bluuebunny/crossref_metadata_2025 huggingface dataset:
Cognitive Regulation Strategies Used by Children with Reading Difficulties
Authors: David Carmen , Costescu Cristina , Frandeș Alina , Roșan Adrian
Published: February 2024

(1) Background: Children with reading difficulties may experience negative emotions and social isolation. The cognitive emotion regulation strategies that they use in different reading tasks can make them more vulnerable in the stressful situations. Using adaptative emotion regulation strategies may help them overcome stressful reading situations. (2) Methods: Children identified with poor reading comprehension skills were compared to children without reading comprehension difficulties on measures of self-evaluation and thought in relation to task, and on cognitive coping strategies relevant to performance. The effect of some relevant demographic factors was also investigated, such as gender and urban/rural setting. (3) Results: Our results indicate that children that have poor reading comprehension skills present higher scores on negative self- evaluation and off-task thoughts that are in relation to performance anxiety. Also, in what concerns cognitive coping strategies, students with difficulties in reading comprehension display a greater use of blaming others strategy, which is in relation to the negative self-evaluations. Also, they displayed less use of putting into perspective. No effect of gender and setting emerged for off and on task thoughts and cognitive coping strategies, except for lower scores of students from rural setting in positive self-evaluation. (4) Conclusions: students presenting difficulties in reading comprehension tend to use more negative self-statements and disengagement through off-task thoughts and employ coping strategies directed to protect self-worth.
Unraveling the Multifaceted Role of the miR-17-92 Cluster in Colorectal Cancer: From Mechanisms to Biomarker Potential
Authors: Al-nakhle Hakeemah H.
Published: February 2024

Colorectal cancer (CRC) is a complex disease driven by intricate mechanisms, making it challenging to understand and manage. The miR-17-92 cluster has gained significant attention in CRC research due to its diverse functions and crucial role in various aspects of the disease. This cluster, consisting of multiple individual miRNAs, influences critical processes like tumor initiation, angiogenesis, metastasis, and the epithelial–mesenchymal transition (EMT). Beyond its roles in tumorigenesis and progression, miR-17-92′s dysregulation in CRC has substantial implications for diagnosis, prognosis, and treatment, including chemotherapy responsiveness. It also shows promise as a diagnostic and prognostic biomarker, offering insights into treatment responses and disease progression. This review provides a comprehensive overview of recent advancements and the context-dependent role of the miR-17-92 cluster in colorectal cancer, drawing from the latest high-quality published data. It summarizes the established mechanisms governing miR-17-92 expression and the molecular pathways under its influence. Furthermore, it examines instances where it functions as an oncogene or a tumor suppressor, elucidating how cellular contexts dictate its biological effects. Ultimately, miR-17-92 holds promise as a biomarker for prognosis and therapy response, as well as a potential target for cancer prevention and therapeutic interventions. In essence, this review underscores the multifaceted nature of miR-17-92 in CRC research, offering promising avenues for enhancing the management of CRC patients.
Oxidation and Heat Shock Resistance of Plasma-Sprayed TiC-CoNi Composite Coatings at 900 °C
Authors: He Jining , Li Baoqiang , Zhao Hongjian , Fu Guanya , Fan Jiawei , Qin Yanfang
Published: February 2024

In this work, the TiC-reinforced CoNi alloy coatings were prepared by the plasma spraying method. Their microstructure, high-temperature oxidation, and thermal shock resistance at 900 °C were studied. The results showed that the CoNi alloy coating exhibited a single phase (c-Co-Ni-Cr-Mo). After adding Ti-graphite mixed powders, the sprayed coating exhibited TiC and TiO2 phases, besides the c-Co-Ni-Cr-Mo matrix phase. For CoNi alloy coating, the main oxidation products were Cr2O3 and CoCr2O4 (NiCr2O4). For TiC-CoNi alloy coating, the main oxidation products were the TiO2 phase, coupled with Cr2O3 and CoCr2O4 (NiCr2O4) phases. The content of oxides increased with the oxidation time. The oxidation weight gain of the TiC-CoNi composite coating was slightly higher than that of the CoNi alloy coating. The formation of TiC could improve the thermal shock resistance of the CoNi alloy coating.
Predicting Time-to-Healing from a Digital Wound Image: A Hybrid Neural Network and Decision Tree Approach Improves Performance
Authors: Kolli Aravind , Wei Qi , Ramsey Stephen A.
Published: February 2024

Despite the societal burden of chronic wounds and despite advances in image processing, automated image-based prediction of wound prognosis is not yet in routine clinical practice. While specific tissue types are known to be positive or negative prognostic indicators, image-based wound healing prediction systems that have been demonstrated to date do not (1) use information about the proportions of tissue types within the wound and (2) predict time-to-healing (most predict categorical clinical labels). In this work, we analyzed a unique dataset of time-series images of healing wounds from a controlled study in dogs, as well as human wound images that are annotated for the tissue type composition. In the context of a hybrid-learning approach (neural network segmentation and decision tree regression) for the image-based prediction of time-to-healing, we tested whether explicitly incorporating tissue type-derived features into the model would improve the accuracy for time-to-healing prediction versus not including such features. We tested four deep convolutional encoder–decoder neural network models for wound image segmentation and identified, in the context of both original wound images and an augmented wound image-set, that a SegNet-type network trained on an augmented image set has best segmentation performance. Furthermore, using three different regression algorithms, we evaluated models for predicting wound time-to-healing using features extracted from the four best-performing segmentation models. We found that XGBoost regression using features that are (i) extracted from a SegNet-type network and (ii) reduced using principal components analysis performed the best for time-to-healing prediction. We demonstrated that a neural network model can classify the regions of a wound image as one of four tissue types, and demonstrated that adding features derived from the superpixel classifier improves the performance for healing-time prediction.
Reducing the Environmental Impacts of Plastic Cosmetic Packaging: A Multi-Attribute Life Cycle Assessment
Authors: Vassallo Nicole , Refalo Paul
Published: February 2024

The global packaging industry has been growing significantly, resulting in an increase in waste and emissions. Social responsibilities, regulations and targets are shifting companies’ priorities to various sustainable practices. This study comprised a life cycle assessment (LCA) to quantify and compare key initiatives influencing the sustainability of plastic cosmetic packaging. The life cycle environmental effects of dematerialisation, recycled content, energy-saving initiatives and renewable energy powering the manufacturing processes, and the end-of-life (EoL) recycling rates of various scenarios, were evaluated. Moreover, a variety of fossil-based and bio-based polymers, such as acrylonitrile butadiene styrene (ABS), polypropylene (PP), polyethylene terephthalate (PET), wood–polymer composite (WPC) and polylactic acid (PLA), were considered. The study determined that dematerialisation and recycled content had the most beneficial impacts on packaging sustainability. When 100% recycled materials were used, an overall impact reduction of 42–60% was noted for all the materials considered. Using 100% renewable energy and applying measures to reduce the energy consumption in the manufacturing stage by 50% reduced the total impact by approximately 9–17% and 7–13%, respectively. Furthermore, it was concluded that PP had the lowest environmental impacts in the majority of the case scenarios considered, by an average of 46%.
The Thermophysical Properties of TcO2
Authors: Zhong Hong , Lonergan Jason , McCloy John S. , Beckman Scott P.
Published: February 2024

Technetium-99 is a highly radioactive isotope with a long half-life that is common in nuclear waste. It volatizes at a low temperature, which poses a significant challenge to the clean-up and containment processes. Due to difficulties in purifying technetium compounds, their thermophysical properties have not been measured or calculated. Here, first principle methods are used along with the quasi quasi-harmonic harmonic approximation to compute the Debye temperature, volumetric thermal expansion coefficient, bulk modulus, and heat capacity of rutile TcO2 for temperatures ranging from 0 to 1500 K and applied pressures ranging from 0 to 255 GPa. The computed atomic structures agree well with the results from diffraction measurements. The computed thermophysical properties are in the neighborhood of other rutile metal oxides and, in particular, are within approximately 10–13% of rutile ReO2, which is frequently used as a substitute for TcO2 in experimental studies.
Growth, Structure, and Electrical Properties of AgNbO3 Antiferroelectric Single Crystal
Authors: Zhao Dengxiaojiang , Chen Zhenpei , Li Borui , Feng Shi , Luo Nengneng
Published: February 2024

AgNbO3 (AN) lead-free antiferroelectric material has attracted great attention in recent years. However, little focus has been directed toward a single crystal that can provide more basic information. In this study, we successfully grew high-quality AN single crystals, using a flux method, with dimensions of 5 × 5 × 3 mm3. A systematic investigation into the crystal structure, domain structure, and electrical properties of a [001]-oriented AN single crystal was conducted. X-ray diffraction and domain structure analysis revealed an orthorhombic phase structure at room temperature. Stripe-like 90° domains aligning parallel to the [110] direction with a thickness of approximately 10–20 μm were observed using a polarized light microscope. The temperature dependence of dielectric permittivity showed M1-M2, M2-M3, and M3-O phase transitions along with increasing temperature, but the phase transition temperatures were slightly higher than those of ceramic. The AN single crystal also exhibited double polarization-electric field (P-E) hysteresis loops, which enabled good recoverable energy-storage density and efficiency comparable to ceramic. Additionally, double P-E loops were kept stable at various temperatures and frequencies, demonstrating robust stability and confirming typical antiferroelectric characteristics. Our work provides valuable insights into understanding the fundamental antiferroelectric properties of AN-based materials.
Clinical Indicators of Bone Deterioration in Alcoholic Liver Cirrhosis and Chronic Alcohol Abuse: Looking beyond Bone Fracture Occurrence
Authors: Stulic Milos , Jadzic Jelena , Dostanic Natasa , Zivkovic Milica , Stojkovic Tihomir , Aleksic Jelena , Stojkovic Stefan , Stojkovic Lalosevic Milica , Vojnovic Marko , Vlaisavljevic Zeljko , Martinov Nestorov Jelena , Nikolić Tatjana , Culafic Vojinovic Violeta , Culafic Djordje , Djonic Danijela
Published: February 2024

Although previous studies indicated that chronic alcohol abuse (CAA) and alcoholic liver cirrhosis (ALC) are associated with increased bone fragility, understanding bone fragility determinants is still modest in these individuals. We used a comprehensive individualized clinical fracture risk assessment approach (vertebral osteodensitometry, femoral osteodensitometry and geometry, and serum bone turnover biomarkers) to compare adult male patients with ALC who have not previously had femoral or vertebral fractures (n = 39), patients with CAA (without liver cirrhosis, n = 78) who have not previously had femoral or vertebral fractures and healthy age- and sex-matched controls (n = 43). Our data suggested that intertrochanteric bone mineral density was significantly lower in ALC and CAA patients than in controls. Also, the trabecular bone score was considerably lower in ALC patients compared with CAA and control individuals. The most significant inter-group differences in femoral geometry were noted on the femoral shaft. Patients with ALC and CAA have a higher 10-year risk of major osteoporotic fractures compared to the controls. Analysis of bone turnover biomarkers showed increased osteoprotegerin and beta-C-terminal telopeptide serum concentrations and decreased insulin growth factor-1 concentrations in patients with ALC compared to CAA and control groups. Our data revealed that bone alterations are present in patients with ALC and CAA even if they did not sustain a nontraumatic bone fracture, but it is also indicative that current bone-assessing clinical methods are not entirely reliable. Thus, future studies should focus on developing a reliable integrative clinical tool that can be used to accurately predict and prevent bone fracture occurrences in patients with ALC and CAA.
Exploring the Relationship between E-Cadherin and β-Catenin Cell Adhesion Proteins and Periacinar Retraction Clefting in Prostatic Adenocarcinoma
Authors: Limani Rinë , Lež Cvjetko , Krušlin Božo
Published: February 2024

Background: Periacinar retraction clefts represent a histopathological criterion supporting the diagnosis of prostatic adenocarcinoma. The origin of these clefts in prostatic adenocarcinoma remains unclear. Exploring the established functions of E-cadherin and β-catenin as intercellular adhesion proteins, and aiming to elucidate the origin of periacinar retraction clefting, we conducted a correlation study between the immunohistochemical expression of E-cadherin and β-catenin and the presence of periacinar retraction clefts in prostatic adenocarcinoma. Methods: We examined 53 cases of morphologically diagnosed prostatic adenocarcinoma, assessing both the neoplastic and adjacent nonneoplastic prostatic tissues for the existence and degree of periacinar retraction clefts. Additionally, we analyzed the immunohistochemical expression of E-cadherin and β-catenin proteins in prostatic tissue and explored their correlation with periacinar retraction clefts, and Gleason score, Grade Group, preoperative serum prostate specific-antigen (sPSA) levels, surgical margin status, and Tumor, Node, Metastasis (TNM) stage in prostatic adenocarcinoma. Results: Our study confirms that periacinar retraction clefting is significantly more extensive in prostatic adenocarcinoma than in nonneoplastic prostatic tissue (p < 0.001). We report a decreased expression of E-cadherin and β-catenin immunostaining in prostatic adenocarcinoma and a negative correlation with Gleason score and Grade Group. Periacinar retraction clefting positively correlated with E-cadherin and β-catenin ((rho = 0.350; p = 0.010) and (rho = 0.340; p = 0.012)) immunostaining in prostatic adenocarcinoma. Conclusions: Periacinar retraction clefts stand out as a dependable criterion in the diagnosis of prostatic adenocarcinoma. E-cadherin and β-catenin proteins are potential markers indicative of tumor progression and invasiveness in prostatic adenocarcinoma. Our discovery of a positive correlation between immunostaining of E-cadherin and β-catenin proteins and periacinar retraction clefts in prostatic adenocarcinoma aligns with the notion that periacinar retraction clefting is more characteristic of Gleason Grade3 pattern in prostatic adenocarcinomas, whereas the immunohistochemical expression of E-cadherin and β-catenin shows a decrease with increasing histopathological tumor grade.
Pediatric Hemangiomas in the Female Genital Tract: A Literature Review
Authors: Merlino Lucia , Volpicelli Agnese Immacolata , Anglana Franco , D’Ovidio Giulia , Dominoni Mattia , Pasquali Marianna Francesca , Gardella Barbara , Inghirami Paolo , Lippa Pietro , Senatori Roberto
Published: February 2024

Background: Hemangiomas are aberrant proliferations of blood vessels and the most frequent benign pediatric soft tissue tumors. Although they are common, genital localization is rare. This study aimed to assist doctors in the diagnosis, management, and treatment of pediatric vulvovaginal hemangiomas by conducting a review of the literature. Methods: We conducted a literature review including papers published between August 2009 and May 2023. Results: While most hemangiomas are usually indolent and resolve with time, in some cases, especially cervicovaginal and uterine ones, they might present with severe symptoms like heavy bleeding and need further instrumental investigation for diagnosis, like CT or MRI. As for the treatment, many options are available, with medical therapy or expectant management being the first choice. Conclusions: Vulvovaginal pediatric hemangiomas are rare and require more research on how to detect and manage these lesions, especially the symptomatic and the psychologically impacting ones. For the time being, treatment should be personalized based on the patient’s situation and clinician’s expertise.
Opinion Models, Election Data, and Political Theory
Authors: Gsänger Matthias , Hösel Volker , Mohamad-Klotzbach Christoph , Müller Johannes
Published: February 2024

A unifying setup for opinion models originating in statistical physics and stochastic opinion dynamics are developed and used to analyze election data. The results are interpreted in the light of political theory. We investigate the connection between Potts (Curie–Weiss) models and stochastic opinion models in the view of the Boltzmann distribution and stochastic Glauber dynamics. We particularly find that the q-voter model can be considered as a natural extension of the Zealot model, which is adapted by Lagrangian parameters. We also discuss weak and strong effects (also called extensive and nonextensive) continuum limits for the models. The results are used to compare the Curie–Weiss model, two q-voter models (weak and strong effects), and a reinforcement model (weak effects) in explaining electoral outcomes in four western democracies (United States, Great Britain, France, and Germany). We find that particularly the weak effects models are able to fit the data (Kolmogorov–Smirnov test) where the weak effects reinforcement model performs best (AIC). Additionally, we show how the institutional structure shapes the process of opinion formation. By focusing on the dynamics of opinion formation preceding the act of voting, the models discussed in this paper give insights both into the empirical explanation of elections as such, as well as important aspects of the theory of democracy. Therefore, this paper shows the usefulness of an interdisciplinary approach in studying real world political outcomes by using mathematical models.
(Non)Resonance Bonds in Molecular Dynamics Simulations: A Case Study concerning C60 Fullerenes
Authors: Siódmiak Jacek
Published: February 2024

In the case of certain chemical compounds, especially organic ones, electrons can be delocalized between different atoms within the molecule. These resulting bonds, known as resonance bonds, pose a challenge not only in theoretical descriptions of the studied system but also present difficulties in simulating such systems using molecular dynamics methods. In computer simulations of such systems, it is often common practice to use fractional bonds as an averaged value across equivalent structures, known as a resonance hybrid. This paper presents the results of the analysis of five forms of C60 fullerene polymorphs: one with all bonds being resonance, three with all bonds being integer (singles and doubles in different configurations), one with the majority of bonds being integer (singles and doubles), and ten bonds (within two opposite pentagons) valued at one and a half. The analysis involved the Shannon entropy value for bond length distributions and the eigenfrequency of intrinsic vibrations (first vibrational mode), reflecting the stiffness of the entire structure. The maps of the electrostatic potential distribution around the investigated structures are presented and the dipole moment was estimated. Introducing asymmetry in bond redistribution by incorporating mixed bonds (integer and partial), in contrast to variants with equivalent bonds, resulted in a significant change in the examined observables.
The Bidirectional Relationship between Meta-Creativity and Academic Performance in University Students
Authors: Villalustre Lourdes , Cueli Marisol , Zarzuelo Daniel
Published: February 2024

Creativity has been studied in relation to academic performance, usually from the perspective of the creative result, with fewer studies focusing on the creative process and the student’s awareness of that process, known as meta-creativity. This study aimed to analyze differences in meta-creativity based on academic performance groups (high or low) and determine the predictive power of meta-creativity belonging to the high or low academic performance groups. A total of 172 university students participated. Meta-creativity was assessed using a Meta-Creativity Questionnaire, which evaluated three dimensions (creative motivation, creative leadership, and divergent thinking). Additionally, academic performance was recorded, allowing for the classification of students based on high and low academic performance. The results of the analysis of variance indicated statistically significant differences between students with high and low academic performance in the three dimensions. Discriminant analysis indicated that the dimensions of meta-creativity were able to predict who belonged to the high and low academic performance groups. The model correctly classified 86.6% of the sample. It can be concluded that academic performance is a good indicator of the level of meta-creativity, and, additionally, meta-creativity has a beneficial effect on academic performance. There is a bidirectional relationship between the two variables.
Research on the Car Searching System in the Multi-Storey Garage with the RSSI Indoor Locating Based on Neural Network
Authors: Ma Jihui , Wang Lijie , Zhu Xianwen , Li Ziyi , Lu Xinyu
Published: February 2024

To solve the problem of reverse car searching in intelligent multi-story garages or parking lots, the reverse car searching method based on the intelligent garage of the PC client and mobile client APP was studied, and the interface design and function development of the system’s PC and mobile client APP were carried out. YOLOv5 network and LPRNet network were used for license plate location and recognition to realize parking and entry detection. The indoor pedestrian location method based on RSSI fingerprint signal fusion BPNet network and KNN algorithm was studied, and the location accuracy within 2.5 m was found to be 100%. The research on the A* algorithm based on spatial accessibility was conducted to realize the reverse car search function. The research results indicate that the guidance of the vehicle finding path can be completed while the number of invalid search nodes for the example maps was reduced by more than 55.0%, and the operating efficiency of the algorithm increased to 28.5%.
An Orientation-Aware Attention Network for Person Re-Identification
Authors: Xu Dongshu , Chen Jun , Chai Xiaoyu
Published: February 2024

Humans always identify persons through their characteristics, salient attributes, and these attributes’ locations on the body. Most person re-identification methods focus on global and local features corresponding to the former two discriminations, cropping person images into horizontal strips to obtain coarse locations of body parts. However, discriminative clues corresponding to location differences cannot be discovered, so persons with similar appearances are often confused because of their alike components. To address the above problem, we introduce pixel-wise relative positions for the invariance of their orientations in viewpoint changes. To cope with the scale change of relative position, we combine relative positions with self-attention modules that perform on multi-level features. Moreover, in the data augmentation stage, mirrored images are given new labels due to the conversion of the relative position along a horizontal orientation and change in visual chirality. Extensive experiments on four challenging benchmarks demonstrate that the proposed approach shows its superiority and effectiveness in discovering discriminating features.
Research on the Evaluation and Prediction of V2I Channel Quality Levels in Urban Environments
Authors: Pang Shengli , Li Zekang , Yao Ziru , Wang Honggang , Long Weichen , Pan Ruoyu
Published: February 2024

The present manuscript introduces a method for evaluating and forecasting the quality of vehicle-to-infrastructure (V2I) communication channels in urban settings. This method precisely classifies and predicts channel quality levels in V2I scenarios based on long-range (LoRa) technology. This approach aims to accurately classify and predict channel quality levels in V2I scenarios. The concept of channel quality scoring was first introduced, offering a more precise description of channel quality compared to traditional packet reception rate (PRR) assessments. In the channel quality assessment model based on the gated recurrent unit (GRU) algorithm, the current channel quality score of the vehicular terminal and the spatial channel parameters (SCP) of its location are utilized as inputs to achieve the classification of channel quality levels with an accuracy of 97.5%. Regarding prediction, the focus lies in forecasting the channel quality score, combined with the calculation of SCP for the vehicle’s following temporal location, thereby achieving predictions of channel quality levels from spatial and temporal perspectives. The prediction model employs the Variational Mode Decomposition-Backoff-Bidirectional Long Short-Term Memory (VMD-BO-BiLSTM) algorithm, which, while maintaining an acceptable training time, exhibits higher accuracy than other prediction algorithms, with an R2 value reaching 0.9945. This model contributes to assessing and predicting channel quality in V2I scenarios and holds significant implications for subsequent channel resource allocation.
Control of a Path Following Cable Trench Caterpillar Robot Based on a Self-Coupling PD Algorithm
Authors: Jia Zhiwei , Fang Wen , Sun Chenhao , Li Ling
Published: February 2024

Underground cable trench inspection robots work in narrow, variable friction coefficient, and complex road environments. The running trajectory easily deviates from the desired path and leads to a collision, or even the destruction of the robot or cable. Addressing this problem, a path-following control method for the dual-tracked chassis robot based on a self-coupling PID (SCPID) control algorithm was developed. The caterpillar robot dynamics were modelled and both the unknown dynamics and external bounded disturbances were defined as sum disturbances, thus mapping the nonlinear system into a linearly disturbed system, then the self-coupling PD (SCPD) controller was designed. The system proved to be a robust stability control system and only one parameter, the velocity factor, needed to be tuned to achieve parameter calibration. Meanwhile, to solve the problem that the error-based speed factor is not universal and to improve the adaptive ability of the SCPD controller, an iterative method was used for adaptive tuning. The simulation results showed that the SCPID can achieve better control. The field test results showed that the SCPD’s maximum offset angle was 56.7% and 10.3% smaller than incremental PID and sliding mode control (SMC), respectively. The inspection time of the SCPD was 20% faster than other methods in the same environment.
Two-Level Excitation Current Driver to Reduce the Driving Power of an Electromagnetic Contactor
Authors: Park Tae-Hwan , Kim Rae-Young , Lim Sang-Kil
Published: February 2024

As the capacity of the electrical system increases, so does the capacity of the electromagnetic contactor (MC). This increases the burden on the MC drive, which consumes unnecessary power in the system. MC is characterized by different initial starting-operating currents and holding currents to maintain contact. However, the operating voltage is constant regardless of the operating state. The initial starting current is considerably larger than that required to maintain contact. However, once the electromagnetic contactor is in the closed state, the current to maintain the contact is relatively small compared to the initial starting operating currents. Therefore, this study proposes two types of two-level excitation-current type MC drives that can reduce the drive power by employing features that have different conditions depending on the operating state of the MC. The overall drive power is reduced by applying different excitation currents based on the operating state. The controller and system proposed in this study were simulated using Powersim 9.1 (PSIM), and the feasibility was verified by manufacturing an analog-type driver using LM2576 and a digital-type driver using an MCU. The simulation and experimental results provide significant data for verifying the high performance and reliability of the proposed controller and system.
Industrial Control Systems Security Validation Based on MITRE Adversarial Tactics, Techniques, and Common Knowledge Framework
Authors: Afenu Divine S. , Asiri Mohammed , Saxena Neetesh
Published: February 2024

Industrial Control Systems (ICSs) have become the cornerstone of critical sectors like energy, transportation, and manufacturing. However, the burgeoning interconnectivity of ICSs has also introduced heightened risks from cyber threats. The urgency for robust ICS security validation has never been more pronounced. This paper provides an in-depth exploration of using the MITRE ATT&CK (Adversarial Tactics, Techniques, and Common Knowledge) framework to validate ICS security. Although originally conceived for enterprise Information Technology (IT), the MITRE ATT&CK framework’s adaptability makes it uniquely suited to address ICS-specific security challenges, offering a methodological approach to identifying vulnerabilities and bolstering defence mechanisms. By zeroing in on two pivotal attack scenarios within ICSs and harnessing a suite of security tools, this research identifies potential weak points and proposes solutions to rectify them. Delving into Indicators of Compromise (IOCs), investigating suitable tools, and capturing indicators, this study serves as a critical resource for organisations aiming to fortify their ICS security. Through this lens, we offer tangible recommendations and insights, pushing the envelope in the domain of ICS security validation.
An Effective Ensemble Learning-Based Real-Time Intrusion Detection Scheme for an In-Vehicle Network
Authors: Alalwany Easa , Mahgoub Imad
Published: February 2024

The emergence of connected and autonomous vehicles has led to complex network architectures for electronic control unit (ECU) communication. The controller area network (CAN) enables the transmission of data inside vehicle networks. However, although it has low latency and enjoys data broadcast capability, it is vulnerable to attacks on security. The lack of effectiveness of conventional security mechanisms in addressing these vulnerabilities poses a danger to vehicle safety. This study presents an intrusion detection system (IDS) that accurately detects and classifies CAN bus attacks in real-time using ensemble techniques and the Kappa Architecture. The Kappa Architecture enables real-time attack detection, while ensemble learning combines multiple machine learning classifiers to enhance the accuracy of attack detection. The scheme utilizes ensemble methods with Kappa Architecture’s real-time data analysis to detect common CAN bus attacks. This study entails the development and evaluation of supervised models, which are further enhanced using ensemble techniques. The accuracy, precision, recall, and F1 score are used to measure the scheme’s effectiveness. The stacking ensemble technique outperformed individual supervised models and other ensembles with accuracy, precision, recall, and F1 of 0.985, 0.987, and 0.985, respectively.
Spatial Transformation Accelerator with Parallel Data Access Scheme for Sample Reconstruction
Authors: Novickis Rihards , Lielāmurs Edgars , Justs Daniels Jānis , Cvetkovs Andrejs , Ozols Kaspars
Published: February 2024

Spatial image transformation is a commonly used component in many image processing pipelines. It enables the correction of optical distortions, image registration onto a common reference plane, electronic image stabilisation, digital zoom, video mosaicking, etc. With the growing tendency to embed image processing in low-power devices, attaining an efficient transformation solution becomes increasingly decisive. Furthermore, interpolation is the key operation in achieving the high quality of the transformed data from the original data. Fortunately, different implementations have already seen several efficiency improvements in recent years. However, interpolation relies on sampling a set of neighbouring points from memory, which has yet to be addressed efficiently for smaller computational platforms with limited memory resources. In this work, we derive a generic mathematical model and circuit design principles for the spatial transformation accelerator design for N-dimensional data. Furthermore, we present an efficient simultaneous access scheme for high-quality signal reconstruction. Finally, the introduced ideas are verified in field programmable gate arrays using one-dimensional and two-dimensional data transformation use cases. The presented solution is able to transform images with sizes ranging from 256 × 256 to 8192 × 8192 and achieves a transfer rate of 275 frames per second with 512 × 512 images.
Determining the Availability of Continuous Systems in Open Pits Using ANFIS and a Simulation Model
Authors: Gomilanovic Miljan , Bugaric Ugljesa , Bankovic Mirjana , Stanic Nikola , Stepanovic Sasa
Published: February 2024

This paper presents a model for determining the availability of continuous systems at open pits using the neuro-fuzzy system. The concept of availability is divided into partial indicators (synthetic indicators and sub-indicators). The presented model in relation to already existing models for determining availability uses a combination of the advantages of artificial neural networks and fuzzy logic. The case study addressed the I ECC (bucket wheel excavator–conveyors–crushing plant) system of the open pit Drmno, Kostolac. In this paper, in addition to the ANFIS model for determining the availability of continuous systems, a simulation model was developed. The obtained results of the ANFIS model were verified with the help of a simulation model that uses certain assumptions about the distribution of failures. This paper was created as a result of several years of field and theoretical research into the availability of continuous systems in open pits, and completes a cycle that consists of several published articles on the subject of modeling the behavior of these systems in real time using a time picture of the state, expert assessment, simulation and AI models, while respecting the multidisciplinarity of the problem (mining technological, mechanical, and information technological aspects). The developed ANFIS model is a key instrument for improving operational efficiency and resource management in the mining sector. Its ability to accurately predict the availability of the ECC system brings not only operational benefits through reduced downtime and optimized maintenance, but also a potential reduction in overall costs at coal open pits. Such an innovative model marks a significant step forward in the mining industry, especially when it comes to continuous systems in coal open pits.
Numerical Simulation of Vertical Well Depressurization with Different Deployments of Radial Laterals in Class 1-Type Hydrate Reservoir
Authors: Wan Tinghui , Yu Miao , Lu Hongfeng , Chen Zongheng , Li Zhanzhao , Tian Lieyu , Li Keliang , Huang Ning , Wang Jingli
Published: February 2024

Gas production efficiency is a key indicator in the commercial development of natural gas hydrates (NGHs). Based on the data from the first natural gas hydrate field test production in the Shenhu Sea area of China, the gas production capability of Class 1-type hydrate reservoirs was numerically evaluated by vertical well depressurization with different deployment schemes for radial laterals. The results showed that the radial laterals can effectively improve production efficiency and that the radial laterals deployed at the three-phase layer (TPL) have the best production performance. Compared with the single vertical well production, the completion length of the radial laterals is 150 m with a radius of 0.05 m, and the production pressure difference is set to 6 MPa. The cumulative gas production Vg reaches up to 594.10 × 104 ST m3, increased by about 208.53% after 360 days of production, which provides a reference for the development of natural gas hydrates with radial jet drilling (RJD) technology.
Battery Management in Electric Vehicle Routing Problems: A Review
Authors: Martin Xabier A. , Escoto Marc , Guerrero Antoni , Juan Angel A.
Published: February 2024

The adoption of electric vehicles (EVs) has gained significant momentum in recent years as a sustainable alternative to traditional internal combustion engine vehicles. However, the efficient utilization of batteries in EVs, coupled with the growing demand for sustainable transportation, has posed complex challenges for battery management in the context of electric vehicle routing problems in a broad sense, which includes vehicle routing problems, team orienteering problems, and arc routing problems, all of them using EVs. This paper presents a comprehensive review of the state-of-the-art approaches, methodologies, and strategies for battery management in each of the aforementioned optimization problems. We explore the relevant factors influencing battery performance and the interplay between routing, charging, and energy management in the context of EVs. The paper also discusses the advances in optimization algorithms, vehicle-to-grid integration, and intelligent decision-making techniques aimed at enhancing the range, reliability, and sustainability of EV operations. Key findings indicate a paradigm shift towards addressing uncertainties, dynamic conditions, and synchronization challenges inherent in large-scale and dynamic routing problems within the context of EVs that require efficient battery management.
A Review on the Classification of Partial Discharges in Medium-Voltage Cables: Detection, Feature Extraction, Artificial Intelligence-Based Classification, and Optimization Techniques
Authors: Kumar Haresh , Shafiq Muhammad , Kauhaniemi Kimmo , Elmusrati Mohammed
Published: February 2024

Medium-voltage (MV) cables often experience a shortened lifespan attributed to insulation breakdown resulting from accelerated aging and anomalous operational and environmental stresses. While partial discharge (PD) measurements serve as valuable tools for assessing the insulation state, complexity arises from the presence of diverse discharge sources, making the evaluation of PD data challenging. The reliability of diagnostics for MV cables hinges on the precise interpretation of PD activity. To streamline the repair and maintenance of cables, it becomes crucial to discern and categorize PD types accurately. This paper presents a comprehensive review encompassing the realms of detection, feature extraction, artificial intelligence, and optimization techniques employed in the classification of PD signals/sources. Its exploration encompasses a variety of sensors utilized for PD detection, data processing methodologies for efficient feature extraction, optimization techniques dedicated to selecting optimal features, and artificial intelligence-based approaches for the classification of PD sources. This synthesized review not only serves as a valuable reference for researchers engaged in the application of methods for PD signal classification but also sheds light on potential avenues for future developments of techniques within the context of MV cables.
Characterization of the Macroscopic Impact of Diverse Microscale Transport Mechanisms of Gas in Micro-Nano Pores and Fractures
Authors: Dong Yintao , Song Laiming , Lai Fengpeng , Zhao Qianhui , Lu Chuan , Chen Guanzhong , Chong Qinwan , Yang Shuo , Wang Junjie
Published: February 2024

The objective of this study is to construct a refined microscopic transport model that elucidates the transport mechanisms of gas flow within micro-nano pores and fractures. The collective impact of various microscopic transport mechanisms was explained through the apparent permeability model, specifically related to gases such as methane and carbon dioxide, within the shale matrix. The apparent permeability models, taking into account microscopic transport mechanisms such as slippage flow, Knudsen diffusion, transition flow, and surface diffusion, were established individually. Subsequently, the influencing factors on apparent permeability were analyzed. The results demonstrate that the apparent permeability of the shale reservoir matrix is significantly influenced by pore pressure, temperature, pore size, and total organic carbon (TOC). As pressure decreases, the apparent permeability of Knudsen diffusion and surface diffusion increases, while the apparent permeability of slippage flow decreases. In addition, the apparent permeability of the reservoir matrix initially decreases and then increases. With increasing temperature, the apparent permeability of slippage flow, Knudsen diffusion, and surface diffusion all increase, as does the apparent permeability of the reservoir matrix. As pore size increases, the apparent permeability of surface diffusion and Knudsen diffusion decreases, while the apparent permeability of slippage flow and the reservoir matrix increases. Furthermore, an increase in TOC leads to no change in the apparent permeability of slippage flow and Knudsen diffusion, but an increase in the apparent permeability of surface diffusion and the reservoir matrix. The model presented in this paper enhances the multi-scale characterization of gas microflow mechanisms in shale and establishes the macroscopic application of these micro-mechanisms. Moreover, this study provides a theoretical foundation for the implementation of carbon capture, utilization, and storage (CCUS) in shale gas production.
Energy Modeling for Electric Vehicles Based on Real Driving Cycles: An Artificial Intelligence Approach for Microscale Analyses
Authors: Mądziel Maksymilian
Published: February 2024

This paper presents the process of creating a model for electric vehicle (EV) energy consumption, enabling the rapid generation of results and the creation of energy maps. The most robust validation indicators were exhibited by an artificial intelligence method, specifically neural networks. Within this framework, two predictive models for EV energy consumption were developed for winter and summer conditions, based on actual driving cycles. These models hold particular significance for microscale road analyses. The resultant model, for test data in summer conditions, demonstrates validation indicators of an R2 of 86% and an MSE of 1.4, while, for winter conditions, its values are 89% and 2.8, respectively, confirming its high precision. The paper also presents exemplary applications of the developed models, utilizing both real and simulated microscale data. The results obtained and the presented methodology can be especially advantageous for decision makers in the management of city roads and infrastructure planners, aiding both cognitive understanding and the better planning of charging infrastructure networks.
The Evolution of Human Social Behavior
Authors: Grinde Bjørn
Published: February 2024

Social life is a key feature in humans; without it, language, science, and technology would not have appeared. The inclination to engage with others is also a main source of pleasure and pain and as such a key factor for quality of life. In this paper, I shall present current knowledge on the evolutionary trajectory leading to the four main types of relations: parent–child, pair-bonding, kinship, and social life (bonding between non-kin for purposes other than breeding). These relationships are not unique to humans; they have evolved independently multiple times across the animal kingdom. In our lineage, the origins of parent–child bonding may be traced back to the early amniotes some 320 Mya (million years ago). Pair-bonding and social life most likely evolved recently. Understanding how these affiliations are rooted in the brain, particularly the role of feelings, provides valuable insights that can help us improve society.
Microbial Factories and Exploiting Synergies of Bioreactor Technologies to Produce Bioproducts
Authors: Granata Tim , Rattenbacher Bernd , Kehl Florian , Egli Marcel
Published: February 2024

Microbial factories, including microalgae biofactories, have the enormous potential to produce biochemicals for manufacturing diverse bioproducts. A strategic approach to biofactories is maintaining cultures in bioreactors with sufficient resource inputs to optimize biochemical precursors for manufacturing bioproducts. Exploiting synergies that use the waste output from a bioreactor containing one microbial culture as a resource input to another bioreactor with a different microbe can lead to overall efficiencies in biofactories. In this paper, two synergies are evaluated. The first is between yeast and algae bioreactors, where data are presented on oxygen (O2) uptake by aerobic yeast cultures and their production of carbon dioxide (CO2) and the uptake of CO2 by algae and their production of O2. The second focuses on a carbon capture reactor, which is utilized to increase CO2 levels to promote higher algal production. This approach of waste as a resource for bioreactor cultures is a novel synergy that can be important to bioreactor designs and, ultimately, to the production of bioproducts.
A Synergistic Elixir-EDA-MQTT Framework for Advanced Smart Transportation Systems
Authors: Li Yushan , Fujita Satoshi
Published: February 2024

This paper proposes a novel event-driven architecture for enhancing edge-based vehicular systems within smart transportation. Leveraging the inherent real-time, scalable, and fault-tolerant nature of the Elixir language, we present an innovative architecture tailored for edge computing. This architecture employs MQTT for efficient event transport and utilizes Elixir’s lightweight concurrency model for distributed processing. Robustness and scalability are further ensured through the EMQX broker. We demonstrate the effectiveness of our approach through two smart transportation case studies: a traffic light system for dynamically adjusting signal timing, and a cab dispatch prototype designed for high concurrency and real-time data processing. Evaluations on an Apple M1 chip reveal consistently low latency responses below 5 ms and efficient multicore utilization under load. These findings showcase the system’s robust throughput and multicore programming capabilities, confirming its suitability for real-time, distributed edge computing applications in smart transportation. Therefore, our work suggests that integrating Elixir with an event-driven model represents a promising approach for developing scalable, responsive applications in edge computing. This opens avenues for further exploration and adoption of Elixir in addressing the evolving demands of edge-based smart transportation systems.