bluuebunny/crossref_metadata_2025 huggingface dataset:
The Dynamic Sea Clutter Simulation of Shore-Based Radar Based on Stokes Waves
Authors: Luo Feng , Feng Yao , Liao Guisheng , Zhang Linrang
Published: August 2022

The sea clutter model based on the physical sea surface can simulate radar echo at different times and positions and is more suitable for describing dynamic sea clutter than the traditional models based on statistical significance. However, when applying the physical surface model to shore-based radar, the effects of wave nonlinearity, breaking wave, shadow, and radar footprint size must be considered. In this paper, a dynamic sea clutter simulation scheme based on a nonlinear wave is proposed that uses random Stokes waves instead of linear superposition waves to simulate the nonlinear dynamic sea surface and then calculates echo in the form of scattering cells. In this process, the relationship between wind speed and the nonlinear factor of the Stokes wave is derived, a simple model of shadow modulation is provided, and a method for appending the sea clutter spikes formed by breaking waves is developed. The experimental results show that the simulated sea clutter and the real measured clutter have good consistency in intensity, amplitude statistical distribution, Doppler spectrum, and spatiotemporal correlation. The proposed scheme is suitable for the sea clutter simulation of shore-based radar and can also adjust the relevant parameters to extend to other types of sea clutter simulation.
FiFoNet: Fine-Grained Target Focusing Network for Object Detection in UAV Images
Authors: Xi Yue , Jia Wenjing , Miao Qiguang , Liu Xiangzeng , Fan Xiaochen , Li Hanhui
Published: August 2022

Detecting objects from images captured by Unmanned Aerial Vehicles (UAVs) is a highly demanding task. It is also considered a very challenging task due to the typically cluttered background and diverse dimensions of the foreground targets, especially small object areas that contain only very limited information. Multi-scale representation learning presents a remarkable approach to recognizing small objects. However, this strategy ignores the combination of the sub-parts in an object and also suffers from the background interference in the feature fusion process. To this end, we propose a Fine-grained Target Focusing Network (FiFoNet) which can effectively select a combination of multi-scale features for an object and block background interference, which further revitalizes the differentiability of the multi-scale feature representation. Furthermore, we propose a Global–Local Context Collector (GLCC) to extract global and local contextual information and enhance low-quality representations of small objects. We evaluate the performance of the proposed FiFoNet on the challenging task of object detection in UAV images. A comparison of the experiment results on three datasets, namely VisDrone2019, UAVDT, and our VisDrone_Foggy, demonstrates the effectiveness of FiFoNet, which outperforms the ten baseline and state-of-the-art models with remarkable performance improvements. When deployed on an edge device NVIDIA JETSON XAVIER NX, our FiFoNet only takes about 80 milliseconds to process an drone-captured image.
Identifiability Analysis for Configuration Calibration in Distributed Sensor Networks
Authors: Liu Xiaoyu , Wang Tong , Chen Jinming
Published: August 2022

In this work, the parameter identifiability of sensor position perturbations in a distributed network is analyzed through establishing the link between rank of the Jacobian matrix and parameter identifiability under Gaussian noise. Here, the calibration is classified as either external or internal, dependent on whether auxiliary sources are exploited. It states that, in the case of internal calibration, sensor position perturbations can be precisely calibrated when the position of a sensor and orientation to a second sensor along with the coordinate of a third sensor along some axis, are known. In the case of external calibration where auxiliary sources are introduced to support the process, the identifiability condition for configuration calibration is to have at least three noncollinear auxiliary sources with the distributed sensor network avoiding the collinear and coplanar geometries. As the assumption of small perturbations is considered, the parameter identifiability is capable of being measured by virtue of the Bayesian Cramer–Rao lower bound (BCRLB), after asymptotical tightness of the BCRLB is verified. Simulations corroborate well with the theoretical development.
Plausible Detection of Feasible Cave Networks Beneath Impact Melt Pits on the Moon Using the Grail Mission
Authors: Hong Ik-Seon , Yi Yu
Published: August 2022

In the future, when humans build their bases on terrestrial planets and their moons, caves will be the safest place for inhabitation. Large holes, believed to be cave entrances, have been discovered on the Moon, along with small features called “impact melt pits.” In the Gravity Recovery and Interior Laboratory (GRAIL) gravity model, which is expressed in spherical harmonics (SH), it is difficult to express the gravity anomaly created by a small empty space below the surface. Nevertheless, we propose that a cave network, akin to an anthill, exists under the impact melt pits discovered on the Moon. This is because we think it is natural to apply a network created by Earth’s small caves to the Moon. We obtained accurate Bouguer gravity measurements by calculating regional crustal density using localized admittance of the study area and detected weak gravity (mass deficit) information. By increasing the degrees and order of SH at regular intervals, we estimated the change in gravity at a specific position at high degrees and order, thereby extracting shallow depth information. To validate our method, we compared our results with those of existing studies that analyzed the previously known Marius Hills Hole (MHH) area. The analysis of seven regions in our study area revealed a mass deficit in some impact melt pits in four lunar regions (Copernicus, King, Stevinus, and Tycho). We propose that there is a cave network in this region, indicated by the gravitation reduction in the impact melt pits region. Our results can be useful for the selection of landing sites for future in situ explorations of lunar caves.
Distribution and Mobility of Coseismic Landslides Triggered by the 2018 Hokkaido Earthquake in Japan
Authors: Lu Jiayan , Li Weile , Zhan Weiwei , Tie Yongbo
Published: August 2022

At 3:08 on 6 September 2018 (UTC +9), massive landslides were triggered by an earthquake of Mw 6.6 that occurred in Hokkaido, Japan. In this paper, a coseismic landslide inventory that covers 388 km2 of the earthquake-impacted area and includes 5828 coseismic landslides with a total landslide area of 23.66 km2 was compiled by using visual interpretations of various high-resolution satellite images. To analyze the spatial distribution and characteristics of coseismic landslides, five factors were considered: the peak ground acceleration (PGA), elevation, slope gradient, slope aspect, and lithology. Results show more than 87% of the landslides occurred at 100 to 200 m elevations. Slopes in the range of 10~20°are the most susceptible to failure. The landslide density of the places with peak ground acceleration (PGA) greater than 0.16 g is obviously larger than those with PGA less than 0.02 g. Compared with the number and scale of coseismic landslides caused by other strong earthquakes and the mobility of the coseismic landslides caused by the Haiyan and Wenchuan earthquakes, it was found that the distribution of coseismic landslides was extremely dense and that the mobility of the Hokkaido earthquake was greater than that of the Wenchuan earthquake and weaker than that of the Haiyuan earthquake, and is described by the following relationship: L = 18.454 ∗ H0.612. Comparative analysis of coseismic landslides with similar magnitude has important guiding significance for disaster prevention and reduction and reconstruction planning of landslides in affected areas.
Exploring Transferable Techniques to Retrieve Crop Biophysical and Biochemical Variables Using Sentinel-2 Data
Authors: Kganyago Mahlatse , Adjorlolo Clement , Mhangara Paidamwoyo
Published: August 2022

The current study aimed to determine the spatial transferability of eXtreme Gradient Boosting (XGBoost) models for estimating biophysical and biochemical variables (BVs), using Sentinel-2 data. The specific objectives were to: (1) assess the effect of different proportions of training samples (i.e., 25%, 50%, and 75%) available at the Target site (DT) on the spatial transferability of the XGBoost models and (2) evaluate the effect of the Source site (DS) (i.e., trained) model accuracy on the Target site (i.e., unseen) retrieval uncertainty. The results showed that the Bothaville (DS) → Harrismith (DT) Leaf Area Index (LAI) models required only fewer proportions, i.e., 25% or 50%, of the training samples to make optimal retrievals in the DT (i.e., RMSE: 0.61 m2 m−2; R2: 59%), while Harrismith (DS) →Bothaville (DT) LAI models required up to 75% of training samples in the DT to obtain optimal LAI retrievals (i.e., RMSE = 0.63 m2 m−2; R2 = 67%). In contrast, the chlorophyll content models for Bothaville (DS) → Harrismith (DT) required significant proportions of samples (i.e., 75%) from the DT to make optimal retrievals of Leaf Chlorophyll Content (LCab) (i.e., RMSE: 7.09 µg cm−2; R2: 58%) and Canopy Chlorophyll Content (CCC) (i.e., RMSE: 36.3 µg cm−2; R2: 61%), while Harrismith (DS) →Bothaville (DT) models required only 25% of the samples to achieve RMSEs of 8.16 µg cm−2 (R2: 83%) and 40.25 µg cm−2 (R2: 77%), for LCab and CCC, respectively. The results also showed that the source site model accuracy led to better transferability for LAI retrievals. In contrast, the accuracy of LCab and CCC source site models did not necessarily improve their transferability. Overall, the results elucidate the potential of transferable Machine Learning Regression Algorithms and are significant for the rapid retrieval of important crop BVs in data-scarce areas, thus facilitating spatially-explicit information for site-specific farm management.
Multi-Scale Object Detection with the Pixel Attention Mechanism in a Complex Background
Authors: Xiao Jinsheng , Guo Haowen , Yao Yuntao , Zhang Shuhao , Zhou Jian , Jiang Zhijun
Published: August 2022

The object detection task is usually affected by complex backgrounds. In this paper, a new image object detection method is proposed, which can perform multi-feature selection on multi-scale feature maps. By this method, a bidirectional multi-scale feature fusion network was designed to fuse semantic features and shallow features to improve the detection effects of small objects in complex backgrounds. When the shallow features are transferred to the top layer, a bottom-up path is added to reduce the number of network layers experienced by the feature fusion network, reducing the loss of shallow features. In addition, a multi-feature selection module based on the attention mechanism is used to minimize the interference of useless information in subsequent classification and regression, allowing the network to adaptively focus on appropriate information for classification or regression to improve detection accuracy. Because the traditional five-parameter regression method has severe boundary problems when predicting objects with large aspect ratios, the proposed network treats angle prediction as a classification task. The experimental results on the DOTA dataset, the self-made DOTA-GF dataset and the HRSC 2016 dataset show that, compared with several popular object detection algorithms, the proposed method has certain advantages in detection accuracy.
Quality by Design Approach for a Multicomponent Quantification Using HPLC-PDA and HPLC-MS: Application to Dosage Form and Biological Body Fluids
Authors: Al-Tannak Naser F. , Al-Shatti Bashayer J. , Al Ali Abdullah S. , Hemdan A.
Published: August 2022

A multicomponent pharmaceutical that contains loratadine, paracetamol, and pseudoephedrine was quantified using HPLC-PDA. The three analytes were well-separated and quantified in the dosage form on a C-18 column using a gradient mobile phase. A quality by design strategy was followed to achieve the challenging separation. Screening and optimization steps were carried out to investigate the effect of many factors on the studied responses with a minimum number of runs. The ANOVA of the factorial model showed that % acetonitrile (factor A), flow rate (factor B), and pH (factor C) were significant. The detection of the analytes’ peaks was carried out using a PDA detector at 248nm for loratadine and paracetamol, and 214 nm for pseudoephedrine. The second method was SPE-HPLC-MS, where the three analytes and desloratadine, the active metabolite of loratadine, were quantified in spiked plasma and urine, using betamethasone valerate as an internal standard. The recovery of the analytes from body fluids was above 96%, and the LOQ was below 0.5 ng/mL. The validation of the developed HPLC-PDA method was achieved as per ICH guidelines, whereas the HPLC-MS method was validated according to FDA guidelines for bioanalytical method validation. The results were compared with the reported method, and no significant differences were found.
Morphological Assessment of River Stability: Review of the Most Influential Parameters
Authors: Haron Nor Azidawati , Yusuf Badronnisa , Sulaiman Mohd Sofiyan , Razak Mohd Shahrizal Ab , Nurhidayu Siti
Published: August 2022

River health assessments in the form of morphological approaches are crucial to determining the stability of a river system. Human interference in the natural river landscapes has altered the regime of river flows in the past. The catastrophes arising from the regime alteration are varied: excessive erosion and sedimentation, low carrying capacity, depletion of water yield, and many more. Past researchers have formulated numerous assessments to examine the stability of a river system. Still, arguments are prevalent due to the opinionated nature of the evaluation and a lack of parameters about river equilibrium. This paper reviews the past approaches to assessing channel stability by revisiting the most influential parameters adopted in the assessment process. An Analytical Hierarchy Process (AHP) was employed to find the prioritization of the selected parameters. This study found that a field survey is the most preferred method of river assessment instead of the other techniques such as remote sensing, modeling, and rapid field assessment. The most influential parameters (top 5) that determine the stability of a river system are (1) channel forms, (2) channel dimensions, (3) channel substrates, (4) channel pattern, and (5) bank profile. Those parameterizations are crucial to determining the stability of a river system.
Marine Construction Waste Recycling Mechanism Considering Public Participation and Carbon Trading: A Study on Dynamic Modeling and Simulation Based on Sustainability Policy
Authors: Wang Junwu , Song Yinghui , Wang Wei , Wang Suikuan , Guo Feng , Lu Jiequn
Published: August 2022

The classification and recycling of construction waste is important for reducing waste emissions, preventing marine pollution, and protecting the natural environment, which can promote carbon trading and carbon sink cycles. Based on the evolutionary game theory, this paper investigated the evolutionary decision-making process and stable strategies of three stakeholders in the construction waste recycling system, namely, the Department of Environment Regulation (DER), the Construction Waste Recycler (CWR), and the Construction Project Contractor (CPC), and analyzed the main factors affecting the stakeholders’ strategies, the evolutionary stable strategies and stable conditions from the perspective of public participation and carbon trading. Then, a DER-CWR-CPC benefit matrix and a replicator dynamics equation representing strategy selection were constructed, in which parameters represent the interest relationship of the three parties, and evolutionary stable strategy (ESS) points were obtained by solving the Jacobian matrix. Finally, the validity of the model was verified by taking the actual values into the simulation. The results showed that DER needs to actively participate in the early stage of the development of the construction waste classification and recycling system, but with the increase of enterprises choosing to recycle construction waste, DER can gradually reduce its intervention in these enterprises. Setting reasonable incentives and penalties, mobilizing public participation, and developing cleaner construction waste sorting equipment to obtain more carbon emission trading targets can facilitate the development of construction waste recycling systems.
A Hybrid Deep Learning Model for Short-Term Traffic Flow Pre-Diction Considering Spatiotemporal Features
Authors: Zhou Shenghan , Wei Chaofan , Song Chaofei , Fu Yu , Luo Rui , Chang Wenbing , Yang Linchao
Published: August 2022

Traffic flow prediction is one of the basic, key problems with developing an intelligent transportation system since accurate and timely traffic flow prediction can provide information support and decision support for traffic control and guidance. However, due to the complex characteristics of traffic information, it is still a challenging task. This paper proposes a novel hybrid deep learning model for short-term traffic flow prediction by considering the inherent features of traffic data. The proposed model consists of three components: the recent, daily and weekly components. The recent component is integrated with an improved graph convolutional network (GCN) and bi-directional LSTM (Bi-LSTM). It is designed to capture spatiotemporal features. The remaining two components are built by multi-layer Bi-LSTM. They are developed to extract the periodic features. The proposed model focus on the important information by using an attention mechanism. We tested the performance of our model with a real-world traffic dataset and the experimental results indicate that our model has better prediction performance than those developed previously.
Modification of Iron-Tailings Concrete with Biochar and Basalt Fiber for Sustainability
Authors: Chen Zhewen , Wu Nianze , Song Yuying , Xiang Junchen
Published: August 2022

Currently, less than 15% of waste iron tailings are utilized. Iron tailings can be used as fine aggregate in concrete, but this kind of concrete has no coarse aggregate, resulting in low strength. Additionally, iron tailings contain some heavy metals, which will cause environmental pollution if improperly treated. In this study, the mechanical properties, sulfate resistance, and pore structure distribution of basalt fiber-biochar-concrete (PFB) were studied. Where basalt is to enhance the mechanical properties of samples, and biochar is to adsorb heavy metals in iron tailings, to prepare environmentally friendly materials. Unconfined compressive strength (UCS) test, flexural strength (FS), sulfate immersion test, leaching behavior, and mercury intrusion porosimetry (MIP) test were used to study the performance of the samples, and X-ray diffraction (XRD), Fourier transform infrared spectrometer (FTIR), and scanning electron microscope (SEM) was used to characterize the samples, explaining the change mechanism of the macroscopic test. The results show that the compressive strength of PFB increased by 2.5% but the flexural strength increased by 12%. The basalt and biochar improve the pore size distribution of samples, that is, the pore size greater than 10 nm is reduced while the pore size between 2 and 6 nm is increased. Biochar can effectively adsorb heavy metals of Cu, Zn, Pb, and Cd, and their leaching concentration is reduced by 50–70%. Basalt fiber improves the mixing performance of concrete, while biochar with a small particle size fills the micro pores in concrete; this paper provides a new idea of sustainability for the preparation of environmentally friendly materials and the utilization of waste iron tailings.
Impact of Producer Service Agglomeration on Carbon Emission Efficiency and Its Mechanism: A Case Study of Urban Agglomeration in the Yangtze River Delta
Authors: Ma Yaoshan , Yao Qingyu
Published: August 2022

As an important part of the economic system of urban agglomeration, the agglomeration of producer services (APS) has become a key contributor to regional low-carbon development. This study analyzes the spatial effect of APS on carbon emission efficiency (CEE) as well as its mechanism and heterogeneity using the panel data of 41 cities in the Yangtze River Delta (YRD) region from 2005 to 2019. First, a U-shaped relationship exists between APS and CEE in both local and neighboring areas. Second, the non-linear relationship between APS and CEE is generated by allocation effects, structural effects and technology effects. Third, the effect of APS on CEE is constrained by the heterogeneity of urban characteristics, in which human capital, fiscal expenditure, and information infrastructure all support and positively moderate the energy-saving and carbon-reduction effect of APS. Fourth, the impact of externalities of APS on CEE varies, both the Marshall–Arrow–Romer (MAR) and Porter externalities having a U-shaped relationship with the CEE of neighboring areas but Jacobs externalities having no significant influence on the CEE of the surrounding areas. The findings of this study indicate that increasing the scale of APS in urban agglomeration, promoting the diversification and division of labor and the cooperation of industries across areas, and promoting the process of city–industry integration are important for achieving the goal of carbon peaking and carbon neutrality in the YRD region.
Community Engagement via Mural Art to Foster a Sustainable Urban Environment
Authors: Petronienė Saulė , Juzelėnienė Saulutė
Published: August 2022

Community is a key element in a sustainable urban environment and possesses a capacity to become a major contributor in the development of it. Therefore, it is essential for communities to be active and engaged. The concept of a community and its engagement via mural art is addressed in this article. The aim of the research is to investigate the impact of mural art on community engagement and the contribution of communities to a sustainable urban environment. The research was based on materials obtained during the implementation of the “Murals for Communities” project. The aim of the project was to address the issues of community engagement in three cities with social disconnection: Waterford (Ireland), Heerlen (the Netherlands), and Kaunas (Lithuania). The engagement of the communities through mural art creation was initiated through community engagement workshops where the communities were encouraged to participate in collaborative actions resulting in the improvement of the urban environment and the strengthening of the communities themselves. In total, 54 community engagement workshops were organized and 18 murals were created in the three partner cities. The entire process of community engagement was a well-coordinated and structured framework of actors and events, where each of them played a significant role. The results of the research revealed that community engagement workshops were used as a successful tool for community bonding and strengthening. Hence, mural art is a successful tool for sustainable community development, and the process of mural creation with its various stages enables community members to be active participants of social interaction and developers of both a sustainable community and a sustainable urban environment.
Arterial Coordination Control Optimization Based on AM–BAND–PBAND Model
Authors: Li Min , Luo Dijia , Liu Bilong , Zhang Xilong , Liu Zhen , Li Mengshan
Published: August 2022

The green wave coordinated control model has evolved from the basic bandwidth maximization model to the multiweight approach to an asymmetrical multiband model and a general signal progression model with phase optimization to improve the operational efficiency of urban arterial roads and reduce driving delays and the amount of exhaust gas generated by vehicles queuing at intersections. However, most of the existing green wave models of arterial roads are based on a single phase pattern and little consider the optimization of the combination of multiple phase patterns. Initial queue clearing time is also considered at the green wave progression line in the time–space diagram, which leads to a waste of green light time. This study proposes a coordination control optimization method based on an asymmetrical multiband model with phase optimization to address the abovementioned problem. This model optimizes four aspects in the time–distance diagram: phase pattern selection, phase sequence, offset, and queue clearing time. Numerical experiments were conducted using the VISSIM micro traffic simulation tool for intersections along Kunlunshan South Road in Qingdao, and the effect of green wave coordination was evaluated using hierarchical analysis and compared with the signal-timing schemes generated by the four models: the multiweight approach, the improved multiweight approach, an asymmetrical multiband model, and a general signal progression model with phase optimization. The results show that an asymmetrical multiband model with phase optimization obtains a total bandwidth of 314 s in both directions. In the outbound direction, average number of stops, average travel speed, average travel time, and average delay time improve by 16%, 7.9%, 17.9%, and 15.6%, respectively. In the inbound direction, they improve by 43.7%, 16.1%, 40.7%, and 36%, respectively. Polluting gas emissions and fuel consumption improve by 17.9%. The applicability of the optimization method under different traffic flow conditions is analyzed, and results indicate a clear control effect when the traffic volume is moderate and the turning vehicles on the feeder roads are few. This work can provide a reference for the optimization of subsequent arterial signal coordination and also has indirect significance for environmental protection to a certain extent.
Travel Time Prediction for Traveler Information System in Heterogeneous Disordered Traffic Conditions Using GPS Trajectories
Authors: Sihag Gurmesh , Parida Manoranjan , Kumar Praveen
Published: August 2022

Precise travel time prediction allows travelers and system controllers to be aware of the future conditions on roadways and helps in pre-trip planning and traffic control strategy formulation to lessen the travel time and mitigate traffic congestion problems. This research investigates the possibility of using the GPS trajectory dataset for travel time prediction in Indian traffic conditions having heterogeneous disordered traffic and improvement in prediction accuracy by shifting from the traditional historical average method to modern machine learning algorithms such as linear regressions, decision tree, random forest, and gradient boosting regression. The present study uses massive location data consisting of historical trajectories that were collected by installing GPS devices on the probe vehicles. A 3.6 km long stretch of the Delhi–Noida Direct (DND) flyway is selected as a case study to predict the travel time and compare the performance as well as the efficiency of various travel time prediction algorithms.
A New Deep Learning Restricted Boltzmann Machine for Energy Consumption Forecasting
Authors: Xu Aoqi , Tian Man-Wen , Firouzi Behnam , Alattas Khalid A. , Mohammadzadeh Ardashir , Ghaderpour Ebrahim
Published: August 2022

A key issue in the desired operation and development of power networks is the knowledge of load growth and electricity demand in the coming years. Mid-term load forecasting (MTLF) has an important rule in planning and optimal use of power systems. However, MTLF is a complicated problem, and a lot of uncertain factors and variables disturb the load consumption pattern. This paper presents a practical approach for MTLF. A new deep learning restricted Boltzmann machine (RBM) is proposed for modelling and forecasting energy consumption. The contrastive divergence algorithm is presented for tuning the parameters. All parameters of RBMs, the number of input variables, the type of inputs, and also the layer and neuron numbers are optimized. A statistical approach is suggested to determine the effective input variables. In addition to the climate variables, such as temperature and humidity, the effects of other variables such as economic factors are also investigated. Finally, using simulated and real-world data examples, it is shown that for one year ahead, the mean absolute percentage error (MAPE) for the load peak is less than 5%. Moreover, for the 24-h pattern forecasting, the mean of MAPE for all days is less than 5%.
Between Expert and Novice: Identity Transition from Teacher to Student as Sustainable Agentic Construction
Authors: Xu Xing , Sit Helena , Li Hui
Published: August 2022

Education for sustainable development in foreign language teaching and learning impacts students’ lives in their current or future endeavours. Despite a wide body of literature elucidating identity navigation of EFL teachers and international doctoral students, a relative scarcity of scholarship casts light on the in-between cohort, namely those who transit from the former to the latter. Via a holistic lens, utilising a qualitative case study based on an Australian university, this research scrutinised how a group of 10 international doctoral students who served as EFL teachers in their home countries enacted agency to navigate the identity transition. The study discusses what generally constitutes sustainable, successful language learning and teaching practices in different social, cultural, and educational contexts. The findings reveal that torn between teaching English as an expert and learning research as a novice, they negotiated the identity transition as a “doing” process subject to different positionings and structural contexts and mediated by various dynamics across a past–present–future trajectory. This study contributes to pertinent literature by shedding nuanced and holistic light on the under-researched topic of teacher-to-student identity transition. It ends with implications for attending to EFL teachers’ complex lived experiences to promote sustainable development in EFL teaching and learning.
Design, Development and Validation of an Intelligent Collision Risk Detection System to Improve Transportation Safety: The Case of the City of Popayán, Colombia
Authors: Yepes Chamorro Santiago Felipe , Paredes Rosero Juan Jose , Salazar-Cabrera Ricardo , Pachón de la Cruz Álvaro , Madrid Molina Juan Manuel
Published: August 2022

Several approaches from different perspectives have been used to solve problems with traffic accidents (TA), which mainly affect low- and middle-income countries. Conditions of certain cities, regarding road infrastructure, enforcement of traffic safety regulations, and motor vehicle numbers, influence the increase in TAs. Therefore, medium-sized cities in developing countries (context of interest), which commonly have worrying conditions, are a relevant scenario. One of the approaches to reduce TAs has been the use of data analysis through Machine Learning (ML); however, these techniques require a large amount of data, and medium-sized cities commonly do not have enough. Techniques such as Naturalistic Driving (ND) can be applied as a data collection method. This work proposes an intelligent collision risk detection system (ICDRS) using ND and ML to improve sustainability and safety of transportation in medium-sized cities. The ICRDS design considered the limitations of the context of interest and uses two data collection devices in the vehicle. The ICRDS validation included the design and execution of tests using ND. This validation identified if the collected data in a certain time interval contained high-risk collision events (sudden acceleration, sudden braking, aggressive left or right turn, aggressive left or right lane change). The system implementation results were satisfactory. The developed ML algorithm obtained an average value 0.98 in all the metrics. Two data sets of driving on routes were collected. In addition, the performed tests were able to identify city areas with high accident rates.
TOC Standards for Sustainably Managing Refractory Organic Matter in Swine Wastewater Effluent
Authors: Park Jae-Hong , Ryu Hong-Duck , Chung Eu-Gene , Oa Seong-Wook , Kim Yong-Seok
Published: August 2022

Swine wastewater effluent is a key source of water contamination since it contains high levels of nutrients, including nitrogen and phosphorus, as well as nitrates and refractory organic matter (ROM). ROM refers to organic compounds that are usually resistant to microbial degradation. When swine wastewater effluent containing high levels of ROM is subsequently discharged into rivers and streams without being adequately treated, purification costs for drinking water increase and there remains a possibility for harmful substances to enter the human body. In this study, we introduce new methods for setting total organic carbon (TOC) water quality standards for discharging swine wastewater effluent containing high levels of ROM after treatment. To set the TOC water quality standards, various analysis methods based on statistics, technology, and experience based on operational data of livestock-manure treatment facilities were applied. In addition, the achievability of the proposed TOC standards in livestock-manure treatment facilities and the financial burden of their implementation on livestock farms were also reviewed. Here, we set tentative values that include all of our results derived from each methodology and set the TOC standards to levels that can be achieved through the normal operation of swine-wastewater treatment facilities (60 mg/L for public treatment facilities and 140 mg/L for treatment facilities operated by individual farms). When setting TOC standards, both single and combined methodologies should be considered and employed after comprehensively assessing livestock management policies, regional conditions, and the burden on stakeholders.
Evaluating the Impacts of Autonomous Vehicles’ Market Penetration on a Complex Urban Freeway during Autonomous Vehicles’ Transition Period
Authors: Abdeen Mohammad A. R. , Yasar Ansar , Benaida Mohamed , Sheltami Tarek , Zavantis Dimitrios , El-Hansali Youssef
Published: August 2022

Autonomous vehicles (AVs) have been a rapidly emerging phenomenon in recent years, with some automated features already available in vehicles. AVs are expected to potentially revolutionize the existing inefficient state of urban transportation and be a step closer to environmental sustainability. This study focuses on simulation modeling in assessing the potential effects of autonomous vehicles (AVs) and on mobility and safety by developing a framework model based on traffic microsimulation for a real network located in Al-Madinah, Saudi Arabia. The market penetration rates (MPRs) will not reach 100% in the near future; instead, penetration will progressively increase. As a result, in our study, we investigated the potential effect of AV technology in five different AV market penetration rates: 0% (baseline), 25%, 50%, 75%, and 100%. The results suggest that Avs significantly improve the network’s safety and operational performance at high penetration rates. Specifically, estimated vehicle delays decreased by 26%, 34.4%, 63.7%, and 74.2% for 25%, 50%, 75%, and 100% AV penetration rates, respectively. Finally, we think this study will help decisionmakers over in the long-term in their attempts to achieve sustainable development through the optimal integration of innovative and novel technologies.
Spatial Pattern and Influencing Factors of Rural Settlements in Qinba Mountains, Shaanxi Province, China
Authors: Chen Sen , Mehmood Muhammad Sajid , Liu Shuchen , Gao Yimin
Published: August 2022

Spatial patterns and the influencing determinants of rural settlements are the most important indicators for understanding the constituent structure of rural regional systems. However, there is little knowledge addressing the characteristics from the settlement perspective by realizing the spatial reconstruction and sustainable development of rural settlements. Therefore, in this study, we analyzed the geographical, size, and morphological properties of rural settlement patterns in the Qinba Mountains in southern Shaanxi Province, China, using rural settlement and remote sensing data through spatial measurement index, gradient transects, demographic-economic index, and geodetector analysis. The results show the following: (1) Overall, rural settlements have spatial characteristics of “high-density multi-core clusters (0.8–1.6/km2) and low-density broadly scattered (<0.08/km2)”. There is a significant positive correlation between the scale of rural settlement density and the characteristics of high-value agglomeration. (2) The spatial disparities of morphological traits of settlement shapes are significant. Furthermore, 1840 NP/piece of plain basin landform types provide high-value areas for each settlement feature value, and locations with moderate slopes are best for settlement dispersal. Moreover, rivers, roads, and distance from township centers are all examples of beneficial directivity. There is consistency between the spatial differentiation of rural settlement areas per capita and the distribution of settlement scale. Conversely, the settlement density is inconsistent with the agricultural production value density’s spatial distribution features. (3) The impact of geographical factors on the diversification of settlement characteristics has significant spatial differences. Moreover, natural ecological characteristics such as elevation and landform and the distribution of cultivated land strongly influence the spatial pattern of the study region. Finally, the study findings can be beneficial for land and space planning and rural governments to develop sustainable rural settlements.
Entrepreneurial University Concept Review from the Perspective of Academicians: A Mixed Method Research Analysis
Authors: Coşkun Hüseyin Emre , Popescu Catalin , Şahin Samaraz Deniz , Tabak Akif , Akkaya Bulent
Published: August 2022

Understanding the role of the factors that contribute to the development and growth of entrepreneurial universities is important for both the university itself and the global economy as a whole. Therefore, this study aimed to gain a deep understanding of the entrepreneurial university. The study was designed with a mixed-method approach in which quantitative and qualitative methods were used together. In the quantitative part of the mixed-method research, a systematic literature review of the entrepreneurial university concept was done and based on the results of the literature search obtained by quantitative methods, general concepts and categories and their relations with the contexts were revealed. In the qualitative part of the mixed-method research, in order to explain the entrepreneurial university understanding, semi-structured interviews were conducted with academicians in universities. According to the findings, it was found that conceptualization was very important to understand the entrepreneurial university. The university was considered as an institution that serves students and companies via some channels, such as research reports, an academic publication, or a patent received at the end of a project. As the entrepreneurial university paradigm is being studied by various institutions in the world, this study contributes to the literature both as a theoretical study and as a study that reveal the concepts of the entrepreneurial university. By providing a conceptual framework, the research will contribute to the literature as a theoretical study by aiming to explain the service systems (universities, government, firms) based on the exchange of skills and the creation of common value in the knowledge-based society on the entrepreneurial university paradigm, which is extremely popular in the world. Fındings from the systematic literature review and interviews with academicians were analysed comparatively, and a conceptual framework that evaluates the concepts and variables of the entrepreneurial university with a holistic view is presented.
Understanding the Complexities of Human Well-Being in the Context of Ecosystem Services within Coastal Ghana
Authors: Duku Eric , Mattah Precious Agbeko Dzorgbe , Angnuureng Donatus Bapentire , Adotey Joshua
Published: August 2022

The understanding of the complexities of human well-being (HWB) within the ecosystem service (ES) context is fundamental to the development of management plans to sustain the flow of ecosystem services (ESs) for HWB. However, research on HWB in the context of ecosystem services is still underrepresented on Africa’s coast. Primary data were collected from 794 household heads in six communities within Ghana’s eastern coastal zone. A sequential logistics regression model was used to assess the effect of the interactions between ESs, socio-economic conditions, and contextual factors on HWB. Respondents’ well-being varied across the study communities, with high well-being reported by 63% of respondents from Anloga and low well-being by 77% in Kedzi. A strong association was found between HWB and relevant characteristics of respondents including marital status, years lived in a community, subjective social position (SSP), main livelihood source, income class, access to a reliable credit facility, and being a member of a local community group. Gender was not a significant predictor of HWB levels. For the effect of ESs on HWB, we found that respondents who had high contentment with provisioning and cultural ESs were more likely to have high well-being as opposed to respondents who had low contentment. Respondents who had low to moderate contentment with regulatory ESs were more likely to have high well-being, but the contextual factors condensed the significance of this relationship. Findings suggest the implementation of deliberate actions to maintain or restore vital ecosystem functions and services for sustainable well-being in coastal communities.
Performance Analysis of Smart City Governance: Dynamic Impact of Beijing 12345 Hotline on Urban Public Problems
Authors: Xiong Nina , Zang Hao , Lu Huijie , Yu Rongxia , Wang Jia , Feng Zhongke
Published: August 2022

The 12345 hotline service has become a widely accepted smart city governance system of citizen contact with the local authorities in Beijing and has enabled leaders to handle public complaints more effectively. However, there have been few studies on the performance of the 12345 hotline. This study, taking Beijing city as the study area, explains how this system improved urban problem management and the degree of improvement in different urban problem categories. First, we studied the quantity distribution of 12345 cases and the public’s top concerns. Then, based on the VAR model, we analyzed the specific dynamic influence process and degree of 12345 cases on UGMS cases, which reflect urban public problems. The main findings of this research are as follows: (1) Illegal parking/charging problems and issues with the city’s dirty conditions were the two primary concerns of the public. (2) The 12345 system hampered the increase in urban public problems with a lag of 3–5 days. (3) Exceptionally, for the construction management and market regulation categories, the hotline cases had no active effect on urban problem management. This paper can help city authorities to assess the strength and weakness of the 12345 system and decide on improvement plans for urban management.
Spatial Analysis of Agriculture Ecological Efficiency and Its Influence on Fiscal Expenditures
Authors: Wu Guoyong , Fan Yang , Riaz Noman
Published: August 2022

The faster development of the agriculture sector has led to the excessive waste of agriculture resources and that development causes serious damage to the ecological environment. So, ecological agriculture has become an important topic to understand the relationship between agriculture and the environment. This study tries to examine the impact of agricultural fiscal expenditures on agricultural ecological efficiency. In the first step, the researcher found the ecological efficiency of agriculture. The second step tries to examine the agricultural fiscal expenditure impact on agriculture’s ecological efficiency. The study used data from 30 provinces of China from 1998 to 2018. For the empirical results, the researcher used the super-efficiency DEA window analysis in the first step. Moreover, we used the spatial Durbin model in the second step. The results of the study revealed that China’s agricultural ecological efficiency shows a downward trend to an upward trend. The efficiency value of the eastern region is higher than the central region’s efficiency value, which is lower than the national average level. The western region’s efficiency value is equal to the national average level, and the differences between regions vary with the time period. The nuclear density of agricultural eco-efficiency in China showed a trend of “left-shift rising—double peaks—right-shifting decline”. In terms of space, China’s agricultural ecological efficiency has a significant positive spatial spillover effect, especially in the eastern and western regions. The results of the spatial Durbin model revealed that agricultural fiscal expenditures have a significant role in promoting China’s agricultural ecological efficiency. While agricultural governance has shown a significant spatial crowd-out effect. Therefore, it is proposed to promote the development of inter-provincial agricultural cooperation, and the formulation of agricultural financial support strategies should be green-oriented and should consider regional heterogeneity.
Changes in the Water Surface Area of Reservoirs of the Crimean Peninsula and Artificial Increases in Precipitation as One of the Possible Solutions to Water Shortages
Authors: Boychenko Svitlana , Kuchma Tetyana , Khlobystov Ievgen V.
Published: August 2022

The climatic conditions of Crimea are semiarid; therefore, alternative sources of fresh water are needed. A significant increase in water use and consumption (due to the intensification of construction, industry, tourism, and militarization), along with the cessation of fresh water supplies from mainland Ukraine, has reduced the water levels in some reservoirs to critical levels, and climate change has exacerbated this situation. There has been a significant decrease in precipitation by 10–15% on the Crimean Peninsula, accompanied by an increase in surface temperature by 0.8 ± 0.1 °C per 10 years during the period 1991–2020. The analysis of satellite-driven drought severity index reveals that the vast area of Crimean steppe is exposed to moderate–high drought risk. According to Landsat satellite imagery, there is a decreasing tendency for the water mirror area of all reservoirs, with a decrease of 34% on average in 2021 compared to 2015. The retrospective analysis of satellite images for 2015–2021 showed that the water surface area of the Simferopol reservoir had decreased on average by ~20% compared to 2015. To solve the problems associated with the shortage of fresh water, a series of works on the active influences on atmospheric processes was conducted, aimed at providing additional artificial precipitation. Two situations were considered, with mixed results: In the first case, on 30 October 2016, wet soil and standing water areas were detected by radar satellite imagery in agricultural fields within a radius of 40 km of the village of Petrovka, Krasnogvardiiskyi district, potentially related to the induction of precipitation by active influences on atmosphere. Meanwhile, in the second case, the realization of atmospheric precipitation occurred over Simferopol on 29–30 September 2020, leading to flooding in the city, but an increase in the water surface area of the Simferopol reservoir after active influences was not recorded.
Introducing Structural Symmetry and Asymmetry Implications in Development of Recent Pharmacy and Medicine
Authors: Jäntschi Lorentz
Published: August 2022

Structural symmetry, anti-symmetry, or asymmetry represent a foundational property that, for chemical compounds, often determines their chemical and biological activity [...]