Miracle-dz/UniFAR-FTUs huggingface dataset:
Economic Implications of Technological and Energy Advancement on CO2 Emission Intensity in Selected Countries

In the pursuit of economic prosperity, many countries have "sacrificed" the quality of their environment to achieve higher rates of economic growth. By focusing on established industrial practices that have destroyed the environment in the long run, they have harmed their own, as well as the global ecosystem. Within this context, the main challenge for a long-term sustainable economic policy is the intertwined focus on two goals - economic growth and environmental protection. The aim of this paper is to analyse the impact of the development of environmentally related technologies, renewable energy supplies, and the total primary energy supply on the intensity of CO2 emissions based on the production within five countries in the period from 1990 to 2019. The observed countries are Italy, Germany, Croatia, Brazil, and Finland. To evaluate the relationship between the observed variables a panel analysis was used, more specifically random effect models (REM) and fixed effect models (FEM). Empirical results show that the development of environmental technologies and the supply of renewable energy have a negative impact on CO2 emissions, while the total supply of primary energy has a positive impact on CO2 emissions. The results of the research suggest that environment-related technologies and renewable energy promote the issue of environmental protection, while the overall supply of primary energy actually limits the perspective of green development.
Kinetic mechanism on elemental mercury adsorption by brominated petroleum coke in simulated flue gas

A waste byproduct of petroleum coke was obtained as a precursor modified with bromine for elemental mercury capture from simulated flue gas on a bench scale fixed-bed reactor. The reaction temperature, the initial inlet elemental mercury concentration and the individual flue gas components of O2, NO, SO2 and HCl were determined to explore their influence on elemental mercury capture by the brominated petroleum coke. Results indicate that high initial inlet mercury concentration can enhance initial mercury accumulation and the optimal temperature for elemental mercury capture by brominated petroleum coke is about 150 °C. Kinetic models reveal that the pseudo-second order and Elovich models are best fitted to the mercury adsorption process, indicating that chemisorption is the control step with the intra-particle diffusion and external mass transfer taking place simultaneously. The kinetic parameters demonstrate that the initial mercury adsorption rate (h or a) and the equilibrium adsorption quantity (Qe) increase remarkably, when higher concentrations of O2 or NO exist in N2 atmosphere. On the contrary, Qe decreases with the presence of high SO2 or HCl, which indicates a two-sided effect on the performance of mercury adsorption owing to their concentrations.
Evaluation of interactions of silibinin with the proteins ALS3 and SAP5 against Candida albicans

Objective: to evaluate the molecular interaction of silibinin with the targets ALS3 and SAP5. Methodology: Molecular docking protocols were conducted to analyze the binding interaction of silibinin with ALS3 and SAP5. Results: Eleven interactions of ALS3 with silibinin and four with fluconazole were found, while six interactions were observed of SAP5 with silibinin and four with fluconazole. Conclusion: Molecular docking between silibinin and ALS3 identified important interactions, but no significant interactions were observed with SAP5, even though silibinin can exhibit affinity and interactions with other SAP5 sites.
An Improved Label Initialization based Label Propagation Method for Detecting Graph Clusters in Complex Networks

—Community structure is one of the fundamental characteristics of complex networks. Detection of community structure can provide insight into the structural and functional or- ganization that helps to understand various dynamical processes such as epidemics and information spreading. Label propagation algorithm (LPA) is a well-known method for community structure identiﬁcation due to linear time complexity. However, the communities extracted by the LPA is unstable since it produces different combinations of communities at each run on the same network. In this paper, a novel label initialization method for label propagation algorithm (ILI-LPA) is proposed to detect stable and accurate community structures. The proposed ILI-LPA focuses on more accurate label initialization rather than assigning unique labels thereby reduce the effect of randomness in LPA. The experiments on several real-world and synthetic networks show that the ILI-LPA improves the quality and stability of communities compared to existing algorithms. The results also demonstrate that appropriate label initialization can signiﬁcantly improve the performance of label propagation algorithms, and the stability has been improved up to 50-78% relative to the standard LPA.
Evaluating Filtering Facepiece Respirator Wearing-Comfort of Lebanese Red Cross Healthcare Providers

Introduction The COVID-19 pandemic significantly increased the usage of various types of face masks. In addition, it triggered the rapid manufacture of new production lines of masks to cope with the unprecedented demand to overcome worldwide shortages. Such masks, which were previously used mostly by the health care personnel, became a daily necessity to the greater mainstream population. This rapid and sudden increase in their usage and the fact that new masks’ innovations are progressively emerging to meet the growing global shortage requires an ongoing analysis on the factors associated with the fit and comfort while using these masks. Methods This paper presents the first translation and validation of the R-COMFI questionnaire to evaluate the comfort of a newly developed filtering face-piece respirator by the research team at the University of Antwerp. The questionnaire, which consists of 3 sections: Discomfort, General wearing experience, and Function, was translated from English to Arabic and involved 43 participants in the Lebanese Red Cross healthcare field based in Lebanon. Results The results showed discomfort factors that are mostly related to breathability and sweating caused by mask usage. Additionally, the results revealed that female respondents found the mask significantly less comfortable than male respondents (p-value with the two-tailed test is 0.0319), which confirmed that future validations should consider the concerns of both genders, and validated the R-COMFI translation exercise detailed in this paper. Discussion The contribution of this paper can be pinned down into three findings. The first finding is related to the discomfort issues. The second finding highlighted a significant difference in comfort experience between females and males. The last finding is the translation validation of the R-COMFI instrument, which confirmed that the questionnaire can be applied among wider geographical locations.
Hybrid Deformable Convolutional with Recurrent Neural Network for Optimal Traffic Congestion Prediction: A Fuzzy Logic Congestion Index System

—In the field of Intelligent Transportation Systems (ITs), traffic congestion is considered as an important problem. Traffic blockage usually affects the quality of time, travel time, economy of the country, and transportability of people. The information of traffic congestion is collected and analyzed in ITs, and the methods to prevent the traffic congestion are predicted. However, the tackling of huge data is still challenging. The rapid increase in vehicle usage and road construction has resulted in traffic congestion. Various studies are undergone in ITs to recognize the traffic management system by adopting few resources. Real time-based traffic services are implemented to prevent the traffic congestion in existing areas. These services provide high expense accuracy. This paper plans to develop a new technique to predict the traffic congestion using improved deep learning approaches. At first, the benchmark dataset is gathered and the pre-processing of data is performed with removing the bad data, organizing the raw data, and filling the null values. The optimized weighted features are selected from the pre-processed data by adopting a new meta-heuristic Hybrid Jaya Harris Hawk Optimization (HJHHO) algorithm. The prediction of congestion parameters such as speed reduction rate, very low speed rate, and volume to capacity ratio of vehicles are performed by the proposed Improved Deformable Convolutional Recurrent Network (IDCRN) prediction model. These predicted measures are subjected to fuzzy interference system for congestion index computation. From the experimental analysis, it has proved that the proposed method has reduced the error rate while comparing with other deep learning and machine learning approaches.
EFFECT OF MACHINING PARAMETERS ON SURFACE ROUGHNESS IN CNC TURNING

Surface roughness is one of the important criteria for product quality in manufacturing processes. It depends on many factors according to different manufacturing processes. In relation to the operation of the lathes, which is the focus of our study, it was found that the controllable factors affecting surface smoothness are (cutting speed, feeding rate and depth of cut). Samples of mild steel are used for the experiment; with different settings. Readings of the surface roughness are measured using sensitive digital devices, and then analyzed using octave programs. The experimental results and graphs showed that the surface roughness is greatly affected by the cutting parameters, and using of cooling liquid. The results obtained from this work showed that the surface roughness decreases with increasing spindle speed, decreasing cutting depth and feed.
A novel method for condition monitoring of wind turbine gearbox in wind farm

In this paper, the gearbox of wind turbine in a wind farm is taken as research object, and its operation condition monitoring model is established by using multivariable long-short term memory networks (LSTM). Firstly, parameters with high correlation are obtained by using maximum information coefficient (MIC) as the input vectors of monitoring model. Then, the oil temperature prediction model of gearbox is constructed based on LSTM network. The residual between actual value and predicted value of gearbox oil temperature is obtained. After that, a gearbox condition monitoring model is established by using residual sequence, exponential weighted moving average (EWMA), and kernel density estimation algorithm. The case analysis shows that the proposed method can carry out fault early warning about 15.7 hours in advance. Compared with univariate LSTM condition monitoring model and SVR condition monitoring model, it can find faults more timely and can be applied to fault early warning of wind turbines in wind farm.
Expression of the GZMB Gene Polymorphism, SNP rs8192917, in 990 Han Chinese Patients with Postoperative Keloids

Background A keloid is a pathological scar hyperplasia that is affected by genetic and environmental factors. Although the involvement of cytotoxic granzyme B in keloids has been recognized, there is almost no research on granzyme B (GZMB) gene polymorphisms and keloids. This study aimed to explore the relationship between genetic polymorphisms of GZMB and postsurgical keloid susceptibility in the Han Chinese population. Material/Methods A total of 3078 participants, including 990 patients with postsurgical keloids and 2088 controls without postsurgical keloids, were enrolled. We selected 15 common DNA variants in the GZMB gene for analysis. Associations were analyzed in both single marker-based and haplotype-based methods. The Genotype-Tissue Expression database was used to examine the biological significance of the targeted single nucleotide polymorphisms (SNPs). Results SNP rs8192917 was found to be associated with the susceptibility of keloids (t statistic=4.82, P=1.47×10−6). An increased risk of keloids was significantly associated with the minor allele (C allele) of rs8192917 (odds ratio=1.33; 95% CI=1.18–1.49], P=1.47×10−6). In addition, a significant association was reported for genotypes of rs8192917 and clinical severity of keloids (χ2=10.61, P=0.03). Conclusions The results suggested there are significant associations between common genetic variants in GZMB and the susceptibility of postsurgical keloids in the Chinese Han population. These genetic polymorphisms were also related with the severity of postsurgical keloid symptoms in participants with keloids. The current study can contribute to future etiological and clinical research of keloids.
Resilient fixed-time bipartite consensus of multi-agent systems with nonlinear dynamics and directed graphs

This article is concerned with the bipartite fixed-time consensus problem for a class of multi-agent systems with nonlinear dynamics and directed graphs. To achieve the fixed-time consensus, a discontinuous control protocol is proposed. Compared to the protocols with undirected graphs, the control parameters are designed using a new Laplacian matrix based on an underlying undirected graph. Besides, to apply the method to the multi-agent systems with malicious agents, the proposed controller is combined with the Weighted Mean Subsequence Reduced (WMSR) algorithm which is modified for signed graphs. Finally, some simulations are presented to demonstrate the performance of the proposed method.
Representing Tibetan Buddhism in Books on Spirituality: A Discourse-Historical Approach

This article looks into how Tibetan Buddhism is framed in terms of East-West dichotomy in six popular books on Buddhism and spirituality. Discourse Historical Approach is employed to uncover the rhetorical representation of Tibetan Buddhism to the readers. A critical post-colonial perspective offers an insight into various power dynamics, arising from these representations, structured according to Yoshikawa's model of intercultural communication between East and West. The various power outcomes of rhetorical styles range from Ethnocentric to Dialogical, with their ethical consequences and problematics discussed.
Hydrogen bonding interactions on molecular properties of pesticidal compound 4-nitrophthalic acid: experimental density functional theory computations, electron localized function, localized orbital locator analysis and molecular docking scrutiny

Abstract Nitroaromatic compounds are expansively used in the manufacturing of pharmaceuticals, pesticides, plasticizers, azo dyes and explosives. Nitrophenolic compounds can accumulate in the soil as a result of hydrolysis of several insecticides such asparathion, methyl parathion, and fenitrothion which can cause propitious effects to the biological systems. In optimized geometry hyperconjugation of nitro and dicarboxylic acid groups with the ring system increases the electron density of the aromatic system. Electron localized Function and Localized orbital Locator Analysis has value in a region wherein indicates high localization of electrons due to the presence of covalent bond, a lone pair of electrons or a nuclear shell in that region. In Nuclear Magnetic Resonance analysis signals in highly deshielded region clearly indicates the presence of carboxylic carbon in the compound. From vibrational assignments, broadening and red shifting depicts the level of intermolecular hydrogen bonding. In-vitro antimicrobial activity results clearly reveal that in all the four strains, activity increases with increase in compound concentration and the inhibition of the strains also increases. From both molecular docking and In-vitro Antimicrobial Activity the title compound can be certified that it inhibits antimicrobial activity and it is a pesticide.
Steel Surface Defect Detection using the RetinaNet Detection Model

Some surface defects make the weak quality of steel materials. To limit these defects, we advocate a one-stage detector model RetinaNet among diverse detection algorithms in deep learning. There are several backbones in the RetinaNet model. We acknowledged two backbones, which are ResNet50 and VGG19. To validate our model, we compared and analyzed several traditional models, one-stage models like YOLO and SSD models and two-stage models like Faster-RCNN, EDDN, and Xception models, with simulations based on steel individual classes. We also performed the correlation of the time factor between one-stage and two-stage models. Comparative analysis shows that the proposed model achieves excellent results on the dataset of the Northeastern University surface defect detection dataset. We would like to work on different backbones to check the efficiency of the model for real world, increasing the datasets through augmentation and focus on improving our limitation.
Understanding school leaders and educators’ attitudes towards youth migrants: An experimental analysis

Due to the global economic and political crises in various regions of the world in recent years, the flow of migrants from countries with deeper structural problems to more developed countries has increased dramatically. At national level, education is the basis on which the values, philosophy, ideology, and culture of a nation are determined. Behavior is analyzed from both psychological and sociological perspectives. The purpose of the paper is, through an experimental analysis of Greek school leaders and educators in the upper level of secondary education, to investigate their attitudes towards young immigrants that attend Greek high schools and determine whether or not their behavior is being influenced by psychological or sociological aspects of the migration phenomenon. The experimental results are in accordance with both sociological and psychological perspectives for the chosen behaviors of school educators. It is clear that familiarity, experience and the exercise of educational policy are critical factors influencing teachers' attitudes towards young immigrants. No difference in behavior has been found except for a slightly more positive attitude towards emigration. In view of the above, recognizing the importance of more cohesive and socially protective educational policies and practices would have far-reaching consequences.
The Approach of Induced Generalized Neutrosophic Cubic Shapley Choquet Integral Aggregation Operators via the CODAS Method to Solve Distance-Based Multicriteria Decision-Making Problems

This study aims to define a conjecture that can handle complex frames of work more efficiently that occurs in daily life problems. In decision-making theory inter-relation of criteria, weights and choice decision-making method subject to the given circumstances which are an important component for appropriate decisions. For this, we define neutrosophic cubic Shapley–Choquet integral (NCSCI) measure; combinative distance-based assessment selection (CODAS) is accomplished over NCSCI and is implemented over a numerical example of a company foreign investment model as an application in decision-making (DM) theory. The neutrosophic cubic set (NCS) is a hybrid of the neutrosophic set (NS) and interval neutrosophic set (INS), which provides a better plate form to handle inconsistent and vague data more conveniently. The novel CODAS method is based on Shapley–Choquet integral and Minkowski distance which contain more information measures than usual criteria weights and distances. The weights of criteria are measured by Shapley–Choquet integral and distance is evaluated by Minkowski distance. The Choquet integral considers the interaction among the criteria, and Shapley considers the overall weight criteria. Motivated by these characteristics NCSCI, we defined two aggregation operators’ induced-generalized neutrosophic cubic Shapley–Choquet integral arithmetic (IGNCSCIA) and operators’ induced-generalized neutrosophic cubic Shapley–Choquet integral geometric (IGNCSCIG) operators. To find the distance between two NC values, Minkowski distance is defined to evaluate neutrosophic cubic combinative distance-based assessment selection (NCCODAS). To examine the feasibility of the proposed method, an example of company investment in a foreign country is considered. To check, the validity of the method, the comparative analysis of the proposed method with other methods is conducted.
Altered Cerebral Curvature in Preterm Infants Is Associated with the Common Genetic Variation Related to Autism Spectrum Disorder and Lipid Metabolism

Preterm births are often associated with neurodevelopmental impairment. In the critical developmental period of the fetal brain, preterm birth disrupts cortical maturation. Notably, preterm birth leads to alterations in the fronto-striatal and temporal lobes and the limbic region. Recent advances in MRI acquisition and analysis methods have revealed an integrated approach to the genetic influence on brain structure. Based on imaging studies, we hypothesized that the altered cortical structure observed after preterm birth is associated with common genetic variations. We found that the presence of the minor allele at rs1042778 in OXTR was associated with reduced curvature in the right medial orbitofrontal gyrus (p < 0.001). The presence of the minor allele at rs174576 in FADS2 (p < 0.001) or rs740603 in COMT (p < 0.001) was related to reduced curvature in the left posterior cingulate gyrus. This study provides biological insight into altered cortical curvature at term-equivalent age, suggesting that the common genetic variations related to autism spectrum disorder (ASD) and lipid metabolism may mediate vulnerability to early cortical dysmaturation in preterm infants.
Dynamic behavior of group pile under lateral spreading

Synopsis Results of a series of centrifuge experiments to study the dynamic response of pile foundations under lateral spreading were compared to the results of numerical analysis. Experiments were carried out under the centrifugal acceleration of 40 G. Piles in the model foundation were lined up 3 by 3 pattern with a spacing of three pile diameter, and both pile head and bottom were rotation fixed. Piles were placed in the inclined ground of saturated sands and applied lateral loads due to the ground deformation. The effective stress finite element analysis was conducted to simulate these experiments. Computed time histories of pile head acceleration and displacement were consistent with those obtained from experiments. However, much smaller surface ground deformation in simulation might cause small amplitude of bending moments. Some calibration in the numerical modeling may be required to have more consistent results on bending moments.
Reactivity of 4,5-Dichlorophthalic Anhydride towards Thiosemicarbazide and Amines: Synthesis, Spectroscopic Analysis, and DFT Study

The cyclic anhydrides are broadly employed in several fields, such as the chemical, plastic, agrochemical, and pharmaceutical industries. This study describes the chemical reactivity of 4,5-dichlorophthalic anhydride towards several nucleophiles, including thiosemicarbazide and different amines, to produce the carboxylic acid derivatives resulting from anhydride’s opening, namely, phthalimide and dicarboxylic acid (1–12) products. Their chemical structures are confirmed by NMR, IR and MS spectra analyses. Density–functional theory (DFT) studies are performed using (DFT/B3LYP) with the 6-311G(d, p) basis sets to recognize different chemical and physical features of the target compounds.