bluuebunny/crossref_metadata_2025 huggingface dataset:
Inflation, Equity Market Volatility, and Bond Prices: Evidence from G7 Countries
Authors: Chen Yu-Fen , Chiang Thomas Chinan , Lin Fu-Lai
Published: October 2023

This study examines the impacts of the US inflation rate on the bond prices of G7 countries across different maturities using inflation-induced equity market volatility (EMV) to better account for bond price determinants. The regression model, a GED-GARCH (1,1) procedure, is adopted to deal with the volatility clustering and fat tail features in bond return estimation. The testing results indicate that the inflation rate has a negative effect on bond returns across different maturities, although an exception occurs for longer maturities in Japan. Evidence shows that US inflation has a significant impact on bond returns for the non-US G7 countries. The negative effects from US inflation are more profound than those from the domestic market (expect in Japan). This study introduces the equity market volatility arising from inflation or the Fed’s interest rate change; this variable produces market volatility that has a positive effect on bond returns, offsetting part of the original negative effect from a rise in inflation.
Cognitive Radar Waveform Design Method under the Joint Constraints of Transmit Energy and Spectrum Bandwidth
Authors: Yang Chen , Yang Wei , Qiu Xiangfeng , Zhang Wenpeng , Lu Zhejun , Jiang Weidong
Published: October 2023

The water-filling (WF) algorithm is a widely used design strategy in the radar waveform design field to maximize the signal-to-interference-plus-noise ratio (SINR). To address the problem of the poor resolution performance of the waveform caused by the inability to effectively control the bandwidth, a novel waveform-related optimization model is established in this paper. Specifically, a corrected SINR expression is first derived to construct the objective function in our optimization model. Then, equivalent bandwidth and energy constraints are imposed on the waveform to formulate the waveform-related non-convex optimization model. Next, the optimal frequency spectrum is obtained using the Karush–Kuhn–Tucker condition of our non-convex model. Finally, the transmit waveform in the time domain is synthesized under the constant modulus constraint. Different experiments based on simulated and real-measured data are constructed to demonstrate the superior performance of the designed waveform on the SINR and equivalent bandwidth compared to the linear frequency modulated signal and waveform designed by the WF algorithm. In addition, to further evaluate the effectiveness of the proposed algorithm in the application of cognitive radar (CR), a closed-loop radar system design strategy is introduced based on our waveform design method. The experiments under real-measured data confirm the advantages of CR compared to the traditional open-loop radar structure.
A Real-Time Permittivity Estimation Method for Stepped-Frequency Ground-Penetrating Radar by Full-Waveform Inversion
Authors: Li Xu , Ye Shengbo , Kong Qingyang , Song Chenyang , Liu Xiaojun , Fang Guangyou
Published: October 2023

Ground-penetrating radar (GPR) has been widely used in estimating the permittivity of mediums. The radar echo amplitude method is an important method used by GPR in this estimation, the basic step of which is to deduce the magnitude of the permittivity according to the relationship between the reflection coefficient and the permittivity. Based on the basic principle of the radar echo amplitude method, this paper proposes a full-wave inversion real-time permittivity estimation method that can be used for stepped-frequency GPR (SFGPR), which offers high efficiency, accuracy, and generalization ability. The characteristics of this method are mainly reflected in the following four aspects: Using the SFGPR system and introducing a layered media detection model, we can complete waveform compensation optimization with high precision. The distance between the antenna and the surface of the reflective medium is extracted from the time domain waveform without manual measurement, avoiding human measurement errors. The inversion of the total reflection waveform at the required height works under the principle of an electromagnetic field, eliminating the need for repeated metal plate calibration experiments and improving work efficiency and waveform accuracy. In a continuous measurement line, the total reflection waveform inversion on each measurement point can be efficiently completed, and the change of permittivity on the measurement line can be obtained. To evaluate the feasibility of the proposed method, we performed experiments on a wall of known thickness, and the permittivity estimation was basically consistent with that of the dielectric probe, physical model calculation, and wall penetration. We also successfully applied this method to the dielectric property analysis of adobe samples. The results indicate that the proposed method can help grasp the condition of a measured medium, which can ensure the accuracy of detection and improve subsequent data processing efficiency.
An Accurate and Efficient BP Algorithm Based on Precise Slant Range Model and Rapid Range History Construction Method for GEO SAR
Authors: Wu Yifan , Huang Lijia , Zhang Bingchen , Wang Xiaochen , Qi Xiyu
Published: October 2023

The Geosynchronous Satellite Synthetic Aperture Radar (GEO SAR) operates at a high orbital altitude, resulting in an extended imaging time and substantial variations in slant range. Additionally, the GEO SAR satellite orbit experiences a bending effect, and the target’s movement, caused by the Earth’s rotation, is influenced by the Earth’s curvature. The back-projection (BP) algorithm has been proven to be a highly effective technique for precise imaging with GEO SAR by processing these specific echo signals. However, this approach necessitates considerable computational resources. Existing BP algorithms, such as the fast BP algorithm, do not consider the “Stop-and-Go” error present in GEO SAR. Consequently, we developed a Precise Slant Range Model that considers the motion of both the satellite and targets. The model incorporates velocity and acceleration factors to accurately represent the signal transmission from transmission to reception. Additionally, we propose a Rapid Range History Construction Method to lessen the computational burden of generating the three-dimensional range history array. By utilizing the Precise Slant Range Model and the Rapid Range History Construction Method, and employing parallel processing through aperture segmentation, we propose an Accurate and Efficient BP imaging algorithm suitable for GEO SAR applications. To validate its effectiveness, simulations were conducted using the parameters of a GEO SAR system. The results indicated that the proposed algorithm enhances the imaging quality of GEO SAR, reduces the processing time, and achieves high-precision rapid imaging, thereby improving operational efficiency.
A Review of Ocean Color Algorithms to Detect Trichodesmium Oceanic Blooms and Quantify Chlorophyll Concentration in Shallow Coral Lagoons of South Pacific Archipelagos
Authors: Dupouy Cécile , Whiteside Andra , Tan Jing , Wattelez Guillaume , Murakami Hiroshi , Andréoli Rémi , Lefèvre Jérôme , Röttgers Rüdiger , Singh Awnesh , Frouin Robert
Published: October 2023

The oceanic waters of the Southwest Tropical Pacific occupy a vast region including multiple Pacific Island Countries. The state of these waters is determinant for fisheries and the blue economy. Ocean color remote sensing is the main tool to survey the variability and long-term evolution of these large areas that are important for economic development but are affected by climate change. Unlike vast oligotrophic gyres, tropical waters are characterized by numerous archipelagos and islands, with deep and shallow lagoons subjected to the large impacts of the land. Strikingly large dendritic phytoplankton (Trichodesmium) blooms with high levels of chlorophyll, developing within archipelagos, as well as coastal enrichments from various origins may be observed. Algorithms to detect the presence of Trichodesmium have been developed or adapted, as well as algorithms to estimate the chlorophyll concentration ([Chl-a)]. Adapting existing [Chl-a] algorithms does not always yield high, i.e., sufficient, accuracy. A review of published regional bio-optical algorithms developed taking into account the specific phytoplankton composition and minimizing the adverse impacts of particles and the seabed bottom on [Chl-a] determination is presented, as well the bio-optical database that allowed their development. The interest of such algorithms for a variety of applications and scientific accomplishments is highlighted, with a view to further addressing the main biology and biogeochemistry questions, e.g., to determine the true impact of diazotrophs and assess lagoon [Chl-a] variability with the highest confidence. This work anticipates the use of future coarse and high-spatial-resolution and multi- and hyper-spectral satellite imagery in the Pacific.
Present-Day Crustal Deformation of the Northwestern Tibetan Plateau Based on InSAR Measurements
Authors: Zhang Guifang , Qu Chunyan , Shan Xinjian , Song Xiaogang , Zhang Yingfeng , Li Yanchuan
Published: October 2023

In this study, The ENVISAT advanced synthetic aperture radar observations from 2003 to 2010 of a descending track covering an area of 100 km × 300 km were used to map the surface velocity field in northwestern Tibet. The derived line-of-sight (LOS) velocity map revealed that interseismic deformation was mainly located on the Altyn Tagh Fault (ATF) and other four immature subsidiary faults (i.e., Tashikule Fault, Muzitage-jingyuhe Fault, Heishibeihu Fault, and Woniuhu Fault). A 2D elastic screw dislocation model was used to interpret the interferometric synthetic aperture radar (InSAR) velocity profiles, which revealed the following results. (a) The oblique movement is partitioned between left-lateral slip at a rate of 6.3 ± 1.4 mm/y on the ATF and 5.9 ± 2.8 mm/y on the subsidiary faults. The low slip rate of the ATF indicates that the ATF does not drive the northeastward extrusion of material, with most of the extrusion occurring in the eastern interior of the plateau and the four subsidiary faults localizing the oblique convergence partitioned in the west. This can reasonably explain why catastrophic earthquakes and rapid slip do not occur all over along the ATF. (b) Based on the four subsidiary faults accommodating the oblique movement and the traces amalgamation with the EKLF (delineated Bayan Har plate boundary to the northeast), we concluded guardedly that the four subsidiary faults are the evoluting plate boundary of the Bayan Har block to the northwest. (c) The Tanan top-up structure had an uplift rate of ~0.6 mm/y at the south of the Tarim Basin.
Land Cover Classification Based on Airborne Lidar Point Cloud with Possibility Method and Multi-Classifier
Authors: Zhao Danjing , Ji Linna , Yang Fengbao
Published: October 2023

As important geospatial data, point cloud collected from an aerial laser scanner (ALS) provides three-dimensional (3D) information for the study of the distribution of typical urban land cover, which is critical in the construction of a “digital city”. However, existing point cloud classification methods usually use a single machine learning classifier that experiences uncertainty in making decisions for fuzzy samples in confusing areas. This limits the improvement of classification accuracy. To take full advantage of different classifiers and reduce uncertainty, we propose a classification method based on possibility theory and multi-classifier fusion. Firstly, the feature importance measure was performed by the XGBoost algorithm to construct a feature space, and two commonly used support vector machines (SVMs) were the chosen base classifiers. Then, classification results from the two base classifiers were quantitatively evaluated to define the confusing areas in classification. Finally, the confidence degree of each classifier for different categories was calculated by the confusion matrix and normalized to obtain the weights. Then, we synthesize different classifiers based on possibility theory to achieve more accurate classification in the confusion areas. DALES datasets were utilized to assess the proposed method. The results reveal that the proposed method can significantly improve classification accuracy in confusing areas.
Internal and External Pipe Defect Characterization via High-Frequency Lamb Waves Generated by Unidirectional EMAT
Authors: Zhang Xu , Li Bo , Zhang Xiaolong , Song Xiaochun , Tu Jun , Cai Chen , Yuan Jundong , Wu Qiao
Published: October 2023

Periodic permanent magnet(PPM) electromagnetic acoustic transducers (EMATs) are commonly employed for axial defect inspection in pipelines. However, the lowest-order shear horizontal waves (SH0) guided waves have difficulties in distinctly differentiating internal and external defects. To enhance the signal-to-noise ratio and resolution, a unidirectional electromagnetic acoustic transducer (EMAT) based on Circumferential Lamb waves (CLamb waves) is developed. Through structural parameter optimization and excitation frequency adjustment, high-amplitude and low-dispersion CLamb waves are successfully generated in the high-frequency-thickness product region of the dispersion curve. Finite element simulations and experimental validation confirm the capability of this EMAT in exciting CLamb waves for the detection of crack-like defects. Experimental results demonstrate that the excitation efficiency of the CLamb EMAT exceeds that of the periodic permanent magnet electromagnetic acoustic transducer by more than tenfold. The defect reflection signal of the CLamb EMAT exhibits higher resolution and more significant amplitude compared to the PPM EMAT. The integration of this method with SH0 mode detection allows for the inspection of both internal and external defects in pipelines, offering a new avenue for EMAT applications in pipeline inspection.
Camera-Based Indoor Positioning System for the Creation of Digital Shadows of Plant Layouts
Authors: Hermann Julian , von Leipzig Konrad H. , Hummel Vera , Basson Anton H.
Published: October 2023

In the past, plant layouts were regarded as highly static structures. With increasing internal and external factors causing turbulence in operations, it has become more necessary for companies to adapt to new conditions in order to maintain optimal performance. One possible way for such an adaptation is the adjustment of the plant layout by rearranging the individual facilities within the plant. Since the information about the plant layout is considered as master data and changes have a considerable impact on interconnected processes in production, it is essential that this data remains accurate and up-to-date. This paper presents a novel approach to create a digital shadow of the plant layout, which allows the actual state of the physical layout to be continuously represented in virtual space. To capture the spatial positions and orientations of the individual facilities, a pan-tilt-zoom camera in combination with fiducial markers is used. With the help of a prototypically implemented system, the real plant layout was captured and converted into different data formats for further use in exemplary external software systems. This enabled the automatic updating of the plant layout for simulation, analysis and routing tasks in a case study and showed the benefits of using the proposed system for layout capturing in terms of accuracy and effort reduction.
Analysis of Light Intensity and Charge Holding Time Dependence of Pinned Photodiode Full Well Capacity
Authors: Miyauchi Ken , Isozaki Toshiyuki , Ikeno Rimon , Nakamura Junichi
Published: October 2023

In this paper, the light intensity and charge holding time dependence of pinned photodiode (PD) full well capacity (FWC) are studied for our pixel structure with a buried overflow path under the transfer gate. The formulae for PDFWC derived from a simple analytical model show that the relation between light intensity and PDFWC is logarithmic because PDFWC is determined by the balance between the photo-generated current and overflow current under the bright condition. Furthermore, with using pulsed light before a charge holding operation in PD, the accumulated charges in PD decrease with the holding time due to the overflow current, and finally, it reaches equilibrium PDFWC. The analytical model has been successfully validated by the technology computer-aided design (TCAD) device simulation and actual device measurement.
Multi-Scene Mask Detection Based on Multi-Scale Residual and Complementary Attention Mechanism
Authors: Zhou Yuting , Lin Xin , Luo Shi , Ding Sixian , Xiao Luyang , Ren Chao
Published: October 2023

Vast amounts of monitoring data can be obtained through various optical sensors, and mask detection based on deep learning integrates neural science into a variety of applications in everyday life. However, mask detection poses technical challenges such as small targets, complex scenes, and occlusions, which necessitate high accuracy and robustness in multi-scene target detection networks. Considering that multi-scale features can increase the receptive field and attention mechanism can improve the detection effect of small targets, we propose the YOLO-MSM network based on the multi-scale residual (MSR) block, multi-scale residual cascaded channel-spatial attention (MSR-CCSA) block, enhanced residual CCSA (ER-CCSA) block, and enhanced residual PCSA (ER-PCSA) block. Considering the performance and parameters, we use YOLOv5 as the baseline network. Firstly, for the MSR block, we construct hierarchical residual connections in the residual blocks to extract multi-scale features and obtain finer features. Secondly, to realize the joint attention function of channel and space, both the CCSA block and PCSA block are adopted. In addition, we construct a new dataset named Multi-Scene-Mask, which contains various scenes, crowd densities, and mask types. Experiments on the dataset show that YOLO-MSM achieves an average precision of 97.51%, showing better performance than other detection networks. Compared with the baseline network, the mAP value of YOLO-MSM is increased by 3.46%. Moreover, we propose a module generalization improvement strategy (GIS) by training YOLO-MSM on the dataset augmented with white Gaussian addition noise to improve the generalization ability of the network. The test results verify that GIS can greatly improve the generalization of the network and YOLO-MSM has stronger generalization ability than the baseline.
Real-Time Road Intersection Detection in Sparse Point Cloud Based on Augmented Viewpoints Beam Model
Authors: Hu Di , Zhang Kai , Yuan Xia , Xu Jiachen , Zhong Yipan , Zhao Chunxia
Published: October 2023

Road intersection is a kind of important navigation landmark, while existing detection methods exhibit clear limitations in terms of their robustness and efficiency. A real-time algorithm for road intersection detection and location in large-scale sparse point clouds is proposed in this paper. Different from traditional approaches, our method establishes the augmented viewpoints beam model to perceive the road bifurcation structure. Explicitly, the spatial features from point clouds are jointly extracted in various viewpoints in front of the robot. In addition, the evaluation metrics are designed to self-assess the quality of detection results, enabling our method to optimize the detection process in real time. Considering the scarcity of datasets for intersection detection, we also collect and annotate a VLP-16 point cloud dataset specifically for intersections, called NCP-Intersection. Quantitative and qualitative experiments demonstrate that the proposed method performs favorably against the other parallel methods. Specifically, our method performs an average precision exceeding 90% and an average processing time of approximately 88 ms/frame.
Unveiling Insights: Harnessing the Power of the Most-Frequent-Value Method for Sensor Data Analysis
Authors: Golovko Victor V. , Kamaev Oleg , Sun Jiansheng
Published: October 2023

The paper explores the application of Steiner’s most-frequent-value (MFV) statistical method in sensor data analysis. The MFV is introduced as a powerful tool to identify the most-common value in a dataset, even when data points are scattered, unlike traditional mode calculations. Furthermore, the paper underscores the MFV method’s versatility in estimating environmental gamma background blue (the natural level of gamma radiation present in the environment, typically originating from natural sources such as rocks, soil, and cosmic rays), making it useful in scenarios where traditional statistical methods are challenging. It presents the MFV approach as a reliable technique for characterizing ambient radiation levels around large-scale experiments, such as the DEAP-3600 dark matter detector. Using the MFV alongside passive sensors such as thermoluminescent detectors and employing a bootstrapping approach, this study showcases its effectiveness in evaluating background radiation and its aptness for estimating confidence intervals. In summary, this paper underscores the importance of the MFV and bootstrapping as valuable statistical tools in various scientific fields that involve the analysis of sensor data. These tools help in estimating the most-common values and make data analysis easier, especially in complex situations, where we need to be reasonably confident about our estimated ranges. Our calculations based on MFV statistics and bootstrapping indicate that the ambient radiation level in Cube Hall at SNOLAB is 35.19 μGy for 1342 h of exposure, with an uncertainty range of +3.41 to −3.59μGy, corresponding to a 68.27% confidence level. In the vicinity of the DEAP-3600 water shielding, the ambient radiation level is approximately 34.80 μGy, with an uncertainty range of +3.58 to −3.48μGy, also at a 68.27% confidence level. These findings offer crucial guidance for experimental design at SNOLAB, especially in the context of dark matter research.
UCO Physical Rehabilitation: New Dataset and Study of Human Pose Estimation Methods on Physical Rehabilitation Exercises
Authors: Aguilar-Ortega Rafael , Berral-Soler Rafael , Jiménez-Velasco Isabel , Romero-Ramírez Francisco J. , García-Marín Manuel , Zafra-Palma Jorge , Muñoz-Salinas Rafael , Medina-Carnicer Rafael , Marín-Jiménez Manuel J.
Published: October 2023

Physical rehabilitation plays a crucial role in restoring motor function following injuries or surgeries. However, the challenge of overcrowded waiting lists often hampers doctors’ ability to monitor patients’ recovery progress in person. Deep Learning methods offer a solution by enabling doctors to optimize their time with each patient and distinguish between those requiring specific attention and those making positive progress. Doctors use the flexion angle of limbs as a cue to assess a patient’s mobility level during rehabilitation. From a Computer Vision perspective, this task can be framed as automatically estimating the pose of the target body limbs in an image. The objectives of this study can be summarized as follows: (i) evaluating and comparing multiple pose estimation methods; (ii) analyzing how the subject’s position and camera viewpoint impact the estimation; and (iii) determining whether 3D estimation methods are necessary or if 2D estimation suffices for this purpose. To conduct this technical study, and due to the limited availability of public datasets related to physical rehabilitation exercises, we introduced a new dataset featuring 27 individuals performing eight diverse physical rehabilitation exercises focusing on various limbs and body positions. Each exercise was recorded using five RGB cameras capturing different viewpoints of the person. An infrared tracking system named OptiTrack was utilized to establish the ground truth positions of the joints in the limbs under study. The results, supported by statistical tests, show that not all state-of-the-art pose estimators perform equally in the presented situations (e.g., patient lying on the stretcher vs. standing). Statistical differences exist between camera viewpoints, with the frontal view being the most convenient. Additionally, the study concludes that 2D pose estimators are adequate for estimating joint angles given the selected camera viewpoints.
Prediction of Pollutant Concentration Based on Spatial–Temporal Attention, ResNet and ConvLSTM
Authors: Chen Cai , Qiu Agen , Chen Haoyu , Chen Yajun , Liu Xu , Li Dong
Published: October 2023

Accurate and reliable prediction of air pollutant concentrations is important for rational avoidance of air pollution events and government policy responses. However, due to the mobility and dynamics of pollution sources, meteorological conditions, and transformation processes, pollutant concentration predictions are characterized by great uncertainty and instability, making it difficult for existing prediction models to effectively extract spatial and temporal correlations. In this paper, a powerful pollutant prediction model (STA-ResConvLSTM) is proposed to achieve accurate prediction of pollutant concentrations. The model consists of a deep learning network model based on a residual neural network (ResNet), a spatial–temporal attention mechanism, and a convolutional long short-term memory neural network (ConvLSTM). The spatial–temporal attention mechanism is embedded in each residual unit of the ResNet to form a new residual neural network with the spatial–temporal attention mechanism (STA-ResNet). Deep extraction of spatial–temporal distribution features of pollutant concentrations and meteorological data from several cities is carried out using STA-ResNet. Its output is used as an input to the ConvLSTM, which is further analyzed to extract preliminary spatial–temporal distribution features extracted from the STA-ResNet. The model realizes the spatial–temporal correlation of the extracted feature sequences to accurately predict pollutant concentrations in the future. In addition, experimental studies on urban agglomerations around Long Beijing show that the prediction model outperforms various popular baseline models in terms of accuracy and stability. For the single-step prediction task, the proposed pollutant concentration prediction model performs well, exhibiting a root-mean-square error (RMSE) of 9.82. Furthermore, even for the pollutant prediction task of 1 to 48 h, we performed a multi-step prediction and achieved a satisfactory performance, being able to achieve an average RMSE value of 13.49.
Employing Deep-Learning Approach for the Early Detection of Mild Cognitive Impairment Transitions through the Analysis of Digital Biomarkers
Authors: Narasimhan Rajaram , Gopalan Muthukumaran , Sikkandar Mohamed Yacin , Alassaf Ahmad , AlMohimeed Ibrahim , Alhussaini Khalid , Aleid Adham , Sheik Sabarunisha Begum
Published: October 2023

Mild cognitive impairment (MCI) is the precursor to the advanced stage of Alzheimer’s disease (AD), and it is important to detect the transition to the MCI condition as early as possible. Trends in daily routines/activities provide a measurement of cognitive/functional status, particularly in older adults. In this study, activity data from longitudinal monitoring through in-home ambient sensors are leveraged in predicting the transition to the MCI stage at a future time point. The activity dataset from the Oregon Center for Aging and Technology (ORCATECH) includes measures representing various domains such as walk, sleep, etc. Each sensor-captured activity measure is constructed as a time series, and a variety of summary statistics is computed. The similarity between one individual’s activity time series and that of the remaining individuals is also computed as distance measures. The long short-term memory (LSTM) recurrent neural network is trained with time series statistics and distance measures for the prediction modeling, and performance is evaluated by classification accuracy. The model outcomes are explained using the SHapley Additive exPlanations (SHAP) framework. LSTM model trained using the time series statistics and distance measures outperforms other modeling scenarios, including baseline classifiers, with an overall prediction accuracy of 83.84%. SHAP values reveal that sleep-related features contribute the most to the prediction of the cognitive stage at the future time point, and this aligns with the findings in the literature. Findings from this study not only demonstrate that a practical, less expensive, longitudinal monitoring of older adults’ activity routines can benefit immensely in modeling AD progression but also unveil the most contributing features that are medically applicable and meaningful.
Incorporating Modern Fault Ride-Through Standards into the Short-Circuit Calculation of Distribution Networks
Authors: Pompodakis Evangelos E. , Katsigiannis Yiannis , Karapidakis Emmanuel S.
Published: October 2023

Modern fault ride-through (FRT) standards in many countries require distributed generators to remain connected for a specified period during the fault by providing reactive current, to support voltage and prevent a massive renewable outage. As a result, short-circuit current is not constant, but it varies depending on the current and disconnection order of distributed generators (DGs). This time-varying short-circuit current complicates the estimation of the time it will take for an overcurrent relay or fuse to trip. The existing short-circuit calculation algorithms usually assume that the fault current is constant throughout the whole period of fault. This assumption may result in incorrect conclusions regarding the tripping time of protective devices in networks with high renewable penetration. This paper incorporates modern FRT standards into the fault analysis by considering the influence of fault current variations on the protective devices (relays, fuses), significantly increasing the accuracy of the estimated tripping time. Simulations carried out in a 13-bus and the IEEE 8500-node network indicate that the traditional short-circuit calculation approaches may miscalculate the tripping time of protective devices, with deviations up to 80 s, when applied to networks complying with modern FRT standards.
Corrosion Assessment in Reinforced Concrete Structures by Means of Embedded Sensors and Multivariate Analysis—Part 1: Laboratory Validation
Authors: Ramón-Zamora José Enrique , Lliso-Ferrando Josep Ramon , Martínez-Ibernón Ana , Gandía-Romero José Manuel
Published: October 2023

Reinforced Concrete Structures (RCS) are a fundamental part of a country’s civil infrastructure. However, RCSs are often affected by rebar corrosion, which poses a major problem because it reduces their service life. The traditionally used inspection and management methods applied to RCSs are poorly operative. Structural Health Monitoring and Management (SHMM) by means of embedded sensors to analyse corrosion in RCSs is an emerging alternative, but one that still involves different challenges. Examples of SHMM include INESSCOM (Integrated Sensor Network for Smart Corrosion Monitoring), a tool that has already been implemented in different real-life cases. Nevertheless, work continues to upgrade it. To do so, the authors of this work consider implementing a new measurement procedure to identify the triggering agent of the corrosion process by analysing the double-layer capacitance of the sensors’ responses. This study was carried out on reinforced concrete specimens exposed for 18 months to different atmospheres. The results demonstrate the proposed measurement protocol and the multivariate analysis can differentiate the factor that triggers corrosion (chlorides or carbonation), even when the corrosion kinetics are similar. Data were validated by principal component analysis (PCA) and by the visual inspection of samples and rebars at the end of the study.
Cluster-Disjoint Multipath Routing Protocol for Real-Time and Reliable Packet Transmission in Wireless Sensor Networks
Authors: Kim Sangdae , Roh Hakgyun , Jung Kwansoo
Published: October 2023

Multipath routing protocol, which is one of the typical schemes to improve end-to-end transmission success ratio in Wireless Sensor Networks (WSNs), exploits two or more paths. However, collisions and interference might occur when each path is adjacent. To prevent this problem, they construct node- or link-disjointed paths. Although the paths could prevent the above-mentioned problem, it still has an issue in the area of time delay. To exploit the disjointed paths, pre-path construction is required. In addition, a lot of time is incurred to recover the whole path when the part of the path is damaged. This phenomenon adversely affects the end-to-end transmission success ratio and transmission time. To alleviate this problem, we propose a cluster-disjoint multipath routing protocol. The proposed scheme divides the whole network into grid clusters of a certain size in the network initialization phase. Each sensor could transmit packets through the pre-constructed cluster structure without the path construction phase. Also, even if any node fails, it could be easily replaced by other nodes within the cluster region. The simulation results show that the proposed scheme has an advantage in terms of transmission delay and end-to-end transmission success ratio compared to existing multipath routing protocols.
Density, Viscosity, and Distillation Temperatures of Binary Blends of Diesel Fuel Mixed with Oxygenated Components at Different Temperatures
Authors: Osman Sibel , Stefaniu Amalia
Published: October 2023

This paper reports density, kinematic viscosity, and distillation curves for blends of diesel fuel with n-butanol, diesel fuel with n-pentanol, and diesel fuel with diethylene glycol dimethyl ether. It is known that these properties affect not only fuel transportation and distribution processes but also the phenomena that occur in an internal combustion engine; therefore, these aspects are intriguing to study. Oxygenated compounds such as n-butanol, n-pentanol, and diethylene glycol dimethyl ether can be used as additives for diesel fuel. Their use can contribute to a significant improvement in the fuel’s ignitability due to their high oxygen contents. Measurements of the experimental properties of various blend compositions were carried out at temperatures ranging between 288.15 K and 323.15 K. Based on density and viscosity data, different mathematical models were verified for the purpose of establishing better quality standards for the production of fuel. Good accuracies were obtained in the cases of density, viscosity, and interaction parameters, with the largest average absolute deviation (AAD%) being 0.4351. Moreover, as the determination of density is uncomplicated, rapid, and requires small sample volumes, correlations with the distillation temperatures used for the fuel blends were investigated to estimate the samples’ cetane indices. These determinations will be useful in the automobile industry when designing transport equipment or pipelines in situations when oxygenated compounds may constitute a fuel component in diesel blends.
The Often-Forgotten Innovation to Improve Sustainability: Assessing Food and Agricultural Sciences Curricula as Interventions in Uganda
Authors: Strong Robert , Baker Mitchell , Dooley Kim , Ray Nicole
Published: October 2023

Needs to improve educational access for women in the Global South have been well documented throughout the literature. The need and impact of non-governmental organizations for food security improvement, educational access, and community sustainability work in the Global South has been heightened since the pandemic. Our study focused on several sustainable development goals (SDGs) where we utilized experiential learning and the Targeting Outcomes of Programs (TOP) model to assess participants’ needs in developing, implementing, and evaluating the adoption of food and agricultural sciences curricula. We utilized a case study method and semi-structured interviews with primary stakeholder groups to answer each research objective. Most participants had increased confidence in growing food for their families due to the knowledge they gained and the practices they learned by participating in the food and agricultural sciences curricula. TOP indicators aligned with reducing hunger and improving sustainable agriculture are identified in SDG 2. Data regarding knowledge, attitudes, skills, and aspirations revealed that participants wanted to pursue careers in agriculture due to the food security and financial advantages the industry offered. Program graduates indicated their desire for food and agricultural sciences curricula to be provided more to improve girls’ self-independence, as well as to address the feasibility and entrepreneurial solutions to reduce poverty, promote learning, and increase community sustainability and vitality.
Control of Silica Gel Formation in the Acidic Leaching of Calcium Aluminate Slags with Aqueous HCl for Al Extraction
Authors: Tsaousi Georgia Maria , Toli Aikaterini , Bempelou Amalia , Kotsanis Dimitrios , Vafeias Michail , Balomenos Efthymios , Panias Dimitrios
Published: October 2023

The present research article investigates the extraction of aluminum (Al) from an engineered CaO-Al2O3-SiO2 slag by leaching with aqueous HCl under atmospheric pressure conditions. The slag is the by-product of an aluminothermic reduction process to produce metallurgical-grade silicon (Si) in a more sustainable way compared to conventional carbothermic reduction. One major challenge in the acidic leaching of aluminosilicate materials, like the slag treated in this study, is the possibility of SiO2 gel formation during the leaching process. Extensive SiO2 gel formation can make the separation of pregnant leach solution (PLS) from the leached residue impossible. Consequently, any acid leaching of aluminosilicate materials must be optimized for avoidance of these gelation phenomena. The present study first explores the leaching behavior of the calcium aluminosilicate slag in relation to the S/L ratio, with all other hydrometallurgical factors remaining stable (20.2% wt. HCl solution, 80 °C, optimized stirring rate), to determine at which value of this parameter SiO2 gelation occurs. After determining the optimum S/L ratio for this system, an analysis of the behavior of Si in the PLS is presented, and the mechanism of SiO2 gelation is explained based on critical assessment of these results against data provided from the scientific literature. It was found that the value of the pH of the PLS and the concentration of dissolved CaCl2 and AlCl3 are critical factors for the formation of filterable amorphous SiO2. Under the optimum leaching conditions, PLS containing 37 g/L Al was obtained with concurrent avoidance of gelation phenomena.
Effects of Sustainability Practices on Farm Continuity in Urban Agriculture: From the Creating Shared Value Perspective
Authors: Yoshida Shingo , Yagi Hironori
Published: October 2023

Urban agriculture is essential in establishing a localized, sustainable food system due to its multifunctional nature in addressing various urban challenges. Despite the recognition that a localized, sustainable food system offers an opportunity to create shared value (CSV), where agriculture generates social and economic value, the impact of environmental and social sustainability practices on farm continuity remains unclear. This paper identifies the impact of sustainability practices on long-term farm development from the perspective of the strategic intentions of urban farmers using a questionnaire survey of 205 urban farms in Japan. Our primary findings are as follows. First, we found that strategic sustainability practices positively impact farm continuity, underscoring the significance of CSV management in sustainable urban farm development. Second, the cluster analysis revealed three strategic groups: socially conscious CSV farms (32.2%), environmentally friendly farms (47.3%), and less sustainable farms (20.5%). Consequently, the socially conscious CSV farms have higher farm continuity than the other groups. For policymakers, promoting awareness of environmentally friendly farms among urban residents and involving diverse stakeholders in supporting less sustainable farms are crucial measures to enhance farm continuity.
Optimizing Green Strategy for Retired Electric Vehicle Battery Recycling: An Evolutionary Game Theory Approach
Authors: Zheng Yi , Xu Yaoqun
Published: October 2023

As the global new energy vehicle (NEV) industry rapidly expands, the disposal and recycling of end-of-life (EOL) power batteries have become imperative. Efficient closed-loop supply chain (CLSC) management, supported by well-designed regulations and strategic investments, plays a crucial role in sustainable waste power battery recycling. In this study, an evolutionary game theory (EGT) methodology is used to construct a tripartite game model to investigate the interactions among manufacturers, recyclers, and the government to study the decision-making dynamics of green investments. In addition, numerical simulations are performed to evaluate the sensitivity of the relevant parameters on the stability of the evolution of the system. The results reveal that government green subsidies can stimulate early period investments in advanced recycling technologies. However, as the battery recycling industry matures, a ‘free-rider’ behavior emerges among enterprises, which can be mitigated through the imposition of a carbon tax. Eventually, as the industry reaches maturity, manufacturers and recyclers autonomously invest for enhanced profitability. This research provides valuable insights for government policy formulation, facilitating the formal recycling of retired batteries and fostering sustainability in the NEV sector.
A Life Cycle Assessment to Evaluate the Environmental Benefits of Applying the Circular Economy Model to the Fertiliser Sector
Authors: El Chami Daniel , Santagata Raffaella , Moretti Stefania , Moreschi Luca , Del Borghi Adriana , Gallo Michela
Published: October 2023

In recent years, the world has witnessed one of the most severe raw material crises ever recorded, with serious repercussions for maintaining its agri-food supply chain. This crisis risks dramatically impacting the poorest areas of the planet and poses profound reflections on global food security. In this complex geopolitical context, the recovery and recycling of renewable resources have become an obligatory path and, today, more than ever, essential in the fertiliser industry. To achieve these objectives, TIMAC AGRO Italia S.p.A. has undertaken a research activity to review the formulation of fertilisers by diversifying the raw materials used and introducing recycled raw materials. This article carried out a life cycle assessment (LCA) on four fertilisers to identify and quantify whether the changes influenced the environmental impacts, highlighting how applying the circular economy within industrial processes can reduce the pressure on natural resources. The results demonstrate that the global warming potential (GWP) impacts of the different reformulated fertilisers show a considerable variation of 4.4–9.2% due to the various raw materials used, the nitrogen content, and related emissions deriving from environmental dispersion. This study shows the importance of the LCA methodology to analyse and quantify the impact categories generated on the life cycle of fertiliser production and to identify the optimal by-products and end-of-waste for the fertiliser industry to find a synergy between environmental and agronomic performance. It also highlights the relevance of the transition to circular production and consumption systems to reduce environmental pressures and their effects on communities and ecosystems without compromising yields. Finally, the positive results encourage accelerating the circular transition and finding alternatives to virgin-mined raw materials.
The Impact of Migration on Forecasting Budget Expenditures on Education: The Sustainability Context
Authors: Zatonatska Tetiana , Liashenko Olena , Fareniuk Yana , Skowron Łukasz , Wołowiec Tomasz , Dluhopolskyi Oleksandr
Published: October 2023

This paper examines the impact of migration and changes in the population’s age structure on government expenditures on education in Ukraine. The cohort method, considering a group of people attending four main types of education, namely, preschool, general secondary, vocational and technical, and higher education, is used to analyze and forecast government expenditures in the medium and long term. The study results show that migration significantly impacts government budget expenditures on education, with the most significant impact being seen on preschool education, followed by higher education, while the impact on general secondary and vocational education is minimal. The number of people receiving education in one of the four main types of institutions funded by state and local budgets was predicted to gradually decrease during the forecast period, except for secondary and vocational education. The overall volume of state expenditures would gradually decrease, both in absolute terms and as a percentage of GDP. Improving educational processes and the quality of budget spending should become an area for state regulation to ensure quality education in all funding conditions. This research’s leading theoretical and practical results show the efficiency of this methodology for analyzing and forecasting budget expenditures on education. They can be helpful in the short and medium terms, considering all potential changes in demographic indicators regarding the population.
An Optimization Design of Hybrid Parking Lots in an Automated Environment
Authors: Chen Taolüe , Sun Chao
Published: October 2023

This paper explores the minimum lateral parking distance and parking acceleration/deceleration distance of vehicles to improve the efficiency of automated valet parking (AVP) lots and save urban land. Specifically, the paper focuses on designing parking lots for automated guided vehicles (AGVs) and their parking attributes. To ensure AGV accessibility and maximize AVP capacity, graph theories and unique path-driving methods are used in designing mobile priority parking lots and decision spaces. Additionally, the paper proposes an optimization design for parking lots with obstacles, considering the layout of load-bearing columns and charging resources for electric vehicles in underground parking lots. The article further proposes an optimization design for hybrid parking lots based on spatio-temporal resource conversion in traffic design and the principle of traffic separation in traffic control since hybrid parking lots that accommodate both conventional vehicles and AGVs are crucial to the future development of urban parking lots. The experimental results show that the proposed optimization design for urban parking lots in automated environments is superior to the traditional parking lots design in terms of capacity and density. This paper provides an optimal layout scheme of urban parking lots in multiple scenarios, which can improve the service level of urban static traffic systems.