bluuebunny/crossref_metadata_2025 huggingface dataset:
Effect of Displacement Pressure Gradient on Oil–Water Relative Permeability: Experiment, Correction Method, and Numerical Simulation
Authors: Wu Jintao , Zhang Lei , Liu Yingxian , Ma Kuiqian , Luo Xianbo
Published: February 2024

Relative permeability is a fundamental parameter affecting reservoir development performance analysis. During the development of oil and gas fields, the displacement pressure gradient changes with time and space. This paper studies the effect of displacement pressure gradient on relative permeability. The oil–water relative permeability curves of a Bohai Oilfield under different displacement pressure gradients are obtained through experimental analysis. Based on the experimental data, a correction model of the permeability curve is established by regression of the Willhite model parameters. The correction model is introduced into the black oil numerical simulation, and the production performance and remaining oil are compared and analyzed. The results show that the displacement pressure gradient can have an obvious impact on the relative permeability curve. As the displacement pressure gradient increases, the two-phase span of the relative permeability curve increases, the oil displacement efficiency increases, and the water relative permeability increases. The relative permeability curves under different displacement pressure gradients can be accurately characterized by the Willhite model. The consideration of the displacement pressure gradient has an obvious impact on numerical simulation results. The conventional method of using a fixed relative permeability curve cannot truly reflect the production performance and the remaining oil distribution. This paper proposes a set of realization methods including obtaining laws from experiments, utilizing the empirical model to correct, and simulating to characterize reservoir changes.
Integrating Intercultural Philosophy into the High School Curriculum: Toward a Deliberative Pedagogy of Tadabbur in Diasporic Muslim Education
Authors: Abdul-Jabbar Wisam Kh. , Makki Yousra
Published: February 2024

This article explores the pedagogical aspects of intercultural philosophy and identifies instructional strategies for Islamic school curricula in diaspora. It combines Western teaching methods peculiar to deliberative pedagogy with the Islamic notion of Tadabbur, Arabic for ‘to deliberate’ and ‘carefully consider’ the outcomes. It aims to provide insights into implementing intercultural philosophy as pedagogy and highlights examples of its application in Muslim educational contexts. How can intercultural philosophy be implemented in class, especially in the high school curriculum in diaspora? It emphasizes the potential benefits and compatibility of intercultural philosophy from a Muslim educational perspective. It offers practical insights and examples for educators who seek to integrate intercultural philosophy into their curricula. Bridging the gap between East and West provides a unique perspective on incorporating diverse philosophical traditions using the same teaching strategies. More specifically, this article introduces Tadabbur through instructional strategies such as Think–Pair–Share and the 5E instructional model, which use deliberative pedagogy. Intercultural philosophy, therefore, contributes to the cultural and religious diversification of curriculum theorizing and implementation.
Schelling’s Critique of Modern Philosophy’s “Impulse toward Spiritualization” in Clara
Authors: Jussaume Andrew
Published: February 2024

The following essay explores Schelling’s critique of what he refers to in Clara as modern philosophy’s “impulse toward spiritualization”, as represented by the character of the clergyman. Schelling’s metaphysics embraces an organicism in which nature is the ground for the revelation of spirit, an organicism which implies that spirit becomes real as truth only insofar as it explains nature while testifying to the divine freedom from which all existence emerges. The following essay shows how Schelling himself understands the uniqueness of his metaphysics vis à vis his criticism of modern philosophy and its impulse to regard spirit as self-grounding. This essay proves that Schelling’s organicism underscores the fact that spirit, without nature as its ground, lacks objectivity, and therefore, is reduced to the mere feelings of the subject.
Modelling Possible Household Uses of Grey Water in Poland using Property Fitting Analysis
Authors: Ober Józef , Karwot Janusz , Sitinjak Charli
Published: February 2024

One of the most important methods of optimising water consumption is grey water recycling. From a technological point of view, the treatment of grey water guarantees that it can be reused for domestic or corporate purposes, but it raises the issue of the social acceptance of the use of such water. This study aimed to assess the possibility of using grey water in households in Poland. The originality of this research study lies in the application of the PROFIT method for the separate construction of models of the benefits of grey water according to user groups. Four groups were identified, differentiated by gender and age; age and possession of an irretrievable water meter; gender and place of residence; place of residence and possession of an irretrievable water meter. To answer the formulated research questions, a diagnostic survey method was used, in which 807 randomly selected respondents from all over Poland were surveyed. The results of the survey indicate that homeowners perceive the potential use of grey water as beneficial, pointing most often to the following factors: rebuilding groundwater levels, reduced extraction of drinking water from rivers and other water bodies, and increased vegetation growth. On the other hand, they are concerned about the need to reconstruct the existing water and sewerage systems in order to produce drinking water from grey water as well as about the high cost and parameter stability of drinking water produced from grey water. Furthermore, men and older people attribute less importance to measures related to the introduction of good practices based on the reuse of recycled water in water management. Women, on the other hand, appreciate almost all opportunities to use grey water to a greater extent than men.
L1 Regularization for High-Dimensional Multivariate GARCH Models
Authors: Yao Sijie , Zou Hui , Xing Haipeng
Published: February 2024

The complexity of estimating multivariate GARCH models increases significantly with the increase in the number of asset series. To address this issue, we propose a general regularization framework for high-dimensional GARCH models with BEKK representations, and obtain a penalized quasi-maximum likelihood (PQML) estimator. Under some regularity conditions, we establish some theoretical properties, such as the sparsity and the consistency, of the PQML estimator for the BEKK representations. We then carry out simulation studies to show the performance of the proposed inference framework and the procedure for selecting tuning parameters. In addition, we apply the proposed framework to analyze volatility spillover and portfolio optimization problems, using daily prices of 18 U.S. stocks from January 2016 to January 2018, and show that the proposed framework outperforms some benchmark models.
Optimization of Targeted Differential Interferometric Measurements for Wellpads Detected by Mask Region-Based Convolutional Neural Network in the Tengiz Oilfield of the Caspian Sea Coast
Authors: Bayramov Emil , Tessari Giulia , Aliyeva Saida , Duisenbiev Askar , Kada Martin
Published: February 2024

Many previous studies have primarily focused on the use of deep learning for interferometric processing or separate recognition purposes rather than targeted measurements of detected wellpads. The present study centered around the integration of deep learning recognition and interferometric measurements for Tengiz oilfield wellpads. This study proposes the optimization, automation, and acceleration of targeted ground deformation wellpad monitoring. Mask Region-based Convolutional Neural Network (R-CNN)-based deep learning wellpad recognition and consequent Small Baseline Subset Synthetic Aperture Radar Interferometry (SBAS-InSAR) analyses were used for the assessment of ground deformation in the wellpads. The Mask R-CNN technique allowed us to detect 159 wells with a confidence level of more than 95%. The Mask R-CNN model achieved a precision value of 0.71 and a recall value of 0.91. SBAS-InSAR interferometric measurements identified 13 wells for Sentinel-1 (SNT1), 8 wells for COSMO-SkyMed (CSK), and 20 wells for TerraSAR-X (TSX) located within the −54–−40 mm/y class of vertical displacement (VD) velocity. Regression analyses for the annual deformation velocities and cumulative displacements (CD) of wells derived from SNT1, CSK, and TSX satellite missions showed a good agreement with R2 > 95. The predictions for cumulative displacements showed that the vertical subsidence processes will continue and reach −339 mm on 31 December 2023, with increasing spatial coverage and the potential to impact a higher number of wells. The hydrological analyses in the Tengiz oilfield clearly demonstrated that water flow has been moving towards the detected hotspot of subsidence and that its accumulation will increase with increasing subsidence. This detected subsidence hotspot was observed at a crossing with a seismic fault that might always be subject to reactivation. The role of this seismic fault should also be investigated as one of the ground deformation-controlling factors, even though this area is not considered seismically active. The primary practical and scientific values of these studies were identified for the operational risk assessment and maintenance needs of oilfield and gas field operators.
Estimating Winter Arctic Sea Ice Motion Based on Random Forest Models
Authors: Zhang Linxin , Shi Qian , Leppäranta Matti , Liu Jiping , Yang Qinghua
Published: February 2024

Sea ice motion (SIM) plays a crucial role in setting the distribution of the ice cover in the Arctic. Limited by images’ spatial resolution and tracking algorithms, challenges exist in obtaining coastal sea ice motion (SIM) based on passive microwave satellite sensors. In this study, we developed a method based on random forest (RF) models to obtain Arctic SIM in winter by incorporating wind field and coastal geographic location information. These random forest models were trained using Synthetic Aperture Radar (SAR) SIM data. Our results show good consistency with SIM data retrieved from satellite imagery and buoy observations. With respect to the SAR data, compared with SIM estimated with RF model training using reanalysis surface wind, the results by additional coastal information input had a lower root mean square error (RMSE) and a higher correlation coefficient by 31% and 14% relative improvement, respectively. The latter SIM result also showed a better performance for magnitude, especially within 100 km of the coastline in the north of the Canadian Arctic Archipelago. In addition, the influence of coastline on SIM is quantified through variable importance calculation, at 22% and 28% importance of all RF variables for east and north SIM components, respectively. These results indicate the great potential of RF models for estimating SIM over the whole Arctic Ocean in winter.
A Method for Retrieving Cloud Microphysical Properties Using Combined Measurement of Millimeter-Wave Radar and Lidar
Authors: Lin Weiqi , He Qianshan , Cheng Tiantao , Chen Haojun , Liu Chao , Liu Jie , Hong Zhecheng , Hu Xinrong , Guo Yiyuan
Published: February 2024

Clouds are an important component of weather systems and are difficult to effectively characterize using current climate models and estimation of radiative forcing. Due to the limitations in observational capabilities, it remains difficult to obtain high-spatiotemporal-resolution, continuous, and accurate observations of clouds. To overcome this issue, we propose a novel and practical combined retrieval method using millimeter-wave radar and lidar, which enables the microphysical properties of thin liquid water clouds, such as cloud droplet effective radius, number concentration, and liquid water content, to be retrieved. This method was utilized to analyze the clouds observed at the Shanghai World Expo Park and was validated through synchronous observations with a microwave radiometer. Furthermore, the most suitable extinction backscatter ratio was determined through sensitivity analysis. This study provides vertical distributions of cloud microphysical properties with a time resolution of 1 min and a spatial resolution of 30 m, demonstrating the scientific potential of this combined retrieval method.
Submesoscale Short-Lived Eddies in the Southwestern Taiwan Strait Observed by High-Frequency Surface-Wave Radars
Authors: Zhao Hong , Yue Xianchang , Wang Li , Wu Xiongbin , Chen Zhangyou
Published: February 2024

Surface currents obtained from the high-frequency surface-wave radars (HFSWRs) installed along the coast of Fujian Province are utilized to characterize submesoscale eddies in the southwestern Taiwan Strait from 29 January to 26 March 2013. The algorithm based on vector geometry (VG) has been applied to datasets and a total of 414 (161 anticyclonic and 253 cyclonic eddies) were obtained. Eddies with both rotations had a relatively short lifespan (≤3.7 h), and their radii were in the range of 5–22.5 km. Eddies with a lifespan of over 30 minutes were more likely to occur north of the Taiwan Strait shoals and move eastward or northeastward. The deviation of moving directions of eddies with a moving distance of more than 20 km was within 18°. Moreover, eddies could hardly hold their original forms with cyclones extending in the east-west and compressing in the north-south direction, and anticyclones were the opposite. The vorticity and strain rate were proportional to the square of the energy intensity (EI). This study shows that the array HFSWRs have a strong capability to observe short-lived submesoscale eddies.
Spectral-Spatial Domain Attention Network for Hyperspectral Image Few-Shot Classification
Authors: Zhang Zhongqiang , Gao Dahua , Liu Danhua , Shi Guangming
Published: February 2024

Recently, many deep learning-based methods have been successfully applied to hyperspectral image (HSI) classification. Nevertheless, training a satisfactory network usually needs enough labeled samples. This is unfeasible in practical applications since the labeling of samples is time-consuming and expensive. The target domain samples that need to be classified are usually limited in HSIs. To mitigate this issue, a novel spectral-spatial domain attention network (SSDA) is proposed for HSI few-shot classification, which can transfer the learned classification knowledge from source domain contained enough labeled samples to target domain. The SSDA includes a spectral-spatial module, a domain attention module, and a multiple loss module. The spectral-spatial module can learn discriminative and domain invariance spectral-spatial features. The domain attention module can further enhance useful spectral-spatial features and avoid the interference of useless features. The multiple loss module, including few-shot loss, coral loss, and mmd loss, can solve the domain adaptation issue. Extensive experimental results demonstrate that on the Salinas, the University of Pavia (UP), the Indian Pines (IP), and the Huoshaoyun datasets, the proposed SSDA obtains higher classification accuracies than state-of-the art methods in the HSI few-shot classification.
Postfire Forest Regrowth Algorithm Using Tasseled-Cap-Retrieved Indices
Authors: Stankova Nataliya , Avetisyan Daniela
Published: February 2024

Wildfires are a common disturbance factor worldwide, especially over the last decade due to global climate change. Monitoring postfire forest regrowth provides fundamental information needed to enhance the management and support of ecosystem recovery after fires. The purpose of this study is to propose an algorithm for postfire forest regrowth monitoring using tasseled-cap-derived indices. A complex approach is used for its implementation, for which a model is developed based on three components—Disturbance Index (DI), Vector of Instantaneous Condition (VIC), and Direction Angle (DA). The final product—postfire regrowth (PFIR)—allows for a quantitative assessment of the intensity of regrowth. The proposed methodology is based on the linear orthogonal transformation of multispectral satellite images—tasseled cap transformation (TCT)—that increases the degree of identification of the three main components that change during a fire—soil, vegetation, and water/moisture—and implies a higher accuracy of the assessments. The results provide a thematic raster representing the intensity of the regrowth classes, which are defined after the PFIR threshold values are determined (HRI—high regrowth intensity; MRI—moderate regrowth intensity; and LRI—low regrowth intensity). The accuracy assessment procedure is conducted using very-high-resolution (VHR) aerial and satellite data from World View (WV) sensors, as well as multispectral Sentinel 2A images. Three different forest test sites affected by fire in Bulgaria are examined. The results show that the classified thematic raster maps are distinguished by a good performance in monitoring the regrowth dynamics, with an average overall accuracy of 62.1% for all three test sites, ranging from 73.9% to 48.4% for the individual forests.
Machine-Learning-Assisted Characterization of Regional Heat Islands with a Spatial Extent Larger than the Urban Size
Authors: Du Yin , Xie Zhiqing , Zhang Lingling , Wang Ning , Wang Min , Hu Jingwen
Published: February 2024

Surface urban heat islands (SUHIs) can extend beyond the urban boundaries and greatly affect the thermal environment of continuous regions over an agglomeration. Traditional urban-rural dichotomy depending on the built-up and non-urban lands is challenged in characterizing regional SUHIs, such as how to accurately quantify the intensity, spatial pattern, and scales of SUHIs, which are vulnerable to SUHIs, and what the optimal scale for conducting measures to mitigate the SUHIs. We propose a machine-learning-assisted solution to address these problems based on the thermal similarity in the Yangtze River Delta of China. We first identified the regional-level SUHI zone of approximately 42,328 km2 and 38,884 km2 and the areas that have no SUHI effects from the annual cycle of land surface temperatures (LSTs) retrieved from Terra and Aqua satellites. Defining SUHI as an anomaly on background condition, random forest (RF) models were further adopted to fit the LSTs in the areas without the SUHI effects and estimate the LST background and SUHI intensity at each grid point in the SUHI zone. The RF models performed well in fitting rural LSTs with a simulation error of approximately 0.31 °C/0.44 °C for Terra/Aqua satellite data and showed a good generalization ability in estimating the urban LST background. The RF-estimated daytime Aqua/SUHI intensity peaked at approximately 6.20 °C in August, and the Terra/SUHI intensity had two peaks of approximately 3.18 and 3.81 °C in May and August, with summertime RF-estimated SUHIs being more reliable than other SUHI types owing to the smaller simulation error of less than 1.0 °C in July–September. This machine-learning-assisted solution identified an optimal SUHI scale of 30,636 km2 and a zone of approximately 23,631 km2 that is vulnerable to SUHIs, and it provided the SUHI intensity and statistical reliability for each grid point identified as being part of the SUHI. Urban planners and decision-makers can focus on the statistically reliable RF-estimated summertime intensities in SUHI zones that have an LST annual cycle similar to that of large cities in developing effective strategies for mitigating adverse SUHI effects. In addition, the selection of large cities might strongly affect the accuracy of identifying the SUHI zone, which is defined as the areas that have an LST annual cycle similar to large cities. Water bodies might reduce the RF performance in estimating the LST background over urban agglomerations.
Proficiency Barrier in Track and Field: Adaptation and Generalization Processes
Authors: Ribeiro M. Teresa S. , Conceição Filipe , Pacheco Matheus M.
Published: February 2024

The literature on motor development and training assumes a hierarchy for learning skills—learning the “fundamentals”—that has yet to be empirically demonstrated. The present study addressed this issue by verifying (1) whether this strong hierarchy (i.e., the proficiency barrier) holds between three fundamental skills and three sport skills and (2) considering different transfer processes (generalization/adaptation) that would occur as a result of the existence of this strong hierarchy. Twenty-seven children/adolescents participated in performing the countermovement jump, standing long jump, leap, high jump, long jump, and hurdle transposition. We identified the proficiency barrier in two pairs of tasks (between the countermovement jump and high jump and between the standing long jump and long jump). Nonetheless, the transfer processes were not related to the proficiency barrier. We conclude that the proposed learning hierarchy holds for some tasks. The underlying reason for this is still unknown.
Enhanced Noise-Resilient Pressure Mat System Based on Hyperdimensional Computing
Authors: Asgarinejad Fatemeh , Yu Xiaofan , Jiang Danlin , Morris Justin , Rosing Tajana , Aksanli Baris
Published: February 2024

Traditional systems for indoor pressure sensing and human activity recognition (HAR) rely on costly, high-resolution mats and computationally intensive neural network-based (NN-based) models that are prone to noise. In contrast, we design a cost-effective and noise-resilient pressure mat system for HAR, leveraging Velostat for intelligent pressure sensing and a novel hyperdimensional computing (HDC) classifier that is lightweight and highly noise resilient. To measure the performance of our system, we collected two datasets, capturing the static and continuous nature of human movements. Our HDC-based classification algorithm shows an accuracy of 93.19%, improving the accuracy by 9.47% over state-of-the-art CNNs, along with an 85% reduction in energy consumption. We propose a new HDC noise-resilient algorithm and analyze the performance of our proposed method in the presence of three different kinds of noise, including memory and communication, input, and sensor noise. Our system is more resilient across all three noise types. Specifically, in the presence of Gaussian noise, we achieve an accuracy of 92.15% (97.51% for static data), representing a 13.19% (8.77%) improvement compared to state-of-the-art CNNs.
Open-Circuit Fault Diagnosis of T-Type Three-Level Inverter Based on Knowledge Reduction
Authors: Chen Xiaojuan , Zhang Zhaohua
Published: February 2024

Compared with traditional two-level inverters, multilevel inverters have many solid-state switches and complex composition methods. Therefore, diagnosing and treating inverter faults is a prerequisite for the reliable and efficient operation of the inverter. Based on the idea of intelligent complementary fusion, this paper combines the genetic algorithm–binary granulation matrix knowledge-reduction method with the extreme learning machine network to propose a fault-diagnosis method for multi-tube open-circuit faults in T-type three-level inverters. First, the fault characteristics of power devices at different locations of T-type three-level inverters are analyzed, and the inverter output power and its harmonic components are extracted as the basis for power device fault diagnosis. Second, the genetic algorithm–binary granularity matrix knowledge-reduction method is used for optimization to obtain the minimum attribute set required to distinguish the state transitions in various fault cases. Finally, the kernel attribute set is utilized to construct extreme learning machine subclassifiers with corresponding granularity. The experimental results show that the classification accuracy after attribute reduction is higher than that of all subclassifiers under different attribute sets, reflecting the advantages of attribute reduction and the complementarity of different intelligent diagnosis methods, which have stronger fault-diagnosis accuracy and generalization ability compared with the existing methods and provides a new way for hybrid intelligent diagnosis.
Armed with Faster Crypto: Optimizing Elliptic Curve Cryptography for ARM Processors
Authors: De Smet Ruben , Blancquaert Robrecht , Godden Tom , Steenhaut Kris , Braeken An
Published: February 2024

Elliptic curve cryptography is a widely deployed technology for securing digital communication. It is the basis of many cryptographic primitives such as key agreement protocols, digital signatures, and zero-knowledge proofs. Fast elliptic curve cryptography relies on heavily optimised modular arithmetic operations, which are often tailored to specific micro-architectures. In this article, we study and evaluate optimisations of the popular elliptic curve Curve25519 for ARM processors. We specifically target the ARM NEON single instruction, multiple data (SIMD) architecture, which is a popular architecture for modern smartphones. We introduce a novel representation for 128-bit NEON SIMD vectors, optimised for SIMD parallelisation, to accelerate elliptic curve operations significantly. Leveraging this representation, we implement an extended twisted Edwards curve Curve25519 back-end within the popular Rust library “curve25519-dalek”. We extensively evaluate our implementation across multiple ARM devices using both cryptographic benchmarks and the benchmark suite available for the Signal protocol. Our findings demonstrate a substantial back-end speed-up of at least 20% for ARM NEON, along with a noteworthy speed improvement of at least 15% for benchmarked Signal functions.
Adaptive Segmentation Algorithm for Subtle Defect Images on the Surface of Magnetic Ring Using 2D-Gabor Filter Bank
Authors: Li Yihui , Ge Manling , Zhang Shiying , Wang Kaiwei
Published: February 2024

In order to realize the unsupervised segmentation of subtle defect images on the surface of small magnetic rings and improve the segmentation accuracy and computational efficiency, here, an adaptive threshold segmentation method is proposed based on the improved multi-scale and multi-directional 2D-Gabor filter bank. Firstly, the improved multi-scale and multi-directional 2D-Gabor filter bank was used to filter and reduce the noise on the defect image, suppress the noise pollution inside the target area and the background area, and enhance the difference between the magnetic ring defect and the background. Secondly, this study analyzed the grayscale statistical characteristics of the processed image; the segmentation threshold was constructed according to the gray statistical law of the image; and the adaptive segmentation of subtle defect images on the surface of small magnetic rings was realized. Finally, a classifier based on a BP neural network is designed to classify the scar images and crack images determined by different threshold segmentation methods. The classification accuracies of the iterative method, the OTSU method, the maximum entropy method, and the adaptive threshold segmentation method are, respectively, 85%, 87.5%, 95%, and 97.5%. The adaptive threshold segmentation method proposed in this paper has the highest classification accuracy. Through verification and comparison, the proposed algorithm can segment defects quickly and accurately and suppress noise interference effectively. It is better than other traditional image threshold segmentation methods, validated by both segmentation accuracy and computational efficiency. At the same time, the real-time performance of our algorithm was performed on the advanced SEED-DVS8168 platform.
Estimation of Muscle Forces of Lower Limbs Based on CNN–LSTM Neural Network and Wearable Sensor System
Authors: Liu Kun , Liu Yong , Ji Shuo , Gao Chi , Fu Jun
Published: February 2024

Estimation of vivo muscle forces during human motion is important for understanding human motion control mechanisms and joint mechanics. This paper combined the advantages of the convolutional neural network (CNN) and long-short-term memory (LSTM) and proposed a novel muscle force estimation method based on CNN–LSTM. A wearable sensor system was also developed to collect the angles and angular velocities of the hip, knee, and ankle joints in the sagittal plane during walking, and the collected kinematic data were used as the input for the neural network model. In this paper, the muscle forces calculated using OpenSim based on the Static Optimization (SO) method were used as the standard value to train the neural network model. Four lower limb muscles of the left leg, including gluteus maximus (GM), rectus femoris (RF), gastrocnemius (GAST), and soleus (SOL), were selected as the studying objects in this paper. The experiment results showed that compared to the standard CNN and the standard LSTM, the CNN–LSTM performed better in muscle forces estimation under slow (1.2 m/s), medium (1.5 m/s), and fast walking speeds (1.8 m/s). The average correlation coefficients between true and estimated values of four muscle forces under slow, medium, and fast walking speeds were 0.9801, 0.9829, and 0.9809, respectively. The average correlation coefficients had smaller fluctuations under different walking speeds, which indicated that the model had good robustness. The external testing experiment showed that the CNN–LSTM also had good generalization. The model performed well when the estimated object was not included in the training sample. This article proposed a convenient method for estimating muscle forces, which could provide theoretical assistance for the quantitative analysis of human motion and muscle injury. The method has established the relationship between joint kinematic signals and muscle forces during walking based on a neural network model; compared to the SO method to calculate muscle forces in OpenSim, it is more convenient and efficient in clinical analysis or engineering applications.
Multi-Stage Learning Framework Using Convolutional Neural Network and Decision Tree-Based Classification for Detection of DDoS Pandemic Attacks in SDN-Based SCADA Systems
Authors: Polat Onur , Türkoğlu Muammer , Polat Hüseyin , Oyucu Saadin , Üzen Hüseyin , Yardımcı Fahri , Aksöz Ahmet
Published: February 2024

Supervisory Control and Data Acquisition (SCADA) systems, which play a critical role in monitoring, managing, and controlling industrial processes, face flexibility, scalability, and management difficulties arising from traditional network structures. Software-defined networking (SDN) offers a new opportunity to overcome the challenges traditional SCADA networks face, based on the concept of separating the control and data plane. Although integrating the SDN architecture into SCADA systems offers many advantages, it cannot address security concerns against cyber-attacks such as a distributed denial of service (DDoS). The fact that SDN has centralized management and programmability features causes attackers to carry out attacks that specifically target the SDN controller and data plane. If DDoS attacks against the SDN-based SCADA network are not detected and precautions are not taken, they can cause chaos and have terrible consequences. By detecting a possible DDoS attack at an early stage, security measures that can reduce the impact of the attack can be taken immediately, and the likelihood of being a direct victim of the attack decreases. This study proposes a multi-stage learning model using a 1-dimensional convolutional neural network (1D-CNN) and decision tree-based classification to detect DDoS attacks in SDN-based SCADA systems effectively. A new dataset containing various attack scenarios on a specific experimental network topology was created to be used in the training and testing phases of this model. According to the experimental results of this study, the proposed model achieved a 97.8% accuracy rate in DDoS-attack detection. The proposed multi-stage learning model shows that high-performance results can be achieved in detecting DDoS attacks against SDN-based SCADA systems.
Effect of Street Asymmetry, Albedo, and Shading on Pedestrian Outdoor Thermal Comfort in Hot Desert Climates
Authors: Necira Hakima , Matallah Mohamed Elhadi , Bouzaher Soumia , Mahar Waqas Ahmed , Ahriz Atef
Published: February 2024

Improving urban walkability in the face of climate change is a critical challenge for urban designers. Street design strategies can mitigate heat stress and enhance pedestrian livability. Most previous studies conducted in hot climates recommend adopting deep canyons to improve summer conditions, overlooking the potential improvement of wide streets as essential structural elements of the urban fabric. This study was conducted in Biskra city, southern Algeria, where several mitigation strategies were applied to ‘Emir Abdelkader Boulevard’, as the main structural street inside the city, to create an optimal street model for arid climates. Five scenarios were developed based on three criteria: (Sc1) asymmetric profile northeast side (NES) > southwest side (SWS); (Sc2) asymmetric profile SWS > NES; (Sc3) cool paving; (Sc4) horizontal shading; and(Sc5) shading with a linear tree arrangement. ENVI-met software version 5.1.1 and the RayMan model were used to estimate the local climate conditions and outdoor thermal comfort levels based on the physiological equivalent temperature (PET). All scenarios reduced PET values across the street, with optimal reductions of −2.0 °C, −3.1 °C, −1.3 °C, −1.7 °C, and −1.2 °C in Sc1, Sc2, Sc3, Sc4, and Sc5, respectively. Concerning pedestrian areas, the optimal results durations were at the southwest side below the arcades’ sidewalks during peak hours: Sc2, Sc3, Sc4, Sc5 (2.2 °C–3 H, 2.3 °C–3 H, 2.4 °C–3 H, 2.5 °C–2 H). Sc1 performed best during daytime hours on the northeast side. The utilizing of these results can strongly help urban planners and landscape architects in creating climate-responsive streets that enhance citizens’ quality of life.
Monthly Global Solar Radiation Model Based on Artificial Neural Network, Temperature Data and Geographical and Topographical Parameters: A Case Study in Spain
Authors: González-Plaza Enrique , García David , Prieto Jesús-Ignacio
Published: February 2024

Solar energy plays an essential role in the current energy context to achieve sustainable development while supplying energy needs, creating jobs, and protecting the environment. Many solar radiation models have provided valid estimates at many different locations, using appropriate input variables for specific climatic conditions, but predictions are less accurate on a regional scale. Since radiometric weather stations are relatively dispersed, even in the most developed countries, it is interesting to develop indirect models based on measurements that are common in secondary network stations. This paper develops a monthly global solar radiation model based on a simple neural network structure, using temperature, geographical, and topographical data from 105 meteorological stations, representative of the whole of peninsular Spain. A hierarchical clustering procedure was employed to select the data used to train and validate the model. To avoid functional dependencies between parameters and variables, which hinder the generality of the model, all input and output variables are dimensionless. The estimates fit the 1260 monthly data with RRMSE values of about 6%, which improves results obtained previously, using regression models, and proves that simplicity is compatible with the generality and accuracy of a model, even in large regions with very varied characteristics.
Enhancing Chub Mackerel Catch Per Unit Effort (CPUE) Standardization through High-Resolution Analysis of Korean Large Purse Seine Catch and Effort Using AIS Data
Authors: Owiredu Solomon Amoah , Onyango Shem Otoi , Song Eun-A , Kim Kwang-Il , Kim Byung-Yeob , Lee Kyoung-Hoon
Published: February 2024

Accurate determination of fishing effort from Automatic Identification System (AIS) data improves catch per unit effort (CPUE) estimation and precise spatial management. By combining AIS data with catch information, a weighted distribution method is applied to allocate catches across various fishing trajectories, accounting for temporal dynamics. A Generalized Linear Model (GLM) and Generalized Additive Model (GAM) were used to examine the influence of spatial–temporal and environmental variables (year, month, Sea Surface Temperature (SST), Sea Surface Salinity (SSS), current velocity, depth, longitude, and latitude) and assess the quality of model fit for these effects on chub mackerel CPUE. Month, SST, and year exhibited the strongest relationship with CPUE in the GLM model, while the GAM model emphasizes the importance of month and year. CPUE peaked within specific temperature and salinity ranges and increased with longitude and specific latitudinal bands. Month emerged as the most influential variable, explaining 38% of the CPUE variance, emphasizing the impact of regulatory measures on fishery performance. The GAM model performed better, explaining 69.9% of the nominal CPUE variance. The time series of nominal and standardized indices indicated strong seasonal cycles, and the application of fine-scale fishing effort improved nominal and standardized CPUE estimates and model performance.
Assessment of the Potential of European Union Member States to Achieve Climate Neutrality
Authors: Bluszcz Anna , Manowska Anna , Mansor Nur Suhaili
Published: February 2024

Climate neutrality is the main environmental goal set for the European Union Member States until 2050. EU economies can achieve this ambitious climate goal by reducing the emission intensity of economies, which has been achieved for many years by reducing pollution emitted by industry. The aim of the study is focused primarily on demonstrating the degree of relationship between the variables describing economic growth, GDP, and the level of CO2 emissions. In the first stage of the research, the potential of countries to achieve climate neutrality was assessed, which was achieved by estimating the correlation between GDP indices in relation to 2013 and the level of CO2 emissions. Research has shown that despite the countries’ differences in the structure of their energy balances, they can achieve independence of economic growth from the emission level of their economies. The research also concerns Poland’s special situation compared to other European Union countries according to energy balance based on coal. A model based on differential equations was used to simulate the impact of GDP, energy intensity, and the share of biofuels on temperature and CO2 concentration until 2030, using data for Poland as an example. The aim of this analysis is to answer the question of whether the energy transformation in the country will achieve the assumed emission reduction goals by 2030.
Leveraging Industry 4.0 Technologies for Sustainable Humanitarian Supply Chains: Evidence from the Extant Literature
Authors: Ülkü M. Ali , Bookbinder James H. , Yun Nam Yi
Published: February 2024

Prevailing and exacerbating impacts of climate change call for robust and resilient humanitarian supply chains (HSCs). To that end, intelligent technologies that brought about the Industry 4.0 (I4.0) revolution, such as the Internet of Things, blockchain, and artificial intelligence, may tremendously impact the optimal design and effective management of HSCs. In this paper, we conduct a systematic literature network analysis and identify trends in I4.0 and HSCs. We posit the need to instill into current HSC efforts the quadruple bottom-line (cultural, economic, environmental, and social) pillars of sustainability and define a Sustainable Humanitarian Supply Chain (SHSC). Based on the extant literature and ongoing practice, we highlight how I4.0 technologies can aid SHSC stages from disaster risk assessment to preparedness to response to relief. The complex nature of SHSCs requires a holistic and multidisciplinary approach and collaboration by scholars, policymakers, and industry practitioners to pool solution resources. We offer future research venues in this fledgling but life-saving scientific discipline. SHSCs can be empowered with I4.0 technologies, a much needed direction in our climate-changed world.
Corporate Social Responsibility Practices and Financial Performance of New Ventures: The Moderating Role of Government Support
Authors: Ji Huanyong , Sheng Shuya , Wan Jun
Published: February 2024

This study enhances our comprehension of the link between corporate social responsibility (CSR) practices and financial performance in the context of new ventures. Specifically, it investigates the impact of primary and secondary stakeholder CSR practices on the financial performance of new ventures. It also examines the moderating roles of direct and indirect government support on the effect of secondary stakeholder CSR practices on new ventures’ financial performance. The data collected from manufacturing firms in China demonstrate the following: (1) primary stakeholder CSR practices can promote the financial performance of new ventures; (2) compared with established firms, the negative effect of secondary stakeholder CSR practices on the financial performance of new ventures is stronger; (3) only indirect government support can positively moderate the relationship between secondary stakeholder CSR practices and new ventures’ financial performance, and such a moderating effect is stronger than that in established firms.
Customizing Management Strategies for Product Introduction in Low-Volume Manufacturing: Enhancing Information Content Quality
Authors: Javadi Siavash , Chirumalla Koteshwar
Published: February 2024

Manufacturing companies characterized by high-mix and low-volume production exhibit distinct features that exert a significant influence on the product introduction process. The quality of information exchange between product design and production interfaces becomes paramount in low-volume manufacturing. Consequently, there is a pressing need to explore various methods for adapting and customizing management strategies to align with the unique characteristics of low-volume manufacturing and its associated product introduction processes. Although this knowledge is critical in low-volume manufacturing, most existing studies focus on the management aspects of high-volume manufacturing. Therefore, this study investigated the customization of management strategies for product introduction in low-volume manufacturing to enhance the quality of information content. Drawing upon a longitudinal analysis of new product introductions within a low-volume manufacturing company, this study identified four management strategies—namely, securing a production test and verification plan, revising the role of product introduction management, developing a formal design and production coordination plan, and developing lessons learned management plan. The paper explains how the examined company adapted and implemented product introduction management strategies to facilitate the enhancement of information content quality across three key stages: pre-implementation, post-implementation, and subsequent modification and refinement of strategies following the initial round of implementation. By shedding light on these strategies, this study offers a comprehensive understanding of management approaches for product introduction in low-volume manufacturing. The study makes a valuable contribution to the discourse on information quality management and design-production interface in the low-volume manufacturing and low-volume product introduction literature.
Effectiveness of Physical Activity Interventions in Sedentary People during COVID-19 Lockdown: A Systematic Review of Randomized Controlled Trials
Authors: Ciriminna Stefano , Veronese Nicola , Cannizzo Claudia , D’Aleo Alessandro , Ganci Antonina , Montana Lampo Sofia Elena , Bifara Federica , Battaglia Giuseppe , Messina Giuseppe , Giustino Valerio , Palma Antonio , Barbagallo Mario , Dominguez Ligia J.
Published: February 2024

Background: The social restriction measures during the COVID-19 pandemic resulted in decreasing physical activity levels. We aimed to evaluate whether the interventions reported in randomized controlled trials (RCTs) assessing the effects of physical activity during the COVID-19 lockdown on people with sedentary behavior were effective. Methods: We searched several databases from their inception until 1 April 2023, including RCTs evaluating the effects of any physical activity intervention in increasing the physical activity level in people with sedentary behavior during COVID-19 confinement, based on evidence derived from intervention studies with a control group. Studies were excluded if they (i) did not include humans; (ii) did not include sufficient information regarding the interventions or regarding the outcomes of interest; (iii) did not have a control group. We used the Rayyan systematic review platform for the selection of the studies to include based on the title/abstract information. Results: Starting with 2461 records from the original literature search, and after reviewing them according to the latest PRISMA recommendations, 11 RCTs including a total of 1770 participants were available and were included in the systematic review. We found that most of the studies examined (73%) reported the beneficial effects of the proposed interventions on improving the physical activity, reducing the sedentary time, and positively contributing to the psychological well-being of the participants. Conclusions: The results of the present systematic review on RCTs of interventions to increase physical activity in sedentary people during the COVID-19 lockdown show the beneficial effects of diverse online-delivered strategies, which can be applied even after the pandemic in conditions in which access to in-person activities is not possible.
On Graphical Symmetric Spaces, Fixed-Point Theorems and the Existence of Positive Solution of Fractional Periodic Boundary Value Problems
Authors: Dubey Nikita , Shukla Satish , Shukla Rahul
Published: February 2024

The rationale of this work is to introduce the notion of graphical symmetric spaces and some fixed-point results are proved for H-(ϑ,φ)-contractions in this setting. The idea of graphical symmetric spaces generalizes various spaces equipped with a function which characterizes the distance between two points of the space. Some topological properties of graphical symmetric spaces are discussed. Some fixed-point results for the mappings defined on graphical symmetric spaces are proved. The fixed-point results of this paper generalize and extend several fixed-point results in this new setting. The main results of this paper are applied to obtain the positive solutions of fractional periodic boundary value problems.