bluuebunny/crossref_metadata_2025 huggingface dataset:
Rethinking the Mengzi’s Concept of Tian 天
Authors: Turner Kevin J.
Published: August 2023

There is an undeniable relationship between humanity and tian 天 in the philosophy of the Mengzi 孟子 where the latter is generally conceived as the metaphysical or ontological source of the morality and ethics of the former. However, this line of interpretation is misleading because it not only imports foreign notions of transcendence into the thought of the Mengzi but also because it ignores the contribution humans make to tian. At the same time, there have been attempts to read the Mengzi in biological terms, thereby naturalizing human morality. This likewise does not satisfy as an adequate account of human morality because it also reduces it to a natural or biological realm where human culture is an external accessory. However, the relationship between humanity and tian in the Mengzi is one of mutual influence and emergence; therefore, this article analyzes their relationship and argues that tian is to be understood as “tradition” whereby humans in their process of becoming humans contribute to and invigorate it as successive generations carry it on into the future.
National-Standards- and Deep-Learning-Oriented Raster and Vector Benchmark Dataset (RVBD) for Land-Use/Land-Cover Mapping in the Yangtze River Basin
Authors: Zhang Pengfei , Wu Yijin , Li Chang , Li Renhua , Yao He , Zhang Yong , Zhang Genlin , Li Dehua
Published: August 2023

A high-quality remote sensing interpretation dataset has become crucial for driving an intelligent model, i.e., deep learning (DL), to produce land-use/land-cover (LULC) products. The existing remote sensing datasets face the following issues: the current studies (1) lack object-oriented fine-grained information; (2) they cannot meet national standards; (3) they lack field surveys for labeling samples; and (4) they cannot serve for geographic engineering application directly. To address these gaps, the national-standards- and DL-oriented raster and vector benchmark dataset (RVBD) is the first to be established to map LULC for conducting soil water erosion assessment (SWEA). RVBD has the following significant innovation and contributions: (1) it is the first second-level object- and DL-oriented dataset with raster and vector data for LULC mapping; (2) its classification system conforms to the national industry standards of the Ministry of Water Resources of the People’s Republic of China; (3) it has high-quality LULC interpretation accuracy assisted by field surveys rather than indoor visual interpretation; and (4) it could be applied to serve for SWEA. Our dataset is constructed as follows: (1) spatio-temporal-spectrum information is utilized to perform automatic vectorization and label LULC attributes conforming to the national standards; and (2) several remarkable DL networks (DenseNet161, HorNet, EfficientNetB7, Vision Transformer, and Swin Transformer) are chosen as the baselines to train our dataset, and five evaluation metrics are chosen to perform quantitative evaluation. Experimental results verify the reliability and effectiveness of RVBD. Each chosen network achieves a minimum overall accuracy of 0.81 and a minimum Kappa of 0.80, and Vision Transformer achieves the best classification performance with overall accuracy of 0.87 and Kappa of 0.86. It indicates that RVBD is a significant benchmark, which could lay a foundation for intelligent interpretation of relevant geographic research about SWEA in the Yangtze River Basin and promote artificial intelligence technology to enrich geographical theories and methods.
GCMTN: Low-Overlap Point Cloud Registration Network Combining Dense Graph Convolution and Multilevel Interactive Transformer
Authors: Wang Xuchu , Yuan Yue
Published: August 2023

A single receptive field limits the expression of multilevel receptive field features in point cloud registration, leading to the pseudo-matching of objects with similar geometric structures in low-overlap scenes, which causes a significant degradation in registration performance. To handle this problem, a point cloud registration network that incorporates dense graph convolution and a mutilevel interaction Transformer (GCMTN) in pursuit of better registration performance in low-overlap scenes is proposed in this paper. In GCMTN, a dense graph feature aggregation module is designed for expanding the receptive field of points and fusing graph features at multiple scales. To make pointwise features more discriminative, a multilevel interaction Transformer module combining Multihead Offset Attention and Multihead Cross Attention is proposed to refine the internal features of the point cloud and perform feature interaction. To filter out the undesirable effects of outliers, an overlap prediction module containing overlap factor and matching factor is also proposed for determining the match ability of points and predicting the overlap region. The final rigid transformation parameters are generated based on the distribution of the overlap region. The proposed GCMTN was extensively verified on publicly available ModelNet and ModelLoNet, 3DMatch and 3DLoMatch, and odometryKITTI datasets and compared with recent methods. The experimental results demonstrate that GCMTN significantly improves the capability of feature extraction and achieves competitive registration performance in low-overlap scenes. Meanwhile, GCMTN has value and potential for application in practical remote sensing tasks.
Comparative Analysis of the Impact of Two Common Residue Burning Parameters on Urban Air Quality Indicators
Authors: Du Jia , Li Dianjia , Song Kaishan , Zheng Zhi , Wang Yan
Published: August 2023

Crop residue burning produces a lot of polluting gases and fine particles, endangering human health, damaging soil structure, and causing fire accidents. In addition to the impact of residue burning on the local environment, pollutants can spread with the wind to more distant areas and impact their air quality. Nevertheless, a comparative analysis of the impact of two common residue burning parameters, the number of residue fire points, and residue burned area on urban air quality indicators has not been reported. In this study, the correlation between these two different residue burning parameters on air quality in Daqing City (Western Heilongjiang Province, China) was investigated comparatively using the Visible Infrared Imaging Radiometer Suite (VIIRS) fire point product, the Moderate-resolution Imaging Spectroradiometer (MODIS) burned area product, and buffer zone analysis. The association between MODIS burned area products and air quality index (AQI) was found to be around 0.8. Meanwhile, it was found that the correlation between the number of residue fire points extracted from the VIIRS active fire products and air quality was above 0.6, again with a maximum of 0.75 at a buffer radius of 50 km. Within other levels of buffer zones, the correlation between residue burned area and AQI was consistently higher than that between residue fire points and AQI. By comparing the correlation between VIIRS fire points, MODIS burned area, and the concentration of each AQI pollutant, it can be found that the correlation between the concentration of each AQI pollutant and the residue burned area was higher than that and the fire points number. MODIS burned area monitoring, on the other hand, detects changes in the time series of images taken by satellite at two transit moments to obtain a new burned area and cumulative burned area during this period, allowing the monitoring of fire traces caused by fire points at non-transit moments. From analyzing the correlation between residue fire points, residue burned area, and the concentration of each pollutant (PM2.5, PM10, CO, NO2, SO2, and O3), we found significant correlations between residue burning and PM2.5, PM10, CO, and NO2 concentrations, with the highest correlation (R2) of 0.81 for PM2.5. Moreover, the correlation between residue burned area and PM2.5, PM10, CO, and NO2 concentrations was significantly higher than that between the number of residue fire points and their concentrations.
E-FPN: Evidential Feature Pyramid Network for Ship Classification
Authors: Dong Yilin , Xu Kunhai , Zhu Changming , Guan Enguang , Liu Yihai
Published: August 2023

Ship classification, as an important problem in the field of computer vision, has been the focus of research for various algorithms over the past few decades. In particular, convolutional neural networks (CNNs) have become one of the most popular models for ship classification tasks, especially using deep learning methods. Currently, several classical methods have used single-scale features to tackle ship classification, without paying much attention to the impact of multiscale features. Therefore, this paper proposes a multiscale feature fusion ship classification method based on evidence theory. In this method, multiple scales of features were utilized to fuse the feature maps of three different sizes (40 × 40 × 256, 20 × 20 × 512, and 10 × 10 × 1024), which were used to perform ship classification tasks separately. Finally, the multiscales-based classification results were treated as pieces of evidence and fused at the decision level using evidence theory to obtain the final classification result. Experimental results demonstrate that, compared to classical classification networks, this method can effectively improve classification accuracy.
Response of Evapotranspiration (ET) to Climate Factors and Crop Planting Structures in the Shiyang River Basin, Northwestern China
Authors: Yang Xueyi , Shi Xiaojing , Zhang Yaling , Tian Fei , Ortega-Farias Samuel
Published: August 2023

Evapotranspiration (ET) is an essential part of energy flow between the surface of the earth and the atmosphere, simultaneously involving the water, carbon, and energy cycles. It is mainly determined by climate, land use, and land cover changes. Additionally, there is still a need for quantitative characterization of the impacts of climate factors and human activities on ET and regional water resource efficiency in arid and semiarid regions. Based on Landsat-8 remote sensing imagery and land use data, the crop planting structures in the Liangzhou District of the middle reaches of the Shiyang River Basin were identified using a multiband and multi-temporal approach in this study. Subsequently, the ET of major cash crops was inverted using the three-temperature model. This research quantitatively describes the responses of wheat and corn to the climate and human activities over a two-year period. Furthermore, the impact of crop planting structures and climatic factors on ET was elucidated. The results indicate that a combination of multi-temporal green and shortwave infrared 1 bands is the optimal spectral combination to extract the planting structures. Compared to 2019, the wheat area decreased by 23.27% in 2020, while the corn area increased by 5.96%. Both crops exhibited significant spatial heterogeneity in ET during the growing season. The typical daily range of ET for wheat was 0.4–7.2 mm/day, and for corn, it was 1.5–4.0 mm/day. Among the climatic factors, temperature showed the highest correlation with ET (R = 0.80, p ≤ 0.05). Our research findings provide valuable insights for the fine identification of crop planting structures and a better understanding of the response of ET to climatic factors and planting structures.
Simulation of the Ecological Service Value and Ecological Compensation in Arid Area: A Case Study of Ecologically Vulnerable Oasis
Authors: Liu Jiamin , Pei Xiutong , Zhu Wanyang , Jiao Jizong
Published: August 2023

In recent years, the delicate balance between economic development and ecological environment protection in ecologically fragile arid areas has gradually become apparent. Although previous research has mainly focused on changes in ecological service value caused by land use, a comprehensive understanding of ecology–economy harmony and ecological compensation remains elusive. To address this, we employed a coupled deep learning model (convolutional neural network-gated recurrent unit) to simulate the ecological service value of the Wuwei arid oasis over the next 10 years. The ecology–economy harmony index was used to determine the priority range of ecological compensation, while the GeoDetector analyzed the potential impact of driving factors on ecological service value from 2000 to 2030. The results show the following: (1) The coupled model, which extracts spatial features in the neighborhood of historical data using a convolutional neural network and adaptively learns time features using the gated recurrent unit, achieved an overall accuracy of 0.9377, outperforming three other models (gated recurrent unit, convolutional neural network, and convolutional neural network—long short-term memory); (2) Ecological service value in the arid oasis area illustrated an overall increasing trend from 2000 to 2030, but urban expansion still caused a decrease in ecological service value; (3) Historical ecology–economy harmony was mainly characterized by low conflict and potential crisis, while future ecology–economy harmony will be characterized by potential crisis and high coordination. Minqin and Tianzhu in the north and south have relatively high coordination between ecological environment and economic development, while Liangzhou and Guluang in the west and east exhibited relatively low coordination, indicating a greater urgency for ecological compensation; (4) Geomorphic, soil, and digital elevation model emerged as the most influential natural factor affecting the spatial differentiation of ecological service value in the arid oasis area. This study is of great significance for balancing economic development and ecological protection and promoting sustainable development in arid areas.
MLGNet: Multi-Task Learning Network with Attention-Guided Mechanism for Segmenting Agricultural Fields
Authors: Luo Weiran , Zhang Chengcai , Li Ying , Yan Yaning
Published: August 2023

The implementation of precise agricultural fields can drive the intelligent development of agricultural production, and high-resolution remote sensing images provide convenience for obtaining precise fields. With the advancement of spatial resolution, the complexity and heterogeneity of land features are accentuated, making it challenging for existing methods to obtain structurally complete fields, especially in regions with blurred edges. Therefore, a multi-task learning network with attention-guided mechanism is introduced for segmenting agricultural fields. To be more specific, the attention-guided fusion module is used to learn complementary information layer by layer, while the multi-task learning scheme considers both edge detection and semantic segmentation task. Based on this, we further segmented the merged fields using broken edges, following the theory of connectivity perception. Finally, we chose three cities in The Netherlands as study areas for experimentation, and evaluated the extracted field regions and edges separately, the results showed that (1) The proposed method achieved the highest accuracy in three cities, with IoU of 91.27%, 93.05% and 89.76%, respectively. (2) The Qua metrics of the processed edges demonstrated improvements of 6%, 6%, and 5%, respectively. This work successfully segmented potential fields with blurred edges, indicating its potential for precision agriculture development.
Investigation of Accuracy of TOA and SNR of Radio Pulsar Signals for Vehicles Navigation
Authors: Kabakchiev Hristo , Behar Vera , Kabakchieva Dorina , Kisimov Valentin , Stefanova Kamelia
Published: August 2023

It is known that X-ray and gamma-ray pulsars can only be observed by spacecraft because signals from these pulsars are impossible to be detected on the Earth’s surface due to their strong absorption by the Earth’s atmosphere. The article is devoted to the theoretical aspects regarding the development of an autonomous radio navigation system for transport with a small receiving antenna, using radio signals from pulsars, similar to navigation systems for space navigation. Like GNSS systems (X-ray and radio), they use signals from four suitable pulsars to position the object. These radio pulsars (out of 50) are not uniformly distributed but are grouped in certain directions (at least 6 clusters can be determined). When using small antennas (with an area of up to tens of square meters) for pulsar navigation, the energy of the pulsar signals received within a few minutes is extremely insufficient to obtain the required level of SNR at the output of the receiver to form TOA estimation, ensuring positioning accuracy up to tens of kilometers. This is one of the scientific tasks that is solved in the paper by studying the relationship between the SNR of the receiver output, which depends on the size of the antenna, the type of signal processing, and the magnitude of the TOA accuracy estimate. The second scientific task that is solved in the paper is the adaptation of all the possible approaches and algorithms suggested in the statistical theory of radars in the suggested signal algorithm for antenna processing and to evaluate the parameters of the TOA and DS pulsar signals, in order to increase the SNR ratio at the receiver output, while preserving the dimensions of the antenna. In this paper, the functional structure of signal processing in a pulsar transport navigation system is proposed, and the choice of the observed second and millisecond pulsars for obtaining a more accurate TOA estimate is discussed. The proposed estimates of positioning accuracy (TOA only, no phase) in an autonomous pulsar vehicle navigation system would only be suitable for the navigation of large vehicles (sea, air, or land) that do not require accurate navigation at sea, air, or desert. Large-sized antennas with an area of tens of square meters to hundreds of square meters can be installed in such vehicles.
Semisupervised Deep Learning for the Detection of Foreign Materials on Poultry Meat with Near-Infrared Hyperspectral Imaging
Authors: Campos Rodrigo Louzada , Yoon Seung-Chul , Chung Soo , Bhandarkar Suchendra M.
Published: August 2023

A novel semisupervised hyperspectral imaging technique was developed to detect foreign materials (FMs) on raw poultry meat. Combining hyperspectral imaging and deep learning has shown promise in identifying food safety and quality attributes. However, the challenge lies in acquiring a large amount of accurately annotated/labeled data for model training. This paper proposes a novel semisupervised hyperspectral deep learning model based on a generative adversarial network, utilizing an improved 1D U-Net as its discriminator, to detect FMs on raw chicken breast fillets. The model was trained by using approximately 879,000 spectral responses from hyperspectral images of clean chicken breast fillets in the near-infrared wavelength range of 1000–1700 nm. Testing involved 30 different types of FMs commonly found in processing plants, prepared in two nominal sizes: 2 × 2 mm2 and 5 × 5 mm2. The FM-detection technique achieved impressive results at both the spectral pixel level and the foreign material object level. At the spectral pixel level, the model achieved a precision of 100%, a recall of over 93%, an F1 score of 96.8%, and a balanced accuracy of 96.9%. When combining the rich 1D spectral data with 2D spatial information, the FM-detection accuracy at the object level reached 96.5%. In summary, the impressive results obtained through this study demonstrate its effectiveness at accurately identifying and localizing FMs. Furthermore, the technique’s potential for generalization and application to other agriculture and food-related domains highlights its broader significance.
Sensor Fusion and Advanced Controller for Connected and Automated Vehicles
Authors: Li Boyuan , Wang Yafei , Papaioannou Georgios , Du Haiping
Published: August 2023

Nowadays, intelligent vehicles are equipped with a number of advanced sensors, such as radar and cameras [...]
Visual-Based Children and Pet Rescue from Suffocation and Incidence of Hyperthermia Death in Enclosed Vehicles
Authors: Moussa Mona M. , Shoitan Rasha , Cho Young-Im , Abdallah Mohamed S.
Published: August 2023

Over the past several years, many children have died from suffocation due to being left inside a closed vehicle on a sunny day. Various vehicle manufacturers have proposed a variety of technologies to locate an unattended child in a vehicle, including pressure sensors, passive infrared motion sensors, temperature sensors, and microwave sensors. However, these methods have not yet reliably located forgotten children in the vehicle. Recently, visual-based methods have taken the attention of manufacturers after the emergence of deep learning technology. However, the existing methods focus only on the forgotten child and neglect a forgotten pet. Furthermore, their systems only detect the presence of a child in the car with or without their parents. Therefore, this research introduces a visual-based framework to reduce hyperthermia deaths in enclosed vehicles. This visual-based system detects objects inside a vehicle; if the child or pet are without an adult, a notification is sent to the parents. First, a dataset is constructed for vehicle interiors containing children, pets, and adults. The proposed dataset is collected from different online sources, considering varying illumination, skin color, pet type, clothes, and car brands for guaranteed model robustness. Second, blurring, sharpening, brightness, contrast, noise, perspective transform, and fog effect augmentation algorithms are applied to these images to increase the training data. The augmented images are annotated with three classes: child, pet, and adult. This research concentrates on fine-tuning different state-of-the-art real-time detection models to detect objects inside the vehicle: NanoDet, YOLOv6_1, YOLOv6_3, and YOLO7. The simulation results demonstrate that YOLOv6_1 presents significant values with 96% recall, 95% precision, and 95% F1.
LSGP-USFNet: Automated Attention Deficit Hyperactivity Disorder Detection Using Locations of Sophie Germain’s Primes on Ulam’s Spiral-Based Features with Electroencephalogram Signals
Authors: Atila Orhan , Deniz Erkan , Ari Ali , Sengur Abdulkadir , Chakraborty Subrata , Barua Prabal Datta , Acharya U. Rajendra
Published: August 2023

Anxiety, learning disabilities, and depression are the symptoms of attention deficit hyperactivity disorder (ADHD), an isogenous pattern of hyperactivity, impulsivity, and inattention. For the early diagnosis of ADHD, electroencephalogram (EEG) signals are widely used. However, the direct analysis of an EEG is highly challenging as it is time-consuming, nonlinear, and nonstationary in nature. Thus, in this paper, a novel approach (LSGP-USFNet) is developed based on the patterns obtained from Ulam’s spiral and Sophia Germain’s prime numbers. The EEG signals are initially filtered to remove the noise and segmented with a non-overlapping sliding window of a length of 512 samples. Then, a time–frequency analysis approach, namely continuous wavelet transform, is applied to each channel of the segmented EEG signal to interpret it in the time and frequency domain. The obtained time–frequency representation is saved as a time–frequency image, and a non-overlapping n × n sliding window is applied to this image for patch extraction. An n × n Ulam’s spiral is localized on each patch, and the gray levels are acquired from this patch as features where Sophie Germain’s primes are located in Ulam’s spiral. All gray tones from all patches are concatenated to construct the features for ADHD and normal classes. A gray tone selection algorithm, namely ReliefF, is employed on the representative features to acquire the final most important gray tones. The support vector machine classifier is used with a 10-fold cross-validation criteria. Our proposed approach, LSGP-USFNet, was developed using a publicly available dataset and obtained an accuracy of 97.46% in detecting ADHD automatically. Our generated model is ready to be validated using a bigger database and it can also be used to detect other children’s neurological disorders.
Laser Safety—What Is the Laser Hazard Distance for an Electro-Optical Imaging System?
Authors: Ritt Gunnar
Published: August 2023

Laser safety is an important topic. Everybody working with lasers has to follow the long-established occupational safety rules to prevent people from eye damage by accidental irradiation. These rules comprise, for example, the calculation of the Maximum Permissible Exposure (MPE), as well as the corresponding laser hazard distance, the so-called Nominal Ocular Hazard Distance (NOHD). At exposure levels below the MPE, laser eye dazzling may occur and is described by a quite new concept, leading to definitions such as the Maximum Dazzle Exposure (MDE) and to its corresponding Nominal Ocular Dazzle Distance (NODD). In earlier work, we defined exposure limits for sensors corresponding to those for the human eye: The Maximum Permissible Exposure for a Sensor, MPES, and the Maximum Dazzle Exposure for a Sensor, MDES. In this publication, we report on our continuative work concerning the laser hazard distances arising from these exposure limits. In contrast to the human eye, unexpected results occur for electro-optical imaging systems: For laser irradiances exceeding the exposure limit, MPES, it can happen that the laser hazard zone does not extend directly from the laser source, but only from a specific distance to it. This means that some scenarios are possible where an electro-optical imaging sensor may be in danger of getting damaged within a certain distance to the laser source but is safe from damage when located close to the laser source. This is in contrast to laser eye safety, where it is assumed that the laser hazard zone always extends directly from the laser source. Furthermore, we provide closed-form equations in order to estimate laser hazard distances related to the damaging and dazzling of the electro-optical imaging systems.
Text Recognition Model Based on Multi-Scale Fusion CRNN
Authors: Zou Le , He Zhihuang , Wang Kai , Wu Zhize , Wang Yifan , Zhang Guanhong , Wang Xiaofeng
Published: August 2023

Scene text recognition is a crucial area of research in computer vision. However, current mainstream scene text recognition models suffer from incomplete feature extraction due to the small downsampling scale used to extract features and obtain more features. This limitation hampers their ability to extract complete features of each character in the image, resulting in lower accuracy in the text recognition process. To address this issue, a novel text recognition model based on multi-scale fusion and the convolutional recurrent neural network (CRNN) has been proposed in this paper. The proposed model has a convolutional layer, a feature fusion layer, a recurrent layer, and a transcription layer. The convolutional layer uses two scales of feature extraction, which enables it to derive two distinct outputs for the input text image. The feature fusion layer fuses the different scales of features and forms a new feature. The recurrent layer learns contextual features from the input sequence of features. The transcription layer outputs the final result. The proposed model not only expands the recognition field but also learns more image features at different scales; thus, it extracts a more complete set of features and achieving better recognition of text. The results of experiments are then presented to demonstrate that the proposed model outperforms the CRNN model on text datasets, such as Street View Text, IIIT-5K, ICDAR2003, and ICDAR2013 scenes, in terms of text recognition accuracy.
Spatial Assessment of the Digital Economy at the National and Sub-National Level
Authors: Chereshnia Olga
Published: August 2023

The study examines the concept of the digital economy, highlights the challenges in measuring it, and explores the development of indexes as a tool for assessing its growth and the effectiveness of policies and strategies. The proposed index is suitable for both national and sub-national spatial assessments, and it was tested and validated using examples from regions in Russia and European countries. The selected indicators comprehensively capture the geographical distribution of digital economic activity, including the concentration of digital businesses in specific regions, the availability of high-speed internet and mobile networks, the distribution of ICT-related jobs across different regions, and the level of e-commerce activity. The authors initiated an analysis and comparison of available data sources, which resulted in a list of indicators that allows for a sufficient assessment of the levels of digital economy development. The resulting index was used to assess the digital economy’s development in regions in Russia and European countries. The top-ranking countries were Scandinavian, while Moscow dominated the rankings among Russian regions. The results also highlighted significant disparities in the development of the digital economy within Russia and Europe. The study’s findings align with those of other international and domestic studies, confirming the reliability of the proposed methodology.
Emotional Education as Coping Strategy for Exhaustion and Dysfunctional Eating Habits
Authors: Urbón Eva , Salavera Carlos
Published: August 2023

Work shifts affect eating habits, social relationships in the workplace, and, more broadly, the physical and mental health of health professionals. The aim of this research was to differentiate BMI, eating behavior, and burnout between shift and non-shift nurses. The study involved 194 nurses (63% of whom work shifts and 37% of whom do not) who completed sociodemographic questionnaires, eating behavior questionnaires, and an exhaustion inventory (Maslach Burnout Inventory). The results showed that shift nurses were twice as likely to skip meals and obtained higher exhaustion scores. In conclusion, the need to establish prevention plans to promote adequate food intake and healthier eating behaviors among health professionals, while promoting specialized training in emotional education in order to prevent mental health problems, is emphasized.
The Effect of Lowering Indoor Air Temperature on the Reduction in Energy Consumption and CO2 Emission in Multifamily Buildings in Poland
Authors: Szałański Paweł , Kowalski Piotr , Cepiński Wojciech , Kęskiewicz Piotr
Published: August 2023

This article analyzes the possibility of reducing the energy consumption from building heating as a result of lowering the indoor air temperature, which is recommended as a response to the energy crisis. Various values of the set-point temperature (16–22 °C), as well as different scenarios for their changes, were assumed for analysis. Changes in clothing that were determined to maintain the same level of thermal comfort after a temperature change were determined. The associated reduction in CO2 emissions emitted into the atmosphere was determined. The effect of reducing CO2 emissions was studied depending on the type of heating source. Simulation calculations were carried out for an exemplary multifamily building. The effect of different building insulations required in Poland over the years 1964–2022 was considered. Analyses were performed for the climatic conditions of cities located in different climatic zones of Poland: Koszalin, Wroclaw, Warsaw, Bialystok, Suwalki. Depending on the scenario, the insulation standard of the building, and the variant of location, the energy reduction achieved ranges from 6.6%/K to 13.2%/K. Taking into account the type of heating source, the reduction in CO2 emissions is from 0.7 to 7.5 kgCO2/(K·m2). The reduction in temperature by 1 or 2 K can be compensated for by wearing an additional sleeveless vest (0.12 clo) or sweater (0.28 clo).
Does the Use of Digital Finance Affect Household Farmland Transfer-Out?
Authors: Lei Haibo , Su Qin
Published: August 2023

Digital finance offers opportunities for inclusive growth in rural areas. This study aims to clarify how digital financiers affect farmland transfer-out. Using the data from the China Household Finance Survey in 2015, this paper establishes Probit and Tobit models to empirically analyze the impact and mechanisms of digital finance on household farmland transfer-out. The study finds that digital financial use significantly increases the probability and proportion of farmland transfer-out and that this effect is greater among households with older heads and lower household per capita income and financial accessibility, suggesting that digital finance has an important role to play in reducing inequality and promoting inclusive growth. Further analysis reveals that off-farm employment and information channels are mediating mechanisms through which digital finance facilitates farmland transfer. Specifically, on the one hand, the financial function of digital finance increases the share of employment and entrepreneurship among rural households. In terms of industry and skill type, digital finance promotes the entry of farmers into tertiary employment, facilitates off-farm employment for low and medium-skilled farmers, and has no impact on the employment of high-skilled farmers. On the other hand, the information function accompanying digital finance broadens households’ access to information, both of which have a favorable effect on farmland transfer-out. This study provides new ideas for supporting agricultural land transfer from a digital finance perspective.
Choosing the Bicycle as a Mode of Transportation, the Influence of Infrastructure Perception, Travel Satisfaction and Pro-Environmental Attitude, the Case of Milan
Authors: Biassoni Federica , Lo Carmine Chiara , Perego Paolo , Gnerre Martina
Published: August 2023

The present study investigates how infrastructure perception, travel satisfaction and pro-environmental attitudes influence the motivations for choosing cycling as a mode of transport. The effects of demographic variables and mobility habits are also taken into account. Data are collected via a survey with cyclists living in Milan and the surrounding areas. The survey comprises a first set of items to explore the cyclists’ transportation habits and three self-assessment questionnaires: The Neighbourhood Environment Walkability Scale (NEWS)—A Short Form, The Satisfaction with Travel Scale (STS) and the Environmental Attitudes Inventory (EAI). A series of different multiple linear regression analyses and mediation models are conducted. The findings suggest that the frequency of cycling may be explained based on several factors. Males report cycling more frequently, younger adults report lower frequencies of bicycle use and the urban setting is more significantly related to bicycle use. Moreover, the results highlight the significant positive role of the perception of neighbourhoods (especially those with an easy access to non-residential areas) and of pro-environmental attitudes (especially of personal conservation behaviour) in promoting the frequency of bicycling, with the mediation effect of the variable ‘purpose of bicycle use’ (for leisure or to reach a destination) as well. Based on these findings, policymakers should focus on tailored strategies to promote cycling in different users.
Environmental Taxation on the Agri-Food Sector and the Farm to Fork Strategy: The Portuguese Case
Authors: Borrego Ana Clara , Abreu Rute , Carreira Francisco Alegria , Caetano Filipe , Vasconcelos Ana Lúcia
Published: August 2023

This research focuses on the environmental taxation applicable to the agri-food sector and aligns with the objectives of the Farm to Fork (F2F) Strategy of the European Green Deal context. Indeed, the methodology of the research develops a theoretical analysis through a literature review to assess environmental taxation and documental analysis of Portuguese tax codes. Furthermore, the Portuguese context of the empirical analysis assesses the contribution to the desired sustainability of the agri-food sector. The results show that the existing tax rules applicable to the agri-food sector are scant, with a few existing rules being misaligned with the objectives of the F2F strategy, because their potential impact on its objectives has been mostly negative or null. Most regulations predated the definition of the F2F strategy, seeking to respond to the need to minimise the price of basic food products, namely agri-food products, without considering concerns such as welfare and sustainability. It is worrisome, however, that a tax rule that does not conform to the F2F strategy has been approved after its definition, indicating that the Portuguese government does not envisage using taxation as an environmental tool in favour of the objectives outlined in the F2F strategy.
Does Intra-Urban Residential Relocation Affect the Elderly’s Health and Well-Being? An Empirical Study of Nanjing, China
Authors: Cao Yang , Wang Yu , Wu Hao , Zhang Chao , Shen Shuwen , Qu Yawei , Yan Shuqi
Published: August 2023

There are few existing studies from a spatial perspective that explore the mechanism of the living environment, relocated direction and relocated distance on the elderly’s physical and mental health. Based on the 2019 household survey questionnaire data, this study utilizes a binomial logistic regression model and residents’ in-depth interviews to study relocated behavior in Nanjing. We analyzed the spatial–temporal characteristics of relocated behaviors, the accumulation effect at different stages and the influence of relocated intention on physical and mental health among the elderly. The findings are as follows: (1) The mode of relocation among the elderly includes long-term cumulative effects and short-term effects. Frequent relocation has a cumulative negative impact on the physical and mental health of the elderly. (2) The elderly relocated in the short-term had a great negative impact on their mental health but had no significant impact on their physical health. (3) In the last relocation, active relocation had a significantly positive impact on physical health. The “centrifugal relocation” from the main urban area to the surrounding new cities has a significant positive impact on mental health. Moreover, long-distance relocations adversely affected mental health.
A Readiness Model and Factors Influencing Blockchain Adoption in Malaysia’s Software Sector: A Survey Study
Authors: AL-Ashmori Ammar , Thangarasu Gunasekar , Dominic P. D. D. , Al-Mekhlafi Al-Baraa Abdulrahman
Published: August 2023

The technology of Blockchain may open up new potential for innovation and distinction. It can enable the software sector to develop more safe and transparent systems that can function in an environment without trust. The adoption rate still needs to be higher despite the potential advantages; the relatively low adoption rate may be attributable to issues such as a lack of awareness, the difficulty of adoption, and ambiguity surrounding legal and regulatory frameworks. Considering technical, organizational, and environmental aspects, this study aims to determine the primary factors impacting the readiness of software firms to adopt Blockchain technology. The research on adopting Blockchain technology in the Malaysian software sector is limited. Using a quantitative method, the researchers used structural equation modeling to analyze 251 survey responses from the Malaysian software sector. In light of the findings, eight hypotheses were considered significant, and one hypothesis was rejected. At the same time, the R2 indicated that all these variables explained 71% of the dependent variable’s variance, which is considered substantial. Overall, it makes it easier for firms in the software sector to use Blockchain technology, which would increase the overall competitiveness of Malaysia’s software sector in the international market.
Reconfiguring Farming Systems of Smallholders with Market-Led Approach: A Case Study in Northeast Thailand
Authors: Promkhambut Arunee , Polthanee Anan , Simma Buppha , Fox Jefferson , Rambo A. T.
Published: August 2023

Agricultural research and development organizations are under pressure to shift from increasing productivity of crops to increasing the profitability and competitiveness of smallholders and linking small-scale farmers to profitable markets. This paper discusses the efforts to assist small-scale farmers to become more sustainable and market oriented. Data were taken from a 5-year participatory action project in a village in Northeast Thailand during 2014–2018. We found that a market-led extension approach can enable farmers to optimize their returns from farming by selecting farm enterprises based on market opportunities for both existing and new products. Farmers need to apply post-harvesting management, including processing, grading, standardization of produce, value adding, packaging, branding, and certification in the process. Collaboration and long-term commitments on the part of scientists, local stakeholders, policymakers, and funding organizations need to be furthered strengthened.
Analyzing Driving Safety on Prairie Highways: A Study of Drivers’ Visual Search Behavior in Varying Traffic Environments
Authors: Ding Xu , Wang Haixiao , Wang Chutong , Guo Min
Published: August 2023

This study aimed to investigate disparities in drivers’ visual search behavior across various typical traffic conditions on prairie highways and analyze driving safety at the visual search level. The study captured eye movement data from drivers across six real-world traffic environments: free driving, vehicle-following, oncoming vehicles, rear vehicles overtaking cut-in, roadside risks, and driving through intersections, by carrying out a real vehicle test on a prairie highway. The drivers’ visual search area was divided into five areas using clustering principles. By integrating the Markov chain and information entropy theory, the information entropy of fixation distribution (IEFD) was constructed to quantify the complexity of drivers’ traffic information search. Additionally, the main area of visual search (MAVS) and the peak-to-average ratio of saccade velocity (PARSV) were introduced to measure visual search range and stability, respectively. The study culminated in the creation of a visual search load evaluation model that utilizes both VIKOR and improved CRITIC methodologies. The findings indicated that while drivers’ visual distribution and transfer modes vary across different prairie highway traffic environments, the current lane consistently remained their primary area of search for traffic information. Furthermore, it was found that each visual search indicator displayed significant statistical differences as traffic environments changed. Particularly when encountering roadside risks, drivers’ visual search load increased significantly, leading to a considerable decrease in driving safety.
Theoretical Aspects for Bayesian Predictions Based on Three-Parameter Burr-XII Distribution and Its Applications in Climatic Data
Authors: Hasaballah Mustafa M. , Al-Babtain Abdulhakim A. , Hossain Md. Moyazzem , Bakr Mahmoud E.
Published: August 2023

Symmetry and asymmetry play vital roles in prediction. Symmetrical data, which follows a predictable pattern, is easier to predict compared to asymmetrical data, which lacks a predictable pattern. Symmetry helps identify patterns within data that can be utilized in predictive models, while asymmetry aids in identifying outliers or anomalies that should be considered in the predictive model. Among the various factors associated with storms and their impact on surface temperatures, wind speed stands out as a significant factor. This paper focuses on predicting wind speed by utilizing unified hybrid censoring data from the three-parameter Burr-XII distribution. Bayesian prediction bounds for future observations are obtained using both one-sample and two-sample prediction techniques. As explicit expressions for Bayesian predictions of one and two samples are unavailable, we propose the use of the Gibbs sampling process in the Markov chain Monte Carlo framework to obtain estimated predictive distributions. Furthermore, we present a climatic data application to demonstrate the developed uncertainty procedures. Additionally, a simulation research is carried out to examine and contrast the effectiveness of the suggested methods. The results reveal that the Bayes estimates for the parameters outperformed the Maximum likelihood estimators.
Excitation Function of Freeze-Out Parameters in Symmetric Nucleus–Nucleus and Proton–Proton Collisions at the Same Collision Energy
Authors: Badshah Murad , Haj Ismail Abd , Waqas Muhammad , Ajaz Muhammad , Mian Mateen Ullah , Dawi Elmuez A. , Adil Khan Muhammad , AbdelKader Atef
Published: August 2023

We analyze the transverse momentum (pT) spectra of π+, π−, K+, K−, p, p¯, Λ, Λ¯, Ξ, Ξ¯, Ω−, Ω¯+ or Ω−+Ω¯+ in different centrality intervals in gold–gold (Au–Au) and lead–lead (Pb–Pb) symmetric collisions at 200 GeV and 2.76 TeV, respectively, by Tsallis–Pareto-type function. Proton–proton collisions at the same centre of mass energies are also analyzed for these particles to compare the results obtained from these systems. The present work extracts the effective temperature T, non-extensivity parameter (q), the mean transverse momentum spectra (⟨pT⟩), the multiplicity parameter (N0), kinetic freeze-out temperature (T0) and transverse flow velocity (βT). We reported a plateau structure of pT, T, T0, βT, pT and q in central collisions. Beyond the plateau region, the excitation function of all the above parameters decreases towards the periphery, except q, which has a reverse trend. The multiplicity parameter is also extracted, which is found to be decreasing towards the periphery from the central collisions. In addition, we observed that the excitation function of pp collisions is nearly the same to that of the most peripheral symmetric nucleus–nucleus collisions at the same colliding energy. Throughout the analyses, the same multiplicity parameters for particles and their antiparticles have been reported, which show the symmetric production of particles and their antiparticles.
Research on the Copyright Value Evaluation Model of Online Movies Based on the Fuzzy Evaluation Method and Analytic Hierarchy Process
Authors: Song Peiyi , Liu Yutong , Sun Jianghua
Published: August 2023

With the rapid development of video websites, copyright management and the protection of online movies are facing severe challenges. With the strengthening of intellectual property protection, copyright value management has become necessary for the transformation of copyright value, which is of great significance for the healthy development of the industry. Based on the current development status of China’s online movie offerings, online movies were collected from China’s three major video platforms between 2016 and 2018 as research objects, and a set of scientific and effective online movie copyright value assessment methods and systems are proposed through the fuzzy comprehensive evaluation method, analytic hierarchy process, Delphi method, and empirical research. In this study, using data collected through a questionnaire survey, a fuzzy evaluation method is applied to establish the evaluation index of the copyright value of online movies. Moreover, according to the Delphi method, expert suggestions are collected, the indexes are scientifically corrected in the market, and the weights of the copyright value evaluation index of online movies both before and after broadcasting are calculated using the analytic hierarchy process. On this basis, by applying big data analysis, the communication effect index, prebroadcast value score evaluation index, and postbroadcast value evaluation index are deeply analyzed, and the copyright value evaluation model of online movies both before and after broadcasting is established. Finally, based on market feedback data, the evaluation models are revised and empirically tested to verify the scientificity and rationality of the copyright evaluation method proposed in this study. The results show that the proposed methods and systems for evaluating the copyright value of online movies are scientific and effective. This study provides new insights for all types of movie and television production organizations and video playback platforms on how to design effective copyright value evaluation models and practice methods for online movies.