bluuebunny/crossref_metadata_2025 huggingface dataset:
Optimization of Coagulation-Flocculation Process in Efficient Arsenic Removal from Highly Contaminated Groundwater by Response Surface Methodology
Authors: Amiri Saba , Vatanpour Vahid , He Tao
Published: November 2022

Elevated arsenic (As) contamination in water, especially groundwater, has been recognized as a major problem of catastrophic proportions. This work explores As(V) removal via the coagulation-flocculation process by use of ferric chloride coagulant and polyacrylamide k16 co-coagulant as a first time. The effects of major operating variables such as coagulant dosing (50, 125 and 200 mg/L), co-coagulant dosing (5, 12.5 and 20 mg/L), pH (6, 7and 8), fast mixing time (1, 2 and 3 min), and fast mixing speed (110, 200 and 300 rpm) on As(V) removal efficiency were investigated by a Box-Behnken statistical experiment design (BBD) and response surface methodology (RSM). According to factors F values, coagulant dosing, rapid mixing speed, pH, and co-coagulant dosing showed the most effect on As(V) removal efficiency, and the rapid mixing time factor indicated the slightest effect. The proposed quadratic model was significant with a p value < 0.0001 and has satisfactorily described the experimental data with R2 and adjusted R2 values of 0.9855 and 0.9738, respectively. Predicted model optimal conditions with target of complete As(V) removal were coagulant dosing = 197.63 ppm, co-coagulant dosing = 19.55 ppm, pH = 7.37, fast mixing time = 1.43 min and fast mixing speed = 286.77 rpm. The treatment of Nazarabad well water sample with an initial As(V) concentration of 5 mg/L under the optimal conditions removed 100% As(V) with the volume of produced sludge of 10.7 mL/200 mL. Increasing coagulant dosing, co-coagulant dosing, fast mixing time and fast mixing speed operation parameters from low-level to high-level values indicated 78%, 20%, 10.52% and 9.47% increases in volume of the produced sludge, respectively. However, a reduction of 13.63% in volume of the produced sludge resulted via pH increases.
Overview of the Polyphenols in Salicornia: From Recovery to Health-Promoting Effect
Authors: Limongelli Francesco , Crupi Pasquale , Clodoveo Maria Lisa , Corbo Filomena , Muraglia Marilena
Published: November 2022

Nowadays, there has been considerable attention paid toward the recovery of waste plant matrices as possible sources of functional compounds with healthy properties. In this regard, we focus our attention on Salicornia, a halophyte plant that grows abundantly on the coasts of the Mediterranean area. Salicornia is used not only as a seasoned vegetable but also in traditional medicine for its beneficial effects in protecting against diseases such as obesity, diabetes, and cancer. In numerous research studies, Salicornia consumption has been highly suggested due to its high level of bioactive molecules, among which, polyphenols are prevalent. The antioxidant and antiradical activity of polyphenols makes Salicornia a functional food candidate with potential beneficial activities for human health. Therefore, this review provides specific and compiled information for optimizing and developing new extraction processes for the recovery of bioactive compounds from Salicornia; focusing particular attention on polyphenols and their health benefits.
Phenotypes, Genotypes, and Treatment Options of Primary Failure of Eruption: A Narrative Review
Authors: Testarelli Luca , Sestito Francesca , De Stefano Adriana , Seracchiani Chiara , Vernucci Roberto , Galluccio Gabriella
Published: November 2022

Tooth eruption is a complex process, during which a series of factors can cause a failure of it. Among this, primary failure of eruption (PFE) is a non-syndromic condition that leads to an incomplete tooth eruption despite the presence of a clear eruption pathway. The aim of this narrative review is to provide an overall view about clinical considerations, genetics-related aspects, and possible treatments of PFE based on the latest findings. A literature search using the PubMed/Medline and Scopus database was performed. The search terms used were “PFE”, “orthodontics”, “primary failure of eruption”, and “treatment”, and all the articles, according to the inclusion criteria, from 2008 until June 2022 were screened. Among them, 12 articles were considered useful to highlight some of the main genotypical and phenotypical aspects and several treatment options. Indeed, if there is a suspicion of primary failure of eruption, a PTH1R screening should be performed, because a mutation in this gene is responsible for an altered balance between the resorptive and the appositional processes during the eruption. This is important to know before starting an orthodontic treatment because it could lead to ankylosis of the affected tooth, exposing patients to iatrogenic damage. Treatment options depend on the growth phase of the patient and on the clinical situation.
Frequency Pulling and the Linewidth Enhancement Factor in Optically Injected Semiconductor Laser
Authors: Al-Hosiny Najm M.
Published: November 2022

The effect of the linewidth enhancement factor (LEF) on the frequency pulling behavior in optically injected lasers is theoretically investigated. The frequency pulling is found to be exponentially dependent on the LEF. This dependence is systematically revealed and explained.
Revealing Impact Characteristics of the Cassava Dust Explosion Process: Experimental and Numerical Research
Authors: Zhang Ruichong , Xie Chengyu
Published: November 2022

The combustion and explosion characteristics of cassava starch and the dispersive physical motion law of dust were systematically studied using a 20 L (=0.02 m3) spherical explosive test device and the numerical simulation method. The experimental results show that the explosion pressure first increases and then decreases with increasing ignition delay time, dust concentration, and spray pressure in the dust storage tank. The maximum explosion pressure was obtained with a dust concentration of 750 g/m3, while the maximum rate of pressure increase was obtained when the concentration was 250 g/m3. The calculated maximum explosion index was 22.3 MPa∙m∙s−1. The simulation results show that the physical movement law of the dust was as follows: high initial velocity → gradual decrease in diffusion velocity → upward linear movement of dust → outward diffusion motion → continuous disorder motion → free settlement → gradual reduction and disorder state → finally, complete settlement. With a powder diffusion time of 120 ms, the dust distribution in the round sphere was the most uniform, which was consistent with the experimental results. After dust ignition, the temperature first gradually increased and then decreased due to heat dissipation. The maximum pressure in the vessel was 46.7 MPa, and the turbulence was the most intense close to the ignition point.
Phytochemical Analysis and Antimicrobial Activity of Rosmarinus officinalis L. Growing in Saudi Arabia: Experimental and Computational Approaches
Authors: Saleh Asmaa , Al Kamaly Omkulthom , Alanazi Ashwag S. , Noman Omar
Published: November 2022

Rosmarinus officinalis L. is widely distributed in Saudi Arabia. However, only a few studies have been reported regarding this species. In this study, we investigated the phytochemical analysis of R. officinalis essential oil using GC-MS analysis in order to identify its major components; the in vitro antimicrobial activity of the essential oil was also evaluated using disc diffusion assay against gram-positive (S. aureus) and gram-negative bacteria (E. coli), the antimicrobial activity was also assessed with molecular docking against several microbial proteins; TyRS, DNA gyrase and DHFR. The GC-MS analysis has indicated the presence of 18 constituents, representing 99.93 % of the total oil content. The major compounds detected were Bornyl acetate (26.59%), Eucalyptol (17.38%), Camphor (10.42%), Borneol (9.78%), Beta-Caryophyllene (7.80%) and α-pinene (3.85%). The antimicrobial study showed that R. officinalis has strong antimicrobial activity against S. aureus with an inhibition zone of 30 mm and E. coli with an inhibition zone of 25 mm. The affinities of molecular docking (TyrRS: between −4.8 and −4.9 Kcal/mol against −8.2 Kcal/mol obtained with Clorobiocin; DNA gyrase: between −4.5 and −4.9 Kcal/mol against −9.1 Kcal/mol obtained with Clorobiocin). However, strong affinities were obtained with the molecules when tested against DHFR (DHFR: between −5.8 and −6.0 Kcal/mol against −6.3 Kcal/mol obtained with SCHEMBL2181345). As a consequence, the pharmaceutical industry may use the essential oils from this plant to develop cutting-edge synthetic drugs to treat this illness infection.
Development of FT-NIR Technique to Determine the Ripeness of Sweet Cherries and Sour Cherries
Authors: Fodor Marietta
Published: November 2022

The FT-NIR technique was used for the rapid and non-destructive determination of sweet cherry and sour cherry ripeness. Titratable acidity (A), water-soluble total solids (SSC), total anthocyanin (TA) content and calculated maturity index (SSC/A = MI) were used as reference values. PLS correlations were validated by seven-fold cross-validation (RMSECV for different parameters: DM = 1.25%, w/w; A = 0.14%, w/w; SSC = 0.97%, w/w; TA = 17.5 g/100 mL; MI = 1.66) and test-validation (RMSEP for different parameters: DM = 1.46%, w/w; A = 0.19%, w/w; SSC = 0.99%, w/w; TA = 17.5 g/100 mL; MI = 1.59). Different discriminant analyses and support vector machine (SVM) classifications were performed for each parameter. The quadratic discriminant analysis (QDA) was found to be the best pattern recognition method. A maturity degree (MD) was developed based on the reference values, which classified the samples into mature and immature categories with an accuracy of 98.44%.
A Combined Neural Network Approach for the Prediction of Admission Rates Related to Respiratory Diseases
Authors: Jose Alex , Macdonald Angus S. , Tzougas George , Streftaris George
Published: November 2022

In this paper, we investigated rates of admission to hospitals (or other health facilities) due to respiratory diseases in a United States working population and their dependence on a number of demographic and health insurance-related factors. We employed neural network (NN) modelling methodology, including a combined actuarial neural network (CANN) approach, and model admission numbers by embedding Poisson and negative binomial count regression models. The aim is to explore the gains in predictive power obtained with the use of NN-based models, when compared to commonly used count regression models, in the context of a large real data set in the area of healthcare insurance. We used nagging predictors, averaging over random calibrations of the NN-based models, to provide more accurate predictions based on a single run, and also employed a k-fold validation process to obtain reliable comparisons between different models. Bias regularisation methods were also developed, aiming at addressing bias issues that are common when fitting NN models. The results demonstrate that NN-based models, with a negative binomial distributional assumption, provide improved predictive performance. This can be important in real data applications, where accurate prediction can drive both personalised and policy-level interventions.
The 2021 Greece Central Crete ML 5.8 Earthquake: An Example of Coalescent Fault Segments Reconstructed from InSAR and GNSS Data
Authors: Famiglietti Nicola Angelo , Golshadi Zeinab , Vallianatos Filippos , Caputo Riccardo , Kouli Maria , Sakkas Vassilis , Atzori Simone , Moschillo Raffaele , Cecere Gianpaolo , D’Ambrosio Ciriaco , Vicari Annamaria
Published: November 2022

The ML 5.8 earthquake that hit the island of Crete on 27 September 2021 is analysed with InSAR (Interferometry from Synthetic Aperture Radar) and GNSS (Global Navigation Satellite System) data. The purpose of this work is to create a model with sufficient detail for the geophysical processes that take place in several kilometres below the earth’s surface and improve our ability to observe active tectonic processes using geodetic and seismic data. InSAR coseismic displacements maps show negative values along the LOS of ~18 cm for the ascending orbit and ~20 cm for the descending one. Similarly, the GNSS data of three permanent stations were used in PPK (Post Processing Kinematic) mode to (i) estimate the coseismic shifts, highlighting the same range of values as the InSAR, (ii) model the deformation of the ground associated with the main shock, and (iii) validate InSAR results by combining GNSS and InSAR data. This allowed us to constrain the geometric characteristics of the seismogenic fault and the slip distribution on it. Our model, which stands on a joint inversion of the InSAR and GNSS data, highlights a major rupture surface striking 214°, dipping 50° NW and extending at depth from 2.5 km down to 12 km. The kinematics is almost dip-slip normal (rake −106°), while a maximum slip of ~1.0 m occurred at a depth of ca. 6 km. The crucial though indirect role of inherited tectonic structures affecting the seismogenic crustal volume is also discussed suggesting their influence on the surrounding stress field and their capacity to dynamically merge distinct fault segments.
Developing and Using Empirical Bio-Optical Algorithms in the Western Part of the Bering Sea in the Late Summer Season
Authors: Salyuk Pavel A. , Stepochkin Igor E. , Sokolova Ekaterina B. , Pugach Svetlana P. , Kachur Vasiliy A. , Pipko Irina I.
Published: November 2022

This study aimed to assess the applicability of global bio-optical algorithms for the estimation of chlorophyll-a (chl-a) concentration (C) and develop regional empirical bio-optical algorithms for estimating C and colored dissolved organic matter (CDOM) content (D) from ocean remote sensing reflectance spectra in the western part of the Bering Sea in the late summer period. The analysis took into account possible problems with the different relative contributions of phytoplankton and CDOM to water-leaving radiance and possible errors associated with the atmosphere correction procedure for ocean color satellite data. Shipborne remote sensing measurements obtained using an above-water hyperspectral ASD HandHeld spectroradiometer, satellite measurements collected via MODIS and VIIRS radiometers, and in situ measurements of C and D in seawater were used. The simulated values of the different multispectral satellite radiometers with daily or 2-day global coverage, obtained by applying the corresponding spectral response functions to ship hyperspectral data, were also analyzed. In this paper, a list of recommended regional bio-optical algorithms is presented. Recommendations are given depending on the possible quality of atmospheric correction and the purpose of use. To obtain more precise estimations of C, OC3/OC4-like algorithms should be used. If the atmosphere correction is poor, then use OC2-like algorithms in which spectral bands in the 476–539 nm range should be used to estimate C and bands near 443 nm to estimate D; however, in the last case, this will provide only the order of magnitude. To estimate more independent fields of C and D, it is necessary to use a spectral range of 501–539 nm for chl-a and bands near 412 nm in the case of modern satellite radiometers (e.g., OLCI or SGLI), for which this band is not the first. Additionally, we showed that global bio-optical algorithms can be applied with acceptable accuracy and similar recommendations.
Fractional Fourier Transform and Distributions in the Ray Space: Application for the Analysis of Radio Occultation Data
Authors: Gorbunov Michael , Dolovova Oksana
Published: November 2022

The concept of the phase space plays a key role in the analysis of oscillating signals. For a 1-D signal, the coordinates of the 2-D phase space are the observation time and the instant frequency. For measurements of propagating wave fields, the time and instant frequency are linked to the spatial location and wave normal, defining a ray. In this case, the phase space is also termed the ray space. Distributions in the ray space find important applications in the analysis of radio occultation (RO) data because they allow the separation of interfering rays in multipath zones. Examples of such distributions are the spectrogram, Wigner distribution function (WDF), and Kirkwood distribution function (KDF). In this study, we analyze the application of the fractional Fourier transform (FrFT) to the construction of distributions in the ray space. The FrFT implements the phase space rotation. We consider the KDF averaged over the rotation group and demonstrate that it equals the WDF convolved with a smoothing kernel. We give examples of processing simple test signals, for which we evaluate the FrFT, KDF, WDF, and smoothed WDF (SWDF). We analyze the advantages of the SWDF and show examples of its application to the analysis of real RO observations.
Breathing Pattern Monitoring by Using Remote Sensors
Authors: Kunczik Janosch , Hubbermann Kerstin , Mösch Lucas , Follmann Andreas , Czaplik Michael , Barbosa Pereira Carina
Published: November 2022

The ability to continuously and unobtrusively monitor and classify breathing patterns can be very valuable for automated health assessments because respiration is tightly coupled to many physiological processes. Pathophysiological changes in these processes often manifest in altered breathing patterns and can thus be immediately detected. In order to develop a breathing pattern monitoring system, a study was conducted in which volunteer subjects were asked to breathe according to a predefined breathing protocol containing multiple breathing patterns while being recorded with color and thermal cameras. The recordings were used to develop and compare several respiratory signal extraction algorithms. An algorithm for the robust extraction of multiple respiratory features was developed and evaluated, capable of differentiating a wide range of respiratory patterns. These features were used to train a one vs. one multiclass support vector machine, which can distinguish between breathing patterns with an accuracy of 95.79 %. The recorded dataset was published to enable further improvement of contactless breathing pattern classification, especially for complex breathing patterns.
Development and Analysis of Electrochemical Reactor with Vibrating Functional Element for AAO Nanoporous Membranes Fabrication
Authors: Cigane Urte , Palevicius Arvydas , Jurenas Vytautas , Pilkauskas Kestutis , Janusas Giedrius
Published: November 2022

Nanoporous anodic aluminum oxide (AAO) is needed for a variety of purposes due to its unique properties, including high hardness, thermal stability, large surface area, and light weight. Nevertheless, the use of AAO in different applications is limited because of its brittleness. A new design of an electrochemical reactor with a vibrating element for AAO nanoporous membranes fabrication is proposed. The vibrating element in the form of a piezoceramic ring was installed inside the developed reactor, which allows to create a high-frequency excitation. Furthermore, mixing and vibration simulations in the novel reactor were carried out using ANSYS 17 and COMSOL Multiphysics 5.4 software, respectively. By theoretical calculations, the possibility to excite the vibrations of five resonant modes at different frequencies in the AAO membrane was shown. The theoretical results were experimentally confirmed. Five vibration modes at close to the theoretical frequencies were obtained in the novel reactor. Moreover, nanoporous AAO membranes were synthesized. The novel aluminum anodization technology results in AAO membranes with 82.6 ± 10 nm pore diameters and 43% porosity at 3.1 kHz frequency excitation and AAO membranes with 86.1 ± 10 nm pore diameters and 46% porosity at 4.1 kHz frequency excitation. Furthermore, the chemical composition of the membrane remained unchanged, and the hardness decreased. Nanoporous AAO has become less brittle but hard enough to be used for template synthesis.
Measurement Method of Temperature of the Face Gear Rim of a Spiroid Gear
Authors: Chodoła Łukasz , Mazurkow Aleksander , Surowaniec Mirosław , Markowski Tadeusz , Homik Wojciech
Published: November 2022

Spiroid gears are used to transfer heavy loads with a significant reduction in input speed. Like most toothed gears, they are lubricated with oil whose physical properties change with temperature fluctuations, affecting the durability and reliability of the gear. Bearing this in mind, gear designers plan systems for measuring oil temperature during gear operation at the design stage. The authors of this paper are of the opinion that, in the case of spiroid gears, it may be insufficient to measure only oil temperature during gear operation. It seems that the working temperature of a pair of mating wheels has a decisive impact on the durability and reliability of the gear. The measurement of oil temperature in a tested gear should be treated as a supplementary measurement with the measurement of temperature on the toothed wheels as the basic measurement. Taking into consideration the above, an innovative test bench was designed and built, making it possible to observe how working parameters of the gear (torque and rotational speed) affect the temperature of the lubricating oil, but most of all, the working temperature of the pair of mating wheels. This paper presents, among others, the results of research on the impact of the rotational speed of the input shaft and load on the distribution of temperature on the toothed rim of the face gear.
Pre-Matching Circuit for High-Frequency Ultrasound Transducers
Authors: Choi Hojong
Published: November 2022

High-frequency ultrasound transducers offer higher spatial resolution than low-frequency ultrasound transducers; however, their maximum sensitivity are lower. Matching circuits are commonly utilized to increase the amplitude of high-frequency ultrasound transducers because the size of the piezoelectric material decreases as the operating frequency of the transducer increases. Thus, it lowers the limit of the applied voltage to the piezoelectric materials. Additionally, the electrical impedances of ultrasound transducers generally differ at the resonant-, center-, and anti-resonant-frequencies. The currently developed most-matching circuits provide electrical matching at the center frequency ranges for ultrasound transmitters and transducers. In addition, matching circuits with transmitters are more difficult to use to control the echo signal quality of the transducers because it is harder to control the bandwidth and gain of an ultrasound transmitter working in high-voltage operation. Therefore, we provide a novel pre-matching circuit method to improve the amplitude and bandwidth of high-frequency ultrasound transducers at the resonant-, center-, and anti-resonant-frequency ranges, with an ultrasound receiver and transducer. To verify the pre-matching circuit, pulse-echo response tests were conducted on the ultrasound transducers. The results show that the designed pre-matching circuits provide higher amplitude (5.63- and 2.02-times) and wider bandwidth (175.55% and 62.01%) for the high-frequency ultrasound transducer compared to the original circuit without a pre-matching circuit, and the parallel capacitor with a series-inductor circuit, respectively; therefore, the proposed pre-matching circuit is an appropriate solution for improving the amplitudes and bandwidths of high-frequency ultrasound transducers over wide frequency ranges.
Countering a Drone in a 3D Space: Analyzing Deep Reinforcement Learning Methods
Authors: Çetin Ender , Barrado Cristina , Pastor Enric
Published: November 2022

Unmanned aerial vehicles (UAV), also known as drones have been used for a variety of reasons and the commercial drone market growth is expected to reach remarkable levels in the near future. However, some drone users can mistakenly or intentionally fly into flight paths at major airports, flying too close to commercial aircraft or invading people’s privacy. In order to prevent these unwanted events, counter-drone technology is needed to eliminate threats from drones and hopefully they can be integrated into the skies safely. There are various counter-drone methods available in the industry. However, a counter-drone system supported by an artificial intelligence (AI) method can be an efficient way to fight against drones instead of human intervention. In this paper, a deep reinforcement learning (DRL) method has been proposed to counter a drone in a 3D space by using another drone. In a 2D space it is already shown that the deep reinforcement learning method is an effective way to counter a drone. However, countering a drone in a 3D space with another drone is a very challenging task considering the time required to train and avoid obstacles at the same time. A Deep Q-Network (DQN) algorithm with dueling network architecture and prioritized experience replay is presented to catch another drone in the environment provided by an Airsim simulator. The models have been trained and tested with different scenarios to analyze the learning progress of the drone. Experiences from previous training are also transferred before starting a new training by pre-processing the previous experiences and eliminating those considered as bad experiences. The results show that the best models are obtained with transfer learning and the drone learning progress has been increased dramatically. Additionally, an algorithm which combines imitation learning and reinforcement learning is implemented to catch the target drone. In this algorithm, called deep q-learning from demonstrations (DQfD), expert demonstrations data and self-generated data by the agent are sampled and the agent continues learning without overwriting the demonstration data. The main advantage of this algorithm is to accelerate the learning process even if there is a small amount of demonstration data.
Application of Unsupervised Machine Learning Classification for the Analysis of Driver Behavior in Work Zones in the State of Qatar
Authors: Khanfar Nour O. , Ashqar Huthaifa I. , Elhenawy Mohammed , Hussain Qinaat , Hasasneh Ahmad , Alhajyaseen Wael K. M.
Published: November 2022

Work zone areas are commonly known as crash-prone areas. Thus, they usually receive high priority by road operators as drivers and workers have higher chances of being involved in road crashes. The paper aims to investigate driving behavior in work zones using unsupervised machine learning and vehicle kinematic data. A dataset of 67 participants was gathered through an experiment using a driving simulator located at the Qatar Transportation and Traffic Safety Center (QTTSC). The study considered two different work zone scenarios where the leftmost lane was closed for maintenance. In the first scenario, drivers drove on the leftmost lane (Drive 1), while in the second, they drove on the second leftmost lane (Drive 2). The results show that the number of aggressive and conservative drivers was surprisingly more than normal drivers, as most participants either cautiously drove through or failed to drive without being aggressive. The results also show that drivers acted more aggressively in the leftmost lane rather than in the second leftmost lane. We also found that female drivers and drivers with relatively little driving experience were more likely to be aggressive as they drove through a work zone. The framework was found to be promising and can help policymakers take optimal safety countermeasures in work zones during construction.
Social Mobility and Firms’ Total Factor Productivity: Evidence from China
Authors: Wang Jinchao , Luo Changfu
Published: November 2022

The key to achieving sustainable growth is to increase total factor productivity. What role does the openness of social structure play in promoting total factor productivity? Based on data from the China Labor-force Dynamic Survey (CLDS), we used the methodology of intergenerational order correlation to measure city-level absolute mobility and to examine the impact of social mobility on the total factor productivity of Chinese manufacturing firms. The results show that the following: (1) Higher social mobility has a significant positive impact on firms’ TFP. Our results still hold after using the instrumental variables strategy to mitigate the endogeneity problem and performing a series of robustness tests. (2) Heterogeneity tests show that the positive effect on total factor productivity is more pronounced in non-state-owned enterprises, small- and medium-sized enterprises (SMEs), and technology-intensive enterprises. Meanwhile, a higher level of marketization reduces the sensitivity of enterprises’ TFP to social mobility. (3) Three identified channels are human capital allocation, fairness perception, and technological innovation. Our findings inspire the government to promote the system and mechanism reform of social flow and improve sustainable development ability.
Exploring Teachers’ Perceptions of the Barriers to Teaching STEM in High Schools in Qatar
Authors: Sellami Abdellatif , Ammar Mohammad , Ahmad Zubair
Published: November 2022

Understanding teachers’ attitudes and perceptions of STEM teaching is a key pathway to enhance effective STEM teaching. Inarguably, teachers are the cornerstone of educational quality and play a central role in students’ academic performance. Specifically, the pedagogical strategies teachers employ and their effective use in the classroom are strong determinants of students’ enrollment or retention in STEM fields of study and eventual careers. This study sought to explore the experiences of high school STEM teachers in Qatar, focusing on the pedagogical approaches they utilize and the challenges they encounter, with the aim of delving into how these approaches and barriers affect the teaching of STEM in the country’s high schools. The study’s design is observational, with data collected using a survey of 299 secondary high school STEM teachers (11th and 12th grades). To attain the goal of this study, we examined the barriers perceived to impede engagement in effective STEM teaching from high school teachers’ perspective. The study’s findings pointed to the influence of student- and school-related factors in shaping STEM teaching. Significant differences were detected based on teachers’ gender, grade level of teaching, age group, and university education. Logistic regressions revealed that teachers’ demographic attributes, including age group and university education, affect their likelihood to use STEM pedagogies in class. This likelihood was significantly affected by student-related barriers and the learning resources/materials employed in classrooms. These findings postulate critical evidence in directing the development of successful STEM learning practices within Qatar’s high schools.
Assessing Open Space in Scotland: Reliability and Construct Validity of the Open Space Scale
Authors: Yu Andrew , Lam Stephanie Kwan Nga
Published: November 2022

The design of open spaces is the subject of interest when searching for solutions to promote well-being and a better quality of life for dwellers, especially those who live in urban areas. A user-friendly open space that meets the needs of an area has become a major concern in sustainable city design, environmental health, and psychological health. Thus, a universal scale that can be applied in different places is needed to study the different needs of different areas. This study systematically adapted the Open Space Scale previously developed in Hong Kong and assessed the reliability and constructed validity of the adapted version in Scotland; 535 samples from Edinburgh and Glasgow completed the revised version of the scale. The Confirmatory Factor Analysis indicated a good model fit and factor loadings in the revised scale. Overall, the Open Space Scale-Revised demonstrated satisfactory measurement properties. In the future, this scale can be used with other scales for further analysis and more complex structural equation models. This scale can also be used in conjunction with other scales for various types of policy analysis to provide policymakers and urban planners with substantial data. For example, one can measure the influence of open spaces on physical and psychological health in an area, such as well-being and sense of belonging, to decide if it is required to improve or expand the proportion of open spaces in that area.
Is Silence Golden? The Influence of Employee Silence on the Transactional Leadership and Job Satisfaction Relationship
Authors: Alarabiat Yousef Ahmad , Eyupoglu Serife
Published: November 2022

The literature has shown that a positive relationship between transactional leadership and job satisfaction in private and public organizations exist. This relationship is critical for sustained organizational performance; however, this relationship can be challenged by the existence of employee silence in the organizational setting. Based on self-determination theory, this study measured the impact of transactional leadership on job satisfaction as well as the part of employee silence in the leadership–satisfaction relationship in a public organizational setting. The study sample consisted of employees working at the Ministry of Justice in Jordon, and 450 questionnaires were applied with 357 useable questionnaires being returned. The data were tested through confirmatory factor analysis, correlation and regression analyses, and structural equation modeling. The results showed a strong positive relationship between transactional leadership and job satisfaction, though employee silence as a mediator indicated reduced job satisfaction. The importance of public organization leaders being more mindful towards the employee silence phenomenon and how it can be detrimental in the transactional leadership–job satisfaction relationship was highlighted.
A Development of Green Finance and Regional Eco-Efficiency in China
Authors: Wang Kaili , Elahi Ehsan , Zhang Yuge , Wang Di , Khalid Zainab
Published: November 2022

Based on panel data collected from 30 Chinese provinces between 2009 and 2020, we used the Super-SBM model to calculate regional eco-efficiency (EE) and analyze its spatial–temporal evolution characteristics. Furthermore, we constructed SDM to analyze the direct impact and spillover effect and tested its mechanism with a mediating effects model. According to the SDM results, it is confirmed that both the direct impact of green financial development on regional eco-efficiency improvement and the spatial spillover effects on neighboring provinces have a “U” shaped relationship, and there is significant heterogeneity among the three major regions of China (East, Central, and West). The intermediary effect model found an asset scale and technological progress effect. Based on the findings of the study, policy implications are proposed to expand the scale of green finance, promote its coordinated regional development, promote technological progress and expand the scale of capital.
Respondent Dynamic Attention to Streetscape Composition in Nanjing, China
Authors: Yue Zhi , Zhong Ying , Cui Zhouxiao
Published: November 2022

Scholars are interested in understanding human responses and perceptions concerning the configuration of streetscape environments that serve multiple functions. However, drivers’ visual attention to the streetscape has seldom been studied dynamically in multi-modal settings. By employing eye-tracking and semantic segmentation, visual attention partitions and objects and patterns are inspected in a per-second count along three typical roadways in Nanjing, China. In our study of 28 participants, it was found that people are likely to focus on the frame center (p-value < 0.005) in all methods of transportation. Roads and buildings are constantly observed along the roadway (p-value < 0.005), while smaller transportation objects across multi-modal conditions are noticed more in per-area counts (p-value < 0.025). Besides, vehicles are focused on more in a higher-speed driving lane (p-values < 0.005), while greenery and humans attract more attention in a slower lane (p-values < 0.005). The results indicate that the previous visual engagement results should be reconsidered on several points, and that the risk of distractions from non-traffic-related elements could be overestimated. The potential of the road surface in integrating safety and information-providing has been ignored in current studies. This study showed that greenery and other functional elements will not distract users in driving lanes; decreasing the calculation burden to two-ninth is possible in smart driving. These results could be helpful in future sustainable cities.
Research on the Factors Influencing the Mechanism of Property Management Enterprises’ Ecological Behavior in China
Authors: Liu Xingming , Qin Beibei , Geng Shuai , Zhang Jun , Zhu Tongsheng , Nie Pingjun
Published: November 2022

The ecological behavior of property management enterprises is crucial to the performance of green buildings. The understanding of property management enterprises’ ecological behavior, however, is rather limited. The purpose of this paper is to analyze the mechanism of the affecting factors upon the ecological behavior of property management enterprises. The qualitative analysis method was used to collect and identify the influencing factors, which were further classified as those of stimulus, status, scenario, and result. A theoretical model of the action mechanism of influencing factors and related hypotheses has been constructed based on the planned behavior theory. The proposed hypotheses have been empirically analyzed by the quantitative analysis of a questionnaire survey. The results indicate that there are significant differences in ecological behavior implementation under different status factors, while the impact of enterprise nature on property management ecological behavior is not significant. Regarding the stimulus factors, market pressure and government regulation are the main external incentive factors. The scenario factors could be resistant forces and moderate the implementation of ecological behavior. For the result factors, property management enterprises pay attention to whether the implementation of ecological behavior can produce direct benefits. The research findings constitute valuable references for further development of the property management industry.
Rural Areas and Well-Being in EU Countries + UK: A Taxonomy and a Cluster Analysis
Authors: Marino Domenico , Tebala Domenico
Published: November 2022

The issue of rural and marginal areas has regained centrality in recent times, also due to the fact that rural areas actively participate in the EU’s green and digital transition. The starting point of the paper is the concept of fair and sustainable well-being, which has been interpreted differently in relation to the diversity of territories and particularly in relation to the differences between urban and rural areas. The objective of this work is the construction of a synthetic index of the welfare of European countries through the Wroclaw taxonomic method and through the use of logit models for the identification of best practices of local realities and the interpretation in a more immediate way of the fair and sustainable welfare of each European country at a rural level.
Pesticide Use Practices among Female Headed Households in the Amhara Region, Ethiopia
Authors: Asmare Birtukan Atinkut , Freyer Bernhard , Bingen Jim
Published: November 2022

Drawing on social practice theory (SPT), we extend our understanding of the existing pesticide use practices among female-headed households (FHHs) in the Amhara region of Ethiopia. We used mixed research methods combining household surveys, focus group discussions (FGDs), key informant interviews, and field observations complemented by photography. A binary logistic regression model was used to investigate the factors that influence the adoption of personal protective equipment (PPE) among FHHs. This finding suggests that pesticide use is an activity consisting of purchasing and using practices with several interacting elements such as materials, competences, and meanings. The main meaning or material element for pesticide purchasing are the perceptions of efficacy on pests, diseases, and weeds (65%), cost and availability in smaller quantities (60.7%), and a woman’s available time and mobility (58.9%). Pesticide hazards to human health or the environment seem not to be relevant for most FHHs. Pesticide use practices among FHHs are done in violation of safety recommendations, motivated by not only material elements (labor, income, time, and the provisioning system), but are notably shaped by competences (skills and knowledge), and meanings (norms, values, rules, and shared ideas). As the regression results show, age and retailers information (p < 0.05) are the significant factors that influence PPE adoption among FHHs. We suggest a change of the practices and processes that sustain women’s lives, a foundational shift of the socioeconomic and cultural environment, and promoting new meanings and competences through advisory services or training.
Eco-Innovation in New Energy Vehicle Supply Chains under Government Subsidies
Authors: Peng Jing , Lin Zhiping
Published: November 2022

The pressure to reduce emissions has induced the government to provide subsidies to urge eco-innovation in the new energy vehicle industry. Although the giving of such subsidies to the new energy vehicle industry has been practiced for a long time, few studies consider how the subsidy policies affect social welfare and the manufacturers’ profits and eco-innovation levels in the presence of the technology gap and the spillover effect. This paper fills the gap in the literature by studying two competitive supply chains consisting of two manufacturers and two retailers. Under three different subsidy policies, we derive the equilibrium outcomes. We find that, as the technology gap increases, the eco-innovation level of the leader increases, whereas the eco-innovation level of the follower decreases. We further investigate the conditions under which subsidy policy is better from the perspective of eco-innovation levels, firms, and social welfare. Specifically, under the centralized setting, the social welfare is lower with the unit production subsidy than with the green technology investment subsidy when the technology gap is low.
Digitizable Product Trade Development and Carbon Emission: Evidence from 94 Countries
Authors: Wang Aihua , Ruan Qiqi , Zhou Teng , Wang Yanzhen
Published: November 2022

In the face of increasingly severe climate change and its destructive effects, how to effectively reduce carbon dioxide emissions has become a challenging task. Developing a digital economy provides opportunities for countries to reduce pollution and carbon emissions and reach a goal of carbon neutrality. As an emerging trade form, digitizable product trade is of great significance to promoting economic growth and carbon emission reduction. This paper selects panel data for 94 countries from 2001 to 2019 and adopts the STIRPAT model to analyze the impact effect and impact mechanism of digitizable product trade on carbon emissions. Research results show that developing digitizable product trade will help countries reduce carbon emissions. The conclusion is robust by replacing the explained variable and core explanatory variable. The carbon emission reduction effect has heterogeneity due to differentiated national income levels and product categories. Mechanism analysis shows that digitizable product trade reduces carbon emissions through the technology effect. Our analysis indicates that countries developing digital trade and digital technology and actively responding to environmental issues have a greater chance of reduced carbon emissions.
Some New Results for (α, β)-Admissible Mappings in 𝔽-Metric Spaces with Applications to Integral Equations
Authors: Faraji Hamid , Mirkov Nikola , Mitrović Zoran D. , Ramaswamy Rajagopalan , Abdelnaby Ola A. Ashour , Radenović Stojan
Published: November 2022

In this paper, we consider and extend some fixed point results in F-complete F-metric spaces by relaxing the symmetry of complete metric spaces. We generalize α,β-admissible mappings in the setting of F-metric spaces. The derived results are supplemented with suitable examples, and the obtained results are applied to find the existence of the solution to the integral equation. The analytical results are compared through numerical simulation. We pose certain open problems for extending and applying our results in the future.
New Research Trends for Textiles
Authors: Dufossé Laurent
Published: November 2022

The Textiles journal is a peer-reviewed, open-access journal, officially launched in 2020 [...]
Detection of Changes in Monoamine Neurotransmitters by the Neonicotinoid Pesticide Imidacloprid Using Mass Spectrometry
Authors: Hirai Anri , Yamazaki Ryo , Kobayashi Atsushi , Kimura Takashi , Nomiyama Kei , Shimma Shuichi , Nakayama Shouta M. M. , Ishizuka Mayumi , Ikenaka Yoshinori
Published: November 2022

Monoamine neurotransmitters (MAs), including dopamine (DA) and serotonin (5-HT), regulate brain functions such as behavior, memory, and learning. Neonicotinoids are pesticides that are being used more frequently. Neonicotinoid exposure has been observed to produce neurological symptoms, such as altered spontaneous movements and anxiety-like behaviors, which are suspected to be caused by altered MA levels. However, current neurotoxicity tests are not sufficiently sensitive enough to make these determinations. In this study, we performed some behavior tests, and derivatization reagents to improve the ionization efficiency, which was applied to liquid chromatography mass spectrometry (LC-MS/MS) to reveal the effect of neonicotinoid administration on MAs in the brain. We orally administered the neonicotinoid imidacloprid (0, 10, and 50 mg/kg body weight) to C57BL/6NCrSlc mice. In the behavior tests, a decrease in activity was observed. The LC-MS/MS quantification of MAs in various brain regions showed a decrease in some MA levels in the olfactory bulb and the striatum. These results showed, for the first time, that even a low dose of imidacloprid could alter MA levels in various parts of the brain.
ACE2-Independent Alternative Receptors for SARS-CoV-2
Authors: Lim Suhyeon , Zhang Monica , Chang Theresa L.
Published: November 2022

Severe acute respiratory syndrome-related coronavirus (SARS-CoV-2), the causative agent of coronavirus disease 2019 (COVID-19), is highly contagious and remains a major public health challenge despite the availability of effective vaccines. SARS-CoV-2 enters cells through the binding of its spike receptor-binding domain (RBD) to the human angiotensin-converting enzyme 2 (ACE2) receptor in concert with accessory receptors/molecules that facilitate viral attachment, internalization, and fusion. Although ACE2 plays a critical role in SARS-CoV-2 replication, its expression profiles are not completely associated with infection patterns, immune responses, and clinical manifestations. Additionally, SARS-CoV-2 infects cells that lack ACE2, and the infection is resistant to monoclonal antibodies against spike RBD in vitro, indicating that some human cells possess ACE2-independent alternative receptors, which can mediate SARS-CoV-2 entry. Here, we discuss these alternative receptors and their interactions with SARS-CoV-2 components for ACE2-independent viral entry. These receptors include CD147, AXL, CD209L/L-SIGN/CLEC4M, CD209/DC-SIGN/CLEC4L, CLEC4G/LSECtin, ASGR1/CLEC4H1, LDLRAD3, TMEM30A, and KREMEN1. Most of these receptors are known to be involved in the entry of other viruses and to modulate cellular functions and immune responses. The SARS-CoV-2 omicron variant exhibits altered cell tropism and an associated change in the cell entry pathway, indicating that emerging variants may use alternative receptors to escape the immune pressure against ACE2-dependent viral entry provided by vaccination against RBD. Understanding the role of ACE2-independent alternative receptors in SARS-CoV-2 viral entry and pathogenesis may provide avenues for the prevention of infection by SARS-CoV-2 variants and for the treatment of COVID-19.