bluuebunny/crossref_metadata_2025 huggingface dataset:
A Sparse Representation Classification Scheme for the Recognition of Affective and Cognitive Brain Processes in Neuromarketing
Authors: Oikonomou Vangelis P. , Georgiadis Kostas , Kalaganis Fotis , Nikolopoulos Spiros , Kompatsiaris Ioannis
Published: February 2023

In this work, we propose a novel framework to recognize the cognitive and affective processes of the brain during neuromarketing-based stimuli using EEG signals. The most crucial component of our approach is the proposed classification algorithm that is based on a sparse representation classification scheme. The basic assumption of our approach is that EEG features from a cognitive or affective process lie on a linear subspace. Hence, a test brain signal can be represented as a linear (or weighted) combination of brain signals from all classes in the training set. The class membership of the brain signals is determined by adopting the Sparse Bayesian Framework with graph-based priors over the weights of linear combination. Furthermore, the classification rule is constructed by using the residuals of linear combination. The experiments on a publicly available neuromarketing EEG dataset demonstrate the usefulness of our approach. For the two classification tasks offered by the employed dataset, namely affective state recognition and cognitive state recognition, the proposed classification scheme manages to achieve a higher classification accuracy compared to the baseline and state-of-the art methods (more than 8% improvement in classification accuracy).
A Study on Optimization of Noise Reduction of Powered Vehicle Seat Movement Using Brushless Direct-Current Motor
Authors: Lee Hyunju , Ko Dongshin , Nam Jaehyeon
Published: February 2023

In this paper, an optimal design model was developed to reduce noise and secure the torque performance of a brushless direct-current motor used in the seat of an autonomous vehicle. An acoustic model using finite elements was developed and verified through the noise test of the brushless direct-current motor. In order to reduce noise in the brushless direct-current motor and obtain a reliable optimization geometry of noiseless seat motion, parametric analysis was performed through the design of experiments and Monte Carlo statistical analysis. The slot depth, stator tooth width, slot opening, radial depth, and undercut angle of the brushless direct-current motor were selected as design parameters for design parameter analysis. Then, a non-linear prediction model was used to determine the optimal slot depth and stator tooth width to maintain the drive torque and minimize the sound pressure level at 23.26 dB or lower. The Monte Carlo statistical method was used to minimize the deviation of the sound pressure level caused by the production deviation of the design parameters. The result is that the SPL was 23.00–23.50 dB with a confidence level of approximately 99.76% when the level of production quality control was set at 3σ.
Convolutional Neural Network Model for Variety Classification and Seed Quality Assessment of Winter Rapeseed
Authors: Rybacki Piotr , Niemann Janetta , Bahcevandziev Kiril , Durczak Karol
Published: February 2023

The main objective of this study is to develop an automatic classification model for winter rapeseed varieties, to assess seed maturity and damage based on seed colour using a convolutional neural network (CNN). A CNN with a fixed architecture was built, consisting of an alternating arrangement of five classes Conv2D, MaxPooling2D and Dropout, for which a computational algorithm was developed in the Python 3.9 programming language, creating six models depending on the type of input data. Seeds of three winter rapeseed varieties were used for the research. Each imaged sample was 20.000 g. For each variety, 125 weight groups of 20 samples were prepared, with the weight of damaged or immature seeds increasing by 0.161 g. Each of the 20 samples in each weight group was marked by a different seed distribution. The accuracy of the models’ validation ranged from 80.20 to 85.60%, with an average of 82.50%. Higher accuracy was obtained when classifying mature seed varieties (average of 84.24%) than when classifying the degree of maturity (average of 80.76%). It can be stated that classifying such fine seeds as rapeseed seeds is a complex process, creating major problems and constraints, as there is a distinct distribution of seeds belonging to the same weight groups, which causes the CNN model to treat them as different.
Research on Two-Round Self-Balancing Robot SLAM Based on the Gmapping Algorithm
Authors: Zhao Jianwei , Li Jinyu , Zhou Jiaxin
Published: February 2023

Aiming at the inconvenience of inspection and monitoring of coal mine pump room equipment in a narrow and complex environment, this paper proposes and designs a two-wheel self-balancing inspection robot based on laser SLAM. Using SolidWorks, the three-dimensional mechanical structure of the robot is designed, and the overall structure of the robot is analyzed by finite element statics. The kinematics model of the two-wheel self-balancing robot was established, and the multi-closed-loop PID controller was used to design the two-wheel self-balancing control algorithm of the robot. The 2D LiDAR-based Gmapping algorithm was used to locate the robot and construct the map. Through the self-balancing test and anti-jamming test, it is verified that the self-balancing algorithm designed in this paper has a certain anti-jamming ability and good robustness. By using Gazebo to build a simulation comparison experiment, it is verified that the selection of the particle number is of great significance for improving the map accuracy. The actual test results show that the constructed map has high accuracy.
Development and Demonstration of a Wireless Ultraviolet Sensing Network for Dose Monitoring and Operator Safety in Room Disinfection Applications
Authors: Cullinan Michael F. , Scott Robert , Linogao Joe , Bradwell Hannah , Cooper Leonie , McGinn Conor
Published: February 2023

The use of mobile ultraviolet-C (UV-C) disinfection devices for the decontamination of surfaces in hospitals and other settings has increased dramatically in recent years. The efficacy of these devices relies on the UV-C dose they deliver to surfaces. This dose is dependent on the room layout, the shadowing, the position of the UV-C source, lamp degradation, humidity and other factors, making it challenging to estimate. Furthermore, since UV-C exposure is regulated, personnel in the room must not be exposed to UV-C doses beyond occupational limits. We proposed a systematic method to monitor the UV-C dose administered to surfaces during a robotic disinfection procedure. This was achieved using a distributed network of wireless UV-C sensors that provide real-time measurements to a robotic platform and operator. These sensors were validated for their linearity and cosine response. To ensure operators could safely remain in the area, a wearable sensor was incorporated to monitor the UV-C exposure of an operator, and it provided an audible warning upon exposure and, if necessary, ceased the UV-C emission from the robot. Enhanced disinfection procedures could then be conducted as items in the room could be rearranged during the procedure to maximise the UV-C fluence delivered to otherwise inaccessible surfaces while allowing UVC disinfection to occur in parallel with traditional cleaning. The system was tested for the terminal disinfection of a hospital ward. During the procedure, the robot was manually positioned in the room by the operator repeatedly, who then used feedback from the sensors to ensure the desired UV-C dose was achieved while also conducting other cleaning tasks. An analysis verified the practicality of this disinfection methodology while highlighting factors which could affect its adoption.
FedDdrl: Federated Double Deep Reinforcement Learning for Heterogeneous IoT with Adaptive Early Client Termination and Local Epoch Adjustment
Authors: Wong Yi Jie , Tham Mau-Luen , Kwan Ban-Hoe , Owada Yasunori
Published: February 2023

Federated learning (FL) is a technique that allows multiple clients to collaboratively train a global model without sharing their sensitive and bandwidth-hungry data. This paper presents a joint early client termination and local epoch adjustment for FL. We consider the challenges of heterogeneous Internet of Things (IoT) environments including non-independent and identically distributed (non-IID) data as well as diverse computing and communication capabilities. The goal is to strike the best tradeoff among three conflicting objectives, namely global model accuracy, training latency and communication cost. We first leverage the balanced-MixUp technique to mitigate the influence of non-IID data on the FL convergence rate. A weighted sum optimization problem is then formulated and solved via our proposed FL double deep reinforcement learning (FedDdrl) framework, which outputs a dual action. The former indicates whether a participating FL client is dropped, whereas the latter specifies how long each remaining client needs to complete its local training task. Simulation results show that FedDdrl outperforms the existing FL scheme in terms of overall tradeoff. Specifically, FedDdrl achieves higher model accuracy by about 4% while incurring 30% less latency and communication costs.
H-BIM and Artificial Intelligence: Classification of Architectural Heritage for Semi-Automatic Scan-to-BIM Reconstruction
Authors: Croce Valeria , Caroti Gabriella , Piemonte Andrea , De Luca Livio , Véron Philippe
Published: February 2023

We propose a semi-automatic Scan-to-BIM reconstruction approach, making the most of Artificial Intelligence (AI) techniques, for the classification of digital architectural heritage data. Nowadays, Heritage- or Historic-Building Information Modeling (H-BIM) reconstruction from laser scanning or photogrammetric surveys is a manual, time-consuming, overly subjective process, but the emergence of AI techniques, applied to the realm of existing architectural heritage, is offering new ways to interpret, process and elaborate raw digital surveying data, as point clouds. The proposed methodological approach for higher-level automation in Scan-to-BIM reconstruction is threaded as follows: (i) semantic segmentation via Random Forest and import of annotated data in 3D modeling environment, broken down class by class; (ii) reconstruction of template geometries of classes of architectural elements; (iii) propagation of template reconstructed geometries to all elements belonging to a typological class. Visual Programming Languages (VPLs) and reference to architectural treatises are leveraged for the Scan-to-BIM reconstruction. The approach is tested on several significant heritage sites in the Tuscan territory, including charterhouses and museums. The results suggest the replicability of the approach to other case studies, built in different periods, with different construction techniques or under different states of conservation.
Pilot Study: Effect of Morton’s Extension on the Subtalar Joint Forces in Subjects with Excessive Foot Pronation
Authors: Palomo-Toucedo Inmaculada C. , González-Elena María Luisa , Balestra-Romero Patricia , Vázquez-Bautista María del Carmen , Castro-Méndez Aurora , Reina-Bueno María
Published: February 2023

This study focuses on the assessment of the mechanical effect produced by Morton’s extension as an orthopedic intervention in patients with bilateral foot pronation posture, through a variation in hindfoot and forefoot prone-supinator forces during the stance phase of gait. A quasi-experimental and transversal research was designed comparing three conditions: barefoot (A); wearing footwear with a 3 mm EVA flat insole (B); and wearing a 3 mm EVA flat insole with a 3 mm thick Morton’s extension (C), with respect to the force or time relational to the maximum time of supination or pronation of the subtalar joint (STJ) using a Bertec force plate. Morton’s extension did not show significant differences in the moment during the gait phase in which the maximum pronation force of the STJ is produced, nor in the magnitude of the force, although it decreased. The maximum force of supination increased significantly and was advanced in time. The use of Morton’s extension seems to decrease the maximum force of pronation and increase supination of the subtalar joint. As such, it could be used to improve the biomechanical effects of foot orthoses to control excessive pronation.
A Facile Fluorometric Assay of Orotate Phosphoribosyltransferase Activity Using a Selective Fluorogenic Reaction for Orotic Acid
Authors: Shibata Takayuki , Narita Tomohiro , Suto Yutaka , Yasmin Hasina , Kabashima Tsutomu
Published: February 2023

Orotate phosphoribosyltransferase (OPRT) exists as a bifunctional enzyme, uridine 5′-monophosphate synthase, in mammalian cells and plays an important role in pyrimidine biosynthesis. Measuring OPRT activity has been considered important for understanding biological events and development of molecular-targeting drugs. In this study, we demonstrate a novel fluorescence method for measuring OPRT activity in living cells. The technique utilizes 4-trifluoromethylbenzamidoxime (4-TFMBAO) as a fluorogenic reagent, which produces selective fluorescence for orotic acid. To perform the OPRT reaction, orotic acid was added to HeLa cell lysate, and a portion of the enzyme reaction mixture was heated at 80 °C for 4 min in the presence of 4-TFMBAO under basic conditions. The resulting fluorescence was measured using a spectrofluorometer, which reflects the consumption of orotic acid by the OPRT. After optimization of the reaction conditions, the OPRT activity was successfully determined in 15 min of enzyme reaction time without further procedures such as purification of OPRT or deproteination for the analysis. The activity obtained was compatible with the value measured by the radiometric method with [3H]-5-FU as the substrate. The present method provides a reliable and facile measurement of OPRT activity and could be useful for a variety of research fields targeting pyrimidine metabolism.
Tone Mapping Operator for High Dynamic Range Images Based on Modified iCAM06
Authors: Li Yumei , Liao Ningfang , Wu Wenmin , Deng Chenyang , Li Yasheng , Fan Qiumei , Liu Chuanjie
Published: February 2023

This study attempted to solve the problem of conventional standard display devices encountering difficulties in displaying high dynamic range (HDR) images by proposing a modified tone-mapping operator (TMO) based on the image color appearance model (iCAM06). The proposed model, called iCAM06-m, combined iCAM06 and a multi-scale enhancement algorithm to correct the chroma of images by compensating for saturation and hue drift. Subsequently, a subjective evaluation experiment was conducted to assess iCAM06-m considering other three TMOs by rating the tone mapped images. Finally, the objective and subjective evaluation results were compared and analyzed. The results confirmed the better performance of the proposed iCAM06-m. Furthermore, the chroma compensation effectively alleviated the problem of saturation reduction and hue drift in iCAM06 for HDR image tone-mapping. In addition, the introduction of multi-scale decomposition enhanced the image details and sharpness. Thus, the proposed algorithm can overcome the shortcomings of other algorithms and is a good candidate for a general purpose TMO.
Slicing Resource Allocation Based on Dueling DQN for eMBB and URLLC Hybrid Services in Heterogeneous Integrated Networks
Authors: Chen Geng , Shao Rui , Shen Fei , Zeng Qingtian
Published: February 2023

In 5G/B5G communication systems, network slicing is utilized to tackle the problem of the allocation of network resources for diverse services with changing demands. We proposed an algorithm that prioritizes the characteristic requirements of two different services and tackles the problem of allocation and scheduling of resources in the hybrid services system with eMBB and URLLC. Firstly, the resource allocation and scheduling are modeled, subject to the rate and delay constraints of both services. Secondly, the purpose of adopting a dueling deep Q network (Dueling DQN) is to approach the formulated non-convex optimization problem innovatively, in which a resource scheduling mechanism and the ϵ-greedy strategy were utilized to select the optimal resource allocation action. Moreover, the reward-clipping mechanism is introduced to enhance the training stability of Dueling DQN. Meanwhile, we choose a suitable bandwidth allocation resolution to increase flexibility in resource allocation. Finally, the simulations indicate that the proposed Dueling DQN algorithm has excellent performance in terms of quality of experience (QoE), spectrum efficiency (SE) and network utility, and the scheduling mechanism makes the performance much more stable. In contrast with Q-learning, DQN as well as Double DQN, the proposed algorithm based on Dueling DQN improves the network utility by 11%, 8% and 2%, respectively.
YOLOv4 with Deformable-Embedding-Transformer Feature Extractor for Exact Object Detection in Aerial Imagery
Authors: Wu Yiheng , Li Jianjun
Published: February 2023

The deep learning method for natural-image object detection tasks has made tremendous progress in recent decades. However, due to multiscale targets, complex backgrounds, and high-scale small targets, methods from the field of natural images frequently fail to produce satisfactory results when applied to aerial images. To address these problems, we proposed the DET-YOLO enhancement based on YOLOv4. Initially, we employed a vision transformer to acquire highly effective global information extraction capabilities. In the transformer, we proposed deformable embedding instead of linear embedding and a full convolution feedforward network (FCFN) instead of a feedforward network in order to reduce the feature loss caused by cutting in the embedding process and improve the spatial feature extraction capability. Second, for improved multiscale feature fusion in the neck, we employed a depth direction separable deformable pyramid module (DSDP) rather than a feature pyramid network. Experiments on the DOTA, RSOD, and UCAS-AOD datasets demonstrated that our method’s average accuracy (mAP) values reached 0.728, 0.952, and 0.945, respectively, which were comparable to the existing state-of-the-art methods.
Wind Energy Conversions, Controls, and Applications: A Review for Sustainable Technologies and Directions
Authors: Hannan M. A. , Al-Shetwi Ali Q. , Mollik M. S. , Ker Pin Jern , Mannan M. , Mansor M. , Al-Masri Hussein M. K. , Mahlia T. M. Indra
Published: February 2023

The use of renewable energy techniques is becoming increasingly popular because of rising demand and the threat of negative carbon footprints. Wind power offers a great deal of untapped potential as an alternative source of energy. The rising demand for wind energy typically results in the generation of high-quality output electricity through grid integration. More sophisticated contemporary generators, power converters, energy management, and controllers have been recently developed to integrate wind turbines into the electricity system. However, a comprehensive review of the role of converters in the wind system’s power conversion, control, and application toward sustainable development is not thoroughly investigated. Thus, this paper proposes a comprehensive review of the impact of converters on wind energy conversion with its operation, control, and recent challenges. The converters’ impact on the integration and control of wind turbines was highlighted. Moreover, the conversion and implementation of the control of the wind energy power system have been analyzed in detail. Also, the recently advanced converters applications for wind energy conversion were presented. Finally, recommendations for future converters use in wind energy conversions were highlighted for efficient, stable, and sustainable wind power. This rigorous study will lead academic researchers and industry partners toward the development of optimal wind power technologies with improved efficiency, operation, and costs.
Long Life Power Factor Corrected LED Driver with Capacitive Energy Mechanism for Street Light Applications
Authors: Faizan Muhammad , Bi Jinshun , Liu Mengxin , Wang Lixin , Stempitsky Viktor , Yousaf Muhammad Zain
Published: February 2023

Conventional switch-mode LED drivers have problems such as poor performance in harmonic distortion, flickering, power factor correction, stresses on the switches, high switching losses, large size, and high cost. To resolve these problems, we propose a long-life LED driver with the ability of power factor correction. The proposed system is based on the integration of a half-bridge LLC resonant converter and two boundary-conducted boost converters. Both boost converters share a common inductor designed in such a way that both boost converters work in boundary conduction mode to attain the natural power factor correction. Half-bridge LLC resonant converter has soft switching characteristics, which assure the zero-voltage switching (ZVS) of primary-side switches and zero-current switching (ZCS) of diodes on the secondary side. This significantly reduces switching losses and improves the overall efficiency of the system. Voltage divider capacitors are used on the input side, which minimizes the bus voltages. The proposed system has two identical secondary windings with a coupled inductor to eliminate the mismatch between them, which powers two independent LED strings. The simulation of a 100-watt 240 V AC converter yields the approximate sinusoidal shape of the input current. It shows that the switches on the primary side are operated in ZVS and the diodes in ZCS. At 240-volt AC input, the efficiency is 87.4%, the total harmonics distortion (THD) is 10.98%, and the power factor (PF) is 0.98.
Impact Assessment of Tropical Cyclones Amphan and Nisarga in 2020 in the Northern Indian Ocean
Authors: Basheer Ahammed K. K. , Pandey Arvind Chandra , Parida Bikash Ranjan , Wasim  , Dwivedi Chandra Shekhar
Published: February 2023

The Northern Indian Ocean (NIO) is one of the most vulnerable coasts to tropical cyclones (TCs) and is frequently threatened by global climate change. In the year 2020, two severe cyclones formed in the NIO and devastated the Indian subcontinent. Super cyclone Amphan, which formed in the Bay of Bengal (BOB) on 15 May 2020, made landfall along the West Bengal coast with a wind speed of above 85 knots (155 km/h). The severe cyclone Nisarga formed in the Arabian Sea (ARS) on 1 June 2020 and made landfall along the Maharashtra coast with a wind speed above 60 knots (115 km/h). The present study has characterized both TCs by employing past cyclonic events (1982–2020), satellite-derived sea surface temperature (SST), wind speed and direction, rainfall dataset, and regional elevation. Long-term cyclonic occurrences revealed that the Bay of Bengal encountered a higher number of cyclones each year than the ARS. Both cyclones had different intensities when making landfall; however, the regional elevation played a significant role in controlling the cyclonic wind and associated hazards. The mountain topography on the east coast weakened the wind, while the deltas on the west coast had no control over the wind. Nisarga weakened to 30 knots (56 km/h) within 6 h from making landfall, while Amphan took 24 h to weaken to 30 knots (56 km/h). We analyzed precipitation patterns during the cyclones and concluded that Amphan had much more (1563 mm) precipitation than Nisarga (684 mm). Furthermore, the impact on land use land cover (LULC) was examined in relation to the wind field. The Amphan wind field damaged 363,837 km2 of land, whereas the Nisarga wind field affected 167,230 km2 of land. This research can aid in the development of effective preparedness strategies for disaster risk reduction during cyclone impacts along the coast of India.
Machine Learning Adoption in Educational Institutions: Role of Internet of Things and Digital Educational Platforms
Authors: Li Jiuxiang , Wang Rufeng
Published: February 2023

The ever-increasing development of information technologies has led to the adoption of advanced learning techniques. In this regard, e-learning and machine learning are two of the emerging instructional means for educational institutes. The current study investigates the role of the Internet of Things (IoT) and digital educational platforms (DEPs) in the adoption of machine learning. The present research additionally investigated the function of DEPs as mediators between IoT and machine learning adoption. The department chairs or heads of 310 departments at 91 Chinese institutions provided the information. In order to analyze the data, we used SPSS 25.0 and SEM (structural equation modeling). The results demonstrated how crucial an impact IoT has on DEPs and the uptake of machine learning. DEPs directly affect machine learning adoption and also act as mediators. The findings also support the mediating role of DEPs in the IoT and machine learning adoption link. The current study contributes to both theory and practical management by examining how IoT is helpful for achieving machine learning adoption. Based on the responses of 91 educational departments, this is a unique study of the mechanisms to achieve machine learning adoption through IoT and DEPs.
Entrepreneurial Initiatives, Education and Culture: Hubs for Enterprise Innovations and Economic Development
Authors: Zemlyak Svetlana , Gusarova Olga , Khromenkova Galina
Published: February 2023

The purpose of this research was to investigate the effects of entrepreneurial initiatives, entrepreneurial education, and entrepreneurial culture on enterprise innovation and economic development. The research was driven by the fact that entrepreneurship is a remarkable force which drives the enterprise growth, societal development, and economic development of a nation. The research adopted the social exchange theory and the economic theory as the basis of the literature. Primary data were used, collected from people engaged in entrepreneurship in Russia. The study used quantitative research method, where empirical data were analyzed using Structural equation modelling (SEM). The findings of the study indicated that entrepreneurship initiatives, entrepreneurship education, and entrepreneurship culture have a significant effect on enterprise innovation. However, only entrepreneurship culture was found to have a direct effect on economic development, but entrepreneurial education and initiatives have indirect effects through enterprise innovation. Enterprise innovation was found to have significant effect on economic development. The research recommended that stakeholder should emphasize creating an entrepreneurial culture to encourage enterprise innovation and economic development. The search also advocates for providing entrepreneurs with the resources, support, and incentives to start businesses in addition to adopting policies that enable and facilitate enterprise innovation, both domestically and internationally, to boost their economic development.
Attributes of Diffusion of Innovation’s Influence on Smallholder Farmers’ Social Media Adoption in Mpumalanga Province, South Africa
Authors: Zondo Welcome Ntokozo Sifisosami , Ndoro Jorine Tafadzwa
Published: February 2023

The adoption and utilization of social media as an advisory tool among smallholder farmers is relatively unexplored. Social media has the potential to enhance communication, making agricultural information easily available in the sector. This study investigated the relationship between the attributes of the diffusion of innovation theory and the socio-economic characteristics that influence social media adoption. An independent samples t-test and a one-way ANOVA were used for data analysis. The sample size was 217 smallholder farmers in the Nkomazi local municipality. The results revealed that the difference in the diffusion of innovation attributes based on gender, age, co-operative membership, and level of education had a statistically significant difference (p < 0.05). The findings suggest that extension services and other stakeholders including those in the private sector and policymakers should encourage wider adoption of social media by smallholder farmers. Governments need to invest in infrastructure such as community computer labs for training and access to social media. Open-access social media platforms that allow free internet access should also be considered for development. Additionally, policymakers should consider developing guidelines that encourage online advisory services, learning and information dissemination.
Modelling Sustainable Non-Renewable and Renewable Energy Based on the EKC Hypothesis for Africa’s Ten Most Popular Tourist Destinations
Authors: Voumik Liton Chandra , Rahman Md. Hasanur , Nafi Shohel Md. , Hossain Md. Akter , Ridzuan Abdul Rahim , Mohamed Yusoff Nora Yusma
Published: February 2023

The main purpose of this study was to examine how tourism, GDP, renewable energy, and fossil fuels cause environmental damage. This study examined ten African countries between 1997 and 2021 to test the environmental Kuznets curve (EKC) theory. Egypt, South Africa, Kenya, Morocco, Tanzania, Tunisia, Mauritius, Ghana, Uganda, and Nigeria are the ten African countries with the most tourists. In this paper, the augmented mean group (AMG), mean group (MG), and common correlated effects mean group (CCEMG) models were used to deal with slope heterogeneity (SH), cross-sectional dependence (CSD), and a mix of first-differenced and level stationary variables. Though the inverted U-shaped exists, the findings are significant only for MG. The impact of renewable energy is favorable for the environment and significant for the AMG estimator. Inversely, impact of tourist arrivals and fossil fuels are detrimental for environment and significant. Based on the findings for each country, the tourism-based EKC theory only works for Kenya, Egypt, and Tanzania. The research found that using more renewable energy minimizes CO2 emissions more effectively in almost all countries except Morocco and Ghana. Ghana, Kenya, and Uganda’s CO2 emissions increase when more tourists come from other countries. For the sake of both tourism and the environment, the government must reconsider its tourism policies and implement ones that include renewable energy. The findings of this study assist in the transition to clean energy, aiding in sustainable tourism growth. As a result, selected countries should develop a new tourism plan that focuses on renewable energy sources and protects the environment.
Decarbonization in Higher Education Institutions as a Way to Achieve a Green Campus: A Literature Review
Authors: da Silva Liziane Araújo , de Aguiar Dutra Ana Regina , de Andrade Guerra José Baltazar Salgueirinho Osório
Published: February 2023

Reducing the carbon footprint (CF) helps to meet the targets of the sustainable development goals (SDGs), with emphasis on SDG 13, which seeks urgent measures to combat climate change and its impacts. Higher Education Institutions (HEIs) or universities, as organizations engaged in education, research, and community service, play an important role in promoting sustainable development. Thus, HEIs are increasingly interested in practices to reduce their CF, in addition to training professionals for this worldwide need. CF reduction is a tool to assess the sustainability and decarbonization of a campus that aligns with Green Campus (GC) initiatives. The objective of this study is to carry out a literature review of the current situation of CF reduction practices in HEIs and the move toward a greener campus, identifying the main sources of emissions according to the GHG Protocol and classification by scope. This article sought to identify the HEIs/universities with already-implemented decarbonization initiatives through the elaboration of a table. This study was based on a portfolio of 33 articles published up to February 2022 that analyze the CFs of HEIs and the implementation of green initiatives. Differences were identified in the methodology and data used by each university. The results show that the main reason for this is the lack of standardization regarding the time metric (year, semester), functional unit (student, employee, area), limitation of data collection (scope 1, 2, 3), and sources of emission and emission factors, mainly about Scope 3 (sometimes not considered in the calculations). However, despite the differences, the search for standardization was observed in the studied articles, as well as the expectation of reduction in the CF of HEIs over time. It also identified the efforts of HEIs in implementing green initiatives aimed at decarbonizing their campuses. It can be concluded that the CF of HEIs requires improvements and solutions to a series of challenges, including the definition of emission sources representative of Scope 3, the creation of a robust database of emission factors, and the development of tools and methodologies—with greater standardization—that cover the needs of the type of institution globally, and that can be used comparatively as an effective model by other HEIs.
Best Management Practices Affect Water Quality in Coastal Watersheds
Authors: Bhattarai Shreeya , Parajuli Prem B.
Published: February 2023

Coastal ecosystems are vulnerable due to watershed sediment and nutrient loads. In this study, the hydrology and water quality of two coastal watersheds, Wolf River watershed (WRW) and Jourdan River watershed (JRW), were assessed using the Soil and Water Assessment Tool (SWAT). Model performance evaluation was performed using the coefficient of determination (R2) and the Nash–Sutcliffe Efficiency (NSE). After model calibration and validation, the effectiveness of ponds, wetlands, riparian buffers, and their combination as best management practices (BMPs) were analyzed. Total suspended solids (TSS) was reduced by 31.15% in the pond scenario and 30.37% in the wetland in WRW. In addition, mineral phosphorous (MinP) was reduced by 11.84% and 9.63% in ponds and wetlands, respectively. Similarly, in JRW, TSS was reduced by 74.89% in ponds and 74.90% in wetlands. Likewise, ponds and wetlands reduced MinP by 8.05% and 13.40%, respectively. Among four riparian buffer scenarios, the 30 m buffer prompted the estimation of higher reductions such as TSS by 28.36% and MinP by 29.77% in WRW, whereas a 13.14% TSS and 22.89% MinP reduction in JRW. In addition, combined BMP, TSS was reduced by 30.37% and MinP by 52.09% in WRW, followed by a 74.92% reduction in TSS and 27.37% in MinP in JRW. All BMP implementations were effective in reducing TSS and MinP. This study suggests that ponds and wetlands were effective in reducing TSS and riparian buffers were effective in reducing MinP. These findings could be insightful in coastal watershed management.
Supporting Cities towards Carbon Neutral Transition through Territorial Acupuncture
Authors: Leone Federica , Hasan Ala , Reda Francesco , Rehman Hassam ur , Nigrelli Fausto Carmelo , Nocera Francesco , Costanzo Vincenzo
Published: February 2023

Since a solution towards carbon neutrality in already highly populated territories that does not profoundly alter the territories has not yet been found, territorial acupuncture, a new methodology presented in this paper, proposes a solution to this challenge and simultaneously helps to counter the dysfunctional dichotomy between large urban centres and small towns. The aim of this study is therefore to present this new concept and its operation. Hence, a phased study was carried out. Territorial acupuncture is the result of merging different theories and practices, such as Biourbanism, urban acupuncture, and energy community design. For Territorial Acupuncture, the territory is conceived as a single organism and, just like acupuncture in traditional Chinese medicine, punctual interventions (in this case, interconnected energy communities) would benefit the entire territory organism. To make the theory work properly, it will be necessary to carry out multi-scalar and multi-disciplinary analyses over the entire territory to identify the intervention points and then proceed to the design and interconnection of the individual district. Thus, Territorial Acupuncture provides a new approach to the resilience of densely populated territories, which, through punctual interventions on a district scale, benefits the entire territory by modifying energy, socio-economic, and environmental dynamics.
Relationship between Psychological Needs and Academic Self-Concept in Physical Education Pre-Service Teachers: A Mediation Analysis
Authors: Granero-Gallegos Antonio , López-García Ginés D. , Baena-Extremera Antonio , Baños Raúl
Published: February 2023

Academic self-concept plays a determining role in the teacher education process. Although research in education has focused on understanding the mechanisms that produce higher academic effort and academic self-confidence, the role that satisfaction and frustration of basic psychological needs and social support and resilience might play on academic self-concept is not known. The aim of the present study was to analyse the mediating effect of social support and resilience in the relationship between satisfaction/frustration of basic psychological needs and academic confidence and academic effort. A non-experimental, cross-sectional, correlational-causal study was designed. In total, 328 Physical Education pre-service teachers (131 women; 197 men) participated from eight Andalusian public universities. The age ranged from 22 to 45 years (M = 24.83; SD = 3.57). The following scales were used: Basic Psychological Needs Satisfaction in Education, Basic Psychological Needs Frustration in Education, Resilience, Social Support, and Academic Self-concept. A structural equations analysis with latent variables was carried out and the results obtained show that the satisfaction of basic psychological needs predicts an improvement in academic confidence and academic effort. In addition, resilience and social support significantly mediated the relationship between satisfaction of basic psychological needs and academic self-concept. This research also highlights the importance, both for teachers and researchers, of creating a context for encouraging the satisfaction of basic psychological needs, to promote academic self-concept in initial teacher training.
How Does Perceived Risk and Trust Affect Mobile Banking Adoption? Empirical Evidence from India
Authors: Kumar Rakesh , Singh Rubee , Kumar Kishore , Khan Shahbaz , Corvello Vincenzo
Published: February 2023

The emergence of high-speed internet (5G) services and the demonetization of the Indian currency by the Government of India in the year 2016 served as catalysts for the growth of banking services, such as internet/mobile banking. The main objective of the study was to investigate the role of perceived financial cost, perceived risk, and trust in the adoption of mobile banking services by users. The study extends the Unified Theory of Acceptance and Use of Technology (UTAUT) to explain the adoption of mobile banking services by young Indian customers. The data from 253 users of age between 18–30 years were collected through a survey questionnaire and were analyzed using structural equation modeling with Amos 22.0. The results revealed that performance expectancy, effort expectancy, social influence, and perceived financial cost exhibited a significant positive influence on behavioral intentions. However, the facilitating conditions were found to exert no effect on actual use. In addition, the results demonstrate that both perceived risk and perceived trust moderate the relationship between behavioral intention and the actual use of mobile banking. The results of the study provide some novel insights into how perceived risk and perceived trust shape the interplay between behavioral intention and the actual use of mobile banking services. The inferences drawn from the study may enhance the understanding of the transformation of behavioral intention into the adoption and actual use of mobile banking services.
A Study on the Learning Early Warning Prediction Based on Homework Habits: Towards Intelligent Sustainable Evaluation for Higher Education
Authors: Wen Wenkan , Liu Yiwen , Zhu Zhirong , Shi Yuanquan
Published: February 2023

Teachers need a technique to efficiently understand the learning effects of their students. Early warning prediction mechanisms constitute one solution for assisting teachers in changing their teaching strategies by providing a long-term process for assessing each student’s learning status. However, current methods of building models necessitate an excessive amount of data, which is not conducive to the final effect of the model, and it is difficult to collect enough information. In this paper, we use educational data mining techniques to analyze students’ homework data and propose an algorithm to extract the three main features: Degree of reliability, degree of enthusiasm, and degree of procrastination. Building a predictive model based on homework habits can provide an individualized evaluation of students’ sustainability processes and support teachers in adjusting their teaching strategies. This was cross-validated using multiple machine learning algorithms, of which the highest accuracy was 93.34%.
Governance Model for a Territory Circularity Index
Authors: Rangoni Gargano Elena , Cornella Alessia , Sacco Pasqualina
Published: February 2023

In a world that seeks to reduce the environmental impact of urban areas and implement the Circular Economy, governance is seen as a key to the ecological transition and the achievement of Sustainable Development Goals. How can we use data, knowledge, and resources at our disposal to put into practice a governance model that implements the Circular Economy of territories? This study devised this model. The comparative assessment of enablers and barriers presented in the literature review conducted allowed for the categorisation of indicators related to the literature sample, leading to the creation of a “Territory Circularity Index” composed of four thematic areas. The index was then incorporated into an innovative governance model intended to serve as a practical tool for local governments and policy makers. In the context of the Circular Economy and Sustainable Development, a “Flexible Governance Model” tailored to the territory could effectively contribute to the creation of coherent policies, an open and transparent process, and facilitated consultation with local stakeholders. The evaluation of the results indicates the potential of the “Flexible Governance Model for a Territory Circularity Index” in promoting effective mechanisms for implementing the circular economy, based on the dual quantitative and qualitative approach from which the model originated. The research could be particularly important for various stakeholders: researchers, policy makers, entrepreneurs, and governments.
The Scale of Attitudes toward the Information Technologies and Software Course: A Scale Development Study
Authors: Şahin Harun , Yeşiltepe Gülden Mediha , Ellez Ahmet Murat , Eraslan Meriç , Karataş Süleyman , Özçetin Serdar
Published: February 2023

In this study, the aim was to develop a Likert-type scale to measure students’ attitudes toward the Information Technologies and Software course taught in secondary school. The developments in the field of science and technology and the changing needs of society necessitate an update to the Information Technologies and Software course curriculum and its implementation, starting from the 2021–2022 academic year. With this update, students are provided with the means to take the first step toward programming with block-based learning activities (Blockly, Scratch, Arduino, etc.). For this reason, it is thought that previously developed scales may be insufficient to measure students’ attitudes toward the updated Information Technologies and Software course. The study was designed by survey method. A total of 600 students for an exploratory factor analysis and 300 different students for a confirmatory factor analysis were included in the study. As a result of conducting the exploratory factor analysis, a scale with 4 factors and 27 items was obtained. Confirmatory factor analysis was performed to confirm the obtained structure. The scale that was developed, as a result of validity and reliability studies, is thought to be a valid and reliable measurement tool that can be used to measure attitudes toward the Information Technologies and Software course.
Using Multiple Data Mining Technologies to Analyze Process Evaluation in the Blended-Teaching Environment
Authors: Li Xiaoting , Yuan Lingyun
Published: February 2023

Under the background of new engineering, the integration of theory and practice in the blended-teaching environment has become the mainstream teaching mode amid science and engineering curriculum reform. Data analysis technology is used to study process evaluation based on the integration of theory and practice in the blended-teaching environment, and a reference for the innovation of process evaluation is provided. This paper makes four key contributions to the blended-teaching environment. The K-means algorithm is used to cluster students into five groups (“serious learners”, “active learners”, “self-directed learners”, “cooperative learners”, and “students with learning difficulties”), according to the results of the students’ process evaluation in the course, integrating theory and practice. The Apriori algorithm and C5.0 model are used to find the key indicators which affected students’ learning performance. They are: classroom performance, assignment submission, classroom testing, problem solving, and online learning. These indicators are used to predict the final learning outcome of students. The Bayesian network model is used to find that there is a strong correlation between learning participation and assignment submission, unit assessment and classroom testing, and classroom performance and work presentation. Data analysis technology is creatively used to strengthen process evaluation. Teaching and learning are promoted by evaluation, so that the true meaning of process evaluation can be revealed. This lays a theoretical and practical foundation for process evaluation, to impact the predominant situation of outcome evaluation and promote the sustainable development of education evaluation.