bluuebunny/crossref_metadata_2025 huggingface dataset:
Power Line Monitoring through Data Integrity Analysis with Q-Learning Based Data Analysis Network
Authors: Maskeliūnas Rytis , Pomarnacki Raimondas , Khang Huynh Van , Damaševičius Robertas , Plonis Darius
Published: December 2022

To monitor and handle big data obtained from electrical, electronic, electro-mechanical, and other equipment linked to the power grid effectively and efficiently, it is important to monitor them continually to gather information on power line integrity. We propose that data transmission analysis and data collection from tools like digital power meters may be used to undertake predictive maintenance on power lines without the need for specialized hardware like power line modems and synthetic data streams. Neural network models such as deep learning may be used for power line integrity analysis systems effectively, safely, and reliably. We adopt Q-learning based data analysis network for analyzing and monitoring power line integrity. The results of experiments performed over 32 km long power line under different scenarios are presented. The proposed framework may be useful for monitoring traditional power lines as well as alternative energy source parks and large users like industries. We discovered that the quantity of data transferred changes based on the problem and the size of the planned data packet. When all phases were absent from all meters, we noted a significant decrease in the amount of data collected from the power line of interest. This implies that there is a power outage during the monitoring. When even one phase is reconnected, we only obtain a portion of the information and a solution to interpret this was necessary. Our Q-network was able to identify and classify simulated 190 entire power outages and 700 single phase outages. The mean square error (MSE) did not exceed 0.10% of the total number of instances, and the MSE of the smart meters for a complete disturbance was only 0.20%, resulting in an average number of conceivable cases of errors and disturbances of 0.12% for the whole operation.
Spatiotemporal Analysis of Regional Ionospheric TEC Prediction Using Multi-Factor NeuralProphet Model under Disturbed Conditions
Authors: Huang Ling , Wu Han , Lou Yidong , Zhang Hongping , Liu Lilong , Huang Liangke
Published: December 2022

The ionospheric total electron content (TEC) is susceptible to factors, such as solar and geomagnetic activities, resulting in the enhancement of its non-stationarity and nonlinear characteristics, which aggravate the impact on radio communications. In this study, based on the NeuralProphet hybrid prediction framework, a regional ionospheric TEC prediction model (multi-factor NeuralProphet model, MF-NPM) considering multiple factors was constructed by taking solar activity index, geomagnetic activity index, geographic coordinates, and IGS GIM data as input parameters. Data from 2009 to 2013 were used to train the model to achieve forecasts of regional ionospheric TEC at different latitudes during the solar maximum phase (2014) and geomagnetic storms by sliding 1 day. In order to verify the prediction performance of the MF-NPM, the multi-factor long short-term memory neural network (LSTMNN) model was also constructed for comparative analysis. At the same time, the TEC prediction results of the two models were compared with the IGS GIM and CODE 1-day predicted GIM products (COPG_P1). The results show that the MF-NPM achieves good prediction performance effectively. The RMSE and relative accuracy (RA) of MF-NPM are 2.33 TECU and 93.75%, respectively, which are 0.77 and 1.87 TECU and 1.91% and 6.68% better than LSTMNN and COPG_P1 in the solar maximum phase (2014). During the geomagnetic storm, the RMSE and RA of TEC prediction results based on the MF-NPM are 3.12 TECU and 92.86%, respectively, which are improved by 1.25 and 2.30 TECU and 2.38% and 7.24% compared with LSTMNN and COPG_P1. Furthermore, the MF-NPM also achieves better performance in low–mid latitudes.
The First Inventory of Rock Glaciers in the Zhetysu Alatau: The Aksu and Lepsy River Basins
Authors: Kaldybayev Azamat , Sydyk Nurmakhambet , Yelisseyeva Alena , Merekeyev Aibek , Nurakynov Serik , Zulpykharov Kanat , Issanova Gulnura , Chen Yaning
Published: December 2022

While rock glaciers (RGs) are widespread in the Zhetysu Alatau mountain range of Tien Shan (Kazakhstan), they have not yet been systematically investigated. In this study, we present the first rock glacier inventory of this region containing 256 rock glaciers with quantitative information about their locations, geomorphic parameters, and downslope velocities, as established using a method that combines SAR interferometry and optical images from Google Earth. Our inventory shows that most of the RGs are talus-derived (61%). The maximum downslope velocity of the active rock glaciers (ARGs) was 252 mm yr−1. The average lower height of rock glaciers in this part of the Zhetysu Alatau was 3036 m above sea level (ASL). The largest area of rock glaciers was located between 2800 and 3400 m ASL and covered almost 86% of the total area. Most rock glaciers had a northern (northern, northeastern, and northwestern) orientation, which indicated the important role of solar insolation in their formation and preservation.
Data Fusion for Satellite-Derived Earth Surface: The 2021 Topographic Map of Etna Volcano
Authors: Ganci Gaetana , Cappello Annalisa , Neri Marco
Published: December 2022

We present a new automatic procedure for updating digital topographic data from multi-source satellite imagery, which consists in the production of Digital Surface Models (DSMs) from high resolution optical satellite images, followed by a context-aware fusion that exploits the complementary characteristics of the multi-source DSMs. The fused DSM minimizes blunders and artifacts due to occlusions (e.g., the presence of clouds, snow or ash plumes) in the source images, resulting in improved accuracy and quality versus those that are not merged. The procedure has been tested to produce the 2021 digital topography of Mt Etna, whose summit area is constantly changing and shows the new peak of 3347 m on the north rim of the South East Crater. We also employ the 2021 DSM to measure the volcanic deposits emplaced in the last five years, finding about 120 million cubic meters, with a yearly average volume of about 24 million cubic meters in agreement with the large eruptive rates registered at Mt Etna since the nineteen seventies. The flexibility and modularity of the presented procedure make it easily exportable to other environmental contexts, allowing for a fast and frequent reconstruction of topographic surfaces even in extreme environments.
LiDAR Point Clouds Usage for Mapping the Vegetation Cover of the “Fryderyk” Mine Repository
Authors: Szostak Marta , Pająk Marek
Published: December 2022

The paper investigates the usage of LiDAR (light detection and ranging) data for the automation of mapping vegetation with respect to the evaluation of the ecological succession process. The study was performed for the repository of the “Fryderyk” mine (southern Poland). The post-flotation area analyzed is a unique refuge habitat—Natura2000, PLH240008—where a forest succession has occurred for several dozen years. Airborne laser scanning (ALS) point clouds were used for deriving detailed information about the morphometry of the spoil heap and about the secondary forest succession process—mainly vegetation parameters i.e., height and canopy cover. The area of the spoil heap is irregular with a flat top and steep slopes above 20°. Analyses of ALS point clouds (2011 and 2019), confirmed progression in the forest succession process, and land cover changes especially in wooded or bushed areas. Precise vegetation parameters (3D LiDAR metrics) were calculated and provided the following parameters: mean value of vegetation height as 6.84 m (2011) and 8.41 m (2019), and canopy cover as 30.0% (2011) and 42.0% (2019). Changes in vegetation volume (3D area) were shown: 2011—310,558 m3, 2019—325,266 m3, vegetation removal—85,136 m3, increasing ecological succession—99,880 m3.
A Comparative Study of Chamomile Essential Oils and Lipophilic Extracts Obtained by Conventional and Greener Extraction Techniques: Chemometric Approach to Chemical Composition and Biological Activity
Authors: Zengin Gökhan , Mollica Adriano , Arsenijević Jelena , Pavlić Branimir , Zeković Zoran , Sinan Kouadio Ibrahime , Yan Linlin , Cvetanović Kljakić Aleksandra , Ražić Slavica
Published: December 2022

Bearing in mind the centuries-old traditional use of chamomile, but also the increasing demand for its products in modern industry, oriented toward sustainable development, there are increasing efforts for the efficient extraction of high-value compounds of this plant, as well as obtaining its products with added value. With that goal, conventional and contemporary separation techniques were applied in this work. Both hydrodistillation processes (HD), conducted in a traditional manner and coupled with microwave irradiation (MWHD), were used for essential oil isolation. In parallel with those procedures, chamomile lipophilic extracts were obtained by Soxhlet extraction applying organic solvents and using supercritical fluid extraction as a greener approach. The obtained extracts and essential oils were characterized in terms of chemical composition (GC analysis, contents of total phenolics and flavonoids) and biological potential. GC analysis revealed that oxygenated sesquiterpenes and non-terpene compounds were the dominant compounds. α-Bisabolol oxide A (29.71–34.41%) and α-bisabolol oxide B (21.06–25.83%) were the most abundant individual components in samples obtained by distillation while in supercritical and Soxhlet extracts, major compounds were α-bisabolol oxide A and pentacosane. The biological potential of essential oils and extracts was tested by applying a set of analyzes to estimate the inhibition of biologically important enzymes (amylase, glucosidase, acetylcholinesterase, butyrylcholinesterase, tyrosinase) and antioxidant capacity (DPPH, ABTS, CUPRAC, FRAP, chelating and total antioxidant capacity). The results suggested essential oils as better antioxidants, while the extracts were proven to be better inhibitors of the tested enzymes. Principal Component Analysis was conducted using the experimental results of the composition of extracts and EOs of chamomile obtained by different separation techniques, showing clear discrimination between methods applied in correlation with the chemical profile. Molecular docking was applied for the identification of the main active principles present in the essential oil, among which α-bisabolol-oxide B (cp3) showed a higher affinity for tyrosinase.
Eating in a Total Institution Considering History, Nutrition, and Gender Issues
Authors: Costa Davide , Serra Raffaele
Published: December 2022

To the authors’ knowledge, this paper is the first to explore the dietary regime provided in a total institution. Specifically, the aim of this study is to highlight gender differences in the dietary regimes of mental health patients. Using archival and documentary evidence, the study uses an unobtrusive research approach to explore the diets of mental health patients living in the Girifalco asylum, in southern Italy, during the second half of the 19th century. The evidence analyzed examines whether the dietary regimes that were rebuilt are perfectly in line with the principles of moral treatment. Concerning the study, limitations should be stated; although archival evidence suggests that the institutional meals provided in Girifalco asylum were nutritionally adequate (by gender), this is impossible to verify. In conclusion, the dietary regime provided at the Girifalco asylum was balanced both from a qualitative and quantitative point of view for healthy living in a total institution.
Applying the Multilevel Approach in Estimation of Income Population Differences
Authors: Timiryanova Venera , Krasnoselskaya Dina , Kuzminykh Natalia
Published: December 2022

Income inequality remains one of the most burning issues discussed in the world. The difficulty of the problem arises from its multiple manifestations at regional and local levels and unique patterns within countries. This paper employs a multilevel approach to identify factors that influence income and wage inequalities at regional and municipal scales in Russia. We carried out the study on data from 2017 municipalities of 75 Russian regions from 2015 to 2019. A Hierarchical Linear Model with Cross-Classified Random Effects (HLMHCM) allowed us to establish that most of the total variances in population income and average wages accounted for the regional scale. Our analysis revealed different variances of income per capita and average wage; we disclosed the reasons for these disparities. We also found a mixed relationship between income inequality and social transfers. These variables influence income growth but change the relationship between income and labour productivity. Our study underlined that the impacts of shares of employees in agriculture and manufacturing should be considered together with labour productivity in these industries.
Psychosocial Stress Induces Orofacial Mechanical Allodynia Due to the Enhancement of Transient Receptor Potential Ankyrin 1 Expression in Trigeminal Ganglion Neurons via the Increment of the Trace Amine-Associated Receptor 7f Expression
Authors: Ito Ko , Shinoda Masamichi , Soma Kumi , Ikutame Daisuke , Ikami Eiji , Mizuno Yosuke , Usui Michihiko , Asoda Seiji , Sato Tsuyoshi
Published: December 2022

(1) Background: Chronic psychosocial stress can lead to oral dysesthesia with tongue pain. We examined whether psychosocial stress causes orofacial pain, and analyzed the comprehensive gene expression patterns of circulating cells and transient receptor potential ankyrin 1 (TRPA1) expression in trigeminal ganglion (TG) neurons in a mouse model of psychosocial stress. (2) Methods: Mice were divided into two groups: one group was kept in confrontational housing, and the other group was kept in single housing. Blood, adrenal gland, and tongue were collected. The head withdrawal threshold (HWT) of mechanical stimulation to the whisker pad skin was measured. TRPA1-positive TG neurons were immunohistochemically examined. DNA microarray analysis and quantitative reverse transcription polymerase chain reaction analysis were performed. (3) Results: The HWT was significantly lower in mice under the psychosocial stress condition compared to non-stressed mice. In stress-loaded mice, the number of TRPA1-positive TG neurons was significantly increased. Moreover, we showed that trace amine-associated receptor 7f expression was upregulated in circulating cells in blood and downregulated in the tongue. (4) Conclusions: Our results indicated that chronic psychosocial stress induced the orofacial mechanical allodynia through enhancement of TRPA1 expression in TG neurons with changes in the levels of trace amine-associated receptor 7f.
Influence of Built Environment on Micromobility–Pedestrian Accidents
Authors: Shin Songhyeon , Choo Sangho
Published: December 2022

The use of micromobility (MM), a form of sustainable urban mobility which has expected effects such as reducing traffic congestion and greenhouse gases, has been rapidly increasing across the world. However, this growth has resulted in a considerable number of MM-related accidents. Most previous studies have explored MM user injuries to improve the safety of MM users, but the threat to pedestrians by MM is not yet fully understood. Therefore, this study aims to identify built environment factors which contribute to MM–pedestrian accidents by using MM–pedestrian crash data in Seoul, Korea from 2020 to 2021. Setting the spatial unit of analysis as a hexagonal grid with an apothem of 150 m, we developed the SZINB (spatial zero-inflated negative binomial) models for the accidents, controlling spatial autocorrelation, zero-inflated, and overdispersion. The model results showed that road intersections, sidewalks, and subway entrances have significant impacts on MM–pedestrian accidents. Thus, it should be suggested that safety measures for both MM and pedestrians are reducing MM speed limits in intersections, preventing MM use on sidewalks through modified sidewalk designs, and installing MM stations near subway stations.
Scientific Knowledge Mapping and Thematic Evolution for Tire Wear Particles
Authors: Wu Wei , Ma Jun , Liu Dong , Xu Qiao , Li Gang
Published: December 2022

In recent years, with the continuous increase of car ownership per capita, tire wear particles (TWPs) from road tire wear have been widely detected in various environmental media, and their environmental behavior and influence have attracted wide attention. Using the Web of Science Core Collection (WOSCC) as a literature search platform, we mapped the research progress of TWPs from publication trends, international cooperation, journal distribution, interdisciplinary areas, and research themes with scientific knowledge mapping methods. Publications in the TWP field have shown an increase year by year, with great contributions from researchers in the USA and Europe, but the efforts and progress of Chinese researchers cannot be ignored. Science of the Total Environment was the most active journal in this field, with 54 relevant articles published. The research area of TWPs was multidisciplinary in nature, with a focus on Environmental Science, Atmospheric Meteorology Science, and Environmental Engineering. The research topics were mainly composed of three thematic groups: suspended particulate matter, air pollution sources, and microplastics in the environment, and research hotspots shifted from particulate matter to microplastics over time. Future research needs to focus on the origin, properties, and transport and dispersion of TWPs in water, atmosphere, and soil environments and to analyze the environmental impacts and ecological risks.
The Moderating Effect of Self-Efficacy on Fitness Use Innovativeness and Usage Pattern
Authors: Satjawathee Theeralak , Ma Shang-Chun , Shu Shih-Tung , Chang Ching-Hung
Published: December 2022

Marketing researchers agree that use innovativeness leads to variety-seeking behavior in service usage in service contexts. As fitness consumers are high-frequency users, user behavior can be enriched by exploring the moderating effect of self-efficacy. This study explores the moderating effect of self-efficacy on the relationship between fitness use innovativeness and usage patterns (usage variety and usage frequency), and investigates their mediation effects on satisfaction and revisit intention. A sample of 234 participants from one of the largest public fitness centers was employed to test the conceptual framework. The findings show that fitness use innovativeness has a positive relationship with usage variety but has no effect on usage frequency. However, self-efficacy demonstrated its moderation effects on the relationships between fitness use innovativeness and usage variety and usage frequency. The evidence of the moderation effect of self-efficacy conforms to the theory of the use-diffusion model. We finally developed a matrix of fitness innovators to illustrate related managerial implications for each segment.
Numerical Analysis of an Aerofoil Fin Integrated Double Pass Solar Air Heater for Thermal Performance Enhancement
Authors: Nagaraj Madhwesh , Reddy Manu Krishna , Honnesara Sheshadri Arun Kumar , Karanth Kota Vasudeva
Published: December 2022

The Solar Air Heater (SAH) is considered to be one of the promising devices for the utilization of solar radiation. Extracting more heat to the flowing air is the focus of researchers, and many novel ideas are adopted to improve the efficiency of such collectors. The objective of the present work is the enhancement of thermal performance using a numerical analysis of a single flow double pass solar air heater with two types of arrangements of aerofoil fin configurations. The effect of the aerofoil fin configurations and the height of the fin are investigated for their thermal and thermohydraulic efficiencies. The height of the fin varied parametrically for the Reynolds number ranging from 3000 to 24,000 by keeping the axial pitch of the fin as a constant. It is found that the thermal efficiency increases with the increase in fin height due to an increased flow turbulence causing more absorption of heat to the working fluid. However, it is seen that the varying height of the fin beyond a certain height has yielded an adverse effect in terms of lesser thermal efficiency due to the expected flow blockage in the main stream. The thermohydraulic efficiency increases as the height of the fin decreases. The Computational Fluid Dynamics (CFD) results revealed that the optimum aerofoil fin configuration yields approximately a 23.24% higher thermal efficiency when compared with that of the single pass solar air heater (base model). The thermohydraulic efficiency exceeds the optimum aerofoil fin configuration compared with the base model by approximately 20.94%.
Terminal Node of Active Distribution Network Correlation Compactness Model and Application Based on Complex Network Topology Graph
Authors: Jiang Peng , Dou Xihao , Dong Jun , Huang Hexiang , Wang Yuanyuan
Published: December 2022

Multiple nodes (such as distributed generation (DG), electric vehicles (EV), energy storage (ES), flexible loads (FL), etc.) are connected to the active distribution network (ADN), which changes its original operational mode. According to the bidirectional current and low-voltage transmission mode, this study proposed a multi voltage and multi electricity flat loop network, AC/DC (Alternating Current/ Direct Current) hybrid network, unified interface and flexible self-organizing network based on Complex network theory. First, the ADN complex network topology of various nodes is established based on the actual grid connected terminal nodes and power flow sensitivity algorithm. Second, using the TOPSIS model, the influence factor matrix of weighted directed network is established. The matrix can be used to guide the formulation of the distribution network operation mode, and the robustness and reliability of this paper are verified by using the standard multi voltage level main distribution hybrid model provided by the Panda Power website as the verification method. Finally, using the influence maximization calculation model of the New Creedy algorithm, the node correlation matrix is expanded to form a super family region set of active distribution network. The results show that the seven nodes in this paper have high correlation, while the other nodes have low correlation. In addition, the change of reactive power has little impact on other nodes, for a node with a change rate of 0, it is obviously not in the same power supply family as node 1, and theoretically it may not have a topological relationship, be a power generation node, or be completely independent. Analyzing the relationship between nodes has a guiding significance for power supply recovery and interaction in distribution network reconfiguration.
A Synergetic Framework for Green and Lean Manufacturing Practices in SMEs: Saudi Arabia Perspective
Authors: Abualfaraa Wadhah , AlManei Mohammed , Kaur Rashmeet , Al-Ashaab Ahmed , McLaughlin Patrick , Salonitis Konstantinos
Published: December 2022

Manufacturers are urged to improve their sustainability performance, in terms of keeping the balance among economic, environmental, and social performances. Nevertheless, top managers and company leaders tend to complain that it is not possible to implement greener solutions without large investments. In this context, the well-known lean manufacturing approach, mainly focused on waste reduction, has come to be viewed with a renewed interest towards improving not only economic but also environmental and social aspects. Unfortunately, while there is no doubt that lean manufacturing increases productivity, efficiency, quality, and customers’ satisfaction by reducing wastes, there is a lack of research about the benefits of lean practices in relation to green manufacturing. The aim of the present work is to investigate the level of integration of green and lean manufacturing approaches, focussing specifically on the context of Saudi Arabia. As a result, a novel theoretical framework capable of combining the lean and green approaches within a synergetic environment, enhancing the former and mitigating the latter, is proposed. A survey was put together for assessing a number of hypotheses and validating the proposed framework. Qualitative analysis based on a survey conducted in different SME manufacturing companies in Saudi Arabia is presented. The statistical analysis of the collected data shows a clear trend that in Saudi Arabia, companies are starting to have a positive view about the integration of lean and green approaches implementation. The results highlighted higher maturity among the respondents with regard to the lean implementation, focussing on operational improvements. The improvements that can be attained with regard to environmental performance are coming next, and the framework proposed can help increase awareness among Saudi manufacturing SMEs.
Perceived Social Support and Engagement in First-Year Students: The Mediating Role of Belonging during COVID-19
Authors: Maluenda-Albornoz Jorge , Berríos-Riquelme José , Infante-Villagrán Valeria , Lobos-Peña Karla
Published: December 2022

Academic engagement and the conditions that favor it have become relevant in recent decades due to their relationship with academic performance, well-being, and university permanence. Variables such as perceived social support and sense of belonging are relevant aspects of social integration to promote engagement. Evidence shows both variables predicting engagement. In addition, the available evidence suggests this possible mediating role, which requires further analysis in freshmen in the emergency teaching context due to the COVID-19. The present study aims to evaluate the mediating role of sense of belonging in the relationship between perceived social support and engagement in university students. Results showed significant predictive relationships between social integration variables and engagement and showed the mediating role of sense of belonging in the relation between perceived social support and the three types of engagement. These results suggest the relevance for degree programs to consider these social integration variables as a key element for university freshmen.
Characterisation of Cellulolytic Bacteria Isolated from Agricultural Soil in Central Lithuania
Authors: Shamshitov Arman , Decorosi Francesca , Viti Carlo , Fornasier Flavio , Kadžienė Gražina , Supronienė Skaidrė
Published: December 2022

Characterisation and evaluation of soil bacteria were conducted in order to select the most potent strains that participate in the degradation of cellulose in unique agroecosystem and climatic conditions. Cellulolytic activity of soil bacteria was estimated using qualitative assays such as growth on selective media followed by screening with Congo red, Gram’s iodine solution, confirmation test on Congo red agar, determination of enzyme production, and sugar utilisation pattern. A total of 159 soil cellulolytic bacterial strains were selected based on shape, size, and colony characteristics. According to the results of all three screening assays, sixty-four, thirty-eight and fifty-one isolates were able to degrade at some level of cellulose, respectively. Partial sequencing of the 16S rRNA gene of 64 bacterial strains obtained using sequences retrieved from the databases indicated the presence of cellulolytic bacteria represented by members of the phyla Actinobacteria (48.44%), followed by Firmicutes (32.81%), Proteobacteria (15.62%) and Bacteroidetes (3.13%). Determination of enzyme production showed that fifteen strains possess endoglucanases activity which ranged from 9.09 to 942.41 nanomoles of MUF (4-methylumbelliferone) mL. Likewise, β-glucosidase enzyme activity was determined in 23.4 % of all isolates. The sugar utilisation pattern of soil bacterial strains displayed the different capabilities of growth and utilisation on various carbon sources, which occur in lignocellulosic materials (cellulose, starch) or their hydrolysates (glucose, galactose, fructose, cellobiose, maltose, lactose, sucrose, trehalose).
Microgrid Protection Using Magneto-Resistive Sensors and Superimposed Reactive Energy
Authors: Mehmood Musfira , Bukhari Syed Basit Ali , Altamimi Abdullah , Khan Zafar A. , Kazmi Syed Ali Abbas , Yousif Muhammad , Shin Dong Ryeol
Published: December 2022

The concept of microgrids has emerged as an effective way to integrate distributed energy resources (DERs) into distribution networks. The presence of DERs in microgrids leads to challenges in the formulation of protection for microgrids. Protection problems arise in a microgrid due to varying fault current levels in different operating scenarios. In order to overcome the practical challenges arising from varying fault current levels leading to short-circuit faults in microgrids, this paper proposes a MagnetoResistive (MR) sensors-based protection scheme, with fault localization through SuperimposedReactiveEnergy (SRE). The process is initiated by employing highly sensitive non-intrusive magnetic sensors to detect the magnetic field at each end of the distribution line. The magnetic field is then used to calculate the total harmonic distortion and thus detect faults in microgrids. After detection of faults, the proposed scheme uses SRE to identify faulty zones in microgrids. Finally, SI components of the current are extracted for fault classification. Extensive simulations on the International Electro-technical Commission (IEC) microgrid are performed in MATLAB/Simulink to validate the efficacy of the proposed scheme. Simulation results show that the proposed scheme can effectively detect, classify and isolate different faults in microgrids, while operating under various modes with varying fault locations and resistances, with the efficiency of approximately 97–98%.
Effects of Injection Timing and Antioxidant on NOx Reduction of CI Engine Fueled with Algae Biodiesel Blend Using Machine Learning Techniques
Authors: Venkatesan Elumalai Perumal , Murugesan Parthasarathy , Narayana Pichika Sri Veera Venkata Satya , Janaki Durga Venkatesh , Javed Yasir , Mahmoud Z. , Saleel C Ahamed
Published: December 2022

Fossil fuels are depended upon often in the transport sector. The use of diesel engines in all areas produce pollutants, such as NOx and CO, which cause serious environmental pollution and hazards, such as global climate change and breathing difficulties. Conventional fuel usage should be reduced, and there should be a shift toward alternative fuels. For compression ignition (CI) engines, microalgae biodiesel has been promoted as a clean, sustainable fuel. This is because it possesses desired traits, such as a quick rate of development, high productivity, and the capacity to turn CO2 into fuel. When algal biodiesel is used, pollutants, such as CO, UBHC, and smoke, are typically reduced, whereas NOx emissions are typically increased. The adoption of an exhaust gas recirculation technology and the advancement or delay of injection timing can effectively reduce NOx formation. Incorporating antioxidant chemicals such as butylated hydroxyl anisole (BHA) into fuel also minimizes NOx formation. In this study, the use of microalgae biodiesel as a substitute fuel for CI engines was investigated by altering the injection timing and adding each antioxidant in two doses. According to ASTM standard test procedures for biodiesel, the fuel qualities of various blends of algal biodiesel with antioxidants were tested and compared with the diesel fuel. The experiments were conducted using CI engines, and parameters were examined, such UBHC, CO, NOx, and smoke opacity. In comparison to diesel fuel, B20 + 30% BHA (21 bTDC) blends produced 49% lower oxides of nitrogen. The smoke, HC, and CO emissions of fuel blend B20 + 30% BHA (25 bTDC) were reduced by 33.33%, 32.37%, and 11.21%, respectively, compared with those of diesel fuel. The fuel blend B20 + 30% BHA (25 bTDC) showed the highest brake thermal efficiency of 14.52% at peak load condition. A multi-output regression deep long short-term memory (MDLSTM) model was designed to predict the performance and emissions of CI engines operating with varied fuel mixtures. The average RMSE and R2 values for the proposed MDLSTM were 0.38 and 0.9579, respectively.
Systematic Evidence Mapping to Assess the Sustainability of Bioplastics Derived from Food Waste: Do We Know Enough?
Authors: Gerassimidou Spyridoula , Martin Olwenn V. , Diaz Gilenny Yamily Feliz , Wan Chaoying , Komilis Dimitrios , Iacovidou Eleni
Published: December 2022

The production of bioplastics from food loss and waste (FLW), termed FLW-derived bioplastics, is considered an attractive alternative to first-generation bioplastics. To our knowledge, a clear understanding of the sustainability performance of FLW-derived bioplastics from environmental, economic, technical, and social aspects is still lacking. This systematic evidence mapping aims to fill this gap by undertaking a reality check on the life cycle sustainability performance of FLW-derived bioplastics from a multidimensional perspective underpinned by systems thinking approach to assess their potential to revolutionise the plastics economy. Results revealed that FLW-derived bioplastic production is highly complex and uncertain. The low technological readiness of FLW valorisation processes and the under-researched logistics of FLW management on a regional scale currently withhold advancement in this field. Nonetheless, progress is looming, and ensuring that FLW-derived bioplastics production enables the transition toward a sustainable bioeconomy is critical. Innovation in both the food and plastics value chains is urgently needed to address their challenges and mitigate pollution. Yet, any steps forward need to be holistically calculated to yield sustainability benefits and prevent unintended consequences.
A Systematic Review of Real-Time Monitoring Technologies and Its Potential Application to Reduce Food Loss and Waste: Key Elements of Food Supply Chains and IoT Technologies
Authors: da Costa Tamíris Pacheco , Gillespie James , Cama-Moncunill Xavier , Ward Shane , Condell Joan , Ramanathan Ramakrishnan , Murphy Fionnuala
Published: December 2022

Continuous monitoring of food loss and waste (FLW) is crucial for improving food security and mitigating climate change. By measuring quality parameters such as temperature and humidity, real-time sensors are technologies that can continuously monitor the quality of food and thereby help reduce FLW. While there is enough literature on sensors, there is still a lack of understanding on how, where and to what extent these sensors have been applied to monitor FLW. In this paper, a systematic review of 59 published studies focused on sensor technologies to reduce food waste in food supply chains was performed with a view to synthesising the experience and lessons learnt. This review examines two aspects of the field, namely, the type of IoT technologies applied and the characteristics of the supply chains in which it has been deployed. Supply chain characteristics according to the type of product, supply chain stage, and region were examined, while sensor technology explores the monitored parameters, communication protocols, data storage, and application layers. This article shows that, while due to their high perishability and short shelf lives, monitoring fruit and vegetables using a combination of temperature and humidity sensors is the most recurring goal of the research, there are many other applications and technologies being explored in the research space for the reduction of food waste. In addition, it was demonstrated that there is huge potential in the field, and that IoT technologies should be continually explored and applied to improve food production, management, transportation, and storage to support the cause of reducing FLW.
Comparative Analysis of Seismic Design Codes for Shallow Foundations Adhering to the Kazakhstani and European Approaches
Authors: Zhanabayeva Assel , Moon Sung-Woo , Ocheme James Innocent , Shokbarov Yeraly , Khomyakov Vitaliy , Kim Jong , Satyanaga Alfrendo
Published: December 2022

Since 2015 the transition from the traditional seismic design regulation to the newly developed code of practice has been initiated in Kazakhstan. The introduced regulatory system involves the application of the European approach for the seismic design of buildings and structures on the territory of Kazakhstan. This study aims to present a comparative analysis of seismic design codes applied in Kazakhstan (i.e., SP RK 5.01-102-2013* Foundations of Buildings and Structures and SP RK 2.03-30-2017* Construction in Seismic Regions) and SP RK EN 1998-5:2004/2012 Design of Structures for Earthquake Resistance, identical to Eurocode 8 (EC8). One of the critical aspects of the research investigates the difficulties of integrating European design standards into the local regulatory system. The necessity of applying the European approach considering the geotechnical features of the country provided in the National Annex (NA) is defined and proved. The designed codes of practice are also compared in terms of conservativeness, when considering a design problem verifying the seismic bearing capacity of a shallow foundation in Almaty city.
Analysis of Influencing Factors and Distribution Simulation of Budget Hotel Room Pricing Based on Big Data and Machine Learning from a Spatial Perspective
Authors: Hu Tao , Song Haoyu
Published: December 2022

The goal of investors in the hotel business is to maximize profits, and the price is an important means of achieving this goal. This has attracted many scholars to study the spatiotemporal relationship between hotel room prices and their possible influencing factors from different perspectives. However, most existing studies adopt the linear assumption of the hedonic model, with limited features and a lack of feature selection procedures. Additionally, there are few forecasts of hotel pricing from a spatial perspective. To overcome these gaps, this study adopts linear and nonlinear machine learning methods based on the “big data” of Sanya City to explore the influencing factors of budget hotel pricing. Based on the spatial perspective, 81 potential factors were considered. They are further selected using a feature extraction model called recursive feature elimination. Six machine-learning algorithms were evaluated and compared: random forest, extreme gradient boosting, multi-linear regression, support vector regression, multilayer perceptron regression, and K-nearest neighbor regression. The optimal value was used to further calculate the feature importance. They disclosed 40 important impact characteristics and predicted the spatial distribution of hotel pricing.
A Framework to Facilitate Advanced Mixed Methods Studies for Investigating Interventions in Road Space for Cycling
Authors: Werner Christian , Füssl Elisabeth , Rieß Jannik , Resch Bernd , Kratochwil Florian , Loidl Martin
Published: December 2022

Cycling mobility contributes to better livability in cites, helps societies to reduce greenhouse gas emissions and their dependency on fossil fuels, and shows positive health effects. However, unattractive conditions, primarily inadequate infrastructure, hinder the further growth of cycling mobility. As interactions of cyclists with the (built) environment are complex, assessing potential impacts of an intervention aimed at improving physical conditions is not trivial. Despite a growing body of literature on various facets of cycling mobility, assessments are widely limited to a single method and thereby either focus on one detailed aspect or on one perspective. While multi-method and mixed methods studies are emerging, they are not embedded into a structured, integrated framework for assessing systemic effects of interventions yet. Therefore, we propose a conceptual integration of several relevant methods such as questionnaires, interviews, GIS analyses and human sensing. In this paper, we present a generic, extensible framework that offers guidance for developing and implementing case-specific mixed methods designs for multifaceted assessments of interventions. The framework supports domain experts and researchers across different stages of conducting a study. Results from this research further indicate the added value of mixed methods studies compared to single-method approaches.
Business Agility and Competitive Advantage of SMEs in Makassar City, Indonesia
Authors: Yusuf Muhammad , Surya Batara , Menne Firman , Ruslan Muhlis , Suriani Seri , Iskandar Iskandar
Published: December 2022

Corporate agility will encourage companies and economic enterprises to respond quickly to the dynamics of internal and external changes in the company. This study aims to analyze the effects of business innovation, CSR implementation, and digital transformation in improving business agility, with competitive advantage as a mediating variable. This study uses a quantitative approach with a survey method. Data were obtained through questionnaires distributed to 100 business units and SMEs as respondents in Makassar City. The results show that competitive advantage, digital transformation, and business innovation have a significant effect on business agility with a coefficient of determination of 72.4%, while CSR implementation and digital transformation have a negative correlation with SME business agility. Digital transformation, CSR implementation, and business innovation affect competitive advantage with a value of 53.9%, while digital transformation, CSR implementation, and business innovation affect business agility through corporate image. This study recommends the use of digital transformation and business innovation for small and medium enterprises (SMEs) to respond and adapt with business agility in Makassar City, Indonesia.
Potential Global Distribution of the Habitat of Endangered Gentiana rhodantha Franch: Predictions Based on MaxEnt Ecological Niche Modeling
Authors: Zhang Huihui , Sun Xiao , Zhang Guoshuai , Zhang Xinke , Miao Yujing , Zhang Min , Feng Zhan , Zeng Rui , Pei Jin , Huang Linfang
Published: December 2022

Continued global climate and environmental changes have led to habitat narrowing or migration of medicinal plants. Gentiana rhodantha Franch. ex Hemsl. is a medicinal plant for ethnic minorities in China, and it has a remarkable curative effect in the treatment of lung-heat cough. However, its habitat is gradually decreasing, and the species has been listed as an endangered ethnic medicine due to excessive harvesting. Here, based on CMIP6 bioclimatic data and 117 species occurrence records, the maximum entropy model (MaxEnt), combined with ArcGIS technology, was applied to predict the potentially suitable habitats for G. rhodantha under different climate scenarios. The results showed that the most critical bioclimatic variables affecting G. rhodantha are the precipitation of the warmest quarter (Bio18) and the mean temperature of the coldest quarter (Bio11). The highly suitable habitats of G. rhodantha are mainly concentrated in Belt and Road (“B&R”) countries, including China, Bhutan, and Vietnam. However, under different climate change scenarios, the fragmentation extent of suitable habitats in China will generally increase, the suitable area will show a decreasing trend as a whole, the distribution center will shift to the northeast, and the distance will increase with time. Notably, the shrinkage of the high suitability area was the most obvious for the 2081–2100 SSP585 scenario, with a total of 358,385.2 km2. These findings contribute to the understanding of the geo-ecological characteristics of this species, and provide guidelines for the conservation, management, monitoring, and cultivation of G. rhodantha.
The Impact of Environmental Pollution on Residents’ Income Caused by the Imbalance of Regional Economic Development Based on Artificial Intelligence
Authors: Ma Binfeng
Published: December 2022

Regional economy is a human economic activity in a geographical region, a prominent and sustainable economy with distinctive regional characteristics. Regional economy is characterized by its integrity, relativity, relative independence, and spatial difference. With the increasing development of science and technology and big data, it has become a normal trend to use artificial intelligence technology to solve current social problems. In this paper, the social problems caused by the imbalance of regional economy are analyzed based on artificial intelligence. Through the application of KNN-SVM algorithm optimization, it is found that AI has relatively little impact on the development of the income level of the population under the age of 25 in each region. Compared with previous studies, the quality is compared with the innovation of this document, which is the development of a conceptual framework approach, an environmental pollution analysis mechanism, and income inequality analysis. The empirical research results show that under the strategic background of improving people’s livelihood, accelerating the reform of ecological civilization and promoting the construction of the health system, the relationship between environmental pollution and population income caused by unbalanced regional economic development can be re-analyzed through the best KNN-SVM algorithm. The implementation of the healthy China strategy has important theoretical and practical significance.
Wearable Artificial Intelligence for Assessing Physical Activity in High School Children
Authors: Ahmed Arfan , Aziz Sarah , Qidwai Uvais , Farooq Faisal , Shan Jingxuan , Subramanian Murugan , Chouchane Lotfi , EINatour Rola , Abd-Alrazaq Alaa , Pandas Satchidananda , Sheikh Javaid
Published: December 2022

Eighty one percent of adolescents aged 11–17 years are inadequately physically active worldwide. Physical activity (PA) recommendations for high school children have not been studied previously in schools in the Qatar region. The objectives of the study were: (i) to assess the level of compliance of the recommended PA and to assess if there are any gender differences; and (ii) to analyze the recommended step count compliance during school and non-school days. An observational cross-sectional study was conducted. Twenty-nine children (12 boys and 17 girls) aged 13–17 years (15.24 ± 1.46) took part in this study. Participants wore Fitbit Charge 5 wrist bands for three weeks to collect various digital biomarkers including moderate-to-vigorous physical activity (MVPA) and step counts (tracking during out-of-school time and school time). Based on this study, high school children in the two Qatar region schools did not meet the MVPA and steps/day recommendation by the established agencies: 38% of the total study group met the recommended 60 min/day of activity (50% boys, 29% girls). Gender differences were also observed in PA levels and steps per day: for non-school days, 17% met the recommended 10,000 steps/day (25% boys, 12% girls). There was a pattern of greater PA performance and steps during the weekdays as opposed to the weekend, but these values showed no robust evidence in favor of H1 or statistical significance for step counts. However, the evidence was robust in favor of H1 (difference between weekend and weekday) due to a statistically significant difference for meeting the 60 min/day activity. While further studies are required to establish if this is a general trend in Qatari schools, this pilot study does highlight the need to design more effective programs and messaging strategies to improve PA levels in the high school population.