bluuebunny/crossref_metadata_2025 huggingface dataset:
Identifying Risk Factors for Premature Birth in the UK Millennium Cohort Using a Random Forest Decision-Tree Approach
Authors: Waynforth David
Published: December 2022

Prior research on causes of preterm birth has tended to focus on pathophysiological processes while acknowledging the role of socioeconomic indicators. The present research explored a wide range of factors plausibly associated with preterm birth informed by pathophysiological and evolutionary life history perspectives on gestation length. To achieve this, a machine learning ensemble classification data analysis approach, random forest (RF), was applied to the UK Millennium Cohort (18,201 births). The results highlighted the importance of socioeconomic variables and parental age in predicting preterm (before 37 completed weeks) and very preterm (before 32 weeks) birth. Infants born in households with low income and with young fathers had an increased risk of both very preterm and preterm birth. Maternal health and health problems during pregnancy were not found to be useful predictors. The best-performing algorithm was for very preterm birth and had 93% sensitivity and 100% specificity using six variables. Algorithms predicting preterm birth before 37 weeks showed increased error, with out-of-bag error rates of about 7% versus only 1% for those predicting very preterm birth. The poorer performance of algorithms predicting preterm births to 37 weeks of gestation suggests that some preterm birth may not result from pathology related to poor maternal health or social or economic disadvantage, but instead represents normal life-history variation.
Multi-Node Joint Power Allocation Algorithm Based on Hierarchical Game Learning in Underwater Acoustic Sensor Networks
Authors: Wang Hui , Huang Yao , Luo Fang , Yang Liejun
Published: December 2022

In order to improve the overall service quality of the network and reduce the level of network interference, power allocation has become one of the research focuses in the field of underwater acoustic communication in recent years. Aiming at the issue of power allocation when channel information is difficult to obtain in complex underwater acoustic communication networks, a completely distributed game learning algorithm is proposed that does not require any prior channel information and direct information exchange between nodes. Specifically, the power allocation problem is constructed as a multi-node multi-armed bandit (MAB) game model. Then, considering nodes as agents and multi-node networks as multi-agent networks, a power allocation algorithm based on a softmax-greedy action selection strategy is proposed. In order to improve the learning efficiency of the agent, reduce the learning cost, and mine the historical reward information, a learning algorithm based on the two-layer hierarchical game learning (HGL) strategy is further proposed. Finally, the simulation results show that the algorithm not only shows good convergence speed and stability but also can adapt to a harsh and complex network environment and has a certain tolerance for incomplete channel information acquisition.
Validation of Expanded Trend-to-Trend Cross-Calibration Technique and Its Application to Global Scale
Authors: Shah Ramita , Leigh Larry , Kaewmanee Morakot , Pinto Cibele Teixeira
Published: December 2022

The expanded Trend-to-Trend (T2T) cross-calibration technique has the potential to calibrate two sensors in much less time and provides trends on a daily assessment basis. The trend obtained from the expanded technique aids in evaluating the differences between satellite sensors. Therefore, this technique was validated with several trusted cross-calibration techniques to evaluate its accuracy. Initially, the expanded T2T technique was validated with three independent RadCaTS RRV, DIMITRI-PICS, and APICS models, and results show a 1% average difference with other models over all bands. Further, this technique was validated with other SDSU techniques to calibrate the newly launched satellite Landsat 9 with 8, demonstrating good agreement in all bands within 0.5%. This technique was also validated for Terra MODIS and ETM+, showing consistency within 1% for all bands compared to four PICS sites. Additionally, the T2T technique was applied to a global scale using EPICS Global sites. The expanded T2T cross-calibration gain result obtained for Landsat 8 versus Landsat 7/8, Sentinel 2A/2B, and Terra/Aqua MODIS presented that the difference between these pairs was within 0.5–1% for most of the spectral bands. Total uncertainty obtained for these pairs of sensors using Monte Carlo Simulation varies from 2.5–4% for all bands except for SWIR2 bands, which vary up to 5%. The difference between EPICS Global and EPICS North Africa was calculated using the ratio of trend gain; the difference among them was within 0.5–1% difference on average for all the sensors and bands within a 0.5% uncertainty level difference.
Intelligent Radar Jamming Recognition in Open Set Environment Based on Deep Learning Networks
Authors: Zhou Yu , Shang Song , Song Xing , Zhang Shiyu , You Tianqi , Zhang Linrang
Published: December 2022

Jamming recognition is an essential step in radar detection and anti-jamming in the complex electromagnetic environment. When radars detect an unknown type of jamming that does not occur in the training set, the existing radar jamming recognition algorithms fail to correctly recognize it. However, these algorithms can only recognize this type of jamming as one that already exists in our jamming library. To address this issue, we present two models for radar jamming open set recognition (OSR) that can accurately classify known jamming and distinguish unknown jamming in the case of small samples. The OSR model based on the confidence score can distinguish known jamming from unknown jamming by assessing the reliability of the sample output probability distribution and setting thresholds. Meanwhile, the OSR model based on OpenMax can output the probability of jamming belonging to not only all known classes but also unknown classes. Experimental results show that the two OSR models exhibit high recognition accuracy for known and unknown jamming and play a vital role in sensing complex jamming environments.
Oriented Object Detection Based on Foreground Feature Enhancement in Remote Sensing Images
Authors: Lin Peng , Wu Xiaofeng , Wang Bin
Published: December 2022

Oriented object detection is a fundamental and challenging task in remote sensing image analysis and has received much attention in recent years. Optical remote sensing images often have more complex background information than natural images, and the number of annotated samples varies in different categories. To enhance the difference between foreground and background, current one-stage object detection algorithms attempt to exploit focus loss to balance the foreground and background weights, thus making the network more focused on the foreground part. However, the current one-stage object detectors still face two main challenges: (1) the detection network pays little attention to the foreground and does not make full use of the foreground information; (2) the distinction of similar object categories has not attracted attention. To address the above challenges, this paper presents a foreground feature enhancement method applied to one-stage object detection. The proposed method mainly includes two important components: keypoint attention module (KAM) and prototype contrastive learning module (PCLM). The KAM is used to enhance the features of the foreground part of the image and reduce the features of the background part of the image, and the PCLM is utilized to enhance the discrimination of samples between foreground categories and reduce the confusion of samples between different categories. Furthermore, the proposed method designs and adopts an equalized modulation focal loss (EMFL) to optimize the training process of the model and increase the loss weight of the foreground later in the model training. Experimental results on the publicly available DOTA datasets and HRSC2016 datasets show that our method exhibits state-of-the-art performance.
Evaluation of the Performance of CLM5.0 in Soil Hydrothermal Dynamics in Permafrost Regions on the Qinghai–Tibet Plateau
Authors: Yang Shuhua , Li Ren , Zhao Lin , Wu Tonghua , Wu Xiaodong , Zhang Yuxin , Shi Jianzong , Qiao Yongping
Published: December 2022

Soil hydrothermal dynamics are crucial processes for understanding the internal physical conditions of the active layer in permafrost regions. It is very difficult to obtain data in permafrost regions, especially on the Qinghai–Tibet Plateau (QTP). Land surface modes (LSMs) provide an effective tool for soil hydrothermal dynamics. However, it is necessary to evaluate the simulation performance before using them. Here, we used two in situ sites along with the latest version of the Community Land Model (CLM5.0) to evaluate the simulated performance in the soil hydrothermal parameters of the model in permafrost regions on the QTP. Meanwhile, the effects of soil properties, thermal roughness length, and the freeze–thaw process on the simulation results were investigated. The results showed that CLM5.0 can capture the dynamic changes in soil hydrothermal changes well in permafrost regions on the QTP. Soil moisture and thermal conductivity were more sensitive to soil properties and the freeze–thaw process, while the thermal roughness length had a greater effect on soil temperature. Notably, although we improved the soil properties and thermal roughness length, there were still some errors, especially in the soil moisture and soil thermal conductivity. It may be caused by inappropriate hydrothermal parameterizations of the model, especially the soil thermal conductivity, hydraulic conductivity, unfrozen water scheme, and snow schemes. There is an urgent need for collaboration between experts in permafrost science, hydrological science, and modelers to develop the appropriate schemes for permafrost regions and enhance the LSMs.
Assessment of the Impact of Rubber Plantation Expansion on Regional Carbon Storage Based on Time Series Remote Sensing and the InVEST Model
Authors: Huang Chong , Zhang Chenchen , Li He
Published: December 2022

Rubber plantations in southeast Asia have grown at an unprecedented rate in recent decades, leading to drastic changes in regional carbon storage. To this end, this study proposes a systematic approach for quantitatively estimating and assessing the impact of rubber expansions on regional carbon storage. First, using Sentinel-1 and Sentinel-2 satellite data, the distributions of forest and rubber, respectively, were extracted. Then, based on the Landsat time series (1999–2019) remote sensing data, the stand age estimation of rubber plantations was studied with the improved shapelet algorithm. On this basis, the Ecosystem Services and Tradeoffs model (InVEST) was applied to assess the regional carbon density and storage. Finally, by setting up two scenarios of actual planting and hypothetical non-planting of rubber forests, the impact of the carbon storage under these two scenarios was explored. The results of the study showed the following: (1) The area of rubber was 1.28 × 105 ha in 2019, mainly distributed at an elevation of 200–400 m (accounting for 78.47% of the total of rubber). (2) The average age of rubber stands was 13.85 years, and the total newly established rubber plantations were converted from cropland and natural forests, accounting for 54.81% and 45.19%, respectively. (3) With the expansion of rubber plantations, the carbon density increased from only 2.25 Mg·C/ha in 1999 to more than 15 Mg·C/ha in 2018. Among them, the carbon sequestration increased dramatically when the cropland was replaced by rubber, while deforestation and replacement of natural forests will cause a significant decrease. (4) The difference between the actual and the hypothetical carbon storage reached −0.15 million tons in 2018, which means that the expansion of rubber led to a decline in carbon storage in our study area. These research findings can provide a theoretical basis and practical application for sustainable regional rubber forest plantation and management, carbon balance maintenance, and climate change stabilization.
Water Quality Grade Identification for Lakes in Middle Reaches of Yangtze River Using Landsat-8 Data with Deep Neural Networks (DNN) Model
Authors: Wei Zeyang , Wei Lifei , Yang Hong , Wang Zhengxiang , Xiao Zhiwei , Li Zhongqiang , Yang Yujing , Xu Guobin
Published: December 2022

Water quality grade is an intuitive element for people to understand the condition of water quality. However, in situ water quality grade measurements are often labor intensive, which makes measurement over large areas very costly and laborious. In recent years, numerous studies have demonstrated the effectiveness of remote sensing techniques in monitoring water quality. In order to automatically extract the water quality information, machine learning technologies have been widely applied in remote sensing data interoperation. In this study, Landsat-8 data and deep neural networks (DNN) were employed to identify the water quality grades of lakes in two cities, Wuhan and Huangshi, in the middle reach of the Yangtze River, central China. Additionally, linear support vector machine (L-SVM), random forest (RF), decision tree (DT), and multi-layer perceptron (MLP) were selected as comparative methods. The experimental results showed that DNN achieved the most promising performance compared to the other approaches. For the lakes in Wuhan, DNN gave water quality results with overall accuracy (OA) of 93.37% and Kappa of 0.9028. For the lakes in Huangshi, OA and kappa given by DNN were 96.39% and 0.951, respectively. The results show that the use of remote sensing images for water quality grade monitoring is effective. In the future, our method can be used for water quality monitoring of lakes in large areas at a low cost.
Sea Surface Salinity Anomaly in the Bay of Bengal during the 2010 Extremely Negative IOD Event
Authors: Chen Shuling , Cha Jing , Qiu Fuwen , Jing Chunsheng , Qiu Yun , Xu Jindian
Published: December 2022

Based on Soil Moisture and Ocean Salinity (SMOS) data and the Ocean Reanalysis System 5 (ORAS5) dataset, positive salinity anomalies exceeding 2 psu in the northern Bay of Bengal (BoB) and negative salinity anomalies with the peak of the freshening anomalies reaching −2 psu around Sri Lanka were observed in autumn 2010. Here, an analysis of the anomalous salt budget revealed that anomalous horizontal advection contributed most to the variability in salinity in the BoB. With the development of La Niña and negative Indian Ocean dipole (nIOD) in summer and autumn, the strong summer monsoon current and Wyrtki jet combined with the anomalous basin-scale cyclonic circulation led to more high-salinity water entering the northern BoB. In addition, more freshwater was transported southward along the eastern coast of India by east Indian coastal current (EICC) in autumn, resulting in extremely negative salinity anomalies around Sri Lanka and positive salinity anomalies in the northern BoB. Moreover, the freshwater around Sri Lanka was carried farther into the southeastern Arabian Sea by the west Indian coastal current (WICC) in November, which affected the salinity stratification in winter and then influenced the variation of the Arabian Sea Mini Warm Pool (ASMWP) in the following spring. The ASMWP could affect the Indian summer monsoon (ISM) through its influence on the monsoon onset vortex (MOV) over the southeast Arabian Sea (SEAS).
A Study on the Application of Machine and Deep Learning Using the Impact Response Test to Detect Defects on the Piston Rod and Steering Rack of Automobiles
Authors: Yoon Young-Geun , Woo Ji-Hoon , Oh Tae-Keun
Published: December 2022

The main parts of automobiles are the piston rod of the shock absorber and the steering rack of the steering gear, and their quality control is critical in the product process. In the process line, these products are normally inspected through visual inspection, sampling, and simple tensile tests; however, if there is a problem or abnormality, it is difficult to identify the type and location of the defect. Usually, these defects are likely to cause surface cracks during processing, which in turn accelerate the deterioration of the shock absorber and steering, causing serious problems in automobiles. As a result, the purpose of this study was to present, among non-destructive methods, a shock response test method and an analysis method that can efficiently and accurately determine the defects of the piston rod and steering rack. A test method and excitation frequency range that can measure major changes according to the location and degree of defects were proposed. A defect discrimination model was constructed using machine and deep learning through feature derivation in the time and frequency domains for the collected data. The analysis revealed that it was possible to effectively distinguish the characteristics according to the location as well as the presence or absence of defects in the frequency domain rather than the time domain. The results indicate that it will be possible to quickly and accurately check the presence or absence of defects in the shock absorber and steering in the automobile manufacturing process line in the future. It is expected that this will play an important role as a key factor in building a smart factory.
Fiber-Based Triboelectric Nanogenerator for Mechanical Energy Harvesting and Its Application to a Human–Machine Interface
Authors: Chung Chen-Kuei , Huang You-Jun , Wang Tun-Kai , Lo Yu-Lung
Published: December 2022

Mechanical energy harvesters including piezoelectric nanogenerators, electromagnetic generators and triboelectric nanogenerators (TENG) used to convert the mechanical motion into electricity are more and more important in the recent decades. Specifically, the fiber-based TENG (FTENG) has gained considerable favors due to its flexibility, light weight, and high environmental tolerance for the wearable devices. The traditional FTENGs made of Teflon result in better performance but are not suitable for long-term wear in person. Here, we propose a novel FTENG using a flexible micro-needle-structured polydimethylsiloxane (MN-PDMS) together with the comfortable commercially available 2D-polyester fibers, and electroless nickel-plated cotton cloth of which two are widely used in human daily life. The MN-PDMS is formed by a laser engraved mold for improving its output performance of FTENG compared to the flat-PDMS. The open-circuit voltage (Voc) and the short-circuit current (Isc) of MN-FTENG increased to 73.6 V and 36 μA, respectively, which are 34% and 37% higher than the flat-FTENG. In terms of power, the performance of MN-FTENG reaches 1.296 mW which is 89% higher than that of flat-TENG and it can also light up 90 LEDs. For application, human motion at the joints can be detected and collected with various signals that are used for the human–machine interface (HMI) through the cooperation of components for the Internet of Things (IoT). It can light up the LED bulb through MN-FTENG to potentially develop IoT HMI systems for human motion control of robot in the future.
An Indoor UWB 3D Positioning Method for Coplanar Base Stations
Authors: Zhou Ning , Si Minghao , Li Dehai , Seow Chee Kiat , Mi Jinzhong
Published: December 2022

As an indispensable type of information, location data are used in various industries. Ultrawideband (UWB) technology has been used for indoor location estimation due to its excellent ranging performance. However, the accuracy of the location estimation results is heavily affected by the deployment of base stations; in particular, the base station deployment space is limited in certain scenarios. In underground mines, base stations must be placed on the roof to ensure signal coverage, which is almost coplanar in nature. Existing indoor positioning solutions suffer from both difficulties in the correct convergence of results and poor positioning accuracy under coplanar base-station conditions. To correctly estimate position in coplanar base-station scenarios, this paper proposes a novel iterative method. Based on the Newton iteration method, a selection range for the initial value and iterative convergence control conditions were derived to improve the convergence performance of the algorithm. In this paper, we mathematically analyze the impact of the localization solution for coplanar base stations and derive the expression for the localization accuracy performance. The proposed method demonstrated a positioning accuracy of 5 cm in the experimental campaign for the comparative analysis, with the multi-epoch observation results being stable within 10 cm. Furthermore, it was found that, when base stations are coplanar, the test point accuracy can be improved by an average of 63.54% compared to the conventional positioning algorithm. In the base-station coplanar deployment scenario, the upper bound of the CDF convergence in the proposed method outperformed the conventional positioning algorithm by about 30%.
Statistical Picking of Multivariate Waveforms
Authors: D’Angelo Nicoletta , Adelfio Giada , Chiodi Marcello , D’Alessandro Antonino
Published: December 2022

In this paper, we propose a new approach based on the fitting of a generalized linear regression model in order to detect points of change in the variance of a multivariate-covariance Gaussian variable, where the variance function is piecewise constant. By applying this new approach to multivariate waveforms, our method provides simultaneous detection of change points in functional time series. The proposed approach can be used as a new picking algorithm in order to automatically identify the arrival times of P- and S-waves in different seismograms that are recording the same seismic event. A seismogram is a record of ground motion at a measuring station as a function of time, and it typically records motions along three orthogonal axes (X, Y, and Z), with the Z-axis being perpendicular to the Earth’s surface and the X- and Y-axes being parallel to the surface and generally oriented in North–South and East–West directions, respectively. The proposed method was tested on a dataset of simulated waveforms in order to capture changes in the performance according to the waveform characteristics. In an application to real seismic data, our results demonstrated the ability of the multivariate algorithm to pick the arrival times in quite noisy waveforms coming from seismic events with low magnitudes.
A Reconfigurable Pseudohairpin Filter Based on MEMS Switches
Authors: Donelli Massimo , Manekiya Mohammedhusen , Tagliapietra Girolamo , Iannacci Jacopo
Published: December 2022

This work presents a bandpass-reconfigurable planar pseudohairpin filter based on RF-MEMS switches. Hairpin-line structures are preferred to design microstrip filters because this class of filters offers a more compact size, and, in general, hairpin filters do not need ground connections for resonators. In this work, the U-shape resonators are arranged to obtain an interdigit capacitor to improve the coupling between the resonators. RF-MEMS switches modify the lengths of coupled resonators by adding microstrip segments to control the filter bandwidth, moving the center frequency and the return loss. An experimental hairpin tunable filter prototype based on RF-MEMS has been designed, fabricated, numerically and experimentally assessed, and compared concerning its tunability, quality factor, and capability with standard tunable filters based on PIN diodes. In conclusion, the tunable hairpin filter based on RF-MEMS switches offers the best performance in center frequency tuning range, compactness, and power consumption regarding reconfigurable filters based on standard PIN diodes switches. The obtained results are appealing and demonstrate the capabilities and potentialities of RF-MEMS to operate with the new communication standards that work at high microwave frequency bands.
Signal Source Localization with Long-Term Observations in Distributed Angle-Only Sensors
Authors: Zhou Shenghua , Wang Linhai , Liu Ran , Chen Yidi , Peng Xiaojun , Xie Xiaoyang , Yang Jian , Gao Shibo , Shao Xuehui
Published: December 2022

Angle-only sensors cannot provide range information of targets and in order to determine accurate position of a signal source, one can connect distributed passive sensors with communication links and implement a fusion algorithm to estimate target position. To measure moving targets with sensors on moving platforms, most of existing algorithms resort to the filtering method. In this paper, we present two fusion algorithms to estimate both the position and velocity of moving target with distributed angle-only sensors in motion. The first algorithm is termed as the gross least square (LS) algorithm, which takes all observations from distributed sensors together to form an estimate of the position and velocity and thus needs a huge communication cost and a huge computation cost. The second algorithm is termed as the linear LS algorithm, which approximates locations of sensors, locations of targets, and angle-only measures for each sensor by linear models and thus does not need each local sensors to transmit raw data of angle-only observations, resulting in a lower communication cost between sensors and then a lower computation cost at the fusion center. Based on the second algorithm, a truncated LS algorithm, which estimates the target velocity through an average operation, is also presented. Numerical results indicate that the gross LS algorithm, without linear approximation operation, often benefits from more observations, whereas the linear LS algorithm and the truncated LS algorithm, both bear lower communication and computation costs, may endure performance loss if the observations are collected in a long period such that the linear approximation model becomes mismatch.
The State-of-the-Art of Smart Cities in the European Union
Authors: Correia Diogo , Marques João Lourenço , Teixeira Leonor
Published: December 2022

Today, policymakers struggle to obtain information from specific smart city case studies. The literature lacks a unified view of current initiatives. This paper performs an empirical study with the aim of collecting evidence from the literature about existing smart city initiatives in the European Union (EU). The contribution of each paper and its geography are analyzed using content analysis to identify the number and type of initiatives in each country. A cluster analysis is performed to find relationships between countries and their development phase as well as the categories (areas) they are focused on. The results suggest that there are different levels of smart city development between the member states despite the initial year of their first result in the literature. Furthermore, 22 smart city categories clustered in four different groups were found. When compared to countries’ socio-economic characteristics, the results suggest the development of smart cities is significantly related to the public budget balance, gross domestic product and EU structural funds. In summary, this paper portrays the state-of-the-art of smart city initiatives in the member states of the European Union. Moreover, it represents a valuable contribution to decision makers to discuss ways to standardize smart city approaches in the European scope. Furthermore, the method used in this paper can inspire the development of collaborative dashboards for the exchange of best practices and data accessibility about case studies’ details.
Staff Stress and Interpersonal Conflict in Secondary Schools—Implications for School Leadership
Authors: Bruce Patrick , Bruce Carol , Hrymak Victor , Hickey Niamh , Mannix McNamara Patricia
Published: December 2022

The importance of school leadership and workplace stress is a recurring theme in education-based research. The literature reports that workplace stress in teaching is a difficult matter to resolve, with mixed outcomes from interventions. The aim of this initial scoping study was to report on the experiences of school leaders with interpersonal conflict (IPC), a known cause of this workplace stress. Accordingly, a sample of twelve school leaders working in Irish post primary schools were recruited to participate in this study using semi-structured interviews. All twelve participants reported experiencing workplace stress and linked other people as a source of this stress. Nine out of twelve had experienced IPC as a school leader. School leaders also noted a fear of reporting workplace stress. Half of the participants reported becoming ill from workplace stress and had taken time off from work. Participants also reported ‘balkanisation’ of like-minded cliques that tried to exert control over other groups. None of the participants expressed confidence in organisational strategies to resolve workplace stress or IPC. This study demonstrates that resolutions for IPC were scant. Further research is needed to conceptualise this phenomenon in the school environment and to support school leaders to effectively manage IPC as a cause of workplace stress.
Adapting Cities to Pluvial Flooding: The Case of Izmir (Türkiye)
Authors: Salata Stefano , Velibeyoğlu Koray , Baba Alper , Saygın Nicel , Couch Virginia Thompson , Uzelli Taygun
Published: December 2022

In the coming decades, climate change will be one of the most significant challenges for urban areas. The quantity, duration and intensity of events, such as flash rains and heat waves, will increase the vulnerability of urban regions while exposing citizens to potentially dangerous conditions. According to the current literature, mainstreaming resilience in urban planning means designing rules that strengthen urban systems’ adaptive and self-regulating functions by reducing their vulnerability. In this work, we aimed to build knowledge for the application of the sponge district concept to Izmir (Türkiye), one of Europe’s most vulnerable areas to pluvial flooding. To do this, we first analyzed the runoff in each urban sub-watershed, then employed a composite index to determine potential areas of intervention for nature-based solutions. Results show that 10% of Izmir’s urban areas are extremely vulnerable to cloudbursts, which means that 40% of the urban population is exposed to this phenomenon. Moreover, the runoff calculation in the sub-watershed demonstrated that the potential flood volume is underestimated, especially in the upslope areas. The results can be used as a template to suggest a stepwise approach to mainstream the resilience of densely-inhabited coastal urban catchments.
Last-Mile Delivery Methods in E-Commerce: Does Perceived Sustainability Matter for Consumer Acceptance and Usage?
Authors: Klein Patrick , Popp Bastian
Published: December 2022

Sustainability has gained significant importance in e-commerce, as it impacts both consumer behavior and company success. This paper contributes to the literature on sustainability in e-commerce by studying the perceived sustainability of three common last-mile delivery methods (home delivery, parcel lockers, and click and collect) and other drivers of consumers’ acceptance of each delivery method (perceived costs, convenience). Based on a review of the relevant literature, a conceptual model integrating key determinants of consumers’ attitudes to delivery methods and their intention to use them is derived and tested using a sample of 536 German online buyers. The results demonstrate that perceived ease of use and perceived usefulness increase the acceptance of the delivery methods, whereas perceived costs constrain it. The findings reveal significant influences of perceived sustainability on the acceptance of the delivery methods, which vary between its three components (environmental, economic, and social) and regarding the delivery method. Retailers and collaborating parcel delivery services are advised to continue making last-mile delivery methods more convenient. However, they are also advised to implement standards in terms of environmental and social sustainability and inform customers about their efforts regarding sustainable last-mile delivery to build a competitive advantage in omni-channel retailing.
Multi-Objective Optimization of Ultra-Low Energy Consumption Buildings in Severely Cold Regions Considering Life Cycle Performance
Authors: Zhang Zihan , Wang Wanjiang , Song Junkang , Wang Zhe , Wang Weiyi
Published: December 2022

Net-zero energy buildings (NZEB) have received widespread attention for their excellent energy and carbon reduction potential in various countries. However, relatively little research has been conducted on the life performance of its primary form: the ultra-low energy building (ULEB). This paper proposes an optimization method combining meta-models to investigate the carbon reduction potential of ultra-low energy buildings in severely cold regions of China. The XGBoost algorithm is used to construct a meta-model of building performance, and the grid search method is used to obtain a high-precision meta-model with an R2 of 0.967. Secondly, NSGA-II is used to find passive technical solutions based on the meta-model that minimize the global warming potential (GWP), global cost (GC), and operation energy consumption (OE). Finally, the variables affecting the life-cycle performance of buildings were ranked by sensitivity analysis. The results show that GWP, GC, and OE are reduced by 12.7%, 6.7%, and 7.4% compared with the original building through the optimization process proposed. Sensitivity analysis showed that for GWP, the top four sensitivities are window type (TW) > WWR of south wall (WWRS) > roof insulation thickness (IR) > WWR of north wall (WWRN). For GC, the top four sensitivities are: TW > WWRS > IR > WWR of west wall (WWRW); for OE, the top four sensitivities are: TW > IR > WWRS > WWRN. This paper’s optimization framework and research results can effectively guide the design of the ULEB in severely cold regions.
Optimal Design of an Inductive MHD Electric Generator
Authors: Carcangiu Sara , Fanni Alessandra , Montisci Augusto
Published: December 2022

In this paper, the problem of optimizing the design of an inductive Magneto-Hydro-Dynamic (MHD) electric generator is formalized as a multi-objective optimization problem where the conflicting objectives consist of maximizing the output power while minimizing the hydraulic losses and the mass of the apparatus. In the proposal, the working fluid is ionized with periodical pulsed discharges and the resulting neutral plasma is unbalanced by means of an intense DC electrical field. The gas is thus split into two charged streams, which induce an electromotive force into a magnetically coupled coil. The resulting generator layout does not require the use of superconducting coils and allows you to manage the issues related to the conductivity of the gas and the corrosion of the electrodes, which are typical limits of the MHD generators. A tailored multi-objective optimization algorithm, based on the Tabu Search meta-heuristics, has been implemented, which returns a set of Pareto optimal solutions from which it is possible to choose the optimal solution according to further applicative or performance constraints.
A Story of Strengths and Weaknesses in Tertiary Education: Evaluating ‘Mobility’ and ‘Opportunities’ in OECD Countries with Composite Indicators
Authors: Chelli Francesco , Ciommi Mariateresa , Mariani Francesca , Polinesi Gloria , Recchioni Maria Cristina , Lamonica Giuseppe Ricciardo , Salvati Luca
Published: December 2022

Assuming a high education level associated with a high probability of job occupancy and greater income, comparative exercises analyzing academic performances and socioeconomic dynamics at regional, country, or supra-national scales have intensified in recent years. As far as tertiary education is concerned, a great disparity in academic performance was characteristic of OECD countries. While adults 25–34 years old were attaining tertiary degrees more frequently than adults 55–64 years old, adults 30–34 years old with at least one tertiary-educated parent were more likely to attain a tertiary degree than individuals from families whose parents have attended secondary—or at least primary—education. ‘Mobility’ and ‘opportunities’ are two dimensions of sustainable education systems that deserve further investigation when assessing disparities in academic performances as a possible source of unsustainable development and social polarizations. ‘Mobility’ refers to the probability of achieving tertiary education for children coming from families with a different—i.e., lower (e.g., secondary or primary)—level of education. ‘Opportunities’ refers to the probability for a child to attain tertiary education, regardless of the education level achieved by the parents. The present study proposes a quantitative assessment of both dimensions through an original approach and novel statistical measures ranking OECD countries. A comparison of individual rankings of ‘mobility’ and ‘opportunities’ reveals counterintuitive results in some cases. To overcome this issue, our study introduces aggregate methods combining the two measures with the aim of developing a bivariate ranking that accounts for both dimensions simultaneously and delineates a more complete evolution of academic performance divides in advanced economies.
The Impacts of Climate Change, Carbon Dioxide Emissions (CO2) and Renewable Energy Consumption on Agricultural Economic Growth in South Africa: ARDL Approach
Authors: Tagwi Aluwani
Published: December 2022

One of the most affected economies by climate change is the agricultural sector. Climate change measured by temperature and precipitation has an impact on agricultural output, which in turn affects the economy of the sector. It is anticipated that using renewable energy will lower carbon emissions that are directly related to climate change. The main objective of this study was to evaluate the impact of carbon dioxide emissions (CO2), renewable energy usage, and climate change on South Africa’s agricultural sector from 1972 to 2021. The nexus was estimated using an Auto Regressive-Distributed Lag (ARDL) Bounds test econometric technique. In the short run, findings indicated that climate change reduces agricultural economic growth and carbon dioxide emissions increase as agricultural economic growth increases. The use of renewable energy was insignificant in the short and long run. Carbon dioxide emissions granger causes temperature and renewable energy unilateral. An ARDL analysis was performed to evaluate the short and long-term relationship between agricultural economic growth, climate change, carbon dioxide emissions and renew able energy usage. The study adds new knowledge on the effects of climate change and carbon emissions on the agricultural economy alongside the use of renewable energy which can be used to inform economic policy on climate change and the energy nexus in the agricultural sector. Study findings point to the prioritization of biomass commercialization, rural and commercial farming sector bioenergy regulations and socioeconomic imperatives research is crucial in order to promote inclusive participation in the production of renewable energy.
The Effects of Tourism Storytelling on Tourism Destination Brand Value, Lovemarks and Relationship Strength in South Korea
Authors: Jo Mina , Cha Jaebin , Kim Jisong
Published: December 2022

This study investigates the effects of tourism storytelling on the tourism destination brand value, lovemarks and relationship strength. The survey data was collected from 259 respondents who had experienced tourism storytelling in South Korea. Among the determinants of tourism storytelling, uniqueness, interestingness and educability have significant effect on tourism destination brand value. Sensibility, descriptiveness and interestingness have significant effect on the lovemarks formation. Both brand value and lovemarks are found to affect relationship strength. The study further found that the rational factor has more influence on the brand value whereas the emotional factor has more influence on the lovemarks. The study made a theoretical contribution by examining whether tourism storytelling affects lovemarks and could boost relationship strength. The managerial implication of this study is that DMO should make efforts to form a lovemarks for tourism destinations.
Growth Mechanism and Synchronization Effect of China’s New Energy Vehicle Enterprises: An Empirical Analysis Based on Moving Logistic and Kuramoto Model
Authors: Chen Wanming , Wang Shengyuan , Wu Xiaolan
Published: December 2022

The primary purpose of this paper is to discuss whether NEV enterprises can achieve synchronous effects with the whole Chinese automobile industry in terms of growth mode. In this paper, we study the development of new energy vehicles from the perspective of ecosystem. Growth mechanisms and synchronization effects also exist in new energy enterprise populations, just like biological populations in natural ecosystems. Here, we propose a moving logistic model to analyze the growth mechanism of new energy vehicle enterprises and obtain serial data of intrinsic growth rate, internal inhibition coefficient, and theoretical maximum sales volume. The intrinsic growth rate and theoretical maximum sales volume show an initial trend of decline followed by recovery. The evaluation results of coupling degree and synchronization of the new energy vehicle population were obtained through the measurement of the Kuramoto model and its derivative model. The coupling degree of the new energy vehicle population is not high, and the synchronization effect fluctuates and oscillates. The change trend of synchronization effect is similar to that of intrinsic growth rate and theoretical maximum sales volume. This phenomenon shows that the new energy vehicle population has been significantly affected by changes in the external market environment. The analysis method of enterprise growth mechanism based on the moving logistic model and the measurement method of coupling degree and synchronization effect based on the Kuramoto model and its derivative models proposed in this paper effectively achieve the research objectives of this paper.
Numerical Optimization of the Dendritic Arm of a Radial Gate Based on Stability and Light Weight
Authors: Han Yifeng , Zhang Xuecai , Cai Kun , Chai Xu , Li Jinyu , Wang Zhengzhong
Published: December 2022

A new dendritic arm structure was proposed to achieve the stability and lightness of a large-scale radial gate arm. The arm structure was arranged in accordance with the current gate design specifications for steel. The optimization aims were to achieve the highest possible overall structural stability and the lowest possible weight. As the failure criterion for the entire structure, the simultaneous instability of the trunks and branches of the dendritic arm was considered. The constraints of strength, stiffness, and stability were identified, and an optimization model of a dendritic arm with two branches was established. A geometric and material nonlinear buckling analysis and optimization solution was implemented for the dendritic arm with a different radius, type, and unit rigidity ratios. Compared to a conventional radial gate structure with two arms, the Y-type dendritic arm structure was lighter and more stable. The optimal dendritic arm had the following parameter settings: the angle ratio between two branches and two arms was [1.92, 2.04], the unit rigidity ratio between the trunk and branch was [5.33, 6.02], the length ratio between the branch and trunk was [0.96, 1.09], the height ratio of the trunk and branch was 1.805, and the flange thickness ratio of the trunk and branch was 1.405. In this case, the overall buckling load ratio between the dendritic arm and two arms was [2.50, 4.34], and the material saving rate was [37, 53] (%). Within the aforementioned parameters, the Y-type dendritic arm performed better overall. Moreover, the radius of the radial gate had minimal influence on the shape and mechanical properties of the Y-type dendritic arm structure. This paper proposes a dendritic arm that can be utilized in most radial gate projects.
Thoughts on Teaching Reform of Resources Majors in Colleges and Universities under the Background of Carbon Peak and Carbon Neutralization—Taking Mineral Resources Exploration Course as an Example
Authors: Yang Wu , Zhang Min , Long Hansheng , Yan Jun
Published: December 2022

On 22 September 2020, General Secretary Xi made a solemn commitment to the international community at the general debate of the seventy-fifth session of the United Nations General Assembly to achieve carbon peak and carbon neutrality, and he announced specific goals at the Climate Ambition Summit. China is striving to achieve carbon peak by 2030 and carbon neutrality by 2060. At the same time, during the Fifth Plenary Session of the Nineteenth Central Committee of the CPC and the Central Economic Work Conference, specific arrangements for related goals have been made. During these two sessions in 2021, carbon peak and carbon neutralization were also mentioned in the government work report for the first time, which became the “hot words” discussed by the delegates and one of the key subjects of the 14th Five-Year Plan. Against the current backdrop of “double carbon”, reforms must urgently be made in the education and teaching of the resource major in colleges and universities to achieve the goal of carbon neutralization with high quality, promote discipline construction, and explore and implement the development model of “new engineering”. With regard to the resource exploration industry, mining, dressing and smelting will produce a large amount of carbon dioxide, solid waste and tailings, which can be neutralized by injecting solid waste or tailings reservoirs through green mine construction and carbon capture. On the one hand, the comprehensive utilization efficiency of mines can be improved in this way. On the other hand, a large amount of carbon can be stored underground, which is an important method of achieving the goal of carbon neutrality. In this study, we took the core course “Mineral Resources Exploration” from the resource exploration major as an example to think about and study the ways the education and teaching of resource majors could be reformed, such as exploration engineering under the guidance of the carbon neutrality target, which has certain guiding significance regarding the improvement of the training quality of resource major students.
The Pricing Strategy of Digital Content Resources Based on a Stackelberg Game
Authors: Zhao Yan , Ni Yuan
Published: December 2022

This paper uses a Stackelberg game model to analyze the profit function composition of digital content resource producers and publishers and uses a numerical simulation method to explore the equilibrium relationships between the various factors that affect the pricing strategy. The findings are as follows: ① platform-based publishers of digital content resources adopt a cost-plus pricing method for a single broadcast price; ② the revenue-sharing ratio of the producers decreases as the single broadcast cost increases; ③ the viewing effect is affected by many factors, such as copyright fees, investment difficulty, sales coefficient, and unit cost. Overall, the main contribution of this manuscript is to make an innovative demonstration and analysis of the factors affecting the pricing strategy of digital content resources, and the results of this paper can promote the transaction of digital content resources and ensure the sustainable development of the digital content industry.