bluuebunny/crossref_metadata_2025 huggingface dataset:
Advances in Visual Simultaneous Localisation and Mapping Techniques for Autonomous Vehicles: A Review
Authors: Bala Jibril Abdullahi , Adeshina Steve Adetunji , Aibinu Abiodun Musa
Published: November 2022

The recent advancements in Information and Communication Technology (ICT) as well as increasing demand for vehicular safety has led to significant progressions in Autonomous Vehicle (AV) technology. Perception and Localisation are major operations that determine the success of AV development and usage. Therefore, significant research has been carried out to provide AVs with the capabilities to not only sense and understand their surroundings efficiently, but also provide detailed information of the environment in the form of 3D maps. Visual Simultaneous Localisation and Mapping (V-SLAM) has been utilised to enable a vehicle understand its surroundings, map the environment, and identify its position within the area. This paper presents a detailed review of V-SLAM techniques implemented for AV perception and localisation. An overview of SLAM techniques is presented. In addition, an in-depth review is conducted to highlight various V-SLAM schemes, their strengths, and limitations. Challenges associated with V-SLAM deployment and future research directions are also provided in this paper.
Experimental Study of Ghost Imaging in Underwater Environment
Authors: Wu Heng , Chen Ziyan , He Chunhua , Cheng Lianglun , Luo Shaojuan
Published: November 2022

Underwater imaging technique is a crucial tool for humans to develop, utilize, and protect the ocean. We comprehensively compare the imaging performance of twenty-four ghost imaging (GI) methods in the underwater environment. The GI methods are divided into two types according to the illumination patterns, the random and orthogonal patterns. Three-group simulations were designed to show the imaging performance of the twenty-four GI methods. Moreover, an experimental system was built, and three-group experiments were implemented. The numerical and experimental results demonstrate that the orthogonal pattern-based compressed sensing GI methods have strong antinoise capability and can restore clear images for underwater objects with a low measurement number. The investigation results are helpful for the practical applications of the underwater GI.
Perception Accuracy of a Multi-Channel Tactile Feedback System for Assistive Technology
Authors: Wersényi György
Published: November 2022

Assistive technology uses multi-modal feedback devices, focusing on the visual, auditory, and haptic modalities. Tactile devices provide additional information via touch sense. Perception accuracy of vibrations depends on the spectral and temporal attributes of the signal, as well as on the body parts they are attached to. The widespread use of AR/VR devices, wearables, and gaming interfaces requires information about the usability of feedback devices. This paper presents results of an experiment using an 8-channel tactile feedback system with vibrators placed on the wrists, arms, ankles, and forehead. Different vibration patterns were designed and presented using sinusoidal frequency bursts on 2, 4, and 8 channels. In total, 27 subjects reported their sensation formally and informally on questionnaires. Results indicate that 2 and 4 channels could be used simultaneously with high accuracy, and the transducers’ optimal placement (best sensitivity) is on the wrists, followed by the ankles. Arm and head positions were inferior and generally inadequate for signal presentation. For optimal performance, signal length should exceed 500 ms. Furthermore, the amplitude level and temporal pattern of the presented signals have to be used for carrying information rather than the frequency of the vibration.
IPD-Net: Infrared Pedestrian Detection Network via Adaptive Feature Extraction and Coordinate Information Fusion
Authors: Zhou Lun , Gao Song , Wang Simin , Zhang Hengsheng , Liu Ruochen , Liu Jiaming
Published: November 2022

Infrared pedestrian detection has important theoretical research value and a wide range of application scenarios. Because of its special imaging method, infrared images can be used for pedestrian detection at night and in severe weather conditions. However, the lack of pedestrian feature information in infrared images and the small scale of pedestrian objects makes it difficult for detection networks to extract feature information and accurately detect small-scale pedestrians. To address these issues, this paper proposes an infrared pedestrian detection network based on YOLOv5, named IPD-Net. Firstly, an adaptive feature extraction module (AFEM) is designed in the backbone network section, in which a residual structure with stepwise selective kernel was included to enable the model to better extract feature information under different sizes of the receptive field. Secondly, a coordinate attention feature pyramid network (CA-FPN) is designed to enhance the deep feature map with location information through the coordinate attention module, so that the network gains better capability of object localization. Finally, shallow information is introduced into the feature fusion network to improve the detection accuracy of weak and small objects. Experimental results on the large infrared image dataset ZUT show that the mean Average Precision (mAP50) of our model is improved by 3.6% compared to that of YOLOv5s. In addition, IPD-Net shows various degrees of accuracy improvement compared to other excellent methods.
Blockchain-Based Secure Authentication with Improved Performance for Fog Computing
Authors: Umoren Otuekong , Singh Raman , Awan Shahid , Pervez Zeeshan , Dahal Keshav
Published: November 2022

Advancement in the Internet of Things (IoT) and cloud computing has escalated the number of connected edge devices in a smart city environment. Having billions more devices has contributed to security concerns, and an attack-proof authentication mechanism is the need of the hour to sustain the IoT environment. Securing all devices could be a huge task and require lots of computational power, and can be a bottleneck for devices with fewer computational resources. To improve the authentication mechanism, many researchers have proposed decentralized applications such as blockchain technology for securing fog and IoT environments. Ethereum is considered a popular blockchain platform and is used by researchers to implement the authentication mechanism due to its programable smart contract. In this research, we proposed a secure authentication mechanism with improved performance. Neo blockchain is a platform that has properties that can provide improved security and faster execution. The research utilizes the intrinsic properties of Neo blockchain to develop a secure authentication mechanism. The proposed authentication mechanism is compared with the existing algorithms and shows that the proposed mechanism is 20 to 90 per cent faster in execution time and has over 30 to 70 per cent decrease in registration and authentication when compared to existing methods.
Isolation Improvement in Reflectarray Antenna-Based FMCW Radar Systems
Authors: Yamani Hesham , Yoon Jihwan
Published: November 2022

This paper presents an optimization of reflectarray-based RF sensors for detecting UAV and human presence. Our previous human detection radar system adapted a center-fed reflectarray antenna to a commercially available radar system, successfully increasing the gains of the transmit (TX) and receive (RX) antennas by 21.18 dB and the range for detecting human targets 3.4 times. However, because the TX and RX antennas were placed in the focal point of the reflectarray, the TX signal reflected by the reflectarray was directly propagated into the RX antenna, causing desensitization or damage to the receiving circuit if high powers were used. To reduce this direct reflection, we propose a novel radar antenna configuration in which the TX and RX antennas are placed back-to-back with each other. In this configuration, the RX antenna does not directly face the reflectarray, thus direct path between the TX to RX through the reflectarray is removed. The results demonstrate that this approach achieves the optimum isolation level of 51.3 dB. With the reflectarray, the TX antenna gain increases to 30.6 dBi, but the RX antenna gain remains at 16 dBi since the RX antenna does not utilize the reflectarray. The TX and RX gain difference (14.6 dB) is a trade-off for good isolation and may be reduced by utilizing a high-gain receiver amplifier.
Real-Time Energy Data Acquisition, Anomaly Detection, and Monitoring System: Implementation of a Secured, Robust, and Integrated Global IIoT Infrastructure with Edge and Cloud AI
Authors: Bin Mofidul Raihan , Alam Md. Morshed , Rahman Md. Habibur , Jang Yeong Min
Published: November 2022

The industrial internet of things (IIoT), a leading technology to digitize industrial sectors and applications, requires the integration of edge and cloud computing, cyber security, and artificial intelligence to enhance its efficiency, reliability, and sustainability. However, the collection of heterogeneous data from individual sensors as well as monitoring and managing large databases with sufficient security has become a concerning issue for the IIoT framework. The development of a smart and integrated IIoT infrastructure can be a possible solution that can efficiently handle the aforementioned issues. This paper proposes an AI-integrated, secured IIoT infrastructure incorporating heterogeneous data collection and storing capability, global inter-communication, and a real-time anomaly detection model. To this end, smart data acquisition devices are designed and developed through which energy data are transferred to the edge IIoT servers. Hash encoding credentials and transport layer security protocol are applied to the servers. Furthermore, these servers can exchange data through a secured message queuing telemetry transport protocol. Edge and cloud databases are exploited to handle big data. For detecting the anomalies of individual electrical appliances in real-time, an algorithm based on a group of isolation forest models is developed and implemented on edge and cloud servers as well. In addition, remote-accessible online dashboards are implemented, enabling users to monitor the system. Overall, this study covers hardware design; the development of open-source IIoT servers and databases; the implementation of an interconnected global networking system; the deployment of edge and cloud artificial intelligence; and the development of real-time monitoring dashboards. Necessary performance results are measured, and they demonstrate elaborately investigating the feasibility of the proposed IIoT framework at the end.
A Deep Learning Approach to Detect Anomalies in an Electric Power Steering System
Authors: Alabe Lawal Wale , Kea Kimleang , Han Youngsun , Min Young Jae , Kim Taekyung
Published: November 2022

As anomaly detection for electrical power steering (EPS) systems has been centralized using model- and knowledge-based approaches, EPS system have become complex and more sophisticated, thereby requiring enhanced reliability and safety. Since most current detection methods rely on prior knowledge, it is difficult to identify new or previously unknown anomalies. In this paper, we propose a deep learning approach that consists of a two-stage process using an autoencoder and long short-term memory (LSTM) to detect anomalies in EPS sensor data. First, we train our model on EPS data by employing an autoencoder to extract features and compress them into a latent representation. The compressed features are fed into the LSTM network to capture any correlated dependencies between features, which are then reconstructed as output. An anomaly score is used to detect anomalies based on the reconstruction loss of the output. The effectiveness of our proposed approach is demonstrated by collecting sample data from an experiment using an EPS test jig. The comparison results indicate that our proposed model performs better in detecting anomalies, with an accuracy of 0.99 and a higher area under the receiver operating characteristic curve than other methods providing a valuable tool for anomaly detection in EPS.
Combination with Continual Learning Update Scheme for Power System Transient Stability Assessment
Authors: Hu Bowen , Hao Zhenghang , Chen Zhuo , Zhang Jing
Published: November 2022

In recent years, the power system transient stability assessment (TSA) based on a data-driven method has been widely studied. However, the topology and modes of operation of power systems may change frequently due to the complex time-varying characteristics of power systems. which makes it difficult for prediction models trained on stationary distributed data to meet the requirements of online applications. When a new working situation scenario causes the prediction model accuracy not to meet the requirements, the model needs to be updated in real-time. With limited storage space, model capacity, and infinite new scenarios to be updated for learning, the model updates must be sustainable and scalable. Therefore, to address this problem, this paper introduces the continual learning Sliced Cramér Preservation (SCP) algorithm to perform update operations on the model. A deep residual shrinkage network (DRSN) is selected as a classifier to construct the TSA model of SCP-DRSN at the same time. With the SCP, the model can be extended and updated just by using the new scenarios data. The updated prediction model not only complements the prediction capability for new scenarios but also retains the prediction ability under old scenarios, which can avoid frequent updates of the model. The test results on a modified New England 10-machine 39-bus system and an IEEE 118-bus system show that the proposed method in this paper can effectively update and extend the prediction model under the condition of using only new scenarios data. The coverage of the updated model for new scenarios is improving.
An Optimized Probabilistic Roadmap Algorithm for Path Planning of Mobile Robots in Complex Environments with Narrow Channels
Authors: Qiao Lijun , Luo Xiao , Luo Qingsheng
Published: November 2022

In this paper, we propose a new path planning algorithm based on the probabilistic roadmaps method (PRM), in order to effectively solve the autonomous path planning of mobile robots in complex environments with multiple narrow channels. The improved PRM algorithm mainly improves the density and distribution of sampling points in the narrow channel, through a combination of the learning process of the PRM algorithm and the APF algorithm. We also shortened the required time and path length by optimizing the query process. The first key technology to improve the PRM algorithm involves optimizing the number and distribution of free points and collision-free lines in the free workspace. To ensure full visibility of the narrow channel, we extend the obstacles through the diagonal distance of the mobile robot while ignoring the safety distance. Considering the safety distance during movement, we re-classify the all sampling points obtained by the quasi-random sampling principle into three categories: free points, obstacle points, and adjacent points. Next, we transform obstacle points into the free points of the narrow channel by combining the APF algorithm and the characteristics of the narrow channel, increasing the density of sampling points in the narrow space. Then, we include potential energy judgment into the construction process of collision-free lines shortening the required time and reduce collisions with obstacles. Optimizing the query process of the PRM algorithm is the second key technology. To reduce the required time in the query process, we adapt the bidirectional A* algorithm to query these local paths and obtain an effective path to the target point. We also combine the path pruning technology with the potential energy function to obtain a short path without collisions. Finally, the experimental results demonstrate that the new PRM path planning technology can improve the density of free points in narrow spaces and achieve an optimized, collision-free path in complex environments with multiple narrow channels.
Estimating the Roll Angle for a Two-Wheeled Single-Track Vehicle Using a Kalman Filter
Authors: Chuang Tzu-Yi , Zhang Xiao-Dong , Chen Chih-Keng
Published: November 2022

This study determines the roll angle for a two-wheeled single-track vehicle during cornering. The kinematics are analyzed by coordinate transformation to determine the relationship between the measured acceleration and the acceleration in the global coordinate. For a measurement error or noise, the state space expression is derived. Using the theory for a Kalman filter, an estimator with two-step measurement updates estimates the yaw rate and roll angle using the acceleration and angular velocity signals from an IMU sensor. A bicycle with relevant electronic products is used as the experimental object for a steady turn, a double lane change and a sine wave turn in real time to determine the effectiveness of the estimator. The results show that the proposed estimator features perfect reliability and accuracy and properly estimates the roll angle for a two-wheeled vehicle using IMU and velocity.
LSTM Attention Neural-Network-Based Signal Detection for Hybrid Modulated Faster-Than-Nyquist Optical Wireless Communications
Authors: Cao Minghua , Yao Ruifang , Xia Jieping , Jia Kejun , Wang Huiqin
Published: November 2022

In order to improve the accuracy of signal recovery after transmitting over atmospheric turbulence channel, a deep-learning-based signal detection method is proposed for a faster-than-Nyquist (FTN) hybrid modulated optical wireless communication (OWC) system. It takes advantage of the long short-term memory (LSTM) network in the recurrent neural network (RNN) to alleviate the interdependence problem of adjacent symbols. Moreover, an LSTM attention decoder is constructed by employing the attention mechanism, which can alleviate the shortcomings in conventional LSTM. The simulation results show that the bit error rate (BER) performance of the proposed LSTM attention neural network is 1 dB better than that of the back propagation (BP) neural network and outperforms by 2.5 dB when compared with the maximum likelihood sequence estimation (MLSE) detection method.
Mirror Clock: A Strategy for Identifying Atomic Clock Frequency Jumps
Authors: Liu Mochi , Chen Yu , Xu Qian , Wang Yuzhuo , Gao Yuan , Zhang Aimin
Published: November 2022

Atomic clock frequency jumps directly influence the accuracy and reliability of timekeeping systems. The necessary corrections are typically implemented by postprocessing mutual comparison data between multiple atomic clocks based on the overly strict assumption that these atomic clocks are independent of each other. This paper describes the concept of a mirror clock, which enables atomic clock frequency jumps to be identified in real time without any assumptions. By comparing whether the real measured data and a corresponding mirror clock prediction fall within a confidence interval determined by the uncertainty of past physical clock data, atomic clock frequency jumps can be effectively identified and corrected. The results of several experiments using three hydrogen masers verify that the precision and recall of simultaneous jump identification reach 96.41% and 73.49%, respectively.
Digital Mapping of Soil Organic Carbon Based on Machine Learning and Regression Kriging
Authors: Zhu Changda , Wei Yuchen , Zhu Fubin , Lu Wenhao , Fang Zihan , Li Zhaofu , Pan Jianjun
Published: November 2022

In the last two decades, machine learning (ML) methods have been widely used in digital soil mapping (DSM), but the regression kriging (RK) model which combines the advantages of the ML and kriging methods has rarely been used in DSM. In addition, due to the limitation of a single-model structure, many ML methods have poor prediction accuracy in undulating terrain areas. In this study, we collected the SOC content of 115 soil samples in a hilly farming area with continuous undulating terrain. According to the theory of soil-forming factors in pedogenesis, we selected 10 topographic indices, 7 vegetation indices, and 2 soil indices as environmental covariates, and according to the law of geographical similarity, we used ML and RK methods to mine the relationship between SOC and environmental covariates to predict the SOC content. Four ensemble models—random forest (RF), Cubist, stochastic gradient boosting (SGB), and Bayesian regularized neural networks (BRNNs)—were used to fit the trend of SOC content, and the simple kriging (SK) method was used to interpolate the residuals of the ensemble models, and then the SOC and residual were superimposed to obtain the RK prediction result. Moreover, the 115 samples were divided into calibration and validation sets at a ratio of 80%, and the tenfold cross-validation method was used to fit the optimal parameters of the model. From the results of four ensemble models: RF performed best in the calibration set (R2c = 0.834) but poorly in the validation set (R2v = 0.362); Cubist had good accuracy and stability in both the calibration and validation sets (R2c = 0.693 and R2v = 0.445); SGB performed poorly (R2c = 0.430 and R2v = 0.336); and BRNN had the lowest accuracy (R2c = 0.323 and R2v = 0.282). The results showed that the R2 of the four RK models in the validation set were 0.718, 0.674, 0.724, and 0.625, respectively. Compared with the ensemble models without superimposed residuals, the prediction accuracy was improved by 0.356, 0.229, 0.388, and 0.343, respectively. In conclusion, Cubist has high prediction accuracy and generalization ability in areas with complex topography, and the RK model can make full use of trends and spatial structural factors that are not easy to mine by ML models, which can effectively improve the prediction accuracy. This provides a reference for soil survey and digital mapping in complex terrain areas.
Preparation and Adsorption Properties of Magnetic Molecularly Imprinted Polymers for Selective Recognition of 17β-Estradiol
Authors: Bi Lanbo , Shen Jimin , Yao Zhuoran , Kang Jing , Zhao Shengxin , Yan Pengwei , Wang Binyuan , Chen Zhonglin
Published: November 2022

In this paper, magnetic molecularly imprinted polymers (MMIPs) were fabricated on the surface of Fe3O4 by surface molecular imprinting technology, which can selectively adsorb 17β-estradiol (E2). The optimized experiments demonstrated that MMIPs possessed the best adsorption capacity when methanol was used as the solvent and MAA was used as the crosslinking agent, with a molar ratio of E2: MMA: EGDMA as 1:4:50. SEM, FTIR, and XRD were employed to investigate the morphologies of MMIPs and the results demonstrated that the MMIPs that can selectively adsorb E2 were successfully prepared on Fe3O4 particles. The adsorption experiments showed that 92.1% of E2 was adsorbed by the MMIPs, which is higher than the magnetic non-molecularly imprinted polymers (MNIPs). The Freundlich isotherm model was more suitable to describe the adsorption process of E2 by MMIPs. Meanwhile, MMIPs had a better recognition ability for E2 and its structural analogs such as estrone and estriol. The MMIPs still had good adsorption performance after methanol regeneration five times. The prepared MMIPs had the advantages of efficient adsorption ability and high reusability, so they can be applied for selective recognition and removal of E2.
Do Families Exposed to Adverse Childhood Experiences Report Family Centered Care?
Authors: Lombardi Brianna M. , Zerden Lisa d. , Lee Hyunji , Moehling Geffel Krissy
Published: November 2022

Background: Youth from marginalized groups may be less likely to receive quality health care services. Adverse Childhood Experiences (ACEs) are known to impact long-term health, but it is unclear if there is a relationship between ACEs and receipt of Family Centered Care (FCC)—one indicator of high-quality health care. To assess this relationship, this study used a nationally representative sample of youth from the National Survey of Children’s Health 2016–2017 combined data set. Caregivers of children who had at least one health care visit in the last 12 months (sub-sample n = 63,662) were asked about five indicators of FCC including if they felt the provider: (1) spent enough time, (2) listened carefully, (3) helped family feel like a partner, (4) provided information requested, and (5) showed sensitivity to culture. Methods: Logistic regression analyses examined the association between ACE score and each FCC quality indicator, as well between ACEs score and the overall FCC dichotomous score. Results: ACE exposure did not significantly predict access to a health care visit in the past 12 months. However, children with higher rates of ACEs were significantly less likely to receive FCC. Other factors that significantly predicted lower FCC included child race and ethnicity, insurance type, language in the home, and access to a regular health provider. Conclusions: Providers and health systems must identify, implement, and advocate for effective trauma-informed and care coordination interventions that ensure quality health care services for vulnerable children and families.
I Consume, Therefore I Am? Hyperconsumption Behavior: Scale Development and Validation
Authors: Dimitrova Teofana , Ilieva Iliana , Stanev Velin
Published: November 2022

Social and material models consider hyperconsumption to be an unsustainable practice of consumer behavior that is responsible for the considerable damage inflicted upon the planet. The primary objective of this research study was to develop and validate a novel measurement scale to assess hyperconsumption behavior (HB) from a consumer’s point of view. Based on the literature on measurement theory, an HB scale was developed and validated over three studies. The first study consisted of item development, while the second study focused on exploring and confirming the factor structure of the scale. The investigations revealed that hyperconsumption behavior was a first-order construct with four underlying dimensions: shopping control (food); perceived repair benefits; possession of a large amount of goods; and experiential consumption. The third study assessed the nomological validity of the proposed scale by testing its association with two relevant scales of materialism and sustainable purchase behavior.
Environmental Impact of Sulaimani Steel Plant (Kurdistan Region, Iraq) on Soil Geochemistry
Authors: Hamarashid Roshna A. , Fiket Željka , Mohialdeen Ibrahim M. J.
Published: November 2022

Steel is an indispensable material that is used in a wide range of products and that contributes significantly to economic development. However, steel production can affect soil quality and is associated with the pollution of local areas. Therefore, the objective of this study was to investigate the mineral and geochemical composition of soils in the vicinity of the Sulaimani Steel Plant (SSP) in Bazian Region, Kurdistan, Iraq. A total of 35 soil samples were collected in the vicinity of SSP. The samples were analyzed for their mineral and geochemical composition, including 10 major, and 40 trace and rare earth elements. In addition, the soils were analyzed for their particle size distribution, pH, and organic matter content. The distribution of elements in the soils was found to be influenced by the texture, organic matter content (0.34–9.28%), and prevailing wind direction. The assessment of soil contamination near the steel plant confirmed high to extremely high enrichment with Cr (EF up to 20.7), Ni (EF up to 14.2), Pb (EF up to 80.4), and Th (EF up to 50.4), indicating that it is a significant source of heavy metals and poses a high risk to soil health.
Overlying Strata Movement and Mine-Pressure Weakening Law of High-Efficiency Longwall Paste Backfilling of Thick Coal
Authors: Ma Qi , Zhang Yidong , Zheng Yu , Li Zexin , Song Guangyuan , Hu Lei
Published: November 2022

This work focused on the serious coal compression under buildings, railways, and water bodies in central and eastern China; the wide range of rock formation damage during the collapse mining process; the high pressure of mines; and difficulties in controlling surface subsidence after mining. The E1302 working face of Shanxi Gaohe Energy was taken as the engineering background in the work. The mechanical properties of gangue paste-filling materials were studied through laboratory tests, and the critical conditions for bending and fracture of the coal seam roof were analyzed. Discrete-element numerical simulation software was used to study the fracturing process of the roof, and the reasonable filling rate to ensure roof stability was determined to be 95%. Meanwhile, overlying stratum movement and mine-pressure weakening law were studied through numerical simulations and field measurement. The results showed that fracture development during the mining process of thick-coal paste filling was divided into the advanced development, re-compaction, and steady-state maintenance of fractures. Fractures advanced in a “sail shape” and developed only in the main-roof rock strata after recompaction. The maximum subsidence angle of the working face was 87.13° after mining, with a subsidence factor of 0.034 and a maximum horizontal movement coefficient of 0.71. The advanced stress value was weakened by 40%, and the influenced area was reduced by 13%. Overlying stratum movement was controlled, and mine pressures were significantly weakened. The work can provide a scientific basis for green backfill mining, roadway support design, and backfill mining equipment selection.
Research on the Construction and Measurement of the HQDMI Evaluation Index System—A Case Study of China
Authors: Su Yongqiang , Shi Jinfa , Zhang Manman
Published: November 2022

Scientific and accurate evaluations of the high-quality development level of the manufacturing industry have important theoretical significance and reference value for the government and for decision-making departments for the formulation of corresponding incentive measures. Firstly, based on rough set theory, this paper proposes an attribute reduction method, which can help to delete redundant indexes and reduce the calculation workload. Secondly, a more scientific combination weighting method is proposed, and the calculation method of the total index in an evaluation index system is given. Finally, the HQDMI evaluation index system is constructed based on the connotations of the high-quality development of the manufacturing industry. Taking China as an example, the total index and sub-index of high-quality development of 30 provinces in China are calculated, the high-quality development level of the manufacturing industry in 30 provinces is clustered based on SPSS24.0, and the visualization of the clustering results is achieved by ArcGIS software. The results show that the high-quality development level of China’s manufacturing industry has regional distribution characteristics. Regions with high development levels are mainly distributed in eastern coastal areas, followed by the central development level, and those of the northeast and west are low. This study provides a theoretical application mode for the evaluation of the high-quality development level of the manufacturing industry, and it has theoretical guidance significance for promoting the high-quality development of the manufacturing industry.
Do Firms That Are Disadvantaged by Unilateral Climate Policy Receive Compensation? Evidence from China’s Energy-Saving Quota Policy
Authors: Lin Weiming , Chen Jianling , Gan Jianbang , Dai Yongwu
Published: November 2022

Inequities caused by a unilateral climate policy may threaten the sustainability of CO2 emission reduction efforts by countries and firms, thus endangering sustainable development for humans and the eco-environment. However, few studies have conducted ex-post evaluations on whether environmentally regulated firms receive external compensation such as subsidies, tax reductions, and loan support. Thus, this study investigates whether firms experiencing inequitable conditions under China’s Energy-Saving Quota Policy (ESQP) are financially compensated. It develops a balanced panel of data from 6189 ESQP-regulated and 6189 unregulated firms from 2010 to 2013, and combines a probit model with the difference-in-differences method to conduct empirical analysis. The results show that ESQP-regulated firms receive more subsidy income and lower tax rates than unregulated firms. Of the ESQP-regulated firms, companies with higher energy-saving burdens receive larger subsidies and lower financial expense ratios than those with lower burdens. Additionally, firms that complete their energy-saving quotas are compensated with larger subsidies and/or lower financial expense ratios and tax rates than those that fail to complete them. Finally, state-owned firms receive more subsidies than private ones. Unlike the emission trading schemes implemented worldwide that formulate an exemption mechanism (i.e., free or over-allocated allowances), the ESQP does not exempt regulated firms from their energy-saving responsibilities. Rather, regulated firms receive a greater amount of external compensation in exchange for their reductions in energy consumption.
Impact of Urbanization on Total Factor Carbon Productivity in Central Asia
Authors: Li Juan , Liu Aifeng
Published: November 2022

Research on the impact of urbanization on total factor carbon productivity is of great significance to promote carbon emission reductions and achieve sustainable development. Based on the panel data of Central Asian countries from 1991 to 2019, the SBM–Malmquist index model and entropy method were used to measure total factor carbon productivity and urbanization, respectively. The nonlinear regression, Tobit, and quantile regression models were used to empirically analyze the impact of urbanization on total factor carbon productivity in Central Asia. The results show that urbanization drives total factor carbon productivity in Central Asia. Urbanization has a nonlinear inverted U-shaped impact on total factor carbon productivity in Central Asia, and it is in the first half of the inverted U-shaped correlation. Urbanization has driving and evolutionary effects on total factor carbon productivity in Central Asia. Urbanization not only directly drives total factor carbon productivity, but also indirectly drives total factor carbon productivity through technical efficiency improvement and technological progress.
Pilot Study on User Service Guarantee Elements for Electric Minivans
Authors: Zhou Xiaomin , Yoo Chaemoon , Sun Xiyan , Lai Yingjie , Pan Younghwan
Published: November 2022

With the introduction of “carbon neutrality”, promoting carbon peaking and carbon neutrality has become contingent upon the vigorous development of pure electric vehicles. The development of electric minivans is receiving substantial backing from the government as China’s logistics vehicle market undergoes a transformation. The drivers of electric minivans in China’s Shanxi Province are the objects of this research, whose purpose was to establish a service model based on the four elements of service design and grounded theory to clarify the key factors of user guarantee for electric minivans. We conducted a pilot study using questionnaires, user patterns, user interviews, and a user journey map to determine the user needs for electric minivan user service guarantees. NVivo was used to analyze and code the user interview data qualitatively. The research findings can serve as a resource for entrepreneurs, service providers, managers, and designers of electric minivans.
Effects of Harvesting Intensity on the Growth of Hydrilla verticillata and Water Quality
Authors: Zhu Shunmei , Wu Xiaodong , Zhou Mengdie , Ge Xuguang , Yang Xingqiang , Wang Nuoxi , Lin Xiaowen , Li Zhenguo
Published: November 2022

The effects of harvesting intensity on the growth of Hydrilla verticillata (L. fil.) Royle as well as water quality were studied in controlled experiments to provide a reference for managing submerged vegetation and purifying the water. The results showed that harvesting had a significant effect on the recovery of shoot growth and H. verticillata height. The harvested group recovered completely or mostly after two harvests, but the recovery time was significantly longer than the control group. The final biomasses of the harvested groups (15%, 30%, 45%, 60%, and 75% harvested) decreased to 66.61%, 49.13%, 43.95%, 43.77%, and 29.94% of the control group, respectively. The greater the harvesting intensity, the fewer the winter buds. Harvesting reduced the number of H. verticillata branches. Repeated harvesting at medium and low intensities during the rapid growth of H. verticillata effectively improved the water quality and inhibited the propagation and growth of phytoplankton. These results show that harvesting controlled the growth of H. verticillata, and that medium and low harvesting intensities were best when considering water quality.
Monitoring Coastal Changes and Assessing Protection Structures at the Damietta Promontory, Nile Delta, Egypt, to Secure Sustainability in the Context of Climate Changes
Authors: El-Asmar Hesham M. , Taha Maysa M. N.
Published: November 2022

The Damietta Promontory is a distinct coastal region in the Nile Delta Egypt, which comprises several communities with strategic economic projects. The promontory has experienced numerous inundation crises due to anthropogenic intervention and/or sea level rise (SLR). The recorded rate of erosion detected is from −18 to −53 m/yr., and −28 to −210 m/yr. along the promontory’s western and eastern coasts, respectively, with a total loss of about 3 km during the past century. It is critical to ensure sustainability of this coastal region in case of future climate changes and expected SLR; accordingly, the state has implemented a long-term plan of coastal protection. The current study updates the coastal changes and assesses the efficiency of the protection structures. For such study, Ikonos satellite images of 1 m high-resolution were acquired on 30 July 2014 and 10 August 2022, respectively. These were compared to multitemporal Landsat images dated 30 June 2015, 29 September 1987, 15 October 1984, and the Landsat 4 MSS images dated 20 October 1972. The results confirm the presence of accretion along the western jetty of the Damietta Harbor with an average of +10.91 m/yr., while erosion of −4.7 m/yr. was detected at the east of the eastern harbor jetty. At the detached breakwaters along Ras El-Bar, an accretion of +4 m/yr. was detected, and then erosion was measured westward to the tip of the detached breakwaters with an average of −1.77 m/yr. At the eastern coast of the promontory, eastward erosion was recorded with rates of −44.16, −34.33, and −20.33 m/yr., respectively, then the erosion stopped after the construction of the seawall. The current study confirms the efficiency of the detached breakwaters and seawalls as coastal protection structures. However, the seawalls lack swimming-friendly long, wide beaches like those found on the detached breakwaters. The groins seem ineffective with rips and reversed currents like those at Ras El -Bar. To develop a fishing community at the Manzala triangle similar in nature to Venice, it is recommended to extend the seawall to 12 km and then construct detached breakwaters eastward to the El-Diba inlet. To secure sustainability of the coast, a continuous maintenance of the protection structures to keep their elevations between 4–6 m above sea level (a.s.l.) is a critical task, in order to reduce the potential risks that could arise from a tsunami, with sand nourishment as a preferred strategy.
What Makes a Good Cabman? Behavioral Patterns Correlated with High-Earning and Low-Earning Taxi Driving
Authors: Jin Shuxin , Su Juan , Wu Zhouhao , Wang Di , Cai Ming
Published: November 2022

The average hourly income of taxi drivers could be improved by understanding the realized income of taxi drivers and investigating the variables that determine their income. Based on 4.85 million taxi-trajectory GPS records in Shenzhen, China, this study built a multi-layer road index system in order to reveal the behavioral patterns of drivers with varying income levels. On this basis, late-shift drivers were further selected and classified into two categories, namely high-earning and low-earning groups. Each driver within these groups was further classified into three income levels and four categories of factors were defined (i.e., occupied trips and duration, operational region, search speed, and taxi service strategies). The sample-based multinomial logit model was used to reveal the significance of these income-influencing factors. The results indicate significant differences in the drivers’ behavioral habits and experience. For instance, high-earning drivers focused more on improving efficiency using mobility intelligence, while low-earning drivers were more likely to invest in working hours to boost their revenue.
Study on the Mechanism and Chain Generation Relationship of Geological Disaster Secondary Coal Mine Accidents
Authors: Qi Qingjie , Yan Bingqian , Liu Wengang , Zhang Bo , Liu Jianzhong
Published: November 2022

Geological disasters induce coal mine accidents, which usually cause casualties and large economic losses in coal mines. However, the chain relationship and disaster mechanisms of geological disasters secondary coal mine accidents are still unclear, and the research on risk assessment methods of geological disasters induced coal mine accidents is relatively scarce. Based on investigating typical cases of secondary coal mine accidents caused by different types of geological disasters, this paper analyzed and studied the disaster-causing factors and chain generation relationship of geological disaster secondary coal mine accidents and studied the disaster-causing mechanism of secondary coal mine accidents caused by geological disasters. On the basis of analyzing the influencing factors of geological disaster secondary coal mine accidents, the risk index system of geological disaster secondary coal mine accidents was constructed, the risk grade assessment method was studied. The risk assessment model of geological disaster secondary coal mine accidents was constructed, and the weight of each index in the assessment system was determined by using the analytic hierarchy process, and the normalized treatment was carried out. According to the safety risk index of geological disaster secondary coal mine accidents, the grade division was carried out to guide the targeted prevention and control measures.
Food Insufficiency among Job-Loss Households during the Pandemic: The Role of Food Assistance Programs
Authors: Li Yingru , Li Dapeng , King Christian
Published: November 2022

During the early stages of the pandemic, many households experienced a loss of employment income, which worsened food insecurity and food insufficiency across the United States. This study aimed to examine whether food assistance programs (FAPs) significantly alleviated food insufficiency (FI) among job-loss households during the pandemic. The analysis was conducted with nationally representative survey data (n = 229,668) collected across 50 states and D.C. in 2020. Spatial analysis and logistic regression were applied to analyze the patterns of FI and to assess the Supplemental Nutrition Assistance Program (SNAP), FAPs for children, and community FAPs. Approximately 18.6% of participants faced FI, 41% of whom experienced FI for the first time. SNAP significantly lowered the odds of being food insufficient by 24.5% among households who were already food insufficient before the pandemic and by 11.9% for households with children. FAPs for children slightly reduced FI among households with children, but those who participated in these programs still had higher prevalence of food insufficiency than those who did not. Community FAPs were not effective. The findings suggest increasing the benefit level of SNAP for households with lowest income, strengthening their partnership with community FAPs, and distributing more emergent assistance to the most vulnerable households.
Research on the Mechanism of Collaborative Value Co-Creation of Enterprise–Science Community: A Case Study Based on the Green Brand Maoduoli
Authors: Shen Wenwen , Nie Yuankun , Long Chao , Song Zibo , Zhang Qian , Tang Decai
Published: November 2022

With the rapid increase of market competition pressure, enterprises’ collaborative innovation plays a more prominent role in competitive advantage. This paper aims to explore how the enterprise–science community can achieve sustainable collaborative value co-creation. Taking the Maoduoli Group as a sample, using the single case study method and grounded theory, a structural model of the enterprise–science community collaborative value co-creation mechanism is constructed. The proposed model is based on the value logic of “advocating value—creating value—delivering value—acquiring value”, which explains how the enterprise–scientific community collaborative value co-creation model is formed, how it is implemented, how it is delivered to customers, and the overall process of jointly harvesting value at last. The findings are as follows: First, government support, market demand, and entrepreneurial spirit are the internal and external factors for the enterprise–science community to develop collaborative value co-creation; secondly, the synergy mechanism of the enterprise–science community is to realize mutual activities such as joint research and development, a joint publication of papers, sharing of research results, joint research and development activities, and joint teaching practice through means of capital investment, concept support, and technical support. Third, the synergy mechanism of the enterprise–science community can realize the value of the economic and scientific research and the ecological and social benefits (narrow sense), and continuously feed back to further promote a deeper level of collaborative value co-creation of the enterprise–science community. This paper introduces the dimension of the scientific community, forms a special construct, and focuses on the collaborative value co-creation model of the enterprise–scientific community, which fills the gap in this research direction, and also provides theoretical support and practical guidance for the collaborative value co-creation model of the enterprise–scientific community.
Short-Term Postoperative Outcome of Baerveldt Glaucoma Implant with Two Tubes Inserted into the Vitreous Cavity
Authors: Tomita Kosei , Akamine Rinko , Morino Kazuya , Kusaka Mami , Akimoto Masayuki
Published: November 2022

Here, we report a new surgical technique designed to increase filtration volume and reduce intraocular pressure (IOP) in glaucoma and its one-year outcome. Two tubes were created from a single Baerveldt glaucoma implant (BI) by folding the tube in a U-shape and incising only the outer edge of the stretched loop tip. The tubes were placed into the vitreous cavity via the pars plana through a long scleral tunnel, without a scleral valve or graft patch. Twenty eyes of 18 patients with neovascular glaucoma were included. This technique was performed in 10 eyes of 10 patients (double group), and outcomes were compared to 10 eyes of eight patients in which a single tube BI was inserted (single group). The primary outcome measures included IOP, supplemental medical therapy score (SMTS), and intraoperative and postoperative complications before and after surgery at 12 months. The mean IOP (SMTS) were 32.0 ± 11.33 mmHg (4.1) in the double group and 29.7 ± 6.31 mmHg (5.7) in the single group, preoperatively reduced to 11.8 ± 2.70 mmHg (0.2) (63% reduction, p < 0.004) and 14.2 ± 4.05 mmHg (1.1) (52% reduction, p < 0.002) after 12 months, respectively. SMTS showed 95% (p = 0.005) and 89% (p = 0.005) reductions, respectively. Although there was no significant difference in IOP between the two groups at 12 months (p = 0.16), there were significant differences in the SMTS between the two groups before, and 6 and 12 months after, surgery (p = 0.01, 0.04 and 0.04, respectively). A reduction in the SMTS suggests that increasing filtration volume by placing two tubes has the potential to further reduce IOP as compared with a single tube.