the Saudi consumers toward the adoption of Apple pay. Methodology: A correlation study design was adopted to answer the research question using a meta-UTAUT method. The study recruited 315 participants through social media to fulfil the questionnaire. Cronbach’s Alpha test was done to test the reliability of the test. Findings: This study resulted that performance expectancy, effort expectancy, personal innovativeness, trust and anxiety factors influences the attitude of Saudi customers towards adapting Apple pay method (p value > 0.05). Whereas, attitude affects behavioral intentions. Furthermore, performance expectancy and grievance redressal affect user behavior (p value > 0.05). Alternatively, social influence and behavioral condition has no significant relationship with behavioral intentions (p value < 0.05). Similarly, performance expectation is also not influencing user behavior (p value < 0.05). In conclusion, these factors will help the marketers and the manufacturers to understand the user demands of Saudi customers and its attention will ultimately help the consumers. Originality: This study will help understand the perception and attitude of Saudi consumers toward the adoption of Apple pay.
MODFLOW-2005 and MT3DMS models used to design and evaluate a bioremediation experiment at the former Naval Air Warfare Center, West Trenton, NJ

MODFLOW-2005 groundwater flow models and MT3DMS solute transport models were developed to represent conditions in the vicinity of a bioremediation experiment conducted in fractured rocks underlying the former Naval Air Warfare Center, West Trenton, New Jersey. The groundwater flow models include transient simulations of water-level changes during six field aquifer tests and steady-state simulations of hydraulic head at the site. The solute transport models include simulations of bromide tracer transport and of injection conditions during the bioremediation experiment. ZoneBudget and MODPATH were used for postprocessing analyses of flow modeling output. UCODE_2014 was used to calibrate the flow models and to conduct uncertainty analyses.
The Correlation Between Grade of Non-Alcoholic Fatty Liver Disease and Lipid Profile in Type 2 Diabetes Mellitus

Background. Non-alcoholic fatty liver disease (NAFLD) is a form of metabolic liver disease in which fat changes (steatosis) are associated with lobular inflammation, hepatocyte injury, polymorphs, or liver fibrosis. The study aimed to assess the relationship between NAFLD grades and lipid profiles in T2DM.Method. The design of the study was cross-sectional with the dependent variable being the grade of NAFLD and the independent variable being profile lipid. The sample of this study was NAFLD sufferers who met the inclusion criteria and were taken on a consecutive sampling basis. Diagnosis of NAFLD from anamnesis and physical, examination. Laboratory and abdominal ultrasound (Sonata SG 30 Ultrasound). Stages of NAFLD are divided into Degree I, Degree II, and Degree III. Lipid profile examination is carried out using a spectrophotometer tool colorimetric enzymatic method.Result. All patients are T2DM, consisting of Grade 1: 32 (40%), Grade II: 29 (36%), and Grade III: 19 (23%) patients. There was a significant difference between TC, TG, and LDL-C (p < 0.01) in the three grades of NAFLD. There is a significant correlation between NAFLD grade with TC and LDL-C (p < 0.05). Conclusion. In T2DM, there is a significant correlation between NAFLD grades and TC and LDL-C, and there was a significant difference between TC, TG, and LDL-C in the three grades of NAFLD.
Application of Machine Learning for Data with an Atmospheric Corrosion Monitoring Sensor Based on Strain Measurements

Machine learning methods were applied to data with an atmospheric corrosion monitoring sensor based on strain measurements to improve the evaluation accuracy of the thickness reduction of a low-carbon steel plate due to atmospheric corrosion. Monitoring data used in this study were taken in a previous study using active–dummy strain gauges for corrosion product experiments. Values measured by the gauges before inducing corrosion via saltwater treatment of the test piece and reference data of the thickness reduction in a reference test piece were used for training data. By using the trained machine learning methods, the errors for the outputs of the machine learning models were smaller than those for the evaluation in monitoring data of our previous study.
Identification of key sectors in Med countries : innovation perspectives

This presentation would like to focus on methodological aspect linked to the identification of key sectors and enterprises in Med countries that have an innovative potential. Today we know a lot more on Mediterranean countries, because a large effort has been done in accumulating data and identifying the S&T capabilities (See MEDA Innovation trend chart, See also UNDP report on Arab countries). But still most of the vision is one from the side of S&T policies, rather than from the “demand” side: enterprises.
Energy Management Strategies and Evaluation for Plug-in Electric Vehicles - On and Off the Road

Electric vehicle (EV) industries are driven by new technologies in batteries and powertrains. This thesis studies the cutting-edge Formula E racing vehicles with vehicle simulation and optimization for energy efficiency. On the consumer side, a new challenge EVs introduce is the need for large-scale charging infrastructure with minimum grid impact. This thesis studies EV charging management on the daily basis, featuring practical smart charging solutions at public locations and bi-directional (dis)charging at workplace and residence. Techniques that support smart charging are also studied. A data-mining based load disaggregation approach is developed to evaluate the general energy usage in the residential context. A machine-learning based load forecasting model is proposed to predict short-term residential loads in ultra-small scales. Overall, this thesis anticipates every aspect of EVs’ daily activities, whether it is on or off the road, and suggests solutions to maximizing EV utilization for both drivers and the smart grid.
The Case of Artificial vs. Natural Intelligence: Philosophy of Information as a Witness, Prosecutor, Attorney or Judge?

The relationship between artificial and natural, human intelligence becomes currently an issue of the primary importance for the world. The threats of technological singularity in the form of ultraintelligent machines occupied many philosophers, but thus far nobody paid attention to the encroaching singularity of relatively low level automation which eliminates need for low skill labor force and threatens wide masses of human population. Economists predict that many occupations regarded as requiring high skills will become spurious too. The only solution for the optimal coexistence of the artificial and natural intelligence is in the reform of education. The solution does not require anything new as the liberal arts education promoted human intellectual development leading to the roles which machines least likely can assume.
Application of mid-infrared vibrational spectroscopy with Fourier transform (FTIR) in quality evaluation in commercial coffees

Currently, Brazil is the largest exporter and producer of coffee in the world, and it is the second most consumed beverage in the world, only behind water. In the years 2019 and 2020 it is estimated that the world consumption of coffee was 168.84 million bags of 60 kg, Brazil consumed 20 million bags of coffee, the second-largest consumer in the world, only behind the United States with 25 million bags. The techniques such as infrared spectroscopy has been applied in the food industry, as it is a fast, easy technique, without the need for reagents, free from polluting processes, and capable of analyzing the simultaneous composition of the constituents. The present study aims to analyze the changes in the chemical constituents of Brazilian commercial coffees as a function of shelf life through Fourier transformed infrared spectroscopy (FT-IR) associated with chemometric methods. The experiments were carried out within the expiration date, 6 months, and a year after the expiration date. Spectra were obtained in the range from 4000 to 500 cm-1. The studies of Principal Component Analysis (PCA) and Hierarchical Cluster Analysis (HCA) were made as discrimination methods. The areas in the region from 2970 to 2830 cm-1 and 1765 to 1720 cm-1 were calculated to analyze the alteration as a function over time. The results suggest that these bands in coffee are sensitive over time and to the storage conditions, promoting changes in aroma and flavor.
The Capillary Morphogenesis Gene 2 Triggers the Intracellular Hallmarks of Collagen VI-Related Muscular Dystrophy

Collagen VI-related disorders (COL6-RD) represent a severe form of congenital disease for which there is no treatment. Dominant-negative pathogenic variants in the genes encoding α chains of collagen VI are the main cause of COL6-RD. Here we report that patient-derived fibroblasts carrying a common single nucleotide variant mutation are unable to build the extracellular collagen VI network. This correlates with the intracellular accumulation of endosomes and lysosomes triggered by the increased phosphorylation of the collagen VI receptor CMG2. Notably, using a CRISPR-Cas9 gene-editing tool to silence the dominant-negative mutation in patients’ cells, we rescued the normal extracellular collagen VI network, CMG2 phosphorylation levels, and the accumulation of endosomes and lysosomes. Our findings reveal an unanticipated role of CMG2 in regulating endosomal and lysosomal homeostasis and suggest that mutated collagen VI dysregulates the intracellular environment in fibroblasts in collagen VI-related muscular dystrophy.
Enhancing fault ride-through capability of DFIG with modified SMES-FCL and RSC control

For grid-connected doubly fed induction generators (DFIGs), fault ride-through (FRT) capability and transient stability are vital problems that urgently need to be addressed. To overcome these problems, a novel superconducting magnetic energy storage-fault current limiter (SMES-FCL)-based protection scheme by modified control of superconducting coil (SC) and rotor-side converter (RSC) is investigated here. A rotor-side mathematical model is used to estimate and optimise the SC inductance parameter. Two modified control strategies of SMES-FCL cooperative operation and positive-sequence d - q current modification (PCM) are proposed to control the SC and RSC for enhancing the transient stability of the overall DFIG system. In addition to maintaining good FRT performance from the SMES-FCL on stabilising the electromagnetic torque, DC-link voltage, active and reactive power, and stator and rotor current during grid fault, the PCM control is also demonstrated in the simulations to suppress the transient oscillations and thus to shorten the recovery time after grid fault. Meanwhile, compared with conventional SMES-FCL scheme, the requirement of SC current capacity is much lowered for smoothing the DFIG output power under a varying wind speed condition and for enhancing the FRT performance under a grid fault condition.
Impact of Protein Content on the Antioxidants, Anti-Inflammatory Properties and Glycemic Index of Wheat and Wheat Bran

Conventional wheat milling generates important volumes of wheat bran (WB), which is a concentrated source of polyphenols and insoluble fiber. In terms of health benefits and based on epidemiological and experimental evidence, these compounds contribute to reducing the risk of certain chronic pathologies. Protein concentration is the main quality factor conditioning wheat use in the agroindustry. When turning waste into feasible resources, it is essential to evaluate the variability of the raw material. The aim of this study was the evaluation of the impact of protein content in the valorization of WB based on its antioxidants, anti-inflammatory properties and glycemic index (GI). A significantly (p ≤ 0.05) lower content of phenolic compounds was found in the whole grain (WG) fractions, both free (FP) and bound (BP), as compared to the WB phenolic fractions, differences that ranged from 4- to 6-fold (538 to 561 mg GAE 100 g−1 in WG vs. 1027 to 1236 in WB mg GAE 100 g−1 in FP and 2245 to 2378 vs. 6344 to 7232 mg GAE 100 g−1 in BP). A significant (p ≤ 0.05) effect of the protein content on the resulting phenolic content and antioxidant capacity was observed, especially in WG, but also in WB, although in the latter a significant (p ≤ 0.05) negative correlation was observed, and increasing the protein content resulted in decreasing total phenolic content, antioxidants, and ferric-reducing capacities, probably due to their different types of proteins. The highest protein content in WB produced a significant (p ≤ 0.05) reduction in GI value, probably due to the role of protein structure in protecting starch from gelatinization, along with phytic acid, which may bind to proteins closely associated to starch and chelate calcium ions, required for α-amylase activity. A significant (p ≤ 0.05) effect of the protein content on the GI was also found, which may be explained by the structural effect of the proteins associated with starch, reducing the GI (21.64). The results obtained show the importance of segregation of WB in valorization strategies in order to increase the efficiency of the processes.
Interval-based Visual-Inertial LiDAR SLAM with Anchoring Poses

We present a novel interval-based visual-inertial LiDAR SLAM (i-VIL SLAM) method that solely assumes sensor errors to be bounded and propagates the error from the input sources to the estimated map and trajectory using interval analysis. The method allows us to restrict the solution set of the robot poses and the position of the landmarks to the set that is consistent with the measurements. If the error limits are not violated, it is guaranteed that the estimated set contains the true solution. The accumulation of the uncertainty is stabilized by anchoring poses derived from GNSS/INS data. Furthermore, for the first time we compare confidence ellipses determined by a classical SLAM graph optimization approach with the interval estimates of the robot poses provided by our method. In this work, we experimentally show that the marginal co-variances computed by the classical SLAM graph optimization are too overconfident and underestimate the uncertainty of the poses. While the 99.9 %-ellipsoids derived from the marginal covariances of the poses only enclose less than 64 % of the ground truth in the worst case, our method provides interval bounds for the pose parameters that enclose the ground truth for more than 96 % of all frames.