bluuebunny/crossref_metadata_2025 huggingface dataset:
Construction of a Green-Comprehensive Evaluation System for Flotation Collectors
Authors: Xu Hongxiang , Cui Jiahua , Cao Yijun , Ma Lin , Fan Guixia , Huang Gen , Ning Kejia , Wang Jingzheng , Kang Yuntao , Sun Xin , Deng Jiushuai , Li Shan
Published: May 2023

The evaluation of flotation reagents performs an important role in the selection and green application of reagents. The green indexes and effect indexes of flotation collectors were selected by data literature method, system analysis method, mathematical model method, and qualitative and quantitative analysis method, and the green evaluation system of flotation collectors, flotation effect evaluation system, and comprehensive evaluation system of flotation collectors were established. The normalization method and expert evaluation methods were adopted to obtain the grade classification of quantitative and qualitative indicators, respectively. The analytic hierarchy process (AHP) was used to determine the weight of secondary indicators and tertiary indicators of the evaluation system and the weight of indicators at a lower level. Applying the fuzzy comprehensive evaluation (FCE), the trapezoidal function is selected to determine the index affiliation, the index system score is calculated according to the weighted average principle, and finally, the established evaluation system is applied in an example. The example application shows that the comprehensive evaluation system of flotation collectors can make a comprehensive evaluation of collectors from the aspects of the greenness of reagent, flotation effect, and cost, and it has a strong target and practicality for collectors evaluation. The establishment of the system has a guiding significance for the selection and use of flotation collectors.
A VHR Bi-Temporal Remote-Sensing Image Change Detection Network Based on Swin Transformer
Authors: Teng Yunhe , Liu Shuo , Sun Weichao , Yang Huan , Wang Bin , Jia Jintong
Published: May 2023

Change detection (CD), as a special remote-sensing (RS) segmentation task, faces challenges, including alignment errors and illumination variation, dense small targets, and large background intraclass variance in very high-resolution (VHR) remote-sensing images. Recent methods have avoided the misjudgment caused by illumination variation and alignment errors by increasing the ability of global modeling, but the latter two problems have still not been fully addressed. In this paper, we propose a new CD model called SFCD, which increases the feature extraction capability for small targets by introducing a shifted-window (Swin) transformer. We designed a foreground-aware fusion module to use attention gates to trim low-level feature responses, enabling increased attention to the changed region compared to the background when recovering the changed region, thus reducing background interference. We evaluated our model on two CD datasets, LEVIR-CD and CDD, and obtained F1 scores of 91.78 and 97.87, respectively. The experimental results and visual interpretation show that our model outperforms several previous CD models. In addition, we adjusted the parameters and structure of the standard model to develop a lightweight version that achieves an accuracy beyond most models with only 1.55 M parameters, further validating the effectiveness of our design.
Quantification of Surface Pattern Based on the Binary Terrain Structure in Mountainous Areas
Authors: Li Sijin , Yang Xin , Zhou Xingyu , Tang Guoan
Published: May 2023

Terrain significantly influences the physical processes and human activities occurring on the Earth’s surface, especially in mountainous areas. The classification and clarification of topographic structures are essential for the quantitative analysis of surface patterns. In this paper, we propose a new method based on the digital elevation model to classify the binary terrain structure. The slope accumulation is constructed to emphasize the accumulated topographic characteristics and is applied to support the segmenting process. The results show that this new method is efficient in increasing the completeness of the segmented results and reducing the classification uncertainty. We verify this method in three areas in South America, North America and Asia to evaluate the method’s robustness. Comparison experiments suggest that this new method outperforms the traditional method in areas with different landforms. In addition, quantitative indices are calculated based on the segmented results. The results indicate that the binary terrain structure benefits the understanding of surface patterns from the perspectives of topographic characteristics, category composition, object morphology and landform spatial distribution. We also assess the transferability of the proposed method, and the results suggest that this method is transferable to different digital elevation models. The proposed method can support the quantitative analysis of land resources, especially in mountainous areas and benefit land management.
Research on the Method of Counting Wheat Ears via Video Based on Improved YOLOv7 and DeepSort
Authors: Wu Tianle , Zhong Suyang , Chen Hao , Geng Xia
Published: May 2023

The number of wheat ears in a field is an important parameter for accurately estimating wheat yield. In a large field, however, it is hard to conduct an automated and accurate counting of wheat ears because of their density and mutual overlay. Unlike the majority of the studies conducted on deep learning-based methods that usually count wheat ears via a collection of static images, this paper proposes a counting method based directly on a UAV video multi-objective tracking method and better counting efficiency results. Firstly, we optimized the YOLOv7 model because the basis of the multi-target tracking algorithm is target detection. Simultaneously, the omni-dimensional dynamic convolution (ODConv) design was applied to the network structure to significantly improve the feature-extraction capability of the model, strengthen the interaction between dimensions, and improve the performance of the detection model. Furthermore, the global context network (GCNet) and coordinate attention (CA) mechanisms were adopted in the backbone network to implement the effective utilization of wheat features. Secondly, this study improved the DeepSort multi-objective tracking algorithm by replacing the DeepSort feature extractor with a modified ResNet network structure to achieve a better extraction of wheat-ear-feature information, and the constructed dataset was then trained for the re-identification of wheat ears. Finally, the improved DeepSort algorithm was used to calculate the number of different IDs that appear in the video, and an improved method based on YOLOv7 and DeepSort algorithms was then created to calculate the number of wheat ears in large fields. The results show that the mean average precision (mAP) of the improved YOLOv7 detection model is 2.5% higher than that of the original YOLOv7 model, reaching 96.2%. The multiple-object tracking accuracy (MOTA) of the improved YOLOv7–DeepSort model reached 75.4%. By verifying the number of wheat ears captured by the UAV method, it can be determined that the average value of an L1 loss is 4.2 and the accuracy rate is between 95 and 98%; thus, detection and tracking methods can be effectively performed, and the efficient counting of wheat ears can be achieved according to the ID value in the video.
Sensor Fusion-Based Vehicle Detection and Tracking Using a Single Camera and Radar at a Traffic Intersection
Authors: Li Shenglin , Yoon Hwan-Sik
Published: May 2023

Recent advancements in sensor technologies, in conjunction with signal processing and machine learning, have enabled real-time traffic control systems to adapt to varying traffic conditions. This paper introduces a new sensor fusion approach that combines data from a single camera and radar to achieve cost-effective and efficient vehicle detection and tracking. Initially, vehicles are independently detected and classified using the camera and radar. Then, the constant-velocity model within a Kalman filter is employed to predict vehicle locations, while the Hungarian algorithm is used to associate these predictions with sensor measurements. Finally, vehicle tracking is accomplished by merging kinematic information from predictions and measurements through the Kalman filter. A case study conducted at an intersection demonstrates the effectiveness of the proposed sensor fusion method for traffic detection and tracking, including performance comparisons with individual sensors.
Simulation and Optimization Design of Inductive Wear Particle Sensor
Authors: Fan Bin , Wang Lianfu , Liu Yong , Zhang Peng , Feng Song
Published: May 2023

In order to monitor the diagnosis of mechanical equipment by monitoring the metal wear particles carried in large aperture lubricating oil tubes, the simulation optimization structure design was carried out based on the traditional three-coil inductance wear particle sensor. The numerical model of electromotive force induced by the wear particle sensor was established, and the coil distance and coil turns were simulated by finite element analysis software. When permalloy is covered on the surface of the excitation coil and induction coil, the background magnetic field at the air gap increases, and the induced electromotive force amplitude generated by wear particles is increased. The effect of alloy thickness on the induced voltage and magnetic field was analyzed to determine the optimum thickness, and increase the induction voltage of the alloy chamfer detection at the air gap. The optimal parameter structure was determined to improve the detection ability of the sensor. Ultimately, by comparing the extreme values of the induced voltage of various types of sensors, the simulation determined that the minimum allowable detection of the optimal sensor was 27.5 µm ferromagnetic particles.
Wide Temperature Range and Low Temperature Drift Eddy Current Displacement Sensor Using Digital Correlation Demodulation
Authors: Ma Tianxiang , Han Yuting , Xu Yongsen , Dai Pengzhang , Shen Honghai , Liu Yunqing
Published: May 2023

Conventional eddy-current sensors have the advantages of being contactless and having high bandwidth and high sensitivity. They are widely used in micro-displacement measurement, micro-angle measurement, and rotational speed measurement. However, they are based on the principle of impedance measurement, so the influence of temperature drift on sensor accuracy is difficult to overcome. A differential digital demodulation eddy current sensor system was designed to reduce the influence of temperature drift on the output accuracy of the eddy current sensor. The differential sensor probe was used to eliminate common-mode interference caused by temperature, and the differential analog carrier signal was digitized by a high-speed ADC. In the FPGA, the amplitude information is resolved using the double correlation demodulation method. The main sources of system errors were determined, and a test device was designed using a laser autocollimator. Tests were conducted to measure various aspects of sensor performance. Testing showed the following metrics for the differential digital demodulation eddy current sensor: nonlinearity 0.68% in the range of ±2.5 mm, resolution 760 nm, maximum bandwidth 25 kHz, and significant suppression in the temperature drift compared to analog demodulation methods. The tests show that the sensor has high precision, low temperature drift and great flexibility, and it can instead of conventional sensors in applications with large temperature variability.
Human Activity Recognition in the Presence of Occlusion
Authors: Vernikos Ioannis , Spyropoulos Theodoros , Spyrou Evaggelos , Mylonas Phivos
Published: May 2023

The presence of occlusion in human activity recognition (HAR) tasks hinders the performance of recognition algorithms, as it is responsible for the loss of crucial motion data. Although it is intuitive that it may occur in almost any real-life environment, it is often underestimated in most research works, which tend to rely on datasets that have been collected under ideal conditions, i.e., without any occlusion. In this work, we present an approach that aimed to deal with occlusion in an HAR task. We relied on previous work on HAR and artificially created occluded data samples, assuming that occlusion may prevent the recognition of one or two body parts. The HAR approach we used is based on a Convolutional Neural Network (CNN) that has been trained using 2D representations of 3D skeletal motion. We considered cases in which the network was trained with and without occluded samples and evaluated our approach in single-view, cross-view, and cross-subject cases and using two large scale human motion datasets. Our experimental results indicate that the proposed training strategy is able to provide a significant boost of performance in the presence of occlusion.
An Online Anomaly Detection Approach for Fault Detection on Fire Alarm Systems
Authors: Sousa Tomé Emanuel , Ribeiro Rita P. , Dutra Inês , Rodrigues Arlete
Published: May 2023

The early detection of fire is of utmost importance since it is related to devastating threats regarding human lives and economic losses. Unfortunately, fire alarm sensory systems are known to be prone to failures and frequent false alarms, putting people and buildings at risk. In this sense, it is essential to guarantee smoke detectors’ correct functioning. Traditionally, these systems have been subject to periodic maintenance plans, which do not consider the state of the fire alarm sensors and are, therefore, sometimes carried out not when necessary but according to a predefined conservative schedule. Intending to contribute to designing a predictive maintenance plan, we propose an online data-driven anomaly detection of smoke sensors that model the behaviour of these systems over time and detect abnormal patterns that can indicate a potential failure. Our approach was applied to data collected from independent fire alarm sensory systems installed with four customers, from which about three years of data are available. For one of the customers, the obtained results were promising, with a precision score of 1 with no false positives for 3 out of 4 possible faults. Analysis of the remaining customers’ results highlighted possible reasons and potential improvements to address this problem better. These findings can provide valuable insights for future research in this area.
Robust H-K Curvature Map Matching for Patient-to-CT Registration in Neurosurgical Navigation Systems
Authors: Kwon Ki Hoon , Kim Min Young
Published: May 2023

Image-to-patient registration is a coordinate system matching process between real patients and medical images to actively utilize medical images such as computed tomography (CT) during surgery. This paper mainly deals with a markerless method utilizing scan data of patients and 3D data from CT images. The 3D surface data of the patient are registered to CT data using computer-based optimization methods such as iterative closest point (ICP) algorithms. However, if a proper initial location is not set up, the conventional ICP algorithm has the disadvantages that it takes a long converging time and also suffers from the local minimum problem during the process. We propose an automatic and robust 3D data registration method that can accurately find a proper initial location for the ICP algorithm using curvature matching. The proposed method finds and extracts the matching area for 3D registration by converting 3D CT data and 3D scan data to 2D curvature images and by performing curvature matching between them. Curvature features have characteristics that are robust to translation, rotation, and even some deformation. The proposed image-to-patient registration is implemented with the precise 3D registration of the extracted partial 3D CT data and the patient’s scan data using the ICP algorithm.
Composite Backscatter Characteristics of Conductive/Dielectric Ships and Sea Surfaces with Breaking Waves under High Sea Conditions
Authors: Zhang Xiaoxiao , Su Xiang , Wu Zhensen
Published: May 2023

When a radar detects marine targets, the radar echo is influenced by the shape, size and dielectric properties of the targets, as well as the sea surface under different sea conditions and the coupling scattering between them. This paper presents a composite backscattering model of the sea surface and conductive and dielectric ships under different sea conditions. The ship scattering is calculated using the equivalent edge electromagnetic current (EEC) theory. The scattering of the sea surface with wedge-like breaking waves is calculated using the capillary wave phase perturbation method combined with the multi-path scattering method. The coupling scattering between ship and sea surface is obtained using the modified four-path model. The results reveal that the backscattering RCS of the dielectric target is significantly reduced compared with the conducting target. Furthermore, the composite backscattering of the sea surface and ship increases significantly in both HH and VV polarizations when considering the effect of breaking waves under high sea conditions at low grazing angles in the upwind direction, especially for HH polarization. This research offers valuable insights into optimizing radar detection of marine targets in varying sea conditions.
Wind Speed Prediction Based on Error Compensation
Authors: Jiao Xuguo , Zhang Daoyuan , Wang Xin , Tian Yanbing , Liu Wenfeng , Xin Liping
Published: May 2023

Wind speed prediction is very important in the field of wind power generation technology. It is helpful for increasing the quantity and quality of generated wind power from wind farms. By using univariate wind speed time series, this paper proposes a hybrid wind speed prediction model based on Autoregressive Moving Average-Support Vector Regression (ARMA-SVR) and error compensation. First, to explore the balance between the computation cost and the sufficiency of the input features, the characteristics of ARMA are employed to determine the number of historical wind speeds for the prediction model. According to the selected number of input features, the original data are divided into multiple groups that can be used to train the SVR-based wind speed prediction model. Furthermore, in order to compensate for the time lag introduced by the frequent and sharp fluctuations in natural wind speed, a novel Extreme Learning Machine (ELM)-based error correction technique is developed to decrease the deviations between the predicted wind speed and its real values. By this means, more accurate wind speed prediction results can be obtained. Finally, verification studies are conducted by using real data collected from actual wind farms. Comparison results demonstrate that the proposed method can achieve better prediction results than traditional approaches.
Two-Color-Thermography for Temperature Determination in Laser Beam Welding of Low-Melting Materials
Authors: Schwarzkopf Karen , Rothfelder Richard , Rasch Michael , Schmidt Michael
Published: May 2023

Spatial and temporal knowledge of temperature evolution is crucial in laser beam welding of low-melting materials such as aluminum alloys. Current temperature measurements are restricted to (i) one-dimensional temperature information (e.g., ratio-pyrometers), (ii) a priori knowledge of emissivity (e.g., thermography), and (iii) high-temperature regions (e.g., two-color-thermography). This study presents a ratio-based two-color-thermography system that enables acquiring spatially and temporally resolved temperature information for low-melting temperature ranges (<1200 K). The study demonstrates that temperature can be accurately determined despite variations in signal intensity and emissivity for objects emitting constant thermal radiation. The two-color-thermography system is further transferred into a commercial laser beam welding set-up. Experiments with varying process parameters are conducted, and the ability of the thermal imaging method to measure dynamic temperature behavior is assessed. Image artifacts presumably caused by internal reflections inside the optical beam path limit the direct application of the developed two-color-thermography system during dynamic temperature evolution.
Cognitive Radio with Machine Learning to Increase Spectral Efficiency in Indoor Applications on the 2.5 GHz Band
Authors: Soares Marilson Duarte , Passos Diego , Castellanos Pedro Vladimir Gonzalez
Published: May 2023

Due to the propagation characteristics in the 2.5 GHz band, the signal is significantly degraded by building entry loss (BEL), making coverage in indoor environments in some cases non-existent. Signal degradation inside buildings is a challenge for planning engineers, but it can be seen as a spectrum usage opportunity for a cognitive radio communication system. This work presents a methodology based on statistical modeling of data collected by a spectrum analyzer and the application of machine learning (ML) to leverage the use of those opportunities by autonomous and decentralized cognitive radios (CRs), independent of any mobile operator or external database. The proposed design targets using as few narrowband spectrum sensors as possible in order to reduce the cost of the CRs and sensing time, as well as improving energy efficiency. Those characteristics make our design especially interesting for internet of things (IoT) applications or low-cost sensor networks that may use idle mobile spectrum with high reliability and good recall.
LearningRlab: Educational R Package for Statistics in Computer Science Engineering
Authors: Cuadrado-Gallego Juan J. , Gómez Josefa , Tayebi Abdelhamid , Usero Luis , Hellín Carlos J. , Valledor Adrián
Published: May 2023

This paper describes and evaluates the educational interest of LearningRlab, an educational R package developed for teaching statistics in computer science engineering. The package was developed by final degree project students to be used as an educational environment for statistics students who evaluated the package and provided feedback for future versions. Such a process increases the motivation of both groups of students. This paper presents how the use of the R packages conceived and developed for engineering education can improve the learning process in the computer science engineering bachelor’s degree. Two different evaluations, one performed by a group of statistics students, and the other performed by final degree project students, were used to evaluate the impact on the learning process of the first version of the package to develop the second version of the package, which corrects and enhances the first version. The evaluation results show a positive effect on the learning process in both subjects. The analysis of the learning outcomes reflected in the grades of the experimental and control groups demonstrates that LearningRlab can be used as a teaching aid for statistics and final degree project subjects of the computer science engineering degree. The average laboratory grade of the students who used the package (5.76) was noticeably higher than the average laboratory grade of students who did not use it (1.84).
Combined Application of Organic and Inorganic Nitrogen and Seed Inoculation with Rhizobacteria (Stenotrophomonas maltophilia FA-9) Improved Productivity, Nitrogen Use Efficiency, and Economic Returns of Pearl Millet
Authors: Dawood Ahmad , Majeed Abdul , Ul-Allah Sami , Naveed Muhammad , Farooq Shahid , Sarwar Naeem , Hussain Mubshar
Published: May 2023

Nitrogen (N) availability and soil microbiota exert significant impacts on plant metabolic systems and yield. Different studies have indicated that yield and nitrogen use efficiency (NUE) of pearl millet (Pennisetum glaucum L.R.Br.) can be improved by the inoculation of N-fixing bacteria. However, the interactive effects of different N sources and bacteria inoculation on growth, productivity, and NUE of pearl millet have been less explored. Therefore, individual and interactive effects of different N sources (organic and inorganic) and bacteria inoculation on growth, productivity, and NUE of pearl millet were investigated in this study. Two different N sources, i.e., organic (farmyard manure) and inorganic (urea) alone or in 50% + 50% combinations (urea + FYM), were used to supply the recommended amount of N. Similarly, seeds were inoculated with two different N-fixing bacteria, i.e., endobacteria (Enterobacter sp. MN17) and rhizobacteria (Stenotrophomonas maltophilia FA-9). Urea + farmyard manure (FYM) and seed inoculation rhizobacteria improved soil attributes, yield-related traits, grain quality, NUE, and net economic returns. Soil porosity was significantly improved by seed inoculation with both bacteria and FYM application. Similarly, seed inoculation with rhizobacteria increased soil organic carbon by 45.45% and 34.88% during the 1st and 2nd year of the study, respectively. Urea + FYM application combined with rhizobacteria seed inoculation improved the number of grains per ear (23.49 and 23.63%), 1000-grain weight (5.76% and 7.85%), grain yield (23.19% and 25.0%), and NUE (33.83 and 48.15%). Similarly, grain quality was significantly improved by seed inoculation with both bacteria. Likewise, urea + FYM combined with rhizobacteria improved nitrogen use efficiency (NUE) by 33.83% and 48.15% in 2020 and 2021, respectively, compared to no N application and no seed inoculation. The highest economic returns (1506.4 and 1506.9 USD) were noted for urea + FYM application combined with rhizobacteria seed inoculation. Therefore, urea + FYM application combined with rhizobacteria (S. maltophilia FA-9) seed inoculation seemed a viable approach to improve grain yield, grain quality, NUE, and net economic returns of pearl millet.
Short-Term Prediction of PV Power Based on Combined Modal Decomposition and NARX-LSTM-LightGBM
Authors: Gao Hongbo , Qiu Shuang , Fang Jun , Ma Nan , Wang Jiye , Cheng Kun , Wang Hui , Zhu Yidong , Hu Dawei , Liu Hengyu , Wang Jun
Published: May 2023

Recently, solar energy has been gaining attention as one of the best promising renewable energy sources. Accurate PV power prediction models can solve the impact on the power system due to the non-linearity and randomness of PV power generation and play a crucial role in the operation and scheduling of power plants. This paper proposes a novel machine learning network framework to predict short-term PV power in a time-series manner. The combination of nonlinear auto-regressive neural networks with exogenous input (NARX), long short term memory (LSTM) neural network, and light gradient boosting machine (LightGBM) prediction model (NARX-LSTM-LightGBM) was constructed based on the combined modal decomposition. Specifically, this paper uses a dataset that includes ambient temperature, irradiance, inverter temperature, module temperature, etc. Firstly, the feature variables with high correlation effects on PV power were selected by Pearson correlation analysis. Furthermore, the PV power is decomposed into a new feature matrix by (EMD), (EEMD) and (CEEMDAN), i.e., the combination decomposition (CD), which deeply explores the intrinsic connection of PV power historical series information and reduces the non-smoothness of PV power. Finally, preliminary PV power prediction values and error correction vector are obtained by NARX prediction. Both are embedded into the NARX-LSTM-LightGBM model pair for PV power prediction, and then the error inverse method is used for weighted optimization to improve the accuracy of the PV power prediction. The experiments were conducted with the measured data from Andre Agassi College, USA, and the root mean square error (RMSE), mean absolute error (MAE), and mean absolute percentage error (MAPE) of the model under different weather conditions were lower than 1.665 kw, 0.892 kw and 0.211, respectively, which are better than the prediction results of other models and proved the effectiveness of the model.
Professionalizing Sharing Platforms for Sustainable Growth in the Hospitality Sector: Insights Gained through Hierarchical Linear Modeling
Authors: Ndaguba Emeka , Zyl Cina Van
Published: May 2023

The sharing economy relating to e-hospitality is threatened globally with sanctions and closure owing to incessant noise and partying complaints, as well as complaints relating to reckless driving, tax evasion, and its social and economic effect on residents and accommodation vendors of longer stay rentals. Because the government is seeking a balance in regulating the e-hospitality sector, we sought to explore how professionalism of the e-hospitality platforms could potentially contribute to the sustainable growth of the sector in local and regional communities. In our study we developed a conceptual narrative that distinguishes two dimensions of professionalism for the sharing economy, namely the ticket clipper and end-to-end model. Data for the research was obtained from Vacation Rental Data (Airdna). Airdna provides a databank for both Airbnb and VRBO/Stayz. For the study a dataset from Airdna for HomeAway, also popularly known as Stayz, was utilized as a representative sample from a tourism town in Western Australia. For analysis of the dataset, path/panel regression was utilized, with a hierarchical linear model subsequently adopted for cross-section and multi-sectional analysis. Findings in the study demonstrate that professionals tend to improve the overall rating, and where the overall rating mediates the relationship between management firm (property/apartment/accommodation venue) and price. It was further observed that no relationship exists between overall rating and the number of HomeAway supply types; nevertheless, professionals promote the image and reputation of the property. Contrary, bad, or negative e-hospitality reviews lead to avoidance by prospective visitors. Lastly, results from the study took the form of two theoretical contributions, namely the ticket clipper model and the end-to-end model. More complaints were received concerning ticket clippers and it was noted that this model has caused severe shutdown in several cities and regions. The end-to-end model appears to be more sustainable. Moreover, literature suggests that there are more complaints from residents concerning ticket clippers and it was noted that this model has caused severe shutdown in several cities, nonetheless the end-to-end model appears to be more sustainable.
How to Exploit Sustainable Food Consumption Habits of Individuals: Evidence from a Household Survey in Izmir, Türkiye
Authors: Biresselioglu Mehmet Efe , Kentmen-Cin Cigdem , Demir Muhittin Hakan , Savas Zehra Funda , Solak Berfu , Onder Burcin , Ceviker-Cinar Gozde , Ozcureci Berker
Published: May 2023

Sustainable production and consumption in the food supply chain are critical for the United Nations’ sustainable development goals (SDGs). Therefore, it is significant to identify the factors that shape individuals’ food consumption behaviour. Türkiye prioritises sustainable food consumption and the prevention of food loss and waste as a national focus. Accordingly, this study aims to identify the drivers of individuals’ food consumption habits in Türkiye through a survey with the participants being households in Izmir, the third most populous city in Türkiye. More specifically, the study has two main objectives: (i) to identify the factors influencing households’ food consumption preferences and (ii) to demonstrate how the factors concerning sustainable food consumption interact through a survey conducted in Izmir. Based on the state-of-art literature, an online survey was completed by 515 respondents in Izmir. Through an analysis of the survey responses, this study provides a descriptive analysis of socio-demographic variables and a correlation analysis between socio-demographics and sustainable food consumption behaviours, including food shopping behaviour, food purchasing and consumption behaviour, dietary habits, and food waste behaviour. The results demonstrate that socio-demographic factors such as age, gender, education level, income level, and the number of residents in the household are significant for sustainable food consumption behaviour. The survey results also demonstrate that the respondents do not consider carbon footprint generation from food production and transportation in their decisions or behaviours. The study’s main limitation is that the survey is implemented in a single city, Izmir. Future research may extend the scope to other cities in Türkiye, allowing a comparative analysis.
Geotechnical Characterization of Quito’s North-Central Zone as Applied to Deep Excavation in the Urban Setting
Authors: Capa Vicente E. , Torrijo F. Javier , Calderón Pedro A. , Hidalgo Signes Carlos
Published: May 2023

This paper describes an in-depth soil characterization study in the La Carolina financial district of Quito (Ecuador). As there was very little information available on the geotechnical structure of Quito’s volcanic soil, particularly in this area, where large-scale property development has taken place, the aim was to provide information on soil parameters to engineers working on large geotechnical and civil engineering projects based on the results of a thorough and comprehensive study of such properties. A series of field tests were performed at three different sites, where thin-walled tube samples were collected for lab testing to estimate the index properties and mechanical parameters. These index properties were then combined with conventional two-way drainage oedometer tests and stress-path triaxial testing to evaluate compressibility, stiffness and strength. The subsoil was found to be partly composed of slightly overconsolidated volcanic soils. X-ray Diffraction (XRD) and Scanning Electron Microscopy (SEM) analyses were also conducted to determine mineralogical and microstructural features and evaluate their influence on the mechanical behavior of the volcanic soil. This type of research is frequently applied to the study of landslides in urban environments, where it is essential to understand their failure mechanisms, especially in slopes generated by the construction of important engineering works. Therefore, based on this geotechnical characterization study, parameters were subsequently determined for the Mohr–Coulomb (MC), Hardening Soil (HS), and Hardening Soil with Small-Strain Stiffness (HSsmall) soil constitutive models, and these were applied to a numerical study of the Soil Nailing system behavior for the construction of a five-level underground car parking structure of an important building located in the north-central sector of the city of Quito. It was verified that the HSsmall and HS constitutive soil models better reproduce the behavior of this type of structure. Finally, the multiple geotechnical parameters determined in this study significantly contribute to the analysis of these structures in this soil type.
Effects of Household Clean Fuel Combustion on the Physical and Mental Health of the Elderly in Rural China
Authors: Chen Huiying , Gu Shuyan , Jia Cangcang , Gu Hai , Xu Qinglin , Lin Zi
Published: May 2023

Against the backdrop of the construction of an ecological civilization and the “Healthy China 2030” initiative, access to clean fuels is crucial for achieving optimal health and wellbeing, as well as sustainable social development. The purpose of this study is to investigate the effect of household clean fuel combustion (HCFC) on multiple dimensions of health among older adults while shedding light on the potential mechanisms. We performed a cross-sectional study of the data from the 2018 China Health and Retirement Longitudinal Survey, and we surmounted the underlying issues of endogeneity with the application of propensity score matching and the instrumental variable strategy. The results revealed that HCFC has positive effects on the health of older adults, particularly by improving their psychological wellbeing. The adoption of clean fuels among the elderly was associated with a significant increase in SRH by 3.06% to 3.42% and a decrease in CES-D by 7.96% to 8.28%. These positive environmental health effects became stronger among the elderly under the age of 75, as well as among those who were educated and had chronic diseases. Moreover, the results demonstrated that HCFC significantly alleviated chronic pain and increased social interaction among older adults, highlighting two potential pathways for promoting their wellbeing. Given that a significant number of rural households in China rely on polluting fuels, targeted strategies are crucial for promoting the use of clean fuels, particularly for vulnerable populations.
The Impact of Carbon Trading Policy on Breakthrough Low-Carbon Technological Innovation
Authors: Fang Chengyu , Wang Wanyi , Wang Weidong
Published: May 2023

This paper studies the impact of carbon trading policies on breakthrough low-carbon technological innovation through a difference-in-differences model based on the panel data of 30 provinces in China. Through descriptive statistics, regression analysis, a robustness test and heterogeneity analysis of the data, the results show that: (1) carbon trading policy has a significant effect on overall low-carbon technological innovation and gradual low-carbon technological innovation, and carbon trading policy lag can significantly promote breakthrough low-carbon technological innovation. (2) The timeliness of carbon trading policy in promoting breakthrough low-carbon technological innovation is different in different regions. The effect can quickly be displayed in some provinces, but there is a lag effect in other provinces. (3) Lags in carbon trading policy have promoted a significant increase in renewable energy technology patents and CCS-related technology patents, and contributed more to the increase in renewable energy technology patents.
The Impact of Carbon Emission Trading Policy on Enterprise ESG Performance: Evidence from China
Authors: Zhang Yadu , Zhang Yiteng , Sun Zuoren
Published: May 2023

The carbon emission trading system profoundly impacts enterprises’ sustainable development as an important market incentive environmental regulation tool. Through data collected from Chinese A-share listed enterprises in Shanghai and Shenzhen from 2011 to 2019 and Bloomberg ESG score data, this paper empirically analyses the impact of carbon emission trading policy on enterprise ESG performance and its channel mechanism using the difference-in-difference (DID) method. Results of this study indicate that carbon emission trading policy improves enterprise ESG performance significantly, and robustness tests confirm these findings. Carbon emission trading policy can encourage enterprises to enhance their R&D investments and promote internal controls, ultimately enhancing their ESG performance. Additionally, carbon emission trading policy positively impacts ESG performance in low-carbon enterprises, enterprises where the CEO is separated from the company, enterprises with a high degree of digital transformation, and enterprises receiving high government subsidies. This paper extends our research into the economic implications of carbon emission trading policy, enriching the literature on market-based environmental regulation policies’ impact on enterprise ESG performance. With respect to governments’ use of carbon emission trading to regulate enterprises environmentally, this paper provides theoretical guidance. It has significant practical implications for improving enterprise ESG performance and sustainability.
Investigating the Impact of Shifting the Brick Kiln Industry from Conventional to Zigzag Technology for a Sustainable Environment
Authors: Bashir Zain , Amjad Muhammad , Raza Syed Farhan , Ahmad Shafiq , Abdollahian Mali , Farooq Muhammad
Published: May 2023

The brick kiln industry is one of the largest and most highly unregulated industrial sectors in developing countries. Most of the kilns use low-quality coal as primary fuel along with small quantities of bagasse, rice husk, and wooden chips. As a result of inefficient methods of combustion in conventional brick kilns, such as fixed chimney Bull’s trench kilns (FCBTKs), harmful pollutants are emitted in high quantities, which ultimately deteriorate the environment and are widely in operation in Pakistan. The most prominent harmful pollutants include carbon dioxide (CO2), carbon monoxide (CO), sulphur dioxide (SO2), black carbon (BC), and particulate matter less than 2.5 microns (PM2.5). Over the years, new technologies have been adopted by developed countries for the reduction of environmental burdens. One of these technologies is induced draught zigzag kilns (IDZKs), or zigzag kilns (ZZKs), technology, which effectively improves the combustion across the path of bricks stacked in a zigzag pattern. For the mass adoption of this technology, environmental assessment and comparison of both technologies is a crucial step. Both types of kiln sites are investigated for the analysis of their emissions and their environmental impact in this work. Carbon mass balance equations are used for the calculation of emission factors. Collected inventory data is then used for the life cycle assessment of both types of kilns using open LCA (version 1.10.3) and the Eco-invent database. According to the study, ZZK technology outperforms FCBTK in all aspects. The analysis of the specific energy consumption (SEC) of fired bricks for each kiln type reveals that ZZKs require 30% less energy than the conventional FCBTK. This implies that ZZKs demand lesser fuel than FCBTKs. The zigzag technology adoption scenario, in particular, can lead to approximately 30% lower CO2 emissions, which can be further reduced by up to 80% when taking into account black carbon (BC) emissions. Additionally, the adoption of zigzag technology can result in a 35% decrease in PM2.5 emissions. The study shows that adopting ZZK technology significantly reduces impact categories, such as particulate matter formation (PMF), photochemical oxidant formation (POF), and terrestrial acidification (TA) by 63%, 93%, and 95%, respectively.
Impact of Pavement Defects on Traffic Operational Performance
Authors: Hashim Ibrahim H. , Badawy Rania M. , Heneash Usama
Published: May 2023

The present research investigates the influence of pavement distresses on traffic operational performance in terms of speed and capacity. The pavement distresses are expressed by the pavement condition index (PCI). Three roads of multi-lane highways located on the road network of Kafrelsheikh governorate, Egypt, were chosen as a case study. Road geometry, pavement condition characteristics, and traffic volume were collected manually from 23 sections for field data surveys. For speed measurements, spot speed data were gathered using the stopwatch method under the free-flow conditions, and the percentile speeds (V50% and V85%) have been calculated for different vehicles classes. Traffic capacity was determined via extrapolation of the quadratic function. Regression analysis models were created to express the relationships of both the percentile speeds and traffic capacity as dependent variables, and three independent variables (PCI, lane, and shoulder widths). These empirical mathematical relations help in developing road design and maintenance works on multi-lane highways. The main conclusion of the study demonstrated that traffic operational performance was more significantly affected by pavement distresses than road geometry. Moreover, the results indicated that pavement distresses have a significant impact on light vehicles more than heavy ones.
Optimization Method of Transfer Streamlines in Integrated Passenger Hubs Based on 3D Spatial Perspective
Authors: Huang Zhaoguo , Chen Junlan , Guo Xiucheng , Ma Changxi
Published: May 2023

To optimize the functional space layout of various transportation modes of the integrated passenger transport hub, and improve the transfer efficiency and service quality of the hub, a quantitative analysis of the transfer flow lines of the integrated passenger hub is carried out. The research clarifies the layout factors of the functional areas of the “get on and drop off areas” for each mode of transportation, generates a candidate set of the placement of each functional area, and determines the priority ranking of the candidate sets and the transfer starting and end locations. Based on the analysis of passenger route selection factors, the basic transfer streamline network is generated. The basic network is distributed according to the improved shortest path allocation algorithm, and the relevant parameters are calculated to simplify the initial transfer streamline network, generate and compare the initial network plan of the transfer streamline. Take Wuxi Integrated Passenger Transport Hub as an example to verify: when the weight coefficient λ = 0.65 and the number of allocations n = 207, the optimal solution T = 2,738,027 s is obtained. As the calculation is based on the 15,000 passenger transfer flow at Wuxi Station, the optimized average transfer time per person is 3 min 2 s. Compared with the current average transfer time per person at Wuxi Station of 4.5 min, the optimization effect of this paper is significant. The initial network generation and comparison method of the transfer flow line enables the space layout of the transportation modes of the hub to be coordinated with the transfer flow line design, and solves the problem of the transfer flow line design when the hub building space layout is determined. The hub is designed to meet the requirements of functional space layout, passenger transfer needs and interchange efficiency at the initial stage of architectural design.
Comprehensive Benefit Evaluation of Power Grid Investment Considering Renewable Energy Development from the Perspective of Sustainability
Authors: Tian Hongliang , Zhao Liang , Guo Sen
Published: May 2023

To cope with the rapid development of renewable energy, the power grid system needs to invest in and construct transmission and distribution projects. Means of evaluating the economic, social, and environmental benefits generated by power grid investment are of great significance for enterprise cost recovery and government policy formulation. In this paper, an evaluation of the comprehensive benefit of a power grid investment considering renewable energy development from the perspective of sustainability is developed through the use of a hybrid multi-criteria decision-making (MCDM) method. The entropy weight method and fuzzy best worst method (BWM) are jointly employed to weight the comprehensive benefit criteria, which include economic, social, and environmental criteria and eight sub-criteria, and the measurement of alternatives and ranking according to compromise solution (MARCOS) method is utilized to evaluate the comprehensive benefit of a power grid investment. Five power grid investment projects for connecting renewable energy generation to a power grid in Ningxia, China, are selected as a case study, and the results indicate that the power grid investment project PGIP#1 has the largest comprehensive benefit (0.7099), followed by PGIP#3 (0.6800), PGIP#2 (0.6709), PGIP#5 (0.5959), and PGIP#4 (0.5861). The sensitivity analysis shows that the comprehensive benefit of PGIP#1 is always the best, indicating the robustness of the proposed method. By employing the proposed MCDM method to assess the comprehensive benefit of power grid investment projects, this research identifies outstanding projects which can provide guidance for the management of power grid investment and promote the sustainable development of the power industry.