bluuebunny/crossref_metadata_2025 huggingface dataset:
Key Factors for Improving the Resolution of Mapped Sea Surface Height from Multi-Satellite Altimeters in the South China Sea
Authors: Liu Lei , Zhang Xiaoya , Fei Jianfang , Li Zhijin , Shi Wenli , Wang Huizan , Jiang Xingliang , Zhang Ze , Lv Xianyu
Published: August 2023

A satellite altimeter measures sea surface height (SSH) along the nadir track. Multiple satellite altimeters have been in orbit, and the measurements been merged for mapping mesoscale eddies of ~100 km in size in the oceans. The capability of the mapped SSH for resolving mesoscale eddies depends on mapping algorithms. A two-dimensional variational (2DVAR) algorithm was implemented to generate mapped SSH at a grid size of 1/12° in the South China Sea. A range of comparisons were performed between the mapped SSH and the commonly used AVISO (Archiving, Validation, and Interpretation of Satellite Oceanographic satellite data) mapped SSH data product at a grid size of 1/8° and 1/4°. The effective resolution, which represents the spatial scale that the data can resolve, was examined. The effective resolution of the mapped SSH using the 2DVAR algorithm is approximately 100 km, while it is 250 km with the 1/8° and 1/4° AVISO data products. The difference in the effective resolution results from the difference in the background state and thus the background error. The result suggests that the effective resolution of the mapped data could be increased by choosing a background state so that the associated errors could have a smaller decorrelation length scale.
A Remote Sensing Method to Assess the Future Multi-Hazard Exposure of Urban Areas
Authors: Salvo Carolina , Vitale Alessandro
Published: August 2023

As more than 75% of the global population is expected to live in urban areas by 2050, there is an urgent need to assess the risk of natural hazards through a future-focused lens so that adequately informed spatial planning decisions can be made to define preventive risk policies in the upcoming decades. The authors propose an innovative methodology to assess the future multi-hazard exposure of urban areas based on remote sensing technologies and statistical and spatial analysis. The authors, specifically, applied remote sensing technologies combined with artificial intelligence to map the built-up area automatically. They assessed and calibrated a transferable Binary Logistic Regression Model (BLRM) to model and predict future urban growth dynamics under different scenarios, such as the business as usual, the slow growth, and the fast growth scenarios. Finally, considering specific socioeconomic exposure indicators, the authors assessed each scenario’s future multi-hazard exposure in urban areas. The proposed methodology is applied to the Municipality of Rende. The results revealed that the multi-hazard exposure significantly changed across the analyzed scenarios and that urban socioeconomic growth is the main driver of risk in urban environments.
RNGC-VIWO: Robust Neural Gyroscope Calibration Aided Visual-Inertial-Wheel Odometry for Autonomous Vehicle
Authors: Zhi Meixia , Deng Chen , Zhang Hongjuan , Tang Hongqiong , Wu Jiao , Li Bijun
Published: August 2023

Accurate and robust localization using multi-modal sensors is crucial for autonomous driving applications. Although wheel encoder measurements can provide additional velocity information for visual-inertial odometry (VIO), the existing visual-inertial-wheel odometry (VIWO) still cannot avoid long-term drift caused by the low-precision attitude acquired by the gyroscope of a low-cost inertial measurement unit (IMU), especially in visually restricted scenes where the visual information cannot accurately correct for the IMU bias. In this work, leveraging the powerful data processing capability of deep learning, we propose a novel tightly coupled monocular visual-inertial-wheel odometry with neural gyroscope calibration (NGC) to obtain accurate, robust, and long-term localization for autonomous vehicles. First, to cure the drift of the gyroscope, we design a robust neural gyroscope calibration network for low-cost IMU gyroscope measurements (called NGC-Net). Following a carefully deduced mathematical calibration model, NGC-Net leverages the temporal convolutional network to extract different scale features from raw IMU measurements in the past and regress the gyroscope corrections to output the de-noised gyroscope. A series of experiments on public datasets show that our NGC-Net has better performance on gyroscope de-noising than learning methods and competes with state-of-the-art VIO methods. Moreover, based on the more accurate de-noised gyroscope, an effective strategy for combining the advantages of VIWO and NGC-Net outputs is proposed in a tightly coupled framework, which significantly improves the accuracy of the state-of-the-art VIO/VIWO methods. In long-term and large-scale urban environments, our RNGC-VIWO tracking system performs robustly, and experimental results demonstrate the superiority of our method in terms of robustness and accuracy.
A Random Forest Algorithm Combined with Bayesian Optimization for Atmospheric Duct Estimation
Authors: Yang Chao , Wang Yulu , Zhang Aoxiang , Fan Hualei , Guo Lixin
Published: August 2023

Inversion of atmospheric ducts is of great importance in the field of performance evaluation for radar and communication systems. Since the model parameters in machine learning play a crucial role in prediction performance, this paper develops a random forest (RF) model integrated with Bayesian optimization (BO) called BO-RF for atmospheric duct prediction, and the BO is adopted to determine appropriate model parameters during the training process. In addition, the K-fold cross-validation (CV) method is also incorporated into the model to obtain the best model partition and overcome the overfitting problem. To test the performance of the proposed model, the results obtained by the BO-RF are compared with other commonly used methods, such as classical RF, extreme gradient boosting (XGBoost) with/without BO, and K-nearest neighbor (KNN) with/without BO. Comparisons demonstrate that BO-RF has the best accuracy and anti-noise ability for the estimation of duct parameters.
Spatiotemporal Information Mining for Emergency Response of Urban Flood Based on Social Media and Remote Sensing Data
Authors: Zhang Hui , Jia Hao , Liu Wenkai , Wang Junhao , Xu Dehe , Li Shiming , Liu Xianlin
Published: August 2023

The emergency response is crucial in preventing and mitigating urban floods. Both remote sensing and social media data offer distinct advantages in large-scale flood monitoring and near-real-time flood monitoring. However, current research lacks a thorough exploration of the application of social media data and remote sensing imagery in the urban flood emergency response. To address this issue, this paper, while extracting disaster information based on social media data, deeply mines the spatiotemporal distribution characteristics and dynamic spatial accessibility of rescue points. Furthermore, SAR imagery and social media data for monitoring urban flooding are compared. This study took the Zhengzhou 7.20 urban flood as a case study and created a methodological framework to quickly extract flood disaster information (flood, landslide, and rescue points) using these two types of data; spatiotemporal analysis and random forest classification were also conducted to mine valuable information. Temporally, the study revealed that disaster information did not increase proportionally with the amount of rainfall during the rainfall process. Spatially, specific regions with higher susceptibility to flooding, landslides, and rescue points were identified, such as the central region characterized by low drainage standards and high-density urban areas, as well as the eastern region with low-lying terrain. Moreover, this study examined the spatial accessibility of rescue resources in real flood scenarios and found that their service coverage varied throughout the day during and after the disaster. In addition, social media excelled in high-density urban areas’ flood point extraction, while SAR performed better in monitoring floods at the edges of low-density urban areas and large water bodies, allowing them to complement each other, to a certain extent. The findings of this study provide scientific reference value for the optimal selection of rescue paths and the allocation of resources in the emergency response to urban floods caused by extreme rainstorms.
A Second-Order Method for Removing Mixed Noise from Remote Sensing Images
Authors: Zhou Ying , Ren Chao , Zhang Shengguo , Xue Xiaoqin , Liu Yuanyuan , Lu Jiakai , Ding Cong
Published: August 2023

Remote sensing image denoising is of great significance for the subsequent use and research of images. Gaussian noise and salt-and-pepper noise are prevalent noises in images. Contemporary denoising algorithms often exhibit limitations when addressing such mixed noise scenarios, manifesting in suboptimal denoising outcomes and the potential blurring of image edges subsequent to the denoising process. To address the above problems, a second-order removal method for mixed noise in remote sensing images was proposed. In the first stage of the method, dilated convolution was introduced into the DnCNN (denoising convolutional neural network) network framework to increase the receptive field of the network, so that more feature information could be extracted from remote sensing images. Meanwhile, a DropoutLayer was introduced after the deep convolution layer to build the noise reduction model to prevent the network from overfitting and to simplify the training difficulty, and then the model was used to perform the preliminary noise reduction on the images. To further improve the image quality of the preliminary denoising results, effectively remove the salt-and-pepper noise in the mixed noise, and preserve more image edge details and texture features, the proposed method employed a second stage on the basis of adaptive median filtering. In this second stage, the median value in the original filter window median was replaced by the nearest neighbor pixel weighted median, so that the preliminary noise reduction result was subjected to secondary processing, and the final denoising result of the mixed noise of the remote sensing image was obtained. In order to verify the feasibility and effectiveness of the algorithm, the remote sensing image denoising experiments and denoised image edge detection experiments were carried out in this paper. When the experimental results are analyzed through subjective visual assessment, images denoised using the proposed method exhibit clearer and more natural details, and they effectively retain edge and texture features. In terms of objective evaluation, the performance of different denoising algorithms is compared using metrics such as mean square error (MSE), peak signal-to-noise ratio (PSNR), and mean structural similarity index (MSSIM). The experimental outcomes indicate that the proposed method for denoising mixed noise in remote sensing images outperforms traditional denoising techniques, achieving a clearer image restoration effect.
Extraction of Human Limbs Based on Micro-Doppler-Range Trajectories Using Wideband Interferometric Radar
Authors: He Xianxian , Zhang Yunhua , Dong Xiao
Published: August 2023

In this paper, we propose to extract the motions of different human limbs by using interferometric radar based on the micro-Doppler-Range signature (mDRS). As we know, accurate extraction of human limbs in motion has great potential for improving the radar performance on human motion detection. Because the motions of human limbs usually overlap in the time-Doppler plane, it is extremely hard to separate human limbs without other information such as the range or the angle. In addition, it is also difficult to identify which part of the body each signal component belongs to. In this work, the overlaps of multiple components can be solved, and the motions from different limbs can be extracted and classified as well based on the extracted micro-Doppler-Range trajectories (MDRTs) along with a proposed three-dimensional constant false alarm (3D-CFAR) detection. Three experiments are conducted with three different people on typical human motions using a 77 GHz radar board of 4 GHz bandwidth, and the results are validated by the measurements of a Kinect sensor. All three experiments were repeatedly conducted for three different people of different heights to test the repeatability and robust of the proposed approach, and the results met our expectations very well.
Digital Forensic Analysis of Vehicular Video Sensors: Dashcams as a Case
Authors: Daraghmi Yousef-Awwad , Shawahna Ibrahim
Published: August 2023

Dashcams are considered video sensors, and the number of dashcams installed in vehicles is increasing. Native dashcam video players can be used to view evidence during investigations, but these players are not accepted in court and cannot be used to extract metadata. Digital forensic tools, such as FTK, Autopsy and Encase, are specifically designed for functions and scripts and do not perform well in extracting metadata. Therefore, this paper proposes a dashcam forensics framework for extracting evidential text including time, date, speed, GPS coordinates and speed units using accurate optical character recognition methods. The framework also transcribes evidential speech related to lane departure and collision warning for enabling automatic analysis. The proposed framework associates the spatial and temporal evidential data with a map, enabling investigators to review the evidence along the vehicle’s trip. The framework was evaluated using real-life videos, and different optical character recognition (OCR) methods and speech-to-text conversion methods were tested. This paper identifies that Tesseract is the most accurate OCR method that can be used to extract text from dashcam videos. Also, the Google speech-to-text API is the most accurate, while Mozilla’s DeepSpeech is more acceptable because it works offline. The framework was compared with other digital forensic tools, such as Belkasoft, and the framework was found to be more effective as it allows automatic analysis of dashcam evidence and generates digital forensic reports associated with a map displaying the evidence along the trip.
Bridged EGFET Design for the Rapid Screening of Sorbents as Sensitisers in Water-Pollution Sensors
Authors: AlQahtani Hadi Rasam , Al-Odayni Abdel-Basit M. , Alhamed Yusif , Grell Martin
Published: August 2023

We further simplify the most ‘user-friendly’ potentiometric sensor for waterborne analytes, the ‘extended-gate field effect transistor’ (EGFET). This is accomplished using a ‘bridge’ design, that links two separate water pools, a ‘control gate’ (CG) pool and a ‘floating gate’ (FG) pool, by a bridge filled with agar-agar hydrogel. We show electric communication between electrodes in the pools across the gel bridge to the gate of an LND150 FET. When loading the gel bridge with a sorbent that is known to act as a sensitiser for Cu2+ water pollution, namely, the ion exchanging zeolite ‘clinoptilolite’, the bridged EGFET acts as a potentiometric sensor to waterborne Cu2+. We then introduce novel sensitisers into the gel bridge, the commercially available resins PurometTM MTS9140 and MTS9200, which are sorbents for the extraction of mercury (Hg2+) pollution from water. We find a response of the bridged EGFET to Hg2+ water pollution, setting a template for the rapid screening of ion exchange resins that are readily available for a wide range of harmful (or precious) metal ions. We fit the potentiometric sensor response vs. pollutant concentration characteristics to the Langmuir–Freundlich (LF) model which is discussed in context with other ion-sensor characteristics.
Security Framework for Network-Based Manufacturing Systems with Personalized Customization: An Industry 4.0 Approach
Authors: Hammad Muhammad , Jillani Rashad Maqbool , Ullah Sami , Namoun Abdallah , Tufail Ali , Kim Ki-Hyung , Shah Habib
Published: August 2023

Smart manufacturing is pivotal in the context of Industry 4.0, as it integrates advanced technologies like the Internet of Things (IoT) and automation to streamline production processes and improve product quality, paving the way for a competitive industrial landscape. Machines have become network-based through the IoT, where integrated and collaborated manufacturing system responds in real time to meet demand fluctuations for personalized customization. Within the network-based manufacturing system (NBMS), mobile industrial robots (MiRs) are vital in increasing operational efficiency, adaptability, and productivity. However, with the advent of IoT-enabled manufacturing systems, security has become a serious challenge because of the communication of various devices acting as mobile nodes. This paper proposes the framework for a newly personalized customization factory, considering all the advanced technologies and tools used throughout the production process. To encounter the security concern, an IoT-enabled NBMS is selected as the system model to tackle a black hole attack (BHA) using the NTRUEncrypt cryptography and the ad hoc on-demand distance-vector (AODV) routing protocol. NTRUEncrypt performs encryption and decryption while sending and receiving messages. The proposed technique is simulated by network simulator NS-2.35, and its performance is evaluated for different network environments, such as a healthy network, a malicious network, and an NTRUEncrypt-secured network based on different evaluation metrics, including throughput, goodput, end-to-end delay, and packet delivery ratio. The results show that the proposed scheme performs safely in the presence of a malicious node. The implications of this study are beneficial for manufacturing industries looking to embrace IoT-enabled subtractive and additive manufacturing facilitated by mobile industrial robots. Implementation of the proposed scheme ensures operational efficiency, enables personalized customization, and protects confidential data and communication in the manufacturing ecosystem.
Clutter Cancellation Methods for Small Target Detection Using High-Resolution W-band Radar
Authors: Hwang Woosung , Jang Hongje , Choi Myungryul
Published: August 2023

Drones are currently being used for various applications. However, the detection of drones for defense or security purposes has become problematic because of the use of plastic materials and the small size of these drones. Any drone can be placed under surveillance to accurately determine its position by collecting high-resolution data using various detectors such as the radar system proposed in this paper. The W-band radar has a high carrier frequency, which makes it easy to design a wide bandwidth system, and the wideband FMCW signal is suitable for creating high resolution images from a distance. Unfortunately, the huge amounts of data gathered in this way also contain clutter (such as background data and noise) that is usually generated from unstable radar systems and complex environmental factors, and which frequently gives rise to distorted data. Accurate extraction of the position of the target from this big data requires the clutter to be suppressed and canceled, but conventional clutter cancellation methods are not suitable. Four clutter cancellation algorithms are assessed and compared: standard deviation, adaptive least mean squares (LMS), recursive least squares (RLS), and the proposed LMS. The proposed LMS has combined LMS with the standard deviation method. First, the big data pertaining to the target position is collected using the W-band radar system. Subsequently, the target position is calculated by applying these algorithms. The performance of the proposed algorithms is measured and compared to that of the other three algorithms by conducting outdoor experiments.
Integrated Positioning System of Kiwifruit Orchard Mobile Robot Based on UWB/LiDAR/ODOM
Authors: Jia Liangsheng , Wang Yinchu , Ma Li , He Zhi , Li Zixu , Cui Yongjie
Published: August 2023

To address the issue of low positioning accuracy of mobile robots in trellis kiwifruit orchards with weak signal environments, this study investigated an outdoor integrated positioning method based on ultra-wideband (UWB), light detection and ranging (LiDAR), and odometry (ODOM). Firstly, a dynamic error correction strategy using the Kalman filter (KF) was proposed to enhance the dynamic positioning accuracy of UWB. Secondly, the particle filter algorithm (PF) was employed to fuse UWB/ODOM/LiDAR measurements, resulting in an extended Kalman filter (EKF) measurement value. Meanwhile, the odometry value served as the predicted value in the EKF. Finally, the predicted and measured values were fused through the EKF to estimate the robot’s pose. Simulation results demonstrated that the UWB/ODOM/LiDAR integrated positioning method achieved a mean lateral error of 0.076 m and a root mean square error (RMSE) of 0.098 m. Field tests revealed that compared to standalone UWB positioning, UWB-based KF positioning, and LiDAR/ODOM integrated positioning methods, the proposed approach improved the positioning accuracy by 64.8%, 13.8%, and 38.3%, respectively. Therefore, the proposed integrated positioning method exhibits promising positioning performance in trellis kiwifruit orchards with potential applicability to other orchard environments.
Satellite Interference Source Direction of Arrival (DOA) Estimation Based on Frequency Domain Covariance Matrix Reconstruction
Authors: Yao Jinjie , Zhao Changchun , Bai Jiansheng , Ren Yang , Wang Yangyang , Miao Jing
Published: August 2023

Direction of arrival (DOA) estimation is an effective method for detecting various active interference signals during the satellite navigation process. It can be utilized for both interference detection and anti-interference applications. This paper proposes a DOA estimation algorithm for satellite interference sources based on frequency domain covariance matrix reconstruction (FDCMR) to address various types of active interference that may occur in the satellite navigation positioning process. This algorithm can estimate the DOA of coherent signals from multiple frequency points under low signal-to-noise ratio (SNR) conditions. The signals received from the array are transformed from the time domain to the frequency domain using a fast Fourier transform (FFT). The data corresponding to the frequency point of the target signal is extracted from the signal in the frequency domain. The frequency domain covariance matrix of the received array signals is reconstructed by utilizing its covariance matrix property. The spatial spectrum search method is used for the final DOA estimation. Simulation experiments have shown that the proposed algorithm performs well in the DOA estimation under low SNR conditions and also resolves coherency. Moreover, the algorithm’s effectiveness is verified through comparison with three other algorithms. Finally, the algorithm’s applicability is validated through simulations of various interference scenarios.
High Value Conversion Technology of Nickel in Waste Electrolytes of Nitrogen Trifluoride by Electrolysis
Authors: Ren Yongzhuan , Ran Yuxuan , Yang Jiaqi , Wei Yonggang
Published: August 2023

The method of precipitation treatment of high-nickel waste electrolytic slag was studied, coupled with multi-step treatment to improve the recovery rate of nickel. In this study, HNO3 was used as dissolving agent, NaOH and Na2CO3 as precipitating agents, and the waste electrolytic slag was dissolved in the liquid phase and then precipitated. The results show that the electroslag with high nickel content can be completely dissolved under the optimal solid-liquid ratio of 1:10. When the pH of the leaching solution was adjusted to 7.5 by 5% NaOH solution, the iron removal rate could reach 99.3%. When the pH was adjusted to 11 with 5% Na2CO3, the nickel could be completely precipitated after standing, and the main component was basic nickel carbonate. The further recovery experiments show that high purity NiO can be obtained after the basic nickel carbonate is oxidized and calcined, and the average recovery rate of nickel is 92.4%. This study is of great practical significance to improve the treatment efficiency of the chemical precipitation method, reduce the operating cost of the chemical precipitation method, and reduce the production of secondary pollutants in the process of nickel recycling.
Mission-Oriented Innovation Policy for Sustainable Development: A Systematic Literature Review
Authors: Al-Jayyousi Odeh , Amin Hira , Al-Saudi Hiba Ali , Aljassas Amjaad , Tok Evren
Published: August 2023

This paper aims to explore effective ways to incorporate a mission-orientated framework to innovation policies that are designed to achieve the United Nations’ Sustainable Development Goals (SDGs). This paper aims to identify the strategies of MOIP to inform innovation and sustainability in the Middle East and North Africa (MENA) region, particularly focusing on the Gulf Corporation Council (GCC) context. It does so through a systematic literature review of current studies related to MOIP, according to the PRISMA protocol, regarding countries in the GCC region that are currently undergoing ambitious national strategic development plans with sustainability and inclusive growth at their core. This particularly targets SDG 9, which is about fostering innovation that is inclusive and sustainable. Implications for this study may inform innovation policy in the GCC and even inspire the wider Middle East and North Africa (MENA) region in terms of innovation metrics, energy transitions, the innovation ecosystem, public participation, and policy implementation and evaluation. Based on the systematic review and current innovation ecosystems and practices in the GCC, recommendations and strategic options are outlined.
Sustainable Leadership Practices Based on the Logic of the Honeybee Pyramid—Comparison of Hungarian and Polish SMEs
Authors: Bencsik Andrea , Pangsy-Kania Sylwia
Published: August 2023

The popularity of sustainability as a research topic in different areas of organisational functioning is marked by the number of studies on the topic. Significantly less research is addressing the factors that fundamentally influence the functioning of organisations. Among these factors, this paper will review the criteria for sustainable leadership. Competition in the economy has made it necessary to build sustainability capabilities in all areas of business, of which the practice of sustainable leadership is a fundamental requirement for making strategic decisions. This paper aims to examine the thinking, leadership styles, and decisions of leaders of organisations in everyday practice based on the pyramid logic of sustainable leadership. In this research, structured interviews with managers of Hungarian (32) and Polish (28) organisations were evaluated using the ‘Voyant tools’ 2.6.9. software. The results show that there is a significant difference between the Hungarian and Polish leadership mindsets, with both samples only partially meeting the expectations of what can be considered a sustainable leadership style in the spirit of the ‘Honeybee’ approach. Out of the 14 basic elements of the pyramid, the leaders of both nations reach the desired level in five cases. This study provides useful lessons for SME managers in developing sustainability management practices.
Experimental and Numerical CFD Modelling of the Hydrodynamic Effects Induced by a Ram Pump Waste Valve
Authors: Evangelista Stefania , Tortora Giuseppe , Viccione Giacomo
Published: August 2023

The hydraulic ram pump or hydram is a machine capable of lifting water to a hydraulic head higher than the level of the supply source. It is a sustainable and self-sufficient device: the working principle is based on the rise of abrupt pressure variations occurring in the feeding pipeline when the liquid inside it undergoes a locally sharp change in velocity as a consequence of the sudden closure of the waste valve. Invented in 1772, the pump has been improved over the decades. Due to its simplicity, low cost and reliability, it has been widely used worldwide to provide adequate domestic water supplies, especially before the spreading of electricity and internal combustion engines. In recent years, the new attention placed on sustainability and energy transition from fossil fuels to renewable energy devices has brought a growing interest to this basic machine, essentially forgotten and abandoned in the last century; it seems promising especially in developing countries. The hydram is, in fact, a very simple machine, with only two moving parts, the waste and delivery valves. The efficiency of the hydraulic ram pump is mainly influenced by the characteristics of the waste valve. However, sufficient data are not available for the design of the hydram and the waste valve. In this work, the behaviour of the waste valve of a hydram was simulated by means of Computational Fluid Dynamics (CFD). Velocity and pressure values were analysed for different scenarios with different closing times of the valve. The data obtained from the developed numerical model were compared, in order to verify the validity of the simulations, with those collected during the operation of the hydram placed at the Laboratory of Environmental and Marine Hydraulics (LIDAM) of the University of Salerno, Italy. The numerical model thus obtained can, therefore, be used to identify the ideal configuration of the valve in order to ensure the best performance of the hydram.
Impact of Climate Change on Inflation in 26 Selected Countries
Authors: Li Cunpu , Zhang Xuetong , He Jing
Published: August 2023

In the era of persistent globalization, climate governance has emerged as a prominent concern within both the theoretical community and government departments of diverse nations. Of particular interest in academic research is the adverse effect of climate shocks on the global economy. This paper employs average temperature as a surrogate indicator for climate shocks and examines the influence of temperature fluctuations on inflation levels using a balanced panel dataset from 1995 to 2021. The findings indicate a positive association between temperature change and inflation within the country, which remains consistent even after subjecting the analysis to multiple robustness tests. Furthermore, accounting for heterogeneity reveals variations in the magnitude of response of inflation levels to temperature fluctuations. Regarding the analysis of underlying mechanisms, this study underscores the significance of energy demand as a pivotal pathway influencing inflationary pressures at the national level. Lastly, by incorporating GDP per capita as a threshold, this research reveals a nonlinear relationship between temperature change and inflation levels.
The Intertwining Effect of Visual Perception of the Reusable Packaging and Type of Logo Simplification on Consumers’ Sustainable Awareness
Authors: Chiu Tseng-Ping , Yang Derrick Jessey , Ma Min-Yuan
Published: August 2023

The COVID-19 pandemic started toward the end of 2019 and social distancing requirements imposed worldwide led to the rapid growth of online shopping, adversely impacted the global efforts for environmental protection. This study examines packaging and logos as stimuli to explore visual attention and attraction, arousal, and environmental awareness using eye tracking and a survey questionnaire. The results suggest that reusable packaging can enhance both attention and attraction. As a result, through the comprehensive data of the eye tracker, it was found that the reusable packaging and monotone logo can attract consumers’ attention faster than the original packaging. Post-event independent sample t-tests should prove that reusable packaging can positively impact consumers.
A System Dynamic Model for Polyethylene Terephthalate Supply Chain in the United Arab Emirates—Status, Projections, and Environmental Impacts
Authors: Al-Shihabi Sameh , Barghash Mahmoud
Published: August 2023

Polyethylene terephthalate (PET) water bottles are widely used in the United Arab Emirates (UAE); however, their production and disposal adversely affect the environment. In collaboration with the private sector, the UAE government has taken serious steps to reduce these impacts, including (i) encouraging people to stop using PET water bottles and to separate their waste, (ii) establishing material recovery facilities, (iii) constructing facilities for incineration with energy recovery, and (iv) creating business opportunities to downcycle and recycle PET water bottles. This paper models the PET supply chain (PSC) using system dynamics (SD) to simulate the current PSC in the UAE and to project its possible evolution from 2023 to 2050, taking greenhouse gas (GHG) emissions into consideration. For decision-makers, the SD model shows that PET reductions must equal population growth to maintain GHG emissions associated with the PSC for the coming years. In addition, the separation efficiency must exceed 33% of PET consumption to meet the current demand for used PET. Moreover, if PET consumption decreases by more than 1.5%, then businesses relying on used PET will face a supply shortage in the year 2050. As for environmental impacts, it is found that if downcycling and recycling capabilities are fully utilized, GHG emissions will decrease by 35%. Furthermore, if demand for recycled PET reaches 10,000 tons, this reduction will exceed 47%, reaching 177,861MtCO2e.
Severity Predictions for Intercity Bus Crashes on Highway Using a Random Parameter Ordered Probit Model
Authors: Kim Kanghyun , Hong Jungyeol
Published: August 2023

As intercity buses are a mode that moves large-scale occupancy between regions, it accounts for the mode share-means for mid- to long-distance movement in South Korea. However, the study of intercity bus safety needs to be more extensive, and safety policies are carried out based on traditional probability models without considering the data characteristics of bus accidents. Therefore, in this study, the Random Parameter Ordered Logit model was applied to derive fixed parameter factors that have the same effect on the severity of intercity bus accidents and Random Parameters that consider the heterogeneity of unique attributes by accident. It also analyzed the marginal effect of intercity bus accident severity. As a result of this study, the influencing factors that reflect heterogeneity with random parameters were driver’s condition: drowsiness, vehicle size: medium, crash type: vehicle–pedestrian accident, road condition: wet pavement, and log form of AADT. The random parameter ordered logit model was traditionally found to be more suitable than the ordinal logit model, which only reflects fixed factors and more reliable predictions considering the heterogeneity of accident characteristics for each observation.
Can Green Finance Drive the Development of the Green Building Industry?—Based on the Evolutionary Game Theory
Authors: Li Shaoliang , Zheng Xiazhong , Zeng Qin
Published: August 2023

The construction industry has a significant impact on the environment, and green buildings provide an effective means of reducing environmental impact. Green finance can facilitate industrial transformation and upgrading, reduce construction costs, attract investment, and promote green industry development. However, traditional financing models still dominate the financing of green building projects, even if they are not in line with the core principles of green building. To solve this problem, this paper establishes a three-party evolutionary game model, which, based on the assumption of finite rationality, considers three core stakeholders: bank and financial institutions, developers, and consumers. The conditions for evolutionary stable equilibrium are identified through an equilibrium analysis of the strategic behavior of each subject, followed by a replication dynamic analysis. Simulation analysis was used to study the effects of key parameters on cooperative behavior and evolution. The results of the study show that (1) The high initial willingness of bank and financial institutions and consumers has a significant driving effect on the market. (2) When the market willingness is high, the government can steer the market towards the desired goal at relatively low policy costs. (3) With the support of green finance, green buildings can still flourish even if the return is slightly lower than traditional building projects. The results of the study reveal cooperative behavior and its evolutionary patterns, which help stakeholders coordinate their goals and promote the development of the green building industry.
Sustainable Micro, Small, and Medium Enterprises: A Developed Model of Entrepreneurial Marketing Behaviors for Thailand’s MSMEs
Authors: Li Liou-Yuan , Syah Ahmad Mujafar , Syukur Muhammad , Limkanchanapa Rachatawan , Srisurat Chamaiporn
Published: August 2023

The main objective of this study is to propose a developed model of entrepreneurial marketing behaviors for sustainable micro, small, and medium enterprises (MSMEs) in Thailand. This study aims to contribute to the existing literature on sustainable business practices and entrepreneurial marketing behaviors after a disaster by providing a framework that can be used for future research in this area, particularly after economic turbulence. The research method used in this study is quantitative. The study was conducted in Bangkok, Thailand. Primary data were collected from respondents via a questionnaire using a non-probability sampling approach (purposive sampling). A total of 511 out of 600 issued online and paper surveys were successfully returned, constituting an 85% response rate. The primary data were acquired from first-hand sources of Thai MSMEs’ business owners and managers. The hypothesis testing of this study proves that entrepreneurial marketing behaviors could influence business rebound during the COVID period. The study also concludes that business resilience practices mediate or carry the influence of entrepreneurial marketing behaviors on the business rebound.
Developing the Actual Precipitation Probability Distribution Based on the Complete Daily Series
Authors: Zhai Wangyuyang , Wang Zhoufeng , Feng Youcan , Xue Lijun , Ma Zhenjie , Tian Lin , Sun Hongliang
Published: August 2023

The defense against urban pluvial flooding relies on the prediction of rainfall frequency, intensity, and long-term trends. The influence of the choice of the complete time series or the wet-day series on the rain analyses remains unclear, which affects the adaptive strategies for the old industrial cities such as Changchun in Northeastern China, with the outdated combined sewer systems. Based on the data from the two separate weather stations, four types of distributions were compared for analyzing the complete daily precipitation series, and their fitting accuracy was found in decreasing order of Pearson III, Pareto–Burr–Feller distribution (PBF), generalized extreme value (GEV), and Weibull. The Pearson III and the PBF probability distribution functions established based on the complete time series were found to be at least 458% and 227%, respectively, more accurate in fitting with the consecutive observations than those built from the wet-day-only series, which did not take account of the probability of the dry periods between the rain events. The rain depths of the return periods determined from the wet-day-only series might be over-predicted by at least 76% if the complete daily series were regarded as being more closely representative of the real condition. A clear threshold of 137 days was found in this study to divide the persistent or autocorrelated time series from the antipersistent or independent time series based on the climacogram analysis, which provided a practical way for independence determination. Due to the significant difference in the rain analyses established from the two time series, this work argued that the complete daily series better represented the real condition and, therefore, should be used for the frequency analysis for flood planning and infrastructure designs.
Creating Synergies among the Sustainable Development Goals and Climate Action: Insights from a Developing Economy
Authors: Khalid Ahmad Mohd
Published: August 2023

Creating synergies and aligning the Sustainable Development Goals and the Paris Agreement offers great opportunity for global climate action that is based on inclusive development and just energy transformation. However, this process is not straight forward and faces several interlinked issues and challenges, including varying national priorities. Research and insights into these issues are lacking in the case of developing countries. This study aims to identify key benefits and opportunities, barriers, and challenges on creating synergies and jointly implementing the Sustainable Development Goals and the Nationally Determined Contributions in the case of India. This is achieved by conducting a structured expert interview with multi-stakeholders in the Delhi National Capital Region of India. The findings of this study intend to benefit and inform national and local governments, individuals, institutions, and organisations across the world on key implementation challenges of the synergies process in the case of a major developing economy and provides important lessons and policy recommendations that may also strengthen and support global efforts towards climate-compatible development and decision making.
Outcome of Primary Anterior Cruciate Ligament Reconstruction with Peroneus Longus and Bone–Patellar Tendon–Bone Autografts: A Clinical Comparative Study
Authors: Goncharov Evgeniy Nikolaevich , Koval Oleg Aleksandrovich , Bezuglov Eduard Nikolaevich , Vetoshkin Aleksandr Aleksandrovich , Goncharov Nikolay Gavriilovich , Encarnación Ramirez Manuel , Nurmukhametov Renat , Montemurro Nicola
Published: August 2023

Background: The aim of this study is to compare the mid-term outcomes of primary tear of the anterior cruciate ligament (ACL) reconstruction via the use of peroneus longus tendon (PLT) and bone–patellar tendon–bone (BPTB) autografts. Methods: 53 patients (group 1) received ACL reconstruction via BPTB, whereas 55 patients (group 2) received arthroscopic ACL reconstruction using PLT autograft. Results: In group 1, the mean preoperative and postoperative scores on the Lysholm Knee Questionnaire (LKQ) scale resulted in 69.2 ± 10.7 points and −92.2 ± 6.4 points, respectively. The average preoperative and postoperative value on the International Knee Documentation Committee (IKDC) scale was 68.2 ± 10.6% and −90.1 ± 9.5%, respectively. For KT-1000, according to the results of surgical treatment, it is −3.7 ± 1.4 mm, and for the American Orthopaedic Foot & Ankle Society (AOSAF) it is −95.3 ± 4.5%. The autograft ruptured within 2 years after operation in 4 of 50 patients. In group 2, the mean score on the LKQ scale before the operation was 70.2 ± 11.6 points, after −94.3 ± 0.5 points. The average value on the IKDC scale before surgery was 68.6 ± 8.7%, after −91.5 ± 8.2%. KT-1000 −3.4 ± 1.2 mm. Conclusion: The results in both groups can be assessed as good; peroneus longus muscle tendon autograft is an alternative graft for the primary reconstruction of ACL, preserving the dynamic stabilizers of the knee and has no effect on the formation of flat feet and other disease in the postoperative period.
An Experimental Protocol for Human Stress Investigation in Manufacturing Contexts: Its Application in the NO-STRESS Project
Authors: Apraiz Ainhoa , Lasa Ganix , Montagna Francesca , Blandino Graziana , Triviño-Tonato Erika , Dacal-Nieto Angel
Published: August 2023

Stress is a critical concern in manufacturing environments, as it impacts the well-being and performance of workers. Accurate measurement of stress is essential for effective intervention and mitigation strategies. This paper introduces a holistic and human-centered protocol to measure stress in manufacturing settings. The three-phased protocol integrates the analysis of physiological signals, performance indicators, and the human perception of stress. The protocol incorporates advanced techniques, such as electroencephalography (EEG), heart rate variability (HRV), galvanic skin response (GSR), and electromyography (EMG), to capture physiological responses associated with stress. Furthermore, the protocol considers performance indicators as an additional dimension of stress measurement. Indicators such as task execution time, errors, production rate, and other relevant performance metrics contribute to a comprehensive understanding of stress in manufacturing environments. The human perception of stress is also integrated into the protocol, recognizing the subjective experience of the individual. This component captures self-assessment and subjective reports, allowing for a more nuanced evaluation of stress levels. By adopting a holistic and human-centered approach, the proposed protocol aims to enhance our understanding of stress factors in manufacturing environments. The protocol was also applied in the automotive industry and plastic component manufacturing. The insights gained from this protocol can inform targeted interventions to improve worker well-being, productivity, and overall organizational performance.
Unraveling the Role of the Zinc-Dependent Metalloproteinase/HTH-Xre Toxin/Antitoxin (TA) System of Brucella abortus in the Oxidative Stress Response: Insights into the Stress Response and Virulence
Authors: Gómez Leonardo A. , Molina Raúl E. , Soto Rodrigo I. , Flores Manuel R. , Coloma-Rivero Roberto F. , Montero David A. , Oñate Ángel A.
Published: August 2023

Toxin/antitoxin (TA) systems have been scarcely studied in Brucella abortus, the causative agent of brucellosis, which is one of the most prevalent zoonotic diseases worldwide. In this study, the roles of a putative type II TA system composed by a Zinc-dependent metalloproteinase (ZnMP) and a transcriptional regulator HTH-Xre were evaluated. The deletion of the open reading frame (ORF) BAB1_0270, coding for ZnMP, used to produce a mutant strain, allowed us to evaluate the survival and gene expression of B. abortus 2308 under oxidative conditions. Our results showed that the B. abortus mutant strain exhibited a significantly reduced capacity to survive under hydrogen peroxide-induced oxidative stress. Furthermore, this mutant strain showed a decreased expression of genes coding for catalase (katE), alkyl hydroperoxide reductase (ahpC) and transcriptional regulators (oxyR and oxyR-like), as well as genes involved in the general stress response, phyR and rpoE1, when compared to the wild-type strain. These findings suggest that this type II ZnMP/HTH-Xre TA system is required by B. abortus to resist oxidative stress. Additionally, previous evidence has demonstrated that this ZnMP also participates in the acidic stress resistance and virulence of B. abortus 2308. Therefore, we propose a hypothetical regulatory function for this ZnMP/HTH-Xre TA system, providing insight into the stress response and its potential roles in the pathogenesis of B. abortus.