bluuebunny/crossref_metadata_2025 huggingface dataset:
Multi Modal Feature Extraction for Classification of Vascular Dementia in Post-Stroke Patients Based on EEG Signal
Authors: Hadiyoso Sugondo , Zakaria Hasballah , Anam Ong Paulus , Erawati Rajab Tati Latifah
Published: February 2023

Dementia is a term that represents a set of symptoms that affect the ability of the brain’s cognitive functions related to memory, thinking, behavior, and language. At worst, dementia is often called a major neurocognitive disorder or senile disease. One of the most common types of dementia after Alzheimer’s is vascular dementia. Vascular dementia is closely related to cerebrovascular disease, one of which is stroke. Post-stroke patients with recurrent onset have the potential to develop dementia. An accurate diagnosis is needed for proper therapy management to ensure the patient’s quality of life and prevent it from worsening. The gold standard diagnostic of vascular dementia is complex, includes psychological tests, complete memory tests, and is evidenced by medical imaging of brain lesions. However, brain imaging methods such as CT-Scan, PET-Scan, and MRI have high costs and cannot be routinely used in a short period. For more than two decades, electroencephalogram signal analysis has been an alternative in assisting the diagnosis of brain diseases associated with cognitive decline. Traditional EEG analysis performs visual observations of signals, including rhythm, power, and spikes. Of course, it requires a clinician expert, time consumption, and high costs. Therefore, a quantitative EEG method for identifying vascular dementia in post-stroke patients is discussed in this study. This study used 19 EEG channels recorded from normal elderly, post-stroke with mild cognitive impairment, and post-stroke with dementia. The QEEG method used for feature extraction includes relative power, coherence, and signal complexity; the evaluation performance of normal-mild cognitive impairment-dementia classification was conducted using Support Vector Machine and K-Nearest Neighbor. The results of the classification simulation showed the highest accuracy of 96% by Gaussian SVM with a sensitivity and specificity of 95.6% and 97.9%, respectively. This study is expected to be an additional criterion in the diagnosis of dementia, especially in post-stroke patients.
Modeling and Parameter Identification of a 3D Measurement System Based on Redundant Laser Range Sensors for Industrial Robots
Authors: Gao Guanbin , Kuang Liulin , Liu Fei , Xing Yashan , Shi Qinghua
Published: February 2023

The low absolute positioning accuracy of industrial robots is one of the bottlenecks preventing industrial robots from precision applications. Kinematic calibration is the main way to improve the absolute positioning accuracy of industrial robots, which greatly relies on three-dimensional (3D) measurement instruments, including laser trackers and pull rope mechanisms. These instruments are costly, and their required intervisibility space is large. In this paper, a precision 3D measurement instrument integrating multiple laser range sensors is designed, which fuses the information of multiple redundant laser range sensors to obtain the coordinates of a 3D position. An identification model of laser beam position and orientation parameters based on redundant distance information and standard spherical constraint is then developed to reduce the requirement for the assembly accuracy of laser range sensors. A hybrid identification algorithm of PSO-LM (particle swarm optimization Levenberg Marquardt) is designed to solve the high-order nonlinear problem of the identification model, where PSO is used for initial value identification, and LM is used for final value identification. Experiments of identification of position and orientation, verifications of the measuring accuracy, and the calibration of industrial robots are conducted, which show the effectiveness of the proposed 3D measurement instrument and identification methods. Moreover, the proposed instrument is small in size and can be used in narrow industrial sites.
A Convolutional Neural Network and Graph Convolutional Network Based Framework for AD Classification
Authors: Lin Lan , Xiong Min , Zhang Ge , Kang Wenjie , Sun Shen , Wu Shuicai ,   Initiative Alzheimer’s Disease Neuroimaging
Published: February 2023

The neuroscience community has developed many convolutional neural networks (CNNs) for the early detection of Alzheimer’s disease (AD). Population graphs are thought of as non-linear structures that capture the relationships between individual subjects represented as nodes, which allows for the simultaneous integration of imaging and non-imaging information as well as individual subjects’ features. Graph convolutional networks (GCNs) generalize convolution operations to accommodate non-Euclidean data and aid in the mining of topological information from the population graph for a disease classification task. However, few studies have examined how GCNs’ input properties affect AD-staging performance. Therefore, we conducted three experiments in this work. Experiment 1 examined how the inclusion of demographic information in the edge-assigning function affects the classification of AD versus cognitive normal (CN). Experiment 2 was designed to examine the effects of adding various neuropsychological tests to the edge-assigning function on the mild cognitive impairment (MCI) classification. Experiment 3 studied the impact of the edge assignment function. The best result was obtained in Experiment 2 on multi-class classification (AD, MCI, and CN). We applied a novel framework for the diagnosis of AD that integrated CNNs and GCNs into a unified network, taking advantage of the excellent feature extraction capabilities of CNNs and population-graph processing capabilities of GCNs. To learn high-level anatomical features, DenseNet was used; a set of population graphs was represented with nodes defined by imaging features and edge weights determined by different combinations of imaging or/and non-imaging information, and the generated graphs were then fed to the GCNs for classification. Both binary classification and multi-class classification showed improved performance, with an accuracy of 91.6% for AD versus CN, 91.2% for AD versus MCI, 96.8% for MCI versus CN, and 89.4% for multi-class classification. The population graph’s imaging features and edge-assigning functions can both significantly affect classification accuracy.
Emotion Classification from Multi-Band Electroencephalogram Data Using Dynamic Simplifying Graph Convolutional Network and Channel Style Recalibration Module
Authors: Zhu Xiaoliang , Liu Gendong , Zhao Liang , Rong Wenting , Sun Junyi , Liu Ran
Published: February 2023

Because of its ability to objectively reflect people’s emotional states, electroencephalogram (EEG) has been attracting increasing research attention for emotion classification. The classification method based on spatial-domain analysis is one of the research hotspots. However, most previous studies ignored the complementarity of information between different frequency bands, and the information in a single frequency band is not fully mined, which increases the computational time and the difficulty of improving classification accuracy. To address the above problems, this study proposes an emotion classification method based on dynamic simplifying graph convolutional (SGC) networks and a style recalibration module (SRM) for channels, termed SGC-SRM, with multi-band EEG data as input. Specifically, first, the graph structure is constructed using the differential entropy characteristics of each sub-band and the internal relationship between different channels is dynamically learned through SGC networks. Second, a convolution layer based on the SRM is introduced to recalibrate channel features to extract more emotion-related features. Third, the extracted sub-band features are fused at the feature level and classified. In addition, to reduce the redundant information between EEG channels and the computational time, (1) we adopt only 12 channels that are suitable for emotion classification to optimize the recognition algorithm, which can save approximately 90.5% of the time cost compared with using all channels; (2) we adopt information in the θ, α, β, and γ bands, consequently saving 23.3% of the time consumed compared with that in the full bands while maintaining almost the same level of classification accuracy. Finally, a subject-independent experiment is conducted on the public SEED dataset using the leave-one-subject-out cross-validation strategy. According to experimental results, SGC-SRM improves classification accuracy by 5.51–15.43% compared with existing methods.
Sievert or Gray: Dose Quantities and Protection Levels in Emergency Exposure
Authors: Ferrari Chiara , Manenti Guglielmo , Malizia Andrea
Published: February 2023

Mitigation or even elimination of adverse effects caused by ionizing radiation is the main scope of the radiation protection discipline. The interaction of radiation with living matter is quantified and correlated with biological effects by dose. The Sievert is the most well-known quantity, and it is used with the equivalent and effective dose to minimize stochastic effects. However, Gray is the reference quantity for sizing tissue reactions that could occur under high-exposure conditions such as in a radiation emergency. The topics addressed in this review are the choice to move from Sievert to Gray, how the operational quantities for environmental and individual monitoring of the detectors should consider such a change of units, and why reference levels substitute dose levels in emergency exposure.
A Low-Profile, Triple-Band, and Wideband Antenna Using Dual-Band AMC
Authors: Gonçalves Licursi de Mello Rafael , Lepage Anne Claire , Begaud Xavier
Published: February 2023

When a wideband antenna is backed by an artificial magnetic conductor (AMC) reflector, the bandwidth is reduced. With the optimization of the shape of the AMC it is possible to exhibit multiband behavior, but the problem becomes complex if the bands are also intended to be wide. In this study, a methodology that exploits both the expected in-band and out-of-band behaviors of a dual-band AMC was used to design a low-profile, triple-band, and wideband directive antenna. The methodology was validated with a prototype suitable for the European standards of 4G/5G and Wi-Fi 2.4/5/6E, operating within the following bands: 2.4–2.7 GHz, 3.4–3.8 GHz, and 5.17–6.45 GHz. The measured results showed respective peak values of 8.0, 9.1, and 10.5 dBi for the broadside realized gain, front-to-back ratios larger than 19 dB, cross-polarized levels lower than -18 dB, and stable half-power beamwidths within each band. Furthermore, 3 dB gain bandwidths of 34.4%, 19.7%, and 31.0% were also measured.
Emerging MIMO Technologies for 6G Networks
Authors: Dala Pegorara Souto Victoria , Dester Plínio Santini , Soares Pereira Facina Michelle , Gomes Silva Daniely , de Figueiredo Felipe Augusto Pereira , Rodrigues de Lima Tejerina Gustavo , Silveira Santos Filho José Cândido , Silveira Ferreira Juliano , Mendes Luciano Leonel , Souza Richard Demo , Cardieri Paulo
Published: February 2023

The demand for wireless connectivity has grown exponentially over the last years. By 2030 there should be around 17 billion of mobile-connected devices, with monthly data traffic in the order of thousands of exabytes. Although the Fifth Generation (5G) communications systems present far more features than Fourth Generation (4G) systems, they will not be able to serve this growing demand and the requirements of innovative use cases. Therefore, Sixth Generation (6G) Networks are expected to support such massive connectivity and guarantee an increase in performance and quality of service for all users. To deal with such requirements, several technical issues need to be addressed, including novel multiple-antenna technologies. Then, this survey gives a concise review of the main emerging Multiple-Input Multiple-Output (MIMO) technologies for 6G Networks such as massive MIMO (mMIMO), extremely large MIMO (XL-MIMO), Intelligent Reflecting Surfaces (IRS), and Cell-Free mMIMO (CF-mMIMO). Moreover, we present a discussion on how some of the expected key performance indicators (KPIs) of some novel 6G Network use cases can be met with the development of each MIMO technology.
Concept and Implementation of Measurement Systems for Stationary and Remote Testing of Sensors for Electrical and Non-Electrical Quantities
Authors: Furmankiewicz Leszek , Kozioł Mirosław , Rybski Ryszard , Szulim Robert
Published: February 2023

In intelligent transportation, various types of sensors are used both in traffic control systems as well as in the control, safety, and entertainment systems of the vehicles themselves. In the process of educating future designers and developers of such systems, it is necessary to familiarize them with the operation and parameters of sensors. The recent years of the COVID-19 pandemic have disturbed this process due to the need to conduct classes remotely. This article presents the general concept of a laboratory stand for testing sensors of electrical and non-electrical quantities, which can be used both in stationary and remote learning. Additionally, the practical implementation of two laboratory stands for testing current and linear displacement sensors was also presented. Both stands have been tested in the remote access mode. The tests showed some shortcomings in the management software but also confirmed the correctness of the adopted concept of their implementation.
Solution Evolution Knowledge Service Based on Design Iteration in Strain Sensor Design
Authors: Zhang Kai , Zhao Wu , Liu Qingjie , Guo Xin , Yu Miao
Published: February 2023

Product design is a process of repeated iteration and gradual improvement, and knowledge push is one of the bottlenecks that needs to be solved to improve the product design level. With the increase in design complexity and iteration rounds, the existing knowledge application methods can hardly meet the needs of product design solution iteration and evolution. In order to better assist designers in acquiring and applying knowledge in the process of product design solution evolution, a knowledge service method for product design solution evolution based on the problem–strategy–solution (PSS) interaction iteration is proposed. The mapping and feedback process between design problems, design strategies, and design solutions are analyzed, a model of the solution evolution process based on design iteration is proposed, and a PSS-based product design solution evolution mechanism is established. On this basis, the product design solution evolution knowledge service dimension is built, and the solution evolution knowledge service model based on design iteration is established. The corresponding solution evolution function module is developed based on the pre-developed computer-aided product innovation design platform. The validity of the iterated-based design was proved in the technical innovation of nanofiber preparation and further application of strain sensors.
Classification of Motor Imagery EEG Signals Based on Data Augmentation and Convolutional Neural Networks
Authors: Xie Yu , Oniga Stefan
Published: February 2023

In brain–computer interface (BCI) systems, motor imagery electroencephalography (MI-EEG) signals are commonly used to detect participant intent. Many factors, including low signal-to-noise ratios and few high-quality samples, make MI classification difficult. In order for BCI systems to function, MI-EEG signals must be studied. In pattern recognition and other fields, deep learning approaches have recently been successfully applied. In contrast, few effective deep learning algorithms have been applied to BCI systems, especially MI-based systems. In this paper, we address these problems from two aspects based on the characteristics of EEG signals: first, we proposed a combined time–frequency domain data enhancement method. This method guarantees that the size of the training data is effectively increased while maintaining the intrinsic composition of the data. Second, our design consists of a parallel CNN that takes both raw EEG images and images transformed through continuous wavelet transform (CWT) as inputs. We conducted classification experiments on a public data set to verify the effectiveness of the algorithm. According to experimental results based on the BCI Competition IV Dataset2a, the average classification accuracy is 97.61%. A comparison of the proposed algorithm with other algorithms shows that it performs better in classification. The algorithm can be used to improve the classification performance of MI-based BCIs and BCI systems created for people with disabilities.
A Critical Lens on Health: Key Principles of Critical Discourse Analysis and Its Benefits to Anti-Racism in Population Public Health Research
Authors: Naidu Jessica , Paolucci Elizabeth Oddone , Turin Tanvir C.
Published: February 2023

Critical discourse analysis (CDA) is an interdisciplinary research methodology used to analyze discourse as a form of “social practice”, exploring how meaning is socially constructed. In addition, the methodology draws from the field of critical studies, in which research places deliberate focus on the social and political forces that produce social phenomena as a means to challenge and change societal practices. The purpose of this article is to demonstrate the benefits of CDA to population public health (PPH) research. We will do this by providing a brief overview of CDA and its history and purpose in research and then identifying and discussing three crucial principles that we argue are crucial to successful CDA research: (1) CDA research should contribute to social justice; (2) CDA is strongly based in theory; and (3) CDA draws from constructivist epistemology. A key benefit that CDA brings to PPH research is its critical lens, which aligns with the fundamental goals of PPH including addressing the social determinants of health and reducing health inequities. Our analysis demonstrates the need for researchers in population public health to strongly consider critical discourse analysis as an approach to understanding the social determinants of health and eliminating health inequities in order to achieve health and wellness for all.
Exploring the Political Debate over the COVID-19 Vaccination on Twitter: Emotions and Polarization in the Spanish Public Sphere
Authors: Pérez-Curiel Concha , Domínguez-García Ricardo , Velasco-Molpeceres Ana
Published: February 2023

In a scenario marked by COVID-19, communication has posed a real challenge for institutions. Since the first case of COVID-19, Spain has faced the enormous challenge of an unprecedented health, economic, and social crisis. The public sphere has put its trust in vaccination as the only chance for the country’s recovery. To determine the role played by political leaders in the debate on vaccination and citizens’ perception of crisis management, this article analyzes the messages posted on Twitter by the four leaders of the parties with the most representation in parliament: Pedro Sánchez (PSOE), Pablo Casado (PP), Santiago Abascal (Vox), and Yolanda Díaz (Podemos). Using a methodology for comparative content analysis on Twitter, a sample was established that began at the start of the vaccination process and inoculation with the first dose, and covered the entire year that followed. The research results, which contrasted with those collected in demographic surveys, reflected an increase in polarization and electoral use of the vaccine, which took precedence over the awareness-raising discourse typical of public campaigns.
A Novel Modeling Approach for Soil and Rock Mixture and Applications in Tunnel Engineering
Authors: Zhang Xiujie , Li Hongzhong , Xu Kaiyan , Yang Wenwei , Yan Rongtao , Ma Zhanwu , Wang Yonghong , Su Zhihua , Wu Haizhi
Published: February 2023

Soil and rock mixtures are complicated geomaterials that are characterized by both continuity and discontinuity. A homogeneous model cannot take into consideration the interactions between rocks and soil, which could lead to misjudgments of the mechanical properties. To simulate the mechanical responses of soil and rock mixtures accurately, a stochastic generation approach to soil and rock mixtures was developed systematically in this study. The proposed approach includes the following three major steps: (1) a combined image filtering technique and multi-threshold binarization method were developed to extract rock block files from raw images. (2) The shapes and sizes of block profiles were controlled and reconstructed randomly using Fourier analysis. (3) A fast-overlapping detection strategy was proposed to allocate the rock blocks efficiently. Finally, models of soil and rock mixtures with a specific rock proportion can be generated. To validate the proposed approach, numerical models were established in tunnel engineering using the conventional homogeneous method and the proposed numerical method, respectively. In addition, a series of field tests on tunnel deformation and stress were conducted. The results showed that the proposed heterogeneous numerical model can model the mechanical response of the soil and rock mixtures well and is much more effective and accurate than the conventional homogeneous approach. Using the proposed numerical approach, the failure mechanism of a tunnel in a soil and rock mixture is discussed, and a reinforcement strategy for the surrounding rocks is proposed. The field tests results indicate that tunnel lining stress can be well controlled within the strength criterion by the proposed reinforcement strategy.
Multicriteria Model for Determining the Best and Low-Cost Methods of Industrial Heritage Transformation and Utilization under Fuzzy Inputs
Authors: Meng Fanlei , Pang Yuxiang , Zhi Yeqing
Published: February 2023

The renovation and utilization of industrial heritage are important issues in the field of sustainable urban renewal. The renovation of industrial heritage is influenced by various factors such as the value of a heritage site, its location, the positioning of renovation, and the cost of renovation. Most existing studies focus on the concept of renovation and the establishment of heritage management techniques. However, a low cost in the context of urban sustainability has a greater impact on renovation. Therefore, this paper takes Beijing Xinhua 1949 Cultural and Creative Industrial Park as an example, incorporates the cost–benefit ratio into the plans for a low-cost construction, and proposes a method to evaluate the performance of holistic low-cost construction during the whole life cycle of industrial heritage renovation and utilization. This study uses the AHP method to create an evaluation index system and fuzzy TOPSIS(FTOPSIS) to rank the solutions so as to establish a comprehensive evaluation system to thus evaluate industrial heritage renovation projects that are difficult to fully quantify, with the aim to obtain performance evaluation conclusions. The results of this study suggest that the definition of a low-cost renovation should not be limited to a reduction in investment costs but should also pay equal attention to the cost–benefit ratio before and after renovation, and that functional and spatial sustainability is another feasible strategy for achieving the sustainable renovation of industrial heritage.
Evaluating the Effects of Logistics Center Location: An Analytical Framework for Sustainable Urban Logistics
Authors: Wang Yanxia , Li Yisong , Lu Changxiang
Published: February 2023

Sustainable urban logistics require support of sustainable logistics center location planning. Nevertheless, urban public authorities generally experience difficulties in choosing a successful planning scheme, due to the lack of a rigorous analytical tool designed to help understand how logistics center location affects sustainability throughout the supply chain. A poor location can keep a firm or a supply chain from reaching its economic, environmental, and social sustainability goals. A good location can help meet these goals. To address the pressing cause of policy failure, this paper developed an analytical framework by integrating the multi-criteria decision-making method and the shortest path model with multiple objectives. The developed framework can be used to evaluate the effects of logistics center location schemes on travel time, transport costs, carbon emission, and road traffic, and further explore the conflict level among them. It is applied to Beijing in this paper. The analysis results reveal that: (1) travel time, transport costs, and carbon emissions all vary along with the distance from logistics center location to the city center; (2) encouraging cargo companies to plan their truck paths with the navigation objective of minimizing travel time is an excellent way to achieve sustainable urban logistics, because in this scenario, freight transport is the quickest, and transport costs and carbon emissions are all more moderate. Additionally, this paper confirmed the conflict level among sustainability goals of urban logistics based on actual contextual data. The proposed framework is a helpful guide tool for urban planners to develop sustainable logistics land-use planning and responsive policy interventions.
User Acceptance Factors Related to Biometric Recognition Technologies of Examination Attendance in Higher Education: TAM Model
Authors: Rukhiran Meennapa , Wong-In Sethapong , Netinant Paniti
Published: February 2023

Identity recognition is influenced at all educational levels by biometric technology. The invention of facial recognition technology has added new efficiencies to the traditional method of tracking student examination attendance. This study aims to determine whether biometric recognition technologies could be utilized to enhance undergraduate examination attendance systems. The study examined the perceptions of first-year college students regarding the system’s use of face recognition technologies. Based on the proposed framework, experimental results were obtained by developing and deploying unimodal and multimodal face recognition methods. Using a quasi-practical design with sample groups, undergraduate students’ perceptions of traditional and biometric examination attendance were compared. Adopting the Theory for Reasoned Action and the Technology Acceptance Model, a questionnaire was distributed and analyzed to determine perception factors. The findings reveal that perceived ease of use, and trust and security significantly impact perceived usefulness. It was discovered that perceived usefulness significantly affects behavioral intention to use a system. According to the research findings, multimodal biometric recognition receives significantly more positive ratings than unimodal biometric recognition. This study proposes that universities utilize biometric technology, particularly facial recognition, to assess users’ acceptance of the system.
A New Approach to Spatial Landslide Susceptibility Prediction in Karst Mining Areas Based on Explainable Artificial Intelligence
Authors: Fang Haoran , Shao Yun , Xie Chou , Tian Bangsen , Shen Chaoyong , Zhu Yu , Guo Yihong , Yang Ying , Chen Guanwen , Zhang Ming
Published: February 2023

Landslides are a common and costly geological hazard, with regular occurrences leading to significant damage and losses. To effectively manage land use and reduce the risk of landslides, it is crucial to conduct susceptibility assessments. To date, many machine-learning methods have been applied to the landslide susceptibility map (LSM). However, as a risk prediction, landslide susceptibility without good interpretability would be a risky approach to apply these methods to real life. This study aimed to assess the LSM in the region of Nayong in Guizhou, China, and conduct a comprehensive assessment and evaluation of landslide susceptibility maps utilizing an explainable artificial intelligence. This study incorporates remote sensing data, field surveys, geographic information system techniques, and interpretable machine-learning techniques to analyze the sensitivity to landslides and to contrast it with other conventional models. As an interpretable machine-learning method, generalized additive models with structured interactions (GAMI-net) could be used to understand how LSM models make decisions. The results showed that the GAMI-net model was valid and had an area under curve (AUC) value of 0.91 on the receiver operating characteristic (ROC) curve, which is better than the values of 0.85 and 0.81 for the random forest and SVM models, respectively. The coal mining, rock desertification, and rainfall greater than 1300 mm were more susceptible to landslides in the study area. Additionally, the pairwise interaction factors, such as rainfall and mining, lithology and rainfall, and rainfall and elevation, also increased the landslide susceptibility. The results showed that interpretable models could accurately predict landslide susceptibility and reveal the causes of landslide occurrence. The GAMI-net-based model exhibited good predictive capability and significantly increased model interpretability to inform landslide management and decision making, which suggests its great potential for application in LSM.
Female Empowerment and Radical Empathy for the Sustainability of Creative Industries: The Case of K-Pop
Authors: Oh Ingyu , Kim Kyeong-Jun , Rowley Chris
Published: February 2023

Contrary to the critical understanding of the cultural and/or creative industry that unduly emphasizes demoralized commercial activities of profit-maximizing, accompanied by the concomitant destruction of individual self-realization, the 21st century perception of the industry highlights its potentials for both creativity and more importantly, sustainability. The global success of Korean pop music (K-pop) unlocks a new possibility for the creative industry in a postcolonial country, with a newly constructed value chain that strategically allows female empowerment through radical empathy, a concept that bridges social empathy among formerly oppressed groups with their new political opportunities of political struggles. Based on survey data and structural equation modeling, this paper empirically corroborates a theorized conceptual link between participation in the K-pop industry and the resulting radical empathy among female fans toward industrial sustainability.
Using Machine Learning Methods to Predict Consumer Confidence from Search Engine Data
Authors: Han Huijian , Li Zhiming , Li Zongwei
Published: February 2023

The consumer confidence index is a leading indicator of regional socioeconomic development. Forecasting research on it helps to grasp the future economic trends and consumption trends of regional development in advance. The data contained on the Internet in the era of big data can truly and timely reflect the current economic trends. This paper constructs a conceptual framework for the relationship between the consumer confidence index and web search keywords. It employed six machine learning and deep learning models: the BP neural network, the convolutional neural network, support vector regression, random forest, the ELMAN neural network, and the extreme learning machine to predict the consumer confidence index. The study shows that the use of machine learning models has a better prediction effect on the consumer confidence index. Compared with other models, the BP neural network and the convolutional neural network have lower error indicators and higher model accuracy, which helps decision-makers forecast the consumer confidence index. Consumers search for various goods and prices, as well as macroeconomics, to understand the economic conditions of the market, which affects the consumer confidence index and consumption decisions. Therefore, web search data can be used to predict consumer confidence. Future research can be extended to other macro indicator-related prediction studies. It is important to promote market consumption and confidence, improve consumption policies, and promote national prosperity.
Integrated Approach for Landslide Risk Assessment Using Geoinformation Tools and Field Data in Hindukush Mountain Ranges, Northern Pakistan
Authors: Shah Nisar Ali , Shafique Muhammad , Ishfaq Muhammad , Faisal Kamil , Van der Meijde Mark
Published: February 2023

Landslides are one of the most recurring and damaging natural hazards worldwide, with rising impacts on communities, infrastructure, and the environment. Landslide hazard, vulnerability, and risk assessments are critical for landslide mitigation, land use and developmental planning. They are, however, often lacking in complex and data-poor regions. This study proposes an integrated approach to evaluate landslide hazard, vulnerability, and risk using a range of freely available geospatial data and semi-quantitative techniques for one of the most landslide-prone areas in the Hindukush mountain ranges of northern Pakistan. Very high-resolution satellite images and their spectral characteristics are utilized to develop a comprehensive landslide inventory and predisposing factors using bi-variate models to develop a landslide susceptibility map. This is subsequently integrated with landslide-triggering factors to derive a Landslide Hazard Index map. A geospatial database of the element-at-risk data is developed from the acquired remote sensing data and extensive field surveys. It contains the building’s footprints, accompanied by typological data, road network, population, and land cover. Subsequently, it is analyzed using a spatial multi-criteria evaluation technique for vulnerability assessment and further applied in a semi-quantitative technique for risk assessment in relative risk classes. The landslide risk assessment map is classified into five classes, i.e., very low (18%), low (39.4%), moderate (26.3%), high (13.3%), and very high (3%). The developed landslide risk index map shall assist in highlighting the landslide risk hotspots and their subsequent mitigation and risk reduction.
A Research Paradigm for Industrial Spatial Layout Optimization and High-Quality Development in The Context of Carbon Peaking
Authors: Zhang Yang , Li Wenlong , Sun Jiawen , Zhao Haidong , Lin Haiying
Published: February 2023

The reasonable spatial layout of industries is crucial to carbon reduction and high-quality economic development. This paper establishes a research paradigm for optimizing the industrial spatial layout and high-quality development in the context of carbon peaking. Based on the perspectives of industrial transfer, the static agglomeration index, dynamic agglomeration index, industrial gradient coefficient, and low-carbon competitiveness index are used to analyze industrial agglomeration, competition status, and low-carbon competitiveness. Taking the Great Bend of the Yellow River (the Bend) as an example, we analyze the current situation in industrial development, guide the orderly transfer of industry, and optimize the spatial layout of industries to achieve high-quality economic development. The results show that resource- and capital-intensive industries have obvious advantages in agglomeration, competitive edge, and low-carbon competitiveness, while labor- and technology-intensive industries have weak advantages. The spatial layout of agglomerated industries was analyzed across four types of factor-intensive industries; these industries are the focus of industrial layout in the Bend. Promising industries were observed in all types of factor-intensive industries except capital-intensive industries, and these industries should be cultivated carefully in all provinces. Scale industries were mainly observed in resource- and capital-intensive industries; these industries should be transformed and upgraded to control the total amount and intensity of carbon emissions. The study’s findings provide a basis for optimizing the spatial layout of industries and reducing carbon emissions through industrial transfer in the context of carbon peaking. The relevant industries should be transformed and upgraded to control the total amount and intensity of carbon emissions.
An Innovative and Alternative Waste Collection Recycling Program Based on Source Separation of Municipal Solid Wastes (MSW) and Operating with Mobile Green Points (MGPs)
Authors: Tsimnadis Konstantinos , Kyriakopoulos Grigorios L. , Arabatzis Garyfallos , Leontopoulos Stefanos , Zervas Efthimios
Published: February 2023

Recently, among European Union (EU) member states, but also globally, there have been available and successful recycling and treatment practices of Municipal Solid Wastes (MSW). Greece has currently implemented low recycling levels, 21%, of the annually produced MSW, compared to the EU regulations. In 2021 the prefectural authorities of Attica Region launched a pilot program of rewarding recycling with source Separation called “THE GREEN CITY”. This program consists of 60 mobile green points (MGPs) that serve at a unified citizen awareness and MSW collection at 7-streams network throughout the prefecture of Attica. In this study, the whole design analysis of “THE GREEN CITY” pilot recycling program contained estimations and calculations of (a) the distances of all waste collection remote itineraries (basic analysis); (b) the annual fuel cost of the MGPs for the realization of all waste collection remote itineraries (financial-based analysis); and (c) the annual carbon dioxide (CO2) emissions into the atmosphere from the IVECO MGPs during the coverage of all waste collection remote itineraries (environmental-based analysis). Then a research synthesis of all these analyses revealed and evaluated the pilot recycling program’s real capabilities and limitations in alignment with: (a) its ultimate goal to help Greece achieve the setting target of Directive 2018/851 for at least 55% by weight recycling and reuse of the total annually generated Greek MSW by 2025 and (b) the MGPs proven ability to support environmental sustainability in densely populated prefectures such as Attica.
Sustainable Technology Diffusion Manufacturing Outwards FDI Firms: Evidence from China Using Numerical Simulation Methods
Authors: Yang Xiaohua , Liu Cheng , Lan Jingling
Published: February 2023

Technology diffusion plays an important role in the improvement of technological innovation capabilities and can provide strong support for sustainable economic development. This paper aims to analyze the influence mechanism of technology diffusion on sustainable development, such as the initial investment willingness, technology spillover effects and absorptive capacity, technology transfer prices and intellectual property protection, government subsidy support and penalties. However, there is a lack of systemic game mechanism and numerical simulation methods for heterogeneous enterprises. Based on the government, OFDI companies, and domestic companies that are not involved in FDI, we have proposed the evolutionary game model for technology diffusion on sustainable development. The results show that: (1) The government, OFDI firms, and domestic firms are influenced by each other’s initial willingness to a different extent; (2) Different combinations of technology spillover and absorptive capacity lead to different sustainable evolution results of technology diffusion; (3) Reasonable technology transfer pricing and enhancing the protection of property rights can improve the efficiency of the technology diffusion sustainable development system; (4) Both financial support and penalties encourage technology diffusion sustainable development, but excessive financial support reduces the government’s willingness to participate ultimately. Therefore, this paper proposes that the government should increase its willingness to participate and increase punishment for unreasonable pricing behaviors. The stronger the stimulus effect of OFDI firm technology spillover and domestic firm absorptive capacity, the stronger the stimulus effect; however, there is a certain threshold effect, at the same time increasing the property rights protection of OFDI firms and the penalties for opportunistic behaviors of domestic firms, and establishing a diversified financial support policy, will increase the efficiency of the technology diffusion sustainable development system.
Characteristics and Temporal Trends of Regional Tourism Along the Border Areas
Authors: Ahn Young-joo , Kim Katie Bokyun , Kim Jin-young
Published: February 2023

This study aims to extract topics from news articles on DMZ tourism published between 1990 and 2020. This study uses LDA to extract topics within the contents of news articles on DMZ tourism. Furthermore, this study aims to examine the trends of each topic within news articles and identify the characteristics of topics over the past 30 years by developing graphs. A total of 23,093 news articles published from 1990 to 2020 were collected. The data were analyzed using LDA, descriptive analysis, odd ratios, and visualized graphs. The results found that news articles on DMZ tourism can provide considerable information in terms of political, social, and environmental issues. The results provide profiles of news articles on DMZ tourism and identify the unique topics and characteristics of these news articles. The study presents findings regarding topic trends over the last 30 years. All topics show an upward trend from 1990 to 2020. Significant issues have attracted attention to the DMZ region. Furthermore, the findings demonstrate spatial and temporal differences in the volumes of topics. This study identified topics from the past 30 years and important issues related to DMZ tourism that can strengthen the unique features of the DMZ and surrounding area, promoting tourism products and content. The present study contributes to identifying topics of DMZ tourism and temporal changes from 1990 to 2020. The findings can provide meaningful insight into developing policies for DMZ tourism.
Genetic Monitoring of the Last Captive Population of Greater Mouse-Deer on the Thai Mainland and Prediction of Habitat Suitability before Reintroduction
Authors: Wongloet Wongsathit , Kongthong Prach , Chaiyes Aingorn , Singchat Worapong , Suksavate Warong , Ariyaraphong Nattakan , Panthum Thitipong , Lisachov Artem , Jaisamut Kitipong , Sonongbua Jumaporn , Budi Trifan , Wannakan Wannapa , Thienpreecha Worawit , Paansri Paanwaris , Ahmad Syed Farhan , Sribuarod Kriangsak , Prayoon Umphornpimon , Aramsirirujiwet Pakpoom , Chamchumroon Wiyada , Muangmai Narongrit , Duengkae Prateep , Srikulnath Kornsorn
Published: February 2023

Developing successful conservation programs for genetically depleted species is challenging. Survival and adaptive potential are related to genetic and habitat factors; therefore, conservation programs are designed to minimize risks associated with inbreeding and loss of genetic diversity. The greater mouse-deer (Tragulus napu) is a true forest species that contributes to seed distribution dynamics in forests. However, with continuous demographic decline over the last century in the wild, only captive populations of the greater mouse-deer remain on the Thai mainland. A restoration program initiated 20 years ago has increased their population to more than 100 individuals but maintaining high genetic diversity in a small captive population is crucial for successful recovery. Microsatellite genotyping and mitochondrial D-loop and SRY gene sequence analyses were performed to examine the genetic diversity and population structure in 123 greater mouse-deer (64 females and 59 males). Highly reduced effective captive population size with trends of inbreeding were observed. No historical bottleneck was observed. These conditions have reduced their reproductive fitness and ability to adapt to environmental change, increasing the risk of population decline and eventual extinction. Demographic analyses suggested a recent captive population expansion due to effective animal welfare and reproduction. The results also suggested that population size at equilibrium is the main factor of allelic diversity (number of alleles). Large habitat carrying capacity, representing each fixed captive population size can support the genetic diversity of greater mouse-deer. We also identified suitable habitat areas for reintroduction and long-term in situ conservation of greater mouse-deer using maximum entropy modeling. Based on the environmental variables, species distribution modeling for greater mouse-deer indicated lowland tropical forest regions in the Khlong Saeng-Khao Sok forest complexes as most suitable and requiring urgent habitat improvement. These findings highlight the relevance of careful genetic monitoring and habitat suitability for the long-term conservation of greater mouse-deer and enhance the success of future conservation plans.
Spatial–Temporal Heterogeneity of Urbanization and Ecosystem Services in the Yellow River Basin
Authors: Zhang Zhongwu , Zhang Jinyuan , Liu Liping , Gong Jian , Li Jinqiang , Kang Lei
Published: February 2023

Taking 736 counties in the Yellow River Basin of China as the research area, the comprehensive urbanization development level and ecosystem service capacity from 2000 to 2020 were measured. Combined with spatial autocorrelation, the spatial pattern evolution characteristics of the two systems in the Yellow River Basin were revealed. The spatio–temporal geographically weighted regression (GTWR) model was used to analyze the spatio–temporal heterogeneity of the impact of various elements of the system on urbanization and ecosystem service capacity. The results showed that (1) the urbanization level and ecosystem service capacity of the Yellow River Basin were on the rise but the urbanization level and ecosystem service capacity were low, while the spatial and temporal heterogeneity was significant. (2) The two systems are positively correlated in space, and the agglomeration characteristics are significant. The evolution trend of urbanization from an L–L agglomeration area to an H–H agglomeration area is occurring gradually. The spatial change in the ecosystem service agglomeration area is small, and the stability is strong. (3) The impact of ecosystem services on comprehensive urbanization is enhanced by time, and the spatial ‘center–periphery’ diffusion characteristics are significant. (4) The influence of urbanization on the comprehensive ecosystem service capacity is enhanced and shows the law of east–west differentiation in space. There are obvious transition zones in the spatial heterogeneity interval of the interaction between the two systems.