bluuebunny/crossref_metadata_2025 huggingface dataset:
Study on the Reservoir Heterogeneity of Different Volcanic Facies Based on Electrical Imaging Log in the Liaohe Eastern Sag
Authors: Liu Zongli , Wu Huanping , Zhang Shanyi , Zhao Xiaoqing
Published: August 2023

The volcanic rocks of the Es3 Formation (the third member of the Eocene Shahejie) in the Liaohe Eastern Sag can be divided into four facies and twelve subfacies. The porosity spectrum, porosity bin, variation coefficient (VC), and porosity width derived from electrical imaging log data were applied to study and characterize the heterogeneity of four facies and nine subfacies, both qualitatively and quantitatively. However, the VC and porosity width cannot be used to quantitatively classify heterogeneity when the VC is small and the porosity width is large. In the present study, the authors propose a new parameter, Pvcd = variation coefficient × porosity width. Based on this parameter, using a combination of porosity spectra, porosity bin features, VC, and porosity width, lithofacies heterogeneity is divided into three categories. The first is weak heterogeneity, which has a Pvcd < 1.1, a VC < 0.15, and a porosity width < 6. The second is moderate heterogeneity, which has a Pvcd 1.1–1.6, a VC 0.15–0.25, and a porosity width 6–9. The third is strong heterogeneity, which has a Pvcd > 1.6, a porosity VC > 0.25, and a porosity width > 9. In these three cases, the porosity spectra mainly display unimodal features, the porosity bins are concentrated, the bimodal features with tails and porosity bins are less concentrated, or the multimodal features with tails and porosity bins are scattered, respectively. Favorable reservoirs of volcanic rocks are controlled by lithofacies. In the study area, the favorable reservoirs appear to be the diatreme subfacies with low or medium heterogeneity, the pyroclastic flow subfacies with low heterogeneity, the compound lava flow subfacies with low or medium heterogeneity, and the outer zone subfacies with strong heterogeneity.
Diaconia and Development: The Study of Religious Social Practice as Lead Discipline in the Religion and Development Debate
Authors: Öhlmann Philipp
Published: August 2023

In this article, I argue that the research field of religion and development and diaconal studies, the study of Christian social practice, share a common subject of inquiry: the social impact of religion. The field of religion and development investigates this mainly with a focus on the Global South and within the discursive framework of the concept of development, while diaconal studies has thus far taken a Christian perspective and a historic focus on the Global North. Recent paradigm shifts in the development discourse (post-development critique, 2030 Agenda for Sustainable Development as a global framework, critique of the secularist approach) put the field of religion and development under pressure to broaden its scope. Moreover, there is no clear lead discipline in the religion and development debate yet, raising questions about its disciplinary location in academic institutions and curricula. The field of diaconal studies is challenged by increasing religious pluralism and under pressure to consider perspectives from the Global South. Impulses from the recent advances in the conceptualisation of ecumenical diaconia as a new paradigm of Christian social service push the field to move beyond its historic focus on assistance and care. The aim of this article is to juxtapose these two fields of academic study and to bring them into mutual dialogue. The article reflects on both fields and their respective advantages and disadvantages and highlights areas of overlap. It goes on to propose a broadened discipline of diaconal studies, reshaped as the Study of Religious Social Practice, as a new academic field. The focus of this field would be the impact of religion on society in global perspective, across religious traditions and geographic contexts.
Design of Bluetooth 5.1 Angle of Arrival Homing Controller for Autonomous Mobile Robot
Authors: Weinmann Katrina , Simske Steve
Published: August 2023

With the improvement of autonomous robot navigation technologies, mobile robots can now be deployed in uncertain, real-world environments. An aspect of autonomous robot navigation in such scenarios is the capability to navigate to a real-time determined (previously unknown) location anywhere in its vicinity. This is especially pertinent for indoor navigation where existing localization technologies such as GPS do not provide sufficient accuracy of target location. In this paper, a controller design is proposed which homes a mobile robot to an object of unknown location using Bluetooth 5.1 Angle of Arrival (AoA) technology. The proposed setup consists of a target object with a Bluetooth beacon and a single Bluetooth antenna array mounted on a mobile robot. The controller uses a hybrid approach to calculating and updating the estimated target position by implementing parallax and vector position calculations from AoA and RSSI Bluetooth data. Simulations with various levels of sensor noise showed convergence to accurate target positions (mean accuracy of 0.12 m or less) in both obstacle-free and obstacle-present environments. The controller can be implemented as a standalone controller by directly commanding robot motion toward the target, or it can integrate with other existing robot navigation techniques by outputting a target position.
Spatial Distributions of Cloud Occurrences in Terms of Volume Fraction as Inferred from CloudSat and CALIPSO
Authors: Ding Yuhao , Liu Qi , Lao Ping , Li Meng , Li Yuan , Zheng Qun , Peng Yanghui
Published: August 2023

The cloud amount, referred to as the frequency of cloud occurrences, is of great importance for the Earth–atmosphere system. It was conventionally quantified as the area fraction of clouds in a given region, discarding the three-dimensional nature of both cloud entities and their spatial distribution. Although the area fraction is explicit, it is the volume fraction that fully depicts cloud occurrences, and the area fraction is just related to a projection of the volume fraction. In this study, by using spaceborne radar measurements, the spatial distribution of cloud volume fraction throughout the troposphere was investigated, and the contributions of various cloud types at each location were clarified. Overall, the volume fraction of total clouds in the whole troposphere is 15.9%, while the corresponding area fraction relative to the global surface is 73.6%. The peak volume fraction occurs at 1 km altitude, mainly contributed by stratocumulus and cumulus. For a single cloud type, the maximum fraction is 48.8%, which is from stratocumulus and occurs at 1 km altitude above the Greenland Sea. Half of the eight cloud types, altostratus, cirrus, nimbostratus, and deep convective clouds, reach the nominal tropopause. In particular, the vertical distribution difference among multiple cloud types in each category (low-level, middle-level, and vertically extending) was clarified, and it was found that the dominant cloud type in a category varies notably with the location in the atmosphere.
A Hybrid Binary Dragonfly Algorithm with an Adaptive Directed Differential Operator for Feature Selection
Authors: Chen Yilin , Gao Bo , Lu Tao , Li Hui , Wu Yiqi , Zhang Dejun , Liao Xiangyun
Published: August 2023

Feature selection is a typical multiobjective problem including two conflicting objectives. In classification, feature selection aims to improve or maintain classification accuracy while reducing the number of selected features. In practical applications, feature selection is one of the most important tasks in remote sensing image classification. In recent years, many metaheuristic algorithms have attempted to explore feature selection, such as the dragonfly algorithm (DA). Dragonfly algorithms have a powerful search capability that achieves good results, but there are still some shortcomings, specifically that the algorithm’s ability to explore will be weakened in the late phase, the diversity of the populations is not sufficient, and the convergence speed is slow. To overcome these shortcomings, we propose an improved dragonfly algorithm combined with a directed differential operator, called BDA-DDO. First, to enhance the exploration capability of DA in the later stages, we present an adaptive step-updating mechanism where the dragonfly step size decreases with iteration. Second, to speed up the convergence of the DA algorithm, we designed a new differential operator. We constructed a directed differential operator that can provide a promising direction for the search, then sped up the convergence. Third, we also designed an adaptive paradigm to update the directed differential operator to improve the diversity of the populations. The proposed method was tested on 14 mainstream public UCI datasets. The experimental results were compared with seven representative feature selection methods, including the DA variant algorithms, and the results show that the proposed algorithm outperformed the other representative and state-of-the-art DA variant algorithms in terms of both convergence speed and solution quality.
Modelling Heat Balance of a Large Lake in Central Tibetan Plateau Incorporating Satellite Observations
Authors: Guo Linan , Zheng Hongxing , Wu Yanhong , Zhu Liping , Wang Junbo , Ju Jianting
Published: August 2023

The thermodynamics of many lakes around the globe are shifting under a warming climate, affecting nutrients and oxygen transportation within the lake and altering lake biota. However, long-term variation in lake heat and water balance is not well known, particularly for regions like the Tibetan Plateau. This study investigates the long-term (1963–2019) variation in the heat balance of a large lake in the Tibetan Plateau (Nam Co) by combining the strengths of modeling and remote sensing. Remotely sensed lake surface water temperatures from the Moderate Resolution Imaging Spectroradiometer (MODIS) and Along Track Scanning Radiometer Reprocessing for Climate: Lake Surface Water Temperature and Ice Cover (ARC-Lake) are used to calibrate and validate a conceptual model (air2water) and a thermodynamic model (LAKE) for the studied lake, for which in situ observation is limited. The results demonstrate that remotely sensed lake surface water temperature can serve as a valuable surrogate for in situ observations, facilitating effective calibration and validation of lake models. Compared with the MODIS-based lake surface water temperature (LSWT) for the period 2000–2019, the correlation coefficient and root mean square error (RMSE) of the LAKE model are 0.8 and 4.2 °C, respectively, while those of the air2water model are 0.9 and 2.66 °C, respectively. Based on modeling, we found that the water temperature of Nam Co increased significantly (p < 0.05) during the period of 1963–2019, corresponding to a warming climate. The rate of water temperature increase is highest at the surface layer (0.41 °C/10a). This warming trend is more noticeable in June and November. From 1963 to 2019, net radiation flux increased at a rate of 0.5 W/m2/10a. The increase in net radiation is primarily responsible for the warming of the lake water, while its impact on changes in lake evaporation is comparatively minor. The approaches developed in this study demonstrate the flexibility of incorporating remote sensing observations into modeling. The results on long-term changes in heat balance could be valuable for a systematic understanding of lake warming in response to a changing climate in the Tibetan Plateau.
Applying Remote Sensing Methods to Estimate Alterations in Land Cover Change and Degradation in the Desert Regions of the Southeast Iberian Peninsula
Authors: Ramírez-Juidias Emilio , Madueño-Luna Antonio , Madueño-Luna José Miguel , López-Gordillo Miguel Calixto , Leiva-Piedra Jorge Luis
Published: August 2023

Numerous drylands worldwide have experienced degradation of both soil and vegetation in proximity to watering areas. Degradation can be observed in satellite imagery as fading radial brightness belts extending away from the water sources. The main objective of this study was to examine the spatio-temporal patterns of land degradation and rehabilitation in the drylands of the southeast Iberian Peninsula. The brightness index of tasseled cap was discovered to be the best form of spectral transformation for enhancing the contrast between the bright-degraded areas near the points and the darker surrounding areas far from and in between these areas. To comprehend the spatial structure present in spaceborne imagery of two desert sites and three key time periods, semi-variograms were created (mid-late 2000s, around 2015 and 2020). To assess spatio-temporal land-cover patterns, a kriging was used to smooth the brightness index values extracted from 30 m spatial resolution images. To assess the direction and intensity of changes between study periods, a change detection analysis based on kriging prediction maps was performed. These findings were linked to the socioeconomic situation prior to and following the EU economic crisis. The study discovered that degradation occurred in some areas as a result of the region’s agricultural activities being exploited.
Adaptive Feature Attention Module for Robust Visual–LiDAR Fusion-Based Object Detection in Adverse Weather Conditions
Authors: Kim Taek-Lim , Arshad Saba , Park Tae-Hyoung
Published: August 2023

Object detection is one of the vital components used for autonomous navigation in dynamic environments. Camera and lidar sensors have been widely used for efficient object detection by mobile robots. However, they suffer from adverse weather conditions in operating environments such as sun, fog, snow, and extreme illumination changes from day to night. The sensor fusion of camera and lidar data helps to enhance the overall performance of an object detection network. However, the diverse distribution of training data makes the efficient learning of the network a challenging task. To address this challenge, we systematically study the existing visual and lidar features based on object detection methods and propose an adaptive feature attention module (AFAM) for robust multisensory data fusion-based object detection in outdoor dynamic environments. Given the camera and lidar features extracted from the intermediate layers of EfficientNet backbones, the AFAM computes the uncertainty among the two modalities and adaptively refines visual and lidar features via attention along the channel and the spatial axis. The AFAM integrated with the EfficientDet performs the adaptive recalibration and fusion of visual lidar features by filtering noise and extracting discriminative features for an object detection network under specific environmental conditions. We evaluate the AFAM on a benchmark dataset exhibiting weather and light variations. The experimental results demonstrate that the AFAM significantly enhances the overall detection accuracy of an object detection network.
Post-Event Surface Deformation of the 2018 Baige Landslide Revealed by Ground-Based and Spaceborne Radar Observations
Authors: Xu Fu , Li Zhenhong , Du Jiantao , Han Bingquan , Chen Bo , Li Yongsheng , Peng Jianbing
Published: August 2023

On 11 October and 3 November 2018, two large landslides occurred in Baige Village, Tibet, China, forcing the Jinsha River to be cut off and form a dammed lake, resulting in massive economic damages and deaths. This paper uses ground-based radar (GBR) and spaceborne interferometric synthetic aperture radar (InSAR) technologies to perform dynamic monitoring of the Baige landslide. Firstly, the GBR results suggest that the cumulative deformation from 4 to 10 December 2018 was 1.4 m, and the landslide still exhibits a risk of instability. Secondly, with the Sentinel-1A ascending and descending orbit images from December 2018 to February 2022, the InSAR-stacking technology assisted by the generic atmospheric correction online service (GACOS) and the multidimensional small baseline subset (MSBAS) method are utilized to obtain the annual deformation velocity and cumulative deformation in the satellite radar line of sight (LOS) direction of the landslide. Finally, according to the spatial–temporal deformation characteristics of feature points, combined with optical images, field investigation, and geological conditions, the development trend and inducing factors of the Baige landslide are comprehensively analyzed. It is shown that the Baige landslide is in constant motion at present, and the deformation is spreading from the slope to its right side. This research establishes a framework of combining emergency monitoring (i.e., GBR) with long-term monitoring (i.e., spaceborne InSAR). The framework is more conducive to obtaining the deformation and evolution of landslides, providing a greater possibility for studying the development trend and risk assessment of landslides, and assisting in reducing or even avoiding the losses caused by landslides.
Learning Adversarially Robust Object Detector with Consistency Regularization in Remote Sensing Images
Authors: Li Yang , Fang Yuqiang , Li Wanyun , Jiang Bitao , Wang Shengjin , Li Zhi
Published: August 2023

Object detection in remote sensing has developed rapidly and has been applied in many fields, but it is known to be vulnerable to adversarial attacks. Improving the robustness of models has become a key issue for reliable application deployment. This paper proposes a robust object detector for remote sensing images (RSIs) to mitigate the performance degradation caused by adversarial attacks. For remote sensing objects, multi-dimensional convolution is utilized to extract both specific features and consistency features from clean images and adversarial images dynamically and efficiently. This enhances the feature extraction ability and thus enriches the context information used for detection. Furthermore, regularization loss is proposed from the perspective of image distribution. This can separate consistent features from the mixed distributions for reconstruction to assure detection accuracy. Experimental results obtained using different datasets (HRSC, UCAS-AOD, and DIOR) demonstrate that the proposed method effectively improves the robustness of detectors against adversarial attacks.
Electrochemical and Optical Sensors for Real-Time Detection of Nitrate in Water
Authors: Lal Kartikay , Jaywant Swapna A. , Arif Khalid Mahmood
Published: August 2023

The health and integrity of our water sources are vital for the existence of all forms of life. However, with the growth in population and anthropogenic activities, the quality of water is being impacted globally, particularly due to a widespread problem of nitrate contamination that poses numerous health risks. To address this issue, investigations into various detection methods for the development of in situ real-time monitoring devices have attracted the attention of many researchers. Among the most prominent detection methods are chromatography, colorimetry, electrochemistry, and spectroscopy. While all these methods have their pros and cons, electrochemical and optical methods have emerged as robust and efficient techniques that offer cost-effective, accurate, sensitive, and reliable measurements. This review provides an overview of techniques that are ideal for field-deployable nitrate sensing applications, with an emphasis on electrochemical and optical detection methods. It discusses the underlying principles, recent advances, and various measurement techniques. Additionally, the review explores the current developments in real-time nitrate sensors and discusses the challenges of real-time implementation.
Piezoelectric Wafer Active Sensor Transducers for Acoustic Emission Applications
Authors: Griffin Connor , Giurgiutiu Victor
Published: August 2023

Piezoelectric materials are defined by their ability to display a charge across their surface in response to mechanical strain, making them great for use in sensing applications. Such applications include pressure sensors, medical devices, energy harvesting and structural health monitoring (SHM). SHM describes the process of using a systematic approach to identify damage in engineering infrastructure. A method of SHM that uses piezoelectric wafers connected directly to the structure has become increasingly popular. An investigation of a novel pitch-catch method of determining instrumentation quality of piezoelectric wafer active sensors (PWASs) used in SHM was conducted as well as an investigation into the effects of defects in piezoelectric sensors and sensor bonding on the sensor response. This pitch-catch method was able to verify defect-less instrumentation quality of pristinely bonded PWASs. Additionally, the pitch-catch method was compared with the electromechanical impedance method in determining defects in piezoelectric sensor instrumentation. Using the pitch-catch method, it was found that defective instrumentation resulted in decreasing amplitude of received and transmitted signals as well as changes in the frequency spectrums of the signals, such as the elimination of high frequency peaks in those with defects in the bonding layer and an increased amplitude of around 600 kHz for a broken PWAS. The electromechanical impedance method concluded that bonding layer defects increase the primary frequency peak’s amplitude and cause a downward frequency shift in both the primary and secondary frequency peaks in the impedance spectrum, while a broken sensor has the primary peak amplitude reduced while shifting upward and nearly eliminating the secondary peak.
Robotic Surgery in Urology: History from PROBOT® to HUGOTM
Authors: Brassetti Aldo , Ragusa Alberto , Tedesco Francesco , Prata Francesco , Cacciatore Loris , Iannuzzi Andrea , Bove Alfredo Maria , Anceschi Umberto , Proietti Flavia , D’Annunzio Simone , Flammia Rocco Simone , Chiacchio Giuseppe , Ferriero Mariaconsiglia , Guaglianone Salvatore , Mastroianni Riccardo , Misuraca Leonardo , Tuderti Gabriele , Simone Giuseppe
Published: August 2023

The advent of robotic surgical systems had a significant impact on every surgical area, especially urology, gynecology, and general and cardiac surgery. The aim of this article is to delineate robotic surgery, particularly focusing on its historical background, its evolution, its present status, and its future perspectives. A comprehensive literature review was conducted upon PubMed/MEDLINE, using the keywords “robotic surgical system”, “robotic surgical device”, “robotics AND urology”. Additionally, the retrieved articles’ reference lists were investigated. Analysis concentrated on urological surgical systems for laparoscopic surgery that have been given regulatory approval for use on humans. From the late 1980s, before daVinci® Era in 2000s, ancestor platform as Probot® and PUMA 560 were described to outline historical perspective. Thus, new robotic competitors of Intuitive Surgical such as Senhance®, Revo-I®, Versius®, Avatera®, Hinotori®, and HugoTM RAS were illustrated. Although daVinci® had high level competitiveness, and for many years represented the most plausible option for robotic procedures, several modern platforms are emerging in the surgical market. Growing competition through unique features of the new robotic technologies might extend applications fields, improve diffusion, and increase cost-effectiveness procedures. More experiences are needed to identify the role of these new advancements in surgical branches and in healthcare systems.
Decentralized Policy-Hidden Fine-Grained Redaction in Blockchain-Based IoT Systems
Authors: Guo Hongchen , Tao Xiaolong , Zhao Mingyang , Wu Tong , Zhang Chuan , Xue Jingfeng , Zhu Liehuang
Published: August 2023

Currently, decentralized redactable blockchains have been widely applied in IoT systems for secure and controllable data management. Unfortunately, existing works ignore policy privacy (i.e., the content of users’ redaction policies), causing severe privacy leakage threats to users since users’ policies usually contain large amounts of private information (e.g., health conditions and geographical locations) and limiting the applications in IoT systems. To bridge this research gap, we propose PFRB, a policy-hidden fine-grained redactable blockchain in decentralized blockchain-based IoT systems. PFRB follows the decentralized settings and fine-grained chameleon hash-based redaction in existing redactable blockchains. In addition, PFRB hides users’ policies during policy matching such that apart from successful policy matching, users’ policy contents cannot be inferred and valid redactions cannot be executed. Some main technical challenges include determining how to hide policy contents and support policy matching. Inspired by Newton’s interpolation formula-based secret sharing, PFRB converts policy contents into polynomial parameters and utilizes multi-authority attribute-based encryption to further hide these parameters. Theoretical analysis proves the correctness and security against the chosen-plaintext attack. Extensive experiments on the FISCO blockchain platform and IoT devices show that PFRB achieves competitive efficiency over current redactable blockchains.
Automatic Detection and Association Analysis of Multiple Surface Defects on Shield Subway Tunnels
Authors: Yin Ziren , Lei Zhanzhan , Zheng Ao , Zhu Jiasong , Liu Xiao-Zhou
Published: August 2023

The surface defects on a shield subway tunnel can significantly affect the serviceability of the tunnel structure and may compromise operation safety. To effectively detect multiple surface defects, this study uses a tunnel inspection trolley (TIT) based on the mobile laser scanning technique. By conducting an inspection of the shield tunnel on a metro line section, various surface defects are identified with the TIT, including water leakage defects, dislocation, spalling, cross-section deformation, etc. To explore the root causes of the surface defects, association rules between different defects are calculated using an improved Apriori algorithm. The results show that: (i) there are significant differences in different association rules for various surface defects on the shield tunnel; (ii) the average confidence of the association rule “dislocation & spalling → water leakage” is as high as 57.78%, indicating that most of the water leakage defects are caused by dislocation and spalling of the shield tunnel in the sections being inspected; (iii) the weakest rule appears at “water leakage → spalling”, with an average confidence of 13%. The association analysis can be used for predicting the critical defects influencing structural reliability and operation safety, such as water leakage, and optimizing the construction and maintenance work for a shield subway tunnel.
Lightweight Model for Pavement Defect Detection Based on Improved YOLOv7
Authors: Huang Peile , Wang Shenghuai , Chen Jianyu , Li Weijie , Peng Xing
Published: August 2023

Existing pavement defect detection models face challenges in balancing detection accuracy and speed while being constrained by large parameter sizes, hindering deployment on edge terminal devices with limited computing resources. To address these issues, this paper proposes a lightweight pavement defect detection model based on an improved YOLOv7 architecture. The model introduces four key enhancements: first, the incorporation of the SPPCSPC_Group grouped space pyramid pooling module to reduce the parameter load and computational complexity; second, the utilization of the K-means clustering algorithm for generating anchors, accelerating model convergence; third, the integration of the Ghost Conv module, enhancing feature extraction while minimizing the parameters and calculations; fourth, introduction of the CBAM convolution module to enrich the semantic information in the last layer of the backbone network. The experimental results demonstrate that the improved model achieved an average accuracy of 91%, and the accuracy in detecting broken plates and repaired models increased by 9% and 8%, respectively, compared to the original model. Moreover, the improved model exhibited reductions of 14.4% and 29.3% in the calculations and parameters, respectively, and a 29.1% decrease in the model size, resulting in an impressive 80 FPS (frames per second). The enhanced YOLOv7 successfully balances parameter reduction and computation while maintaining high accuracy, making it a more suitable choice for pavement defect detection compared with other algorithms.
A Liquid Crystal-Modulated Metastructure Sensor for Biosensing
Authors: Liao Siyuan , Chen Qi , Ma Haocheng , Huang Jingwei , Sui Junyang , Zhang Haifeng
Published: August 2023

In this paper, a liquid crystal-modulated metastructure sensor (MS) is proposed that can detect the refractive index (RI) of a liquid and change the detection range under different applied voltages. The regulation of the detection range is based on the different bias states of the liquid crystal at different voltages. By changing the sample in the cavity that is to be detected, the overall electromagnetic characteristics of the device in the resonant state are modified, thus changing the position of the absorption peaks so that different RI correspond to different absorption peaks, and finally realizing the sensing detection. The refractive index unit is denoted as RIU. The range of the refractive index detection is 1.414–2.828 and 2.121–3.464, and the corresponding absorption peak variation range is 0.8485–1.028 THz and 0.7295–0.8328 THz, with a sensitivity of 123.8 GHz/RIU and 75.6 GHz/RIU, respectively. In addition, an approach to optimizing resonant absorption peaks is explored, which can suppress unwanted absorption generated during the design process by analyzing the energy distribution and directing the current flow on the substrate. Four variables that have a more obvious impact on performance are listed, and the selection and change trend of the numerical values are focused on, fully considering the errors that may be caused by manufacturing and actual use. At the same time, the incident angle and polarization angle are also included in the considered range, and the device shows good stability at these angles. Finally, the influence of the number of resonant rings on the sensing performance is also discussed, and its conclusion has guiding value for optimizing the sensing demand. This new liquid crystal-modulated MS has the advantages of a small size and high sensitivity and is expected to be used for bio-detection, sensing, and so on. All results in this work were obtained with the aid of simulations based on the finite element method.
Experimental Analysis of the Magnetic Leakage Detection of a Corroded Steel Strand Due to Vibration
Authors: Zhang Hong , Ding Yaxi , Yuan Ye , Xia Runchuan , Zhou Jianting
Published: August 2023

The self-magnetic flux leakage (SMFL) detection technique has great potential in the corrosion detection of bridge stay cables due to its advantages of small testing equipment, high accuracy, and fast testing rate. However, the vibration effect in the cable’s SMFL detection is unclear. To address this, the influence of vibration on the magnetic field distribution of cable structure is analyzed theoretically. According to the theoretical model, the effect of vibration on SMFL detection primarily manifests as displacement changes (displacement-added magnetic field) and defect shape changes (deformation-added magnetic field). SMFL detection experiments are conducted on steel strands. The results demonstrate that the displacement-added magnetic field exhibits statistical characteristics in the form of a normal distribution, fluctuating around the zero value. The impact of the deformation-added magnetic field on SMFL is linearly correlated with the corrosion ratio c. Moreover, a corrosion characterization index A was proposed and has an excellent linear fit with the corrosion ratio c. The index A effectively improves the accuracy of corrosion detection and provides early warning for the maintenance of cable structures.
Evaluation of the Use of the 12 Bands vs. NDVI from Sentinel-2 Images for Crop Identification
Authors: Lozano-Tello Adolfo , Siesto Guillermo , Fernández-Sellers Marcos , Caballero-Mancera Andres
Published: August 2023

Today, machine learning applied to remote sensing data is used for crop detection. This makes it possible to not only monitor crops but also to detect pests, a lack of irrigation, or other problems. For systems that require high accuracy in crop identification, a large amount of data is required to generate reliable models. The more plots of and data on crop evolution used over time, the more reliable the models. Here, a study has been carried out to analyse neural network models trained with the Sentinel satellite’s 12 bands, compared to models that only use the NDVI, in order to choose the most suitable model in terms of the amount of storage, calculation time, accuracy, and precision. This study achieved a training time gain of 59.35% for NDVI models compared with 12-band models; however, models based on 12-band values are 1.96% more accurate than those trained with the NDVI alone when it comes to making predictions. The findings of this study could be of great interest to administrations, businesses, land managers, and researchers who use satellite image data mining techniques and wish to design an efficient system, particularly one with limited storage capacity and response times.
Design of A Finite-Time Adaptive Controller for Image-Based Uncalibrated Visual Servo Systems with Uncertainties in Robot and Camera Models
Authors: Zhao Zhuoqun , Wang Jiang , Zhao Hui
Published: August 2023

Aiming at the time-varying uncertainties of robot and camera models in IBUVS (image-based uncalibrated visual servo) systems, a finite-time adaptive controller is proposed based on the depth-independent Jacobian matrix. Firstly, the adaptive law of depth parameters, kinematic parameters, and dynamic parameters is proposed for the uncertainty of a robot model and a camera model. Secondly, a finite-time adaptive controller is designed by using a nonlinear proportional differential plus a dynamic feedforward compensation structure. By applying a continuous non-smooth nonlinear function to the feedback error, the control quality of the closed-loop system is improved, and the desired trajectory of the image is tracked in finite time. Finally, using the Lyapunov stability theory and the finite-time stability theory, the global finite-time stability of the closed-loop system is proven. The experimental results show that the proposed controller can not only adapt to the changes in the EIH and ETH visual configurations but also adapt to the changes in the relative pose of feature points and the camera’s relative pose parameters. At the same time, the convergence rate near the equilibrium point is improved, and the controller has good dynamic stability.
Comparison of Miniaturized Chromatographic Columns and 96-Well Plates for Automated Antibody Purification under Economic and Sustainable Aspects
Authors: Bach Anna , Fleischer Heidi , Thurow Kerstin
Published: August 2023

Therapeutic antibodies are an area of major interest in treating infectious diseases, autoimmune diseases, and cancer. Efficient purification strategies are required to discover and produce antibodies, representing a significant cost driver. Miniaturization and automation of the chromatographic purification process may contribute to cost reduction. In this article, miniaturized chromatographic columns and 96-well filter plates are used to purify an IgG antibody from cell cultures and are compared in terms of quality, cost, and sustainability. The validation results of the two miniaturized chromatographic antibody purification methods indicate high efficacy and are comparable except for minor differences in yield. The lowest cost per sample can be obtained with the miniaturized chromatographic columns, as annual throughput can be maximized with the miniaturized plates. By evaluating sustainability, significant reductions in laboratory waste produced during antibody purification can be shown, along with an increase in employee safety by using the automation system. Overall, the comparison of results shows that, in the development of new purification strategies, the type of implementation has a decisive influence on the outcomes in terms of recovery, cost-effectiveness, and sustainability.
Multi-Step-Ahead Prediction Intervals for Nonparametric Autoregressions via Bootstrap: Consistency, Debiasing, and Pertinence
Authors: Politis Dimitris N. , Wu Kejin
Published: August 2023

To address the difficult problem of the multi-step-ahead prediction of nonparametric autoregressions, we consider a forward bootstrap approach. Employing a local constant estimator, we can analyze a general type of nonparametric time-series model and show that the proposed point predictions are consistent with the true optimal predictor. We construct a quantile prediction interval that is asymptotically valid. Moreover, using a debiasing technique, we can asymptotically approximate the distribution of multi-step-ahead nonparametric estimation by the bootstrap. As a result, we can build bootstrap prediction intervals that are pertinent, i.e., can capture the model estimation variability, thus improving the standard quantile prediction intervals. Simulation studies are presented to illustrate the performance of our point predictions and pertinent prediction intervals for finite samples.
Industry and Stakeholder Impacts on Corporate Social Responsibility (CSR) and Financial Performance: Consumer vs. Industrial Sectors
Authors: Arian Adam , Sands John , Tooley Stuart
Published: August 2023

We examine the longitudinal relationship between corporate social responsibility (CSR) performance and financial performance by investigating attributes among firms operating in different industry sectors longitudinally. Using panel regression analysis on Australian publicly listed firms from 2007 to 2021, we find that CSR performance positively influences financial performance. Furthermore, our industry-specific analysis uncovers notable distinctions. Specifically, within the consumer product markets, including recreational facilities, travel and tourism, lodging, dining, and leisure products, firms benefit from stakeholder rewards for their CSR efforts, leading to sustained financial gains. However, this positive association is absent for firms operating in industrial product markets, where stakeholders do not offer similar rewards for CSR performance. The significance of stakeholder engagement becomes evident in consumer market sectors, as firms with higher levels of CSR performance secure stakeholder support, resulting in superior long-term financial performance. Our findings contribute to the existing CSR literature and offer practical insights and implications for managers operating in diverse product market industries, including the dynamic field of tourism and hospitality seeking to harness CSR performance, meet stakeholder expectations, and achieve financial advantages.
Evaluating Renewable Energy and Ranking 17 Autonomous Communities in Spain: A TOPSIS Method
Authors: Esfandiary Abdolmaleki Danial , Faraji Abdolmaleki Shoeib , Bello Bugallo Pastora M.
Published: August 2023

Transitioning to renewable energy is becoming increasingly imperative, particularly for countries with limited energy supplies. To achieve this transition, all sectors must cooperate and coordinate to focus on renewable energy as a pillar of sustainable development. This study evaluates the status of renewable energy in each Spanish community, considering policies, frameworks, and elements of sustainable development. Using the Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS) methodology, seventeen autonomous communities of Spain were analyzed based on ten selected indicators. The results show that Castile and León, La Rioja, Murcia, and Aragon are in the top ranks of the first sustainable group, while Navarre, the Balearic Islands, Catalonia, the Basque Country, and Asturias are in the less sustainable group. This research presents novel contributions towards the adoption of sustainability-oriented policies aimed at enhancing energy systems, stimulating economic growth, and achieving zero-carbon energy targets in Spain. By enhancing the self-sufficiency of energy communities, they can attain demand-side energy independence, decreasing the requirement for government intervention, and mitigating concerns regarding the adequacy of energy resources. This can ultimately enhance social well-being, emphasizing the importance of sustainable energy systems for regional development in Spain.
Assessing Established Residential Compounds between Regulation and Reality Utilizing Space Syntax Theories
Authors: Rauof Tara Azad
Published: August 2023

Planners and architects should be able to incorporate digital technologies and data utilization to fulfil their professional duty of speculating how people will interact and transact with urban configurations. Analysing spatial patterns and their association with social behaviour contributes to harnessing the impact of connectivity on economic, social, environmental, and land value growth. This research presents a mathematical relationship between three variables; as a result, by the transitive property, the connectivity value of every individual street in the urban spatial layout should indicate the location of the urban block according to their sizes and be related in the same manner. The research aims to investigate the following question: is there a positive of negative statistical coefficient correlation between residential plot sizes and connectivity/integration values presented by Depth Map X in two urban villages in Sulaimanya city? The first section’s findings support the transitive property discussed in the research; furthermore, the second section shows an imbalance in the quantity of residential plots of different sizes on each street, causing the effectiveness of the exploitation of urban land in the case studies to decrease, which disagrees with the standards outlined by the Iraqi Roads and Buildings Regulation and the theories of space syntax. This study contributes to the future design of similar projects in the region by emphasizing the need for the authorities to compel building enterprises to adhere to these standards.
Optimizing Vehicle Replacement in Sustainable Urban Freight Transportation Subject to Presence of Regulatory Measures
Authors: Ahani Parisa , Arantes Amílcar , Garmanjani Rohollah , Melo Sandra
Published: August 2023

Since the 1990s, studies and pilot tests have been conducted to reduce traffic, accidents, and pollution due to urban freight transport (UFT). These ended up in several policies, regulations, and restrictions for UFT, such as low emission zones, delivery time windows, and vehicle size and weight restrictions. However, issues in UFT under regulatory measures still persist. This study introduces an optimization framework for deriving an optimal combination of various types of vehicles with different capacities for vehicle replacement with UFT. This framework allows an understanding of how an urban freight company with a limited budget efficiently satisfies its freight demand within an urban area in the presence of regulatory measures by urban administrators. The introduced formulation, which is mixed-integer linear programming, will assist the operator in choosing the best investment strategy for introducing new vehicles of certain types and sizes, for operation in different zones, into its fleet while gaining economic benefits and having a positive impact on the liveability of the urban area. Furthermore, an elasticity analysis is performed to consider the effects of specific uncertain parameters on the total cost. The numerical results show that the share of electric vehicles in the fleet increases, and they are more competitive than diesel vehicles.
The Spatial Synergy of the Ice–Snow Tourism Industry and Its Related Industries in Jilin Province
Authors: Cai Weiying , Yu Jie , Yang Wei
Published: August 2023

The rapid development of the ice–snow tourism industry in recent years has promoted the sustainable development of the regional economy, and it has become one of the critical supporting industries affecting local economic development. However, there is an obvious imbalance in the ice–snow tourism industry in the region, resulting in significant differences in the synergy between the ice–snow tourism industry and its related industries in the country. This study used grey relation analysis, Copula joint function, and structural equation methods to analyze the correlation and interaction between the ice–snow tourism industry and other regional industries. It analyzed the synergistic relationship between the ice–snow tourism industry and its related industries in Jilin Province. The results showed that the ice–snow tourism industry highly correlates with the financial, real estate, and other service industries. The contribution rate of ice–snow tourism to the three closely related service industries has reached 25.1%. This study provides decision-making support for the synergistic development of regional and ice–snow industries.
Modification Mechanism and Technical Performance of Recycled PE-Modified Asphalt
Authors: Yu Haiyang , Wu Shaopeng , Chen Anqi , Li Yuanyuan
Published: August 2023

Waste plastic pollution is a serious issue. In order to adhere to the concept of green development and rationally dispose of polyethylene waste plastic products, polyethylene (PE)-modified asphalt was prepared using recycled polyethylene (RPE) and low-density polyethylene (LDPE) as raw materials. The chemical structures of the RPE- and LDPE-modified asphalt were studied using a Fourier transform infrared spectrometer (FTIR), and the dispersion of RPE was studied using a fluorescence microscope (FM). Subsequently, the modification mechanism of the PE-modified asphalt was revealed. The physical properties and high- and low-temperature rheological characteristics of the PE-modified asphalt were examined using physical property tests, a dynamic shear rheometer (DSR), and a bending beam rheometer (BBR). The creep performance of the PE-modified asphalt was analyzed using multiple-stress creep recovery (MSCR). In addition, a laboratory-made inexpensive inorganic stabilizer was added to enhance the storability of the PE-modified asphalt. The results show that PE and asphalt are similarly compatible and form an S-C bond with an inorganic stabilizer. The resulting product’s storage stability is enhanced via the cross linking between the PE and asphalt and the subsequent formation of a network structure. The segregation softening point increased from 2 °C to 45 °C with the increase in PE content, and the increase in RPE was more obvious than that of LDPE. The high-temperature failure of the 2–6% RPE-modified asphalt can reach 70 °C, while that of the 8% RPE-modified asphalt can reach 76 °C. Low-temperature performance was reduced slightly: the 8% PE-doping low-temperature failure temperature was −14.7 °C. The low-temperature performance was somewhat reduced, but it was still within a PG rating.
Multi-Objective Optimization Method of Industrial Workshop Layout from the Perspective of Low Carbon
Authors: Li Rui , Chen Yali , Song Jinzhao , Li Ming , Yu Yu
Published: August 2023

A crucial measure to accelerate the low-carbon transformation of enterprises in the industrial sector involves stringent control over carbon emissions attributed to logistics and transportation activities. In this study, a multi-objective workshop layout optimization model is developed, aiming to minimize logistics cost per unit area and carbon emissions, and maximize the non-logistics relationship. The objective is to mitigate avoidable transportation-related carbon emissions during enterprise operations, while facilitating the co-development of the enterprise’s economy and environment. The model is solved utilizing an enhanced NSGA-II algorithm, with the initial solution set optimized through a combination of system layout design method, dynamic adaptive crossover, and variation strategies. Additionally, the distribution function is introduced to enhance the elite retention strategy and boost the algorithm’s search rate. By using an actual case study, the usefulness of the enhanced algorithm is demonstrated, and the plant’s initial low-carbon layout is realized in order to advance the enterprises’ sustainable growth.