bluuebunny/crossref_metadata_2025 huggingface dataset:
Holocene Activity of the Wudaoliang–Changshagongma Fault of the Eastern Tibetan Plateau
Authors: Liang Mingjian , Dong Yun , Liao Cheng , Qin Yulong , Zhang Huiping , Wu Weiwei , Zuo Hong , Zhou Wenying , Xiong Changli , Yang Li , Gong Yue , Li Tian
Published: May 2023

The Wudaoliang–Changshagongma fault is one of the NW-trending faults located within the southern Bayan Har Block of the Tibetan Plateau in China. In this paper, we used high-resolution imagery and digital elevation model data to study the geomorphological and geological characteristics of the fault. Furthermore, the result also determined the fault trace and estimated the average horizontal slip rate of the fault since the late Quaternary to have been 2.6 ± 0.6 mm/a. This slip rate is approximately equivalent to that of the Awancang, Madoi–Garde, and Dari faults, which are also located within the block. Furthermore, the slip rates of these faults obtained by remote sensing and geological methods are consistent with GPS observations. It indicates that tectonic deformation within the block is continuous and diffuse. Using trenching study results and sedimentary radiocarbon dating, we identified four paleoearthquake events that occurred at 42,378–32,975, 33,935–20,663, 5052–4862, and after 673–628 cal BP, respectively. The recurrence intervals of large earthquakes on the faults within the block are much longer than those of the boundary faults, and the slip rates are also smaller, indicating that faults within the block play a regulatory role in the tectonic deformation of the Bayan Har Block.
Comparative Analysis of Remote Sensing Storage Tank Detection Methods Based on Deep Learning
Authors: Fan Lu , Chen Xiaoying , Wan Yong , Dai Yongshou
Published: May 2023

Since the Industrial Revolution, methane has become the second most important greenhouse gas component after CO2 and the second most important culprit of global warming, leading to serious climate change problems such as droughts, fires, floods, and glacial melting. While most of the methane in the atmosphere comes from emissions from energy activities such as petroleum refining, storage tanks are an important source of methane emissions during the extraction and processing of crude oil and natural gas. Therefore, the use of high-resolution remote sensing image data for oil and gas production sites to achieve efficient and accurate statistics for storage tanks is important to promote the strategic goals of “carbon neutrality and carbon peaking”. Compared with traditional statistical methods for studying oil storage tanks, deep learning-based target detection algorithms are more powerful for multi-scale targets and complex background conditions. In this paper, five deep learning detection algorithms, Faster RCNN, YOLOv5, YOLOv7, RetinaNet and SSD, were selected to conduct experiments on 3568 remote sensing images from five different datasets. The results show that the average accuracy of the Faster RCNN, YOLOv5, YOLOv7 and SSD algorithms is above 0.84, and the F1 scores of YOLOv5, YOLOv7 and SSD algorithms are above 0.80, among which the highest detection accuracy is shown by the SSD algorithm at 0.897 with a high F1 score, while the lowest average accuracy is shown by RetinaNet at only 0.639. The training results of the five algorithms were validated on three images containing differently sized oil storage tanks in complex backgrounds, and the validation results obtained were better, providing more accurate references for practical detection applications in remote sensing of oil storage tank targets in the future.
A New Method for Crop Type Mapping at the Regional Scale Using Multi-Source and Multi-Temporal Sentinel Imagery
Authors: Wang Xiaohu , Fang Shifeng , Yang Yichen , Du Jiaqiang , Wu Hua
Published: May 2023

Crop type mapping at high resolution is crucial for various purposes related to agriculture and food security, including the monitoring of crop yields, evaluating the potential effects of natural disasters on agricultural production, analyzing the potential impacts of climate change on agriculture, etc. However, accurately mapping crop types and ranges on large spatial scales remains a challenge. For the accurate mapping of crop types at the regional scale, this paper proposed a crop type mapping method based on the combination of multiple single-temporal feature images and time-series feature images derived from Sentinel-1 (SAR) and Sentinel-2 (optical) satellite imagery on the Google Earth Engine (GEE) platform. Firstly, crop type classification was performed separately using multiple single-temporal feature images and the time-series feature image. Secondly, with the help of information entropy, this study proposed a pixel-scale crop type classification accuracy evaluation metric, i.e., the CA-score, which was used to conduct a vote on the classification results of multiple single-temporal images and the time-series feature image to obtain the final crop type map. A comparative analysis showed that the proposed classification method had excellent performance and that it can achieve accurate mapping of multiple crop types at a 10 m resolution for large spatial scales. The overall accuracy (OA) and the kappa coefficient (KC) were 84.15% and 0.80, respectively. Compared with the classification results that were based on the time-series feature image, the OA was improved by 3.37%, and the KC was improved by 0.03. In addition, the CA-score proposed in this study can effectively reflect the accuracy of crop identification and can serve as a pixel-scale classification accuracy evaluation metric, providing a more comprehensive visual interpretation of the classification accuracy. The proposed method and metrics have the potential to be applied to the mapping of larger study areas with more complex land cover types using remote sensing.
Predicting Dust-Storm Transport Pathways Using a Convolutional Neural Network and Geographic Context for Impact Adaptation and Mitigation in Urban Areas
Authors: Yarmohamadi Mahdis , Alesheikh Ali Asghar , Sharif Mohammad , Vahidi Hossein
Published: May 2023

Dust storms are natural disasters that have a serious impact on various aspects of human life and physical infrastructure, particularly in urban areas causing health risks, reducing visibility, impairing the transportation sector, and interfering with communication systems. The ability to predict the movement patterns of dust storms is crucial for effective disaster prevention and management. By understanding how these phenomena travel, it is possible to identify the areas that are most at risk and take appropriate measures to mitigate their impact on urban environments. Deep learning methods have been demonstrated to be efficient tools for predicting moving processes while considering multiple geographic information sources. By developing a convolutional neural network (CNN) method, this study aimed to predict the pathway of dust storms that occur in arid regions in central and southern Asia. A total of 54 dust-storm events were extracted from the modern-era retrospective analysis for research and applications, version 2 (MERRA-2) product to train the CNN model and evaluate the prediction results. In addition to dust-storm data (aerosol optical depth (AOD) data), geographic context information including relative humidity, surface air temperature, surface wind direction, surface skin temperature, and surface wind speed was considered. These features were chosen using the random forest feature importance method and had feature importance values of 0.2, 0.1, 0.06, 0.03, and 0.02, respectively. The results show that the CNN model can promisingly predict the dust-transport pathway, such that for the 6, 12, 18, and 24-h time steps, the overall accuracy values were 0.9746, 0.975, 0.9751, and 0.9699, respectively; the F1 score values were 0.7497, 0.7525, 0.7476, and 0.6769, respectively; and the values of the kappa coefficient were 0.7369, 0.74, 0.7351, and 0.6625, respectively.
Machine Fault Detection Using a Hybrid CNN-LSTM Attention-Based Model
Authors: Borré Andressa , Seman Laio Oriel , Camponogara Eduardo , Stefenon Stefano Frizzo , Mariani Viviana Cocco , Coelho Leandro dos Santos
Published: May 2023

The predictive maintenance of electrical machines is a critical issue for companies, as it can greatly reduce maintenance costs, increase efficiency, and minimize downtime. In this paper, the issue of predicting electrical machine failures by predicting possible anomalies in the data is addressed through time series analysis. The time series data are from a sensor attached to an electrical machine (motor) measuring vibration variations in three axes: X (axial), Y (radial), and Z (radial X). The dataset is used to train a hybrid convolutional neural network with long short-term memory (CNN-LSTM) architecture. By employing quantile regression at the network output, the proposed approach aims to manage the uncertainties present in the data. The application of the hybrid CNN-LSTM attention-based model, combined with the use of quantile regression to capture uncertainties, yielded superior results compared to traditional reference models. These results can benefit companies by optimizing their maintenance schedules and improving the overall performance of their electric machines.
Design and Validation of Experimental Setup for Cell Spheroid Radiofrequency-Induced Heating
Authors: Androulakis Ioannis , Ferrero Riccardo , van Oossanen Rogier , Manzin Alessandra , Denkova Antonia G. , Djanashvili Kristina , Nadar Robin , van Rhoon Gerard C.
Published: May 2023

While hyperthermia has been shown to induce a variety of cytotoxic and sensitizing effects on cancer tissues, the thermal dose–effect relationship is still not well quantified, and it is still unclear how it can be optimally combined with other treatment modalities. Additionally, it is speculated that different methods of applying hyperthermia, such as water bath heating or electromagnetic energy, may have an effect on the resulting biological mechanisms involved in cell death or in sensitizing tumor cells to other oncological treatments. In order to further quantify and characterize hyperthermia treatments on a cellular level, in vitro experiments shifted towards the use of 3D cell spheroids. These are in fact considered a more representative model of the cell environment when compared to 2D cell cultures. In order to perform radiofrequency (RF)-induced heating in vitro, we have recently developed a dedicated electromagnetic field applicator. In this study, using this applicator, we designed and validated an experimental setup which can heat 3D cell spheroids in a conical polypropylene vial, thus providing a reliable instrument for investigating hyperthermia effects at the cellular scale.
Coalition Formation Game for Cost-Efficient Multiparty Payment Channel in Payment Channel Networks
Authors: Kim Wooseong
Published: May 2023

Blockchain has introduced a new era for online payment services and its economy with tamper-proof cryptocurrencies. However, blockchain, which is based on global peer-to-peer networks, has its limitations due to payment delays from global consensus and transaction costs for maintenance. Thus, payment channel networks (PCN) have been proposed as one of the most promising off-chain solutions, allowing users to pay directly through payment channels (PC), with minimal blockchain involvement. However, payment delays and cost problems still exist, especially given the large size of the PCN. This study proposes a multiparty payment channel (MPC) that enables multiple users to join the same PC and exchange payment transactions, compared to the legacy PC. To avoid a consensus procedure among users in the PC, we introduce sequential and parallel updates for the PC status. Since increasing the MPC size limits the advantages in terms of the delay and cost, we propose a distributed coalition formation algorithm to form the MPC group, in which each user has the choice to join or leave the group. Simulations show that the proposed algorithm establishes MPCs successfully, considering the trade-off between the payoff gain and the MPC delay cost.
An Approach to Integrated Scheduling of Flexible Job-Shop Considering Conflict-Free Routing Problems
Authors: Sun Jiachen , Xu Zifeng , Yan Zhenhao , Liu Lilan , Zhang Yixiang
Published: May 2023

This study proposes an approach to minimize the maximum makespan of the integrated scheduling problem in flexible job-shop environments, taking into account conflict-free routing problems. A hybrid genetic algorithm is developed for production scheduling, and the optimal ranges of crossover and mutation probabilities are also discussed. The study applies the proposed algorithm to 82 test problems and demonstrates its superior performance over the Sliding Time Window (STW) heuristic proposed by Bilge and the Genetic Algorithm proposed by Ulusoy (UGA). For conflict-free routing problems of Automated Guided Vehicles (AGVs), the genetic algorithm based on AGV coding is used to study the AGV scheduling problem, and specific solutions are proposed to solve different conflicts. In addition, sensors on the AGVs provide real-time data to ensure that the AGVs can navigate through the environment safely and efficiently without causing any conflicts or collisions with other AGVs or objects in the environment. The Dijkstra algorithm based on a time window is used to calculate the shortest paths for all AGVs. Empirical evidence on the feasibility of the proposed approach is presented in a study of a real flexible job-shop. This approach can provide a highly efficient and accurate scheduling method for manufacturing enterprises.
Neuroimaging Technology in Exercise Neurorehabilitation Research in Persons with MS: A Scoping Review
Authors: Sandroff Brian M. , Rafizadeh Caroline M. , Motl Robert W.
Published: May 2023

There is increasing interest in the application of neuroimaging technology in exercise neurorehabilitation research among persons with multiple sclerosis (MS). The inclusion and focus on neuroimaging outcomes in MS exercise training research is critical for establishing a biological basis for improvements in functioning and elevating exercise within the neurologist’s clinical armamentarium alongside disease modifying therapies as an approach for treating the disease and its consequences. Indeed, the inclusion of selective neuroimaging approaches and sensor-based technology among physical activity, mobility, and balance outcomes in such MS research might further allow for detecting specific links between the brain and real-world behavior. This paper provided a scoping review on the application of neuroimaging in exercise training research among persons with MS based on searches conducted in PubMed, Web of Science, and Scopus. We identified 60 studies on neuroimaging-technology-based (primarily MRI, which involved a variety of sequences and approaches) correlates of functions, based on multiple sensor-based measures, which are typically targets for exercise training trials in MS. We further identified 12 randomized controlled trials of exercise training effects on neuroimaging outcomes in MS. Overall, there was a large degree of heterogeneity whereby we could not identify definitive conclusions regarding a consistent neuroimaging biomarker of MS-related dysfunction or singular sensor-based measure, or consistent neural adaptation for exercise training in MS. Nevertheless, the present review provides a first step for better linking correlational and randomized controlled trial research for the development of high-quality exercise training studies on the brain in persons with MS, and this is timely given the substantial interest in exercise as a potential disease-modifying and/or neuroplasticity-inducing behavior in this population.
Investigating the Path Tracking Algorithm Based on BP Neural Network
Authors: Liu Lu , Xue Mengyuan , Guo Nan , Wang Zilong , Wang Yuwei , Tang Qixing
Published: May 2023

In this paper, we propose an adaptive path tracking algorithm based on the BP (back propagation) neural network to increase the performance of vehicle path tracking in different paths. Specifically, based on the kinematic model of the vehicle, the front wheel steering angle of the vehicle was derived with the PP (Pure Pursuit) algorithm, and related parameters affecting path tracking accuracy were analyzed. In the next step, BP neural networks were introduced and vehicle speed, radius of path curvature, and lateral error were used as inputs to train models. The output of the model was used as the control coefficient of the PP algorithm to improve the accuracy of the calculation of the front wheel steering angle, which is referred to as the BP–PP algorithm in this paper. As a final step, simulation experiments and real vehicle experiments are performed to verify the algorithm’s performance. Simulation experiments show that compared with the traditional path tracking algorithm, the average tracking error of BP–PP algorithm is reduced by 0.025 m when traveling at a speed of 3 m/s on a straight path, and the average tracking error is reduced by 0.27 m, 0.42 m, and 0.67 m, respectively, at a speed of 1.5 m/s with a curvature radius of 6.8 m, 5.5 m, and 4.5 m, respectively. In the real vehicle experiment, an electric patrol vehicle with an autonomous tracking function was used as the experimental platform. The average tracking error was reduced by 0.1 m and 0.086 m on a rectangular road and a large curvature road, respectively. Experimental results show that the proposed algorithm performs well in both simulation and actual scenarios, improves the accuracy of path tracking, and enhances the robustness of the system. Moreover, facing paths with changes in road curvature, the BP–PP algorithm achieved significant improvement and demonstrated great robustness. In conclusion, the proposed BP–PP algorithm reduced the interference of nonlinear factors on the system and did not require complex calculations. Furthermore, the proposed algorithm has been applied to the autonomous driving patrol vehicle in the park and achieved good results.
Exploring Tactile Temporal Features for Object Pose Estimation during Robotic Manipulation
Authors: Galaiya Viral Rasik , Asfour Mohammed , Alves de Oliveira Thiago Eustaquio , Jiang  Xianta , Prado da Fonseca Vinicius
Published: May 2023

Dexterous robotic manipulation tasks depend on estimating the state of in-hand objects, particularly their orientation. Although cameras have been traditionally used to estimate the object’s pose, tactile sensors have recently been studied due to their robustness against occlusions. This paper explores tactile data’s temporal information for estimating the orientation of grasped objects. The data from a compliant tactile sensor were collected using different time-window sample sizes and evaluated using neural networks with long short-term memory (LSTM) layers. Our results suggest that using a window of sensor readings improved angle estimation compared to previous works. The best window size of 40 samples achieved an average of 0.0375 for the mean absolute error (MAE) in radians, 0.0030 for the mean squared error (MSE), 0.9074 for the coefficient of determination (R2), and 0.9094 for the explained variance score (EXP), with no enhancement for larger window sizes. This work illustrates the benefits of temporal information for pose estimation and analyzes the performance behavior with varying window sizes, which can be a basis for future robotic tactile research. Moreover, it can complement underactuated designs and visual pose estimation methods.
Predicting Cybersecurity Threats in Critical Infrastructure for Industry 4.0: A Proactive Approach Based on Attacker Motivations
Authors: Alqudhaibi Adel , Albarrak Majed , Aloseel Abdulmohsan , Jagtap Sandeep , Salonitis Konstantinos
Published: May 2023

In Industry 4.0, manufacturing and critical systems require high levels of flexibility and resilience for dynamic outcomes. Industrial Control Systems (ICS), specifically Supervisory Control and Data Acquisition (SCADA) systems, are commonly used for operation and control of Critical Infrastructure (CI). However, due to the lack of security controls, standards, and proactive security measures in the design of these systems, they have security risks and vulnerabilities. Therefore, efficient and effective security solutions are needed to secure the conjunction between CI and I4.0 applications. This paper predicts potential cyberattacks and threats against CI systems by considering attacker motivations and using machine learning models. The approach presents a novel cybersecurity prediction technique that forecasts potential attack methods, depending on specific CI and attacker motivations. The proposed model’s accuracy in terms of False Positive Rate (FPR) reached 66% with the trained and test datasets. This proactive approach predicts potential attack methods based on specific CI and attacker motivations, and doubling the trained data sets will improve the accuracy of the proposed model in the future.
Celestial Bodies Far-Range Detection with Deep-Space CubeSats
Authors: Franzese Vittorio , Topputo Francesco
Published: May 2023

Detecting celestial bodies while in deep-space travel is a critical task for the correct execution of space missions. Major bodies such as planets are bright and therefore easy to observe, while small bodies can be faint and therefore difficult to observe. A critical task for both rendezvous and fly-by missions is to detect asteroid targets, either for relative navigation or for opportunistic observations. Traditional, large spacecraft missions can detect small bodies from far away, owing to the large aperture of the onboard optical cameras. This is not the case for deep-space miniaturized satellites, whose small-aperture cameras pose new challenges in detecting and tracking the line-of-sight directions to small bodies. This paper investigates the celestial bodies far-range detection limits for deep-space CubeSats, suggesting active measures for small bodies detection. The M–ARGO CubeSat mission is considered as the study case for this activity. The analyses show that the detection of small asteroids (with absolute magnitude fainter than 24) is expected to be in the range of 30,000–50,000 km, exploiting typical miniaturized cameras for deep-space CubeSats. Given the limited detection range, this paper recommends to include a zero-phase-angle way point at close range in the mission design phase of asteroid rendezvous missions exploiting deep-space CubeSats to allow detection.
Petrochemical Equipment Tracking by Improved Yolov7 Network and Hybrid Matching in Moving Scenes
Authors: Wei Zhenqiang , Dong Shaohua , Wang Xuchu
Published: May 2023

Petrochemical equipment tracking is a fundamental and important technology in petrochemical industry security monitoring, equipment working risk analysis, and other applications. In complex scenes where the multiple pipelines present different directions and many kinds of equipment have huge scale and shape variation in seriously mutual occlusions captured by moving cameras, the accuracy and speed of petrochemical equipment tracking would be limited because of the false and missed tracking of equipment with extreme sizes and severe occlusion, due to image quality, equipment scale, light, and other factors. In this paper, a new multiple petrochemical equipment tracking method is proposed by combining an improved Yolov7 network with attention mechanism and small target perceive layer and a hybrid matching that incorporates deep feature and traditional texture and location feature. The model incorporates the advantages of channel and spatial attention module into the improved Yolov7 detector and Siamese neural network for similarity matching. The proposed model is validated on the self-built petrochemical equipment video data set and the experimental results show it achieves a competitive performance in comparison with the related state-of-the-art tracking algorithms.
A Deep Learning-Driven Self-Conscious Distributed Cyber-Physical System for Renewable Energy Communities
Authors: Cicceri Giovanni , Tricomi Giuseppe , D’Agati Luca , Longo Francesco , Merlino Giovanni , Puliafito Antonio
Published: May 2023

The Internet of Things (IoT) is transforming various domains, including smart energy management, by enabling the integration of complex digital and physical components in distributed cyber-physical systems (DCPSs). The design of DCPSs has so far been focused on performance-related, non-functional requirements. However, with the growing power consumption and computation expenses, sustainability is becoming an important aspect to consider. This has led to the concept of energy-aware DCPSs, which integrate conventional non-functional requirements with additional attributes for sustainability, such as energy consumption. This research activity aimed to investigate and develop energy-aware architectural models and edge/cloud computing technologies to design next-generation, AI-enabled (and, specifically, deep-learning-enhanced), self-conscious IoT-extended DCPSs. Our key contributions include energy-aware edge-to-cloud architectural models and technologies, the orchestration of a (possibly federated) edge-to-cloud infrastructure, abstractions and unified models for distributed heterogeneous virtualized resources, innovative machine learning algorithms for the dynamic reallocation and reconfiguration of energy resources, and the management of energy communities. The proposed solution was validated through case studies on optimizing renewable energy communities (RECs), or energy-aware DCPSs, which are particularly challenging due to their unique requirements and constraints; in more detail, in this work, we aim to define the optimal implementation of an energy-aware DCPS. Moreover, smart grids play a crucial role in developing energy-aware DCPSs, providing a flexible and efficient power system integrating renewable energy sources, microgrids, and other distributed energy resources. The proposed energy-aware DCPSs contribute to the development of smart grids by providing a sustainable, self-consistent, and efficient way to manage energy distribution and consumption. The performance demonstrates our approach’s effectiveness for consumption and production (based on RMSE and MAE metrics). Our research supports the transition towards a more sustainable future, where communities adopting REC principles become key players in the energy landscape.
Effect of a Patient-Specific Structural Prior Mask on Electrical Impedance Tomography Image Reconstructions
Authors: Chen Rongqing , Krueger-Ziolek Sabine , Battistel Alberto , Rupitsch Stefan J. , Moeller Knut
Published: May 2023

Electrical Impedance Tomography (EIT) is a low-cost imaging method which reconstructs two-dimensional cross-sectional images, visualising the impedance change within the thorax. However, the reconstruction of an EIT image is an ill-posed inverse problem. In addition, blurring, anatomical alignment, and reconstruction artefacts can hinder the interpretation of EIT images. In this contribution, we introduce a patient-specific structural prior mask into the EIT reconstruction process, with the aim of improving image interpretability. Such a prior mask ensures that only conductivity changes within the lung regions are reconstructed. To evaluate the influence of the introduced structural prior mask, we conducted numerical simulations with two scopes in terms of their different ventilation statuses and varying atelectasis scales. Quantitative analysis, including the reconstruction error and figures of merit, was applied in the evaluation procedure. The results show that the morphological structures of the lungs introduced by the mask are preserved in the EIT reconstructions and the reconstruction artefacts are decreased, reducing the reconstruction error by 25.9% and 17.7%, respectively, in the two EIT algorithms included in this contribution. The use of the structural prior mask conclusively improves the interpretability of the EIT images, which could facilitate better diagnosis and decision-making in clinical settings.
Hierarchical Multi-Objective Optimization for Dedicated Bus Punctuality and Supply–Demand Balance Control
Authors: Shang Chunlin , Zhu Fenghua , Xu Yancai , Liu Xiaoming , Jiang Tianhua
Published: May 2023

Public transportation is a crucial component of urban transportation systems, and improving passenger sharing rates can help alleviate traffic congestion. To enhance the punctuality and supply–demand balance of dedicated buses, we propose a hierarchical multi-objective optimization model to optimize bus guidance speeds and bus operation schedules. Firstly, we present an intelligent decision-making method for bus driving speed based on the mathematical description of bus operation states and the application of the Lagrange multiplier method, which improves the overall punctuality rate of the bus line. Secondly, we propose an optimization method for bus operation schedules that respond to passenger needs by optimizing departure time intervals and station schedules for supply–demand balance. The experiments were conducted in Future Science City, Beijing, China. The results show that the bus line’s punctuality rate has increased to 90.53%, while the retention rate for platform passengers and the intersection stop rate have decreased by 36.22% and 60.93%, respectively. These findings verify the effectiveness and practicality of the proposed hierarchical multi-objective optimization model.
Advanced Field-Effect Sensors
Authors: Di Bartolomeo Antonio
Published: May 2023

Sensors based on the field-effect principle have been used for more than fifty years in a variety of applications ranging from bio-chemical sensing to radiation detection or environmental parameter monitoring [...]
An Investigation about Modern Deep Learning Strategies for Colon Carcinoma Grading
Authors: Carcagnì Pierluigi , Leo Marco , Signore Luca , Distante Cosimo
Published: May 2023

Developing computer-aided approaches for cancer diagnosis and grading is currently receiving an increasing demand: this could take over intra- and inter-observer inconsistency, speed up the screening process, increase early diagnosis, and improve the accuracy and consistency of the treatment-planning processes.The third most common cancer worldwide and the second most common in women is colorectal cancer (CRC). Grading CRC is a key task in planning appropriate treatments and estimating the response to them. Unfortunately, it has not yet been fully demonstrated how the most advanced models and methodologies of machine learning can impact this crucial task.This paper systematically investigates the use of advanced deep models (convolutional neural networks and transformer architectures) to improve colon carcinoma detection and grading from histological images. To the best of our knowledge, this is the first attempt at using transformer architectures and ensemble strategies for exploiting deep learning paradigms for automatic colon cancer diagnosis. Results on the largest publicly available dataset demonstrated a substantial improvement with respect to the leading state-of-the-art methods. In particular, by exploiting a transformer architecture, it was possible to observe a 3% increase in accuracy in the detection task (two-class problem) and up to a 4% improvement in the grading task (three-class problem) by also integrating an ensemble strategy.
DNA Sensing Platforms: Novel Insights into Molecular Grafting Using Low Perturbative AFM Imaging
Authors: Rotondi Silvia Maria Cristina , Canepa Paolo , Angeli Elena , Canepa Maurizio , Cavalleri Ornella
Published: May 2023

By using AFM as a nanografting tool, we grafted micrometer-sized DNA platforms into inert alkanethiol SAMs. Tuning the grafting conditions (surface density of grafting lines and scan rate) allowed us to tailor the molecular density of the DNA platforms. Following the nanografting process, AFM was operated in the low perturbative Quantitative Imaging (QI) mode. The analysis of QI AFM images showed the coexistence of molecular domains of different heights, and thus different densities, within the grafted areas, which were not previously reported using contact AFM imaging. Thinner domains corresponded to low-density DNA regions characterized by loosely packed, randomly oriented DNA strands, while thicker domains corresponded to regions with more densely grafted DNA. Grafting with densely spaced and slow scans increased the size of the high-density domains, resulting in an overall increase in patch height. The structure of the grafted DNA was compared to self-assembled DNA, which was assessed through nanoshaving experiments. Exposing the DNA patches to the target sequence produced an increase in the patch height, indicating that hybridization was accomplished. The relative height increase of the DNA patches upon hybridization was higher in the case of lower density patches due to hybridization leading to a larger molecular reorganization. Low density DNA patches were therefore the most suitable for targeting oligonucleotide sequences.
Design and Experimental Validation of a Switchable Frequency Selective Surface with Incorporated Control Network
Authors: Silaghi Andrei-Marius , Mir Farzad , De Sabata Aldo , Matekovits Ladislau
Published: May 2023

Tunable/switchable devices are more and more required in modern communication systems. However, the realization of the tuning requires the presence of active devices, which in turn must be biased. The current paper comes up with a solution for designing and experimentally validating such a switchable Frequency Selective Surface. Two different metallic structures are simulated and measured, having incorporated the same topology control network (CN). In this scenario, the main innovation of this paper is the presence of the feeding part, namely the control network. In this work, the main FSS structure is flanked by three parallel CN microstrip lines and several via holes that allow biasing the active elements, namely PIN diodes. The switchability of the proposed structure is achieved through PIN diodes, whose bias determines the values of the elements in the equivalent circuit. At different biases, the response of the FSS changes accordingly. From all possible values of the bias, the extreme cases when the diodes act as (almost) short- and open-circuits are considered in the submitted manuscript for the sake of brevity. These cases are modeled by the main and cut-slot structures, respectively. The proposed structures have been evaluated using electromagnetic simulation and implemented on an FR4 substrate having a thickness of 1.58 mm. With the periodicity of the square-shaped unit cell of 18 mm edge length, different filtering bands are obtained below 12 GHz. Another novelty that has received very little consideration in the existing literature is the use of a finite array of unit cells instead of an infinite one. And finally, tests in an anechoic chamber have proved that there is a good agreement between practical and simulation results and also demonstrated the proper performance of the devices for wide angular incidence for both TE and TM polarizations.
Purchase Behavior According to the Development of Sustainable Pistachio (Pistacia vera L.) Rice Cake: For Korean Consumers
Authors: Han Ji Ahn , Kwon Ki Han
Published: May 2023

In recent years, the rice cake market has grown considerably in terms of research and development of products suitable for consumer needs and beneficial to health at the same time. Since the outbreak of COVID-19, the number of consumers consuming antioxidant-rich foods and immunity-boosting foods to improve health and promote healthy eating habits has increased. Pistachio rice cakes have been tested and found to be high in polyphenols, which have good antioxidant activity and strengthen the immune system. Therefore, research was conducted on pistachio rice cakes to develop safe, health functional, and sustainable food. In order to maximize the sales potential of pistachio rice cakes, a survey was conducted among Korean adults to investigate perception of pistachio, pistachio rice cake purchasing behavior, and popularization plans. Pearson’s correlation analysis was performed to examine the correlation between pistachio perception, pistachio rice cake purchasing behavior, and pistachio rice cake popularization measures, and they were found to have a close relationship. In addition, as a result of multiple regression analysis, perception and purchasing behavior of pistachio rice cake products were found to have a significant positive (+) effect on rice cake technology development, development of brand specializing in rice cakes, and cultivation of traditional rice cake experts. This study suggests that continuous research and development of food safety and sustainability of rice cakes are required, and it is expected to be utilized as basic data to form a research basis that can be a strategy for increasing sales in the rice cake industry.
Snow Cover Temporal Dynamic Using MODIS Product, and Its Relationship with Precipitation and Temperature in the Tropical Andean Glaciers in the Alto Santa Sub-Basin (Peru)
Authors: Calizaya Elmer , Laqui Wilber , Sardón Saul , Calizaya Fredy , Cuentas Osmar , Cahuana José , Mindani Carmen , Huacani Walquer
Published: May 2023

The retreats of the planet’s tropical glaciers are natural indicators of the variation of precipitation, temperature, and other variables. The glaciers of the Alto Santa sub-basin are sources of freshwater storage for consumptive and non-consumptive use for different sectors. As a result of climatic variations, it is essential to analyze the dynamics of the snow cover area (SCA). The methodology consisted of processing 6578 MODIS Snow Cover MOD10A1 product images and generating 18-year time series using the Platform Google Earth Engine (GEE). Normalized Difference Snow Index (NDSI) was used to estimate the extent of snow cover, and to validate the MODIS snow cover product, we used the same overlapping date of Landsat 5 and 8 Surface Reflectance Tier 1, to examine the relationships between daily precipitation and temperature. The standardized correlation results gave good results with stations over 4500 m.a.s.l., such as Artesonraju AP2 (4828 m.a.s.l.) of −0.84 and −0.74, precipitation, and temperature. These results show coherent behaviors of the retreat due to the variation of the climatological variables. In some years, there were anomalies in the conduct of the three variables, but these originated from events of natural weather phenomena. Regarding the dynamics of the SCA in 18 years, it decreased from 649 km2 to 311.6. km2 between 2000 and 2017, representing a retreat of 41%; we can conclude and confirm that the glacier retreat is imminent due to the consequences of climate change, which would affect the security of freshwater from the tropical glaciers of the Peruvian Andes.
Evaluating the Consumer Attitude and Behavioral Consumption of Green Products in Vietnam
Authors: Nguyen Dat Duy
Published: May 2023

For the past few years, environmental degradation has become increasingly worse. Most nations, particularly developing nations, are experiencing excessive economic expansion, which has decreased natural resources and increased environmental pollution. Numerous nations have created plans to safeguard the environment and lessen harmful human effects on its resources. Transforming to using green products is the most powerful strategy to protect the environment. This study aims to investigate Vietnamese consumers’ decision to buy green products by assessing influencing factors, typically consumer consciousness. Data were collected from consumers across several major cities in Vietnam and analyzed using structural equation modeling (SEM). The primary factors include consumer awareness, consumption trends, reference groups, government policies, demand for green products, and economic factors. Research results show that most of the variables have a positive impact on the transition to green product consumption. Consumer awareness strongly impacts the transition to the consumption of green products and environmental responsibility on green purchase intention. The findings also reinforce the current view that pro-environmental factors override self-interest in buyer decision-making. Research results also show that government policies also affect green consumption behavior. Therefore, the government needs to develop policies to encourage green consumption.
Understanding the Relationship between Big Data Analytics Capabilities and Sustainable Performance: The Role of Strategic Agility and Firm Creativity
Authors: Alyahya Mansour , Aliedan Meqbel , Agag Gomaa , Abdelmoety Ziad
Published: May 2023

The most successful organisations create businesses that can respond to sudden and unexpected changes in the market. The purpose of this research is to examine how big data analytics capabilities might, through strategic agility, impact on sustainable performance. We grounded our theoretical framework in two perspectives: the resource-based view and the dynamic capabilities view. In order to gather data from Saudi Arabian managers, we used the positivist methodology of a survey. Data were collected from 410 managers. The data were analysed using the SEM method. The findings indicated that big data analytics capabilities have a significant effect on economic, environmental, and social performance. They also revealed that strategic agility partially mediates the relationship between the capabilities of big data analytics and sustainable performance. Furthermore, the impact of big data analytics capabilities on strategic agility is stronger in a creative environment, while the strategic agility–sustainable performance relationship is more pronounced in more creative environments. The findings offer firms an insight into the actual benefits that big data analytics may generate and how firms may align the use of big data analytics with industrial conditions to foster sustainable performance.
The Environmental Sustainability Study of an Airport Building System Based on an Integrated LCA-Embodied Energy (Emergy)-ANN Analysis
Authors: Xie Fei , Zhang Junxue , Wu Guodong , Zhang Chunxia , Wang Hechi
Published: May 2023

From a global perspective, the ecological sustainability of building systems has always been a hot research topic, especially in China, where the annual amount of new construction is nearly half of the world. The difficulty is making a complete and accurate ecological assessment of the building system. This study has designed and adopted the LCA-Emergy-ANN framework to assess and analyze an airport building system for sustainability. The results demonstrate that building material emergy and operational stage emergy play a critical role and account for 92.4% of the entire emergy, which are the primary contributors. As the vital indicator, the emergy sustainability index (ESI) is 0.669, which is unsustainable (The eligibility standard is 1). Simultaneously, to ensure the accuracy of the data results, sensitivity analysis was performed. The artificial neural network (ANN) was used by integrating the LCA method and emergy approach to predict the sustainability trend in the long run. In the end, the optimization strategy is proposed to enhance the sustainability of the building system.
Challenges to Global Health Emerging from the COVID-19 Pandemic
Authors: Albreht Tit
Published: May 2023

The COVID-19 pandemic deepened challenges and opened new fronts where old and unresolved problems met with new problems triggered by the pandemic itself. There are several issues, problems, and challenges related to the post-COVID world we are in. This paper discusses some of them from different perspectives and elaborates on the future challenges and their solutions, which need to be tackled. Apart from the pre-existing problems, we are now also facing new military conflicts, interrupted supply chains, even in medicines supplies, as well as the worsening of climate change, further complicated by the military conflicts and a potential new global economic crisis. The COVID-19 pandemic left the world with many uncertainties about the future as there seem to be few answers ready to tackle the combination of the fallout of the COVID-19 pandemic coupled with the pre-existing problems and challenges. Inequalities in terms of socio-economic differences and inequalities in health are driving disease burden, which will again be marked by the population aging and non-communicable diseases (NCDs). Global collaboration and the joint search for solutions to global challenges have become inevitable. Research into behavioral determinants of health and health promotion are essential in understanding the sources of controversy regarding and resistance to proven public health interventions.
Research and Development of a New Sustainable Functional Food under the Scope of Nutrivigilance
Authors: Matran Irina Mihaela , Tarcea Monica , Rus Diana Carmen , Voda Rares , Muntean Daniela-Lucia , Cirnatu Daniela
Published: May 2023

Background: The New Global Economy is represented by a series of major features, such as the use of green energy, the reduction of the carbon footprint in all industrial and civil fields, as well as finding alternative food resources. Our main objective was the research of a sustainable food product with a special nutritional purpose in the vision of nutrivigilance, developed in Romania, as an adjuvant in the repair of gastric mucosa. Methods: The materials used in the research and development of the new food are the following: inulin, lactoferrin, sericin, and sodium bicarbonate. The new adjuvant food product in the repair of the gastric mucosa was added to certain foods in order to prevent the patients from being satiated by a single food from a sensory point of view. The resulting food products were organoleptically and physico-chemically analyzed. Results: The new food is sustainable and has versatile uses. It can be hydrated with water, non-carbonated drinks, mixed with cottage cheese, or with fruit puree and oatmeal. It is stable under normal storage conditions and microbiologically safe. Conclusions: Through its versatile use, the new food product for special nutritional conditions represents a worldwide novelty. Through the development of forestry for the cultivation of white or black mulberry (Morus alba and Morus nigra), the raising of silkworms (Bombyx mori), the processing of fibroin to obtain natural silk and the processing of sericin resulting as a residue in the textile industry, the new food product developed actively contribute to the global economy II.