bluuebunny/crossref_metadata_2025 huggingface dataset:
miRNA Expression Profiling in Human Breast Cancer Diagnostics and Therapy
Authors: Dziechciowska Iga , Dąbrowska Małgorzata , Mizielska Anna , Pyra Natalia , Lisiak Natalia , Kopczyński Przemysław , Jankowska-Wajda Magdalena , Rubiś Błażej
Published: November 2023

Breast cancer is one of the most commonly diagnosed cancer types worldwide. Regarding molecular characteristics and classification, it is a heterogeneous disease, which makes it more challenging to diagnose. As is commonly known, early detection plays a pivotal role in decreasing mortality and providing a better prognosis for all patients. Different treatment strategies can be adjusted based on tumor progression and molecular characteristics, including personalized therapies. However, dealing with resistance to drugs and recurrence is a challenge. The therapeutic options are limited and can still lead to poor clinical outcomes. This review aims to shed light on the current perspective on the role of miRNAs in breast cancer diagnostics, characteristics, and prognosis. We discuss the potential role of selected non-coding RNAs most commonly associated with breast cancer. These include miR-21, miR-106a, miR-155, miR-141, let-7c, miR-335, miR-126, miR-199a, miR-101, and miR-9, which are perceived as potential biomarkers in breast cancer prognosis, diagnostics, and treatment response monitoring. As miRNAs differ in expression levels in different types of cancer, they may provide novel cancer therapy strategies. However, some limitations regarding dynamic alterations, tissue-specific profiles, and detection methods must also be raised.
The Roles of Caloric Restriction Mimetics in Central Nervous System Demyelination and Remyelination
Authors: Kaffe Despoina , Kaplanis Stefanos Ioannis , Karagogeos Domna
Published: November 2023

The dysfunction of myelinating glial cells, the oligodendrocytes, within the central nervous system (CNS) can result in the disruption of myelin, the lipid-rich multi-layered membrane structure that surrounds most vertebrate axons. This leads to axonal degeneration and motor/cognitive impairments. In response to demyelination in the CNS, the formation of new myelin sheaths occurs through the homeostatic process of remyelination, facilitated by the differentiation of newly formed oligodendrocytes. Apart from oligodendrocytes, the two other main glial cell types of the CNS, microglia and astrocytes, play a pivotal role in remyelination. Following a demyelination insult, microglia can phagocytose myelin debris, thus permitting remyelination, while the developing neuroinflammation in the demyelinated region triggers the activation of astrocytes. Modulating the profile of glial cells can enhance the likelihood of successful remyelination. In this context, recent studies have implicated autophagy as a pivotal pathway in glial cells, playing a significant role in both their maturation and the maintenance of myelin. In this Review, we examine the role of substances capable of modulating the autophagic machinery within the myelinating glial cells of the CNS. Such substances, called caloric restriction mimetics, have been shown to decelerate the aging process by mitigating age-related ailments, with their mechanisms of action intricately linked to the induction of autophagic processes.
Effects of Fire on Pyrodiversity of Terricolous Non-Tracheophytes Photoautotrophs in a Páramo of Southern Ecuador
Authors: Yangua-Solano Erika , Carrión-Paladines Vinicio , Benítez Ángel
Published: November 2023

The páramos have a great diversity of flora, including terricolous non-tracheophyte photoautotrophs (bryophytes and lichens). Bryophytes and lichens are very sensitive to environmental changes related to anthropogenic fires, livestock, and agricultural activities. We determined for the first time in Ecuador the effects of prescribed fires on the pyrodiversity of terricolous non-tracheophyte photoautotroph in a páramo of South Ecuador. Three permanent sampling plots (T1, T2, and control) were established, each with a dimension of 4 m × 20 m and separated by 3 m (T1: one with fire-induced uphill and T2: one with fire-induced downhill and one control). They were installed in three different blocks, obtaining a total of nine plots. Three samplings (2, 6, and 12 months) were carried out in each plot, where the cover and richness of terricolous bryophytes and lichens were estimated in 216 quadrats of 20 × 30 cm. A total of 27 species (11 lichens and 16 bryophytes) were studied, where the lichen families, that is, Cladoniaceae and Baeomycetaceae, as well as the bryophytes families, namely, Dicranaceae, Jungermanniaceae, Bartramiaceae, Rhacocarpaceae, and Pallaviciniaceae, have been recorded as pioneers in areas under fire effects. Richness and diversity (calculated using the Shannon–Weaver and Simpson indexes) were affected by fire treatments; on the other hand, monitoring time (M3) positively affected species diversity. The composition of terrestrial non-tracheophyte photoautotroph communities showed slight changes between the control and T1 and T2, but the changes were more marked with time after the burns (M1 vs. M3), related to fire severity. Therefore, terricolous lichen and bryophyte communities (richness and diversity) can be used as model organisms for the assessment of the effects of prescribed fires on tropical páramos for subsequent management and conservation.
Development and Performance Evaluation of Fibrous Pseudoplastic Quaternary Cement Systems for Aerial Additive Manufacturing
Authors: Dams Barrie , Shepherd Paul , Ball Richard J.
Published: November 2023

Aerial additive manufacturing (AAM) represents a paradigm shift in using unmanned aerial vehicles (UAVs, often called ‘drones’) in the construction industry, using self-powered and untethered UAVs to extrude structural cementitious material. This requires miniaturisation of the deposition system. Rheological properties and known hydration times are important material parameters. Calcium aluminate cement (CAC) systems can be advantageous over purely ordinary Portland cement (OPC) binders as they promote hydration and increase early strength. A quaternary OPC/pulverised fuel ash (PFA)/CAC/calcium sulphate (CS) system was combined with polyvinyl alcohol (PVA) fibres and pseudoplastic hydrocolloids to develop a novel AAM material for miniaturised deposition. CAC hydration is affected by environmental temperature. Intending material to be extruded in situ, mixes were tested at multiple temperatures. OPC/PFA/CAC/CS mixes with PVA fibres were successfully extruded with densities of ≈1700 kg/m3, yield stresses of 1.1–1.3 kPa and a compressive strength of 25 MPa. Pseudoplastic OPC/PFA/CAC/CS quaternary cementitious systems are demonstrated to be viable for AAM, provided mixes are modified with retarders as temperature increases. This study can significantly impact industry by demonstrating structural material which can be extruded using UAVs in challenging or elevated in situ construction, reducing safety risks.
Correlation between Subchondral Insufficiency Fracture of the Knee and Osteoarthritis Progression in Patients with Medial Meniscus Posterior Root Tear
Authors: Chen Bing-Kuan , Lin Yi-Cheng , Liu Yu-Hsin , Weng Pei-Wei , Chen Kuan-Hao , Chiang Chang-Jung , Wong Chin-Chean
Published: November 2023

A medial meniscus posterior root tear (MMPRT) contributes to knee joint degeneration. Arthroscopic transtibial pullout repair (ATPR) may restore biomechanical integrity for load transmission. However, degeneration persists after ATPR in certain patients, particularly those with preoperative subchondral insufficiency fracture of the knee (SIFK). We explored the relationship between preoperative SIFK and osteoarthritis (OA) progression in retrospectively enrolled patients who were diagnosed as having an MMPRT and had received ATPR within a single institute. Based on their preoperative magnetic resonance imaging (MRI), these patients were then categorized into SIFK and non-SIFK groups. OA progression was evaluated by determining Kellgren–Lawrence (KL) grade changes and preoperative and postoperative median joint widths. SIFK characteristics were quantified using Image J (Version 1.52a). Both groups exhibited significant post-ATPR changes in medial knee joint widths. The SIFK group demonstrated significant KL grade changes (p < 0.0001). A larger SIFK size in the tibia and a greater lesion-to-tibia length ratio in the coronal view were positively correlated with more significant KL grade changes (p = 0.008 and 0.002, respectively). Thus, preoperative SIFK in patients with an MMPRT was associated with knee OA progression. Moreover, a positive correlation was observed between SIFK lesion characteristics and knee OA progression.
Investigating the Feasibility of Predicting KRAS Status, Tumor Staging, and Extramural Venous Invasion in Colorectal Cancer Using Inter-Platform Magnetic Resonance Imaging Radiomic Features
Authors: Alshuhri Mohammed S. , Alduhyyim Abdulaziz , Al-Mubarak Haitham , Alhulail Ahmad A. , Alomair Othman I. , Madkhali Yahia , Alghuraybi Rakan A. , Alotaibi Abdullah M. , Alqahtani Abdullalh G. M.
Published: November 2023

(1) Background: Colorectal cancer is the third most common type of cancer with a high mortality rate and poor prognosis. The accurate prediction of key genetic mutations, such as the KRAS status, tumor staging, and extramural venous invasion (EMVI), is crucial for guiding personalized treatment decisions and improving patients’ outcomes. MRI radiomics was assessed to predict the KRAS status and tumor staging in colorectal cancer patients across different imaging platforms to improve the personalized treatment decisions and outcomes. (2) Methods: Sixty colorectal cancer patients (35M/25F; avg. age 56.3 ± 12.9 years) were treated at an oncology unit. The MRI scans included T2-weighted (T2W) and diffusion-weighted imaging (DWI) or the apparent diffusion coefficient (ADC). The manual segmentation of colorectal cancer was conducted on the T2W and DWI/ADC images. The cohort was split into training and validation sets, and machine learning was used to build predictive models. (3) Results: The neural network (NN) model achieved 73% accuracy and an AUC of 0.71 during training for predicting the KRAS mutation status, while during testing, it achieved 62.5% accuracy and an AUC of 0.68. In the case of tumor grading, the support vector machine (SVM) model excelled with a training accuracy of 72.93% and an AUC of 0.7, and during testing, it reached an accuracy of 72% and an AUC of 0.69. (4) Conclusions: ML models using radiomics from ADC maps and T2-weighted images are effective for distinguishing KRAS genes, tumor grading, and EMVI in colorectal cancer. Standardized protocols are essential to improve MRI radiomics’ reliability in clinical practice.
Where to Draw Cerebrospinal Fluid from an External Ventricular Drain? Comparison of Cerebrospinal Fluid Parameters between Two Different Collection Sites
Authors: Khalaveh Farjad , Mischkulnig Mario , Zeiser Vitalij , Vossen Matthias G. , Reinprecht Andrea , Dorfer Christian , Rössler Karl , Herta Johannes
Published: November 2023

Background: High cerebrospinal fluid (CSF) sampling frequency is considered a risk factor for external ventricular drain (EVD)-associated infections. To reduce manipulation at the proximal port and potentially minimize the risk of an infection, we aimed to analyze whether CSF parameters sampled from the far distal collection bag could provide reliable results compared to the proximal port. Methods: We included patients who were treated with an EVD at our neurosurgical intensive care unit (ICU) between June 2021 and September 2022. CSF sampling, including microbiological analysis, was performed simultaneously from the proximal port and the collection bag. Spearman’s correlation coefficients were calculated to assess the correlation of CSF cell count, protein, lactate and glucose between the two sample sites. Results: We analyzed 290 pairs of CSF samples in 77 patients. Ventriculitis was identified in 4/77 (5%) patients. In 3/4 patients, microbiological analysis showed the same bacterial species at both sample sites at the same time. Spearman’s correlation coefficient showed that CSF cell count (r = 0.762), lactate (r = 0.836) and protein (r = 0.724) had a high positive correlation between the two collection sites, while CSF glucose (r = 0.663) showed a moderate positive correlation. Conclusion: This study shows that biochemical CSF parameters can be reliably assessed from the EVD collection bag.
The Human Nature of Generative AIs and the Technological Nature of Humanity: Implications for Education
Authors: Dron Jon
Published: November 2023

This paper analyzes the ways that the widespread use of generative AIs (GAIs) in education and, more broadly, in contributing to and reflecting the collective intelligence of our species, can and will change us. Methodologically, the paper applies a theoretical model and grounded argument to present a case that GAIs are different in kind from all previous technologies. The model extends Brian Arthur’s insights into the nature of technologies as the orchestration of phenomena to our use by explaining the nature of humans’ participation in their enactment, whether as part of the orchestration (hard technique, where our roles must be performed correctly) or as orchestrators of phenomena (soft technique, performed creatively or idiosyncratically). Education may be seen as a technological process for developing these soft and hard techniques in humans to participate in the technologies, and thus the collective intelligence, of our cultures. Unlike all earlier technologies, by embodying that collective intelligence themselves, GAIs can closely emulate and implement not only the hard technique but also the soft that, until now, was humanity’s sole domain; the very things that technologies enabled us to do can now be done by the technologies themselves. Because they replace things that learners have to do in order to learn and that teachers must do in order to teach, the consequences for what, how, and even whether learning occurs are profound. The paper explores some of these consequences and concludes with theoretically informed approaches that may help us to avert some dangers while benefiting from the strengths of generative AIs. Its distinctive contributions include a novel means of understanding the distinctive differences between GAIs and all other technologies, a characterization of the nature of generative AIs as collectives (forms of collective intelligence), reasons to avoid the use of GAIs to replace teachers, and a theoretically grounded framework to guide adoption of generative AIs in education.
Energy Minimization in Reconfigurable Intelligent Surface-Assisted Unmanned Aerial Vehicle-Enabled Wireless Powered Mobile Edge Computing Systems with Rate-Splitting Multiple Access
Authors: Kim Jihyung , Hong Eunhye , Jung Jaemin , Kang Jinkyu , Jeong Seongah
Published: November 2023

In this study, a reconfigurable intelligent surface (RIS)-assisted wireless-powered mobile edge computing (WP-MEC) system is proposed, where a single-antenna unmanned aerial vehicle (UAV)-mounted cloudlet provides offloading opportunities to K user equipments (UEs) with a single antenna, and the K UEs can harvest the energy from the broadcast radio-frequency signals of the UAV. In addition, rate-splitting multiple access is used to provide offloading opportunities to multiple UEs for effective power control and high spectral efficiency. The aim of this paper is to minimize the total energy consumption by jointly optimizing the resource allocation in terms of time, power, computing frequency, and task load, along with the UAV trajectory and RIS phase-shift matrix. Since coupling issues between optimization variable designs are caused, however, an alternating optimization-based algorithm is developed. The performance of the proposed algorithm is verified via simulations and compared with the benchmark schemes of partial optimizations of resource allocation, path planning, and RIS phase design. The proposed algorithm exhibits high performance in WP-MEC systems with insufficient resources, e.g., achieving up to 40% energy reduction for a UAV with eight elements of RIS.
Event-Triggered Hierarchical Planner for Autonomous Navigation in Unknown Environment
Authors: Chen Changhao , Song Bifeng , Fu Qiang , Xue Dong , He Lei
Published: November 2023

End-to-end deep neural network (DNN)-based motion planners have shown great potential in high-speed autonomous UAV flight. Yet, most existing methods only employ a single high-capacity DNN, which typically lacks generalization ability and suffers from high sample complexity. We propose a novel event-triggered hierarchical planner (ETHP), which exploits the bi-level optimization nature of the navigation task to achieve both efficient training and improved optimality. Specifically, we learn a depth-image-based end-to-end motion planner in a hierarchical reinforcement learning framework, where the high-level DNN is a reactive collision avoidance rerouter triggered by the clearance distance, and the low-level DNN is a goal-chaser that generates the heading and velocity references in real time. Our training considers the field-of-view constraint and explores the bi-level structural flexibility to promote the spatio–temporal optimality of planning. Moreover, we design simple yet effective rules to collect hindsight experience replay buffers, yielding more high-quality samples and faster convergence. The experiments show that, compared with a single-DNN baseline planner, ETHP significantly improves the success rate and generalizes better to the unseen environment.
Centrality and System Size Dependence among Freezeout Parameters and the Implications for EOS and QGP in High-Energy Collisions
Authors: Waqas Muhammad , Haj Ismail Abd , Alrebdi Haifa I. , Ajaz Muhammad
Published: November 2023

Utilizing the Modified Hagedorn function with embedded flow, we analyze the transverse momenta (pT) and transverse mass (mT) spectra of π+ in Au–Au, Cu–Cu, and d–Au collisions at sNN = 200 GeV across various centrality bins. Our study reveals the centrality and system size dependence of key freezeout parameters, including kinetic freezeout temperature (T0), transverse flow velocity (βT), entropy-related parameter (n), and kinetic freezeout volume (V). Specifically, T0 and n increase from central to peripheral collisions, while βT and V show the opposite trend. These parameters also exhibit system size dependence; T0 and βT are smaller in larger collision systems, whereas V is larger. Importantly, central collisions correspond to a stiffer Equation of State (EOS), characterized by larger βT and smaller T0, while peripheral collisions indicate a softer EOS. These insights are crucial for understanding the properties of Quark–Gluon Plasma (QGP) and offer valuable constraints for Quantum Chromodynamics (QCD) models at high temperatures and densities.
Cluster Persistence for Weighted Graphs
Authors: Bobrowski Omer , Skraba Primoz
Published: November 2023

Persistent homology is a natural tool for probing the topological characteristics of weighted graphs, essentially focusing on their 0-dimensional homology. While this area has been thoroughly studied, we present a new approach to constructing a filtration for cluster analysis via persistent homology. The key advantages of the new filtration is that (a) it provides richer signatures for connected components by introducing non-trivial birth times, and (b) it is robust to outliers. The key idea is that nodes are ignored until they belong to sufficiently large clusters. We demonstrate the computational efficiency of our filtration, its practical effectiveness, and explore into its properties when applied to random graphs.
Part-Aware Point Cloud Completion through Multi-Modal Part Segmentation
Authors: Yu Fuyang , Tian Runze , Wang Xuanjun , Liang Xiaohui
Published: November 2023

Point cloud completion aims to generate high-resolution point clouds using incomplete point clouds as input and is the foundational task for many 3D visual applications. However, most existing methods suffer from issues related to rough localized structures. In this paper, we attribute these problems to the lack of attention to local details in the global optimization methods used for the task. Thus, we propose a new model, called PA-NET, to guide the network to pay more attention to local structures. Specifically, we first use textual embedding to assist in training a robust point assignment network, enabling the transformation of global optimization into the co-optimization of local and global aspects. Then, we design a novel plug-in module using the assignment network and introduce a new loss function to guide the network’s attention towards local structures. Numerous experiments were conducted, and the quantitative results demonstrate that our method achieves novel performance on different datasets. Additionally, the visualization results show that our method efficiently resolves the issue of poor local structures in the generated point cloud.
Nonequilibrium Effects on Information Recoverability of the Noisy Channels
Authors: Zeng Qian , Li Ran , Wang Jin
Published: November 2023

We investigated the impact of nonequilibrium conditions on the transmission and recovery of information through noisy channels. By measuring the recoverability of messages from an information source, we demonstrate that the ability to recover information is connected to the nonequilibrium behavior of the information flow, particularly in terms of sequential information transfer. We discovered that the mathematical equivalence of information recoverability and entropy production characterizes the dissipative nature of information transfer. Our findings show that both entropy production (or recoverability) and mutual information increase monotonically with the nonequilibrium strength of information dynamics. These results suggest that the nonequilibrium dissipation cost can enhance the recoverability of noise messages and improve the quality of information transfer. Finally, we propose a simple model to test our conclusions and found that the numerical results support our findings.
Social Commitment at Higher Education Institutions: Analysis of Their Strategic Plans
Authors: García-Aracil Adela , Isusi-Fagoaga Rosa , Monteiro Sílvia , Almeida Leandro
Published: November 2023

The social contribution of higher education institutions (HEIs) tends to be constrained as a description of HEI activities in terms of outcomes, but no examination of how HEIs face societal problems through collaborative activities has been addressed. One way to explore social strategy pursued by HEIs is through the analysis of their strategic plans. We analyze the strategic plans of public HEIs in Portugal and Spain due to their similarities after the 2008 international financial crisis, which led to a major recession and gave rise to social actions to address societal problems. In doing so, we propose a framework that could guide future research in providing empirical evidence on the formulation, articulation, and implementation of social issues in institutional strategic plans. We interviewed HEI representatives to confirm some of our findings, highlighting several factors that enhance or suppress the attainment of social issues. Our research shows that HEI responses to social commitment differ according to each institution’s regulations and social circumstances. We aspire to encourage management scholars to engage in tackling social strategy through their collaborative activities.
Nomophobia and Its Association with Depression, Anxiety and Stress (DASS Scale), among Young Adults in Greece
Authors: Gnardellis Charalambos , Vagka Elissavet , Lagiou Areti , Notara Venetia
Published: November 2023

Smartphones with their numerous applications have become essential daily equipment, prompting scientific research to deal with the impact of their use on psychosocial health. Under this spectrum, the aim of the present cross-sectional study was to examine the association between nomophobia and the negative emotional states of depression, anxiety, and stress, in relation to self-esteem and sociodemographic data, among the young adult population. The study sample consisted of 1408 young adults aged 18–25 years, participating on a voluntary basis with an online anonymous questionnaire. Data were collected through the “Nomophobia Questionnaire (NMP-Q)”, “Depression Anxiety Stress Scales—short form (DASS-21)”, and Rosenberg Self-Esteem Scale (RSES). The questionnaire also included socio-demographic characteristics and smartphone use variables. Data analysis showed that women were identified with severe depression and stress to a greater extent than men (63.3% vs. 55.1% for depression and 18.1% vs. 13.8% for stress scale). With respect to nomophobia, participants with severe levels of nomophobia also exhibited severe levels of negative emotional states in all DASS components, i.e., 40.6% in depression, 73.7% in anxiety, and 32.7% in stress (all p values < 0.001). Participants with severe levels of depression and anxiety were very often checking their phone and used it in all daily activities. Moreover, correlation analysis revealed that self-esteem had a moderating effect on the relationship between nomophobia and DASS, a fact that modifies the association between the involved variables: stronger relationships appeared between nomophobia and DASS components in individuals with normal/high self-esteem than in individuals with low self-esteem.
Trends in Suicidal Mortality and Motives among Working-Ages Individuals in Japan during 2007–2022
Authors: Matsumoto Ryusuke , Motomura Eishi , Onitsuka Toshiaki , Okada Motohiro
Published: November 2023

Suicides in Japan consistently decreased from 2009–2019, but increased during the COVID-19 pandemic. To identify causes of increasing suicides, age-dependent and temporal fluctuations of suicide mortality rate per 100,000 (SMRP) in working-age generations (20–59 years) disaggregated by suicidal motives (7-categories; 52-subcategories) and sex from 2007 to 2022, were analyzed by analysis of variance and joinpoint regression, respectively, using the government suicide database “Suicide Statistics”. The SMRP of 20–29 year-old males and 20–49 year-old females began to increase in the late 2010s. SMRPs of these high-risk groups for suicides caused by depression (the leading suicidal motive for all groups) began increasing in the late 2010s. Economic-related, employment-related, and romance-related problems contributed to the increasing SMRPs in 20–29 males in the late 2010s. Romance-related and family-related problems contributed to the increasing SMRPs of 20–29 females in the late 2010s. Increasing SMRPs caused by child-raising stress in 20–39 year-old females from the late 2010s was a remarkable finding. In contrast, SMRPs of 30–59 year-old males consistently decreased until 2021; however, in these groups, SMRPs for suicides caused by various motives sharply increased in 2022. The consistent increase in SMRPs of high-risk groups from the late 2010s to the pandemic suggest recent socioeconomic and psychosocial problems in Japan possibly contributed to the increasing SMRPs in these high-risk groups independently of pandemic-associated factors, whereas the SMRPs of males of 30–59 years were probably associated with the ending of the pandemic rather than pandemic-associated factors.
Autonomous Vehicles for Healthcare Applications: A Review on Mobile Robotic Systems and Drones in Hospital and Clinical Environments
Authors: Marmaglio Paolo , Consolati Davide , Amici Cinzia , Tiboni Monica
Published: November 2023

The development of autonomous vehicles, both ground and airborne, for hospitals and clinical settings is an extremely interesting topic that has developed rapidly in recent years. Given their significant potential to improve operational efficiency and safety protocols, these devices are gradually gaining an important place in the healthcare industry. This is true for both in-hospital and out-of-hospital functions. The integration of autonomous vehicles into these environments will greatly increase operational efficiency and enrich the experience for both medical staff and patients. This document provides an updated and comprehensive overview of the current state-of-the-art in the development of autonomous vehicles for the healthcare sector. Particular emphasis is placed on design, functionality, and level of autonomy. The review is organized on two levels: a prospective review highlights the main trends in the design and application of autonomous vehicles, and an analytical review performs an in-depth analysis of the main aspects of the technical solutions developed and implemented in the scientific research reviewed. The results are presented in a schematic approach.
Semantic Segmentation Network Slimming and Edge Deployment for Real-Time Forest Fire or Flood Monitoring Systems Using Unmanned Aerial Vehicles
Authors: Lee Youn Joo , Jung Ho Gi , Suhr Jae Kyu
Published: November 2023

In recent years, there has been a significant increase in the demand for unmanned aerial vehicle (UAV)-based monitoring systems to ensure proper emergency response during natural disasters such as wildfires, hurricanes, floods, and earthquakes. This paper proposes a real-time UAV monitoring system for responding to forest fires or floods. The proposed system consists of a hardware part and a software part. The hardware configuration is an embedded camera board mounted on the UAV, a Qualcomm QCS610 SoC with cores suitable for running deep learning-based algorithms. The software configuration is a deep learning-based semantic segmentation model for detecting fires or floods. To execute the model in real time on edge devices with limited resources, we used a network slimming technique which generates a lightweight model with reduced model size, number of parameters, and computational complexity. The performance of the proposed system was evaluated on the FLAME dataset consisting of forest fire images and the FloodNet dataset consisting of flood images. The experimental results showed that the mIoU of slimmed DeepLabV3+ for FLAME is 88.29%, and the inference speed is 10.92 fps. For FloodNet, the mIoU of the slimmed DeepLabV3+ is 94.15%, and the inference speed is 13.26 fps. These experimental results confirm that the proposed system is appropriate for accurate, low-power, real-time monitoring of forest fires and floods using UAVs.
Distributed, Dynamic and Recursive Planning for Holonic Multi-Agent Systems: A Behavioural Model-Based Approach
Authors: Dehimi Nour El Houda , Galland Stéphane , Tolba Zakaria , Allaoua Nora , Ferkani Mouhamed
Published: November 2023

In this work, we propose a new distributed, dynamic, and recursive planning approach able to consider the hierarchical nature of the holonic agent and the unpredictable evolution of its behaviour. For each new version of the holonic agent, introduced because of the agent members obtaining new roles to achieve new goals and adapt to the changing environment, the approach generates a new plan that can solve the new planning problem associated with this new version against which the plans, executed by the holonic agent, become obsolete. To do this, the approach starts by generating sub-plans capable of solving the planning subproblems associated with the groups of the holonic agent at its different levels. It then recursively links the sub-plans, according to their hierarchical and behavioural dependency, to obtain a global plan. To generate the sub-plans, the approach exploits the behavioural model of the holonic agent’s groups, thereby minimising the computation rate imposed by other multi-agent planning methods. In our work, we have used a concrete case to show and illustrate the usefulness of our approach.
Unsteady Magnetohydrodynamic Radiative Casson Nanofluid within Chemically Reactive Flow over a Stretchable Surface with Variable Thickness through a Porous Medium
Authors: Sedki Ahmed M. , Qahiti Raed
Published: November 2023

This study presents a mathematical investigation into the phenomena of radiative heat with an unsteady MHD electrically conducting boundary layer of chemically reactive Casson nanofluid flow due to a pored stretchable sheet immersed in a porous medium in the presence of heat generation, thermophoretic force, and Brownian motion. The surface is assumed to be not flat, and has variable thickness. The magnetic field is time-dependent, and the chemical reaction coefficient is inversely varied with the distance. The nanofluid’s velocity, heat, and concentration at the surface are nonlinearly varied. A similarity transformation is introduced, and the controlling equations are converted into nondimensional forms involving many significant physical factors. The transformed forms are analyzed numerically using a computational method based on the finite difference scheme and Newton’s linearization procedure. The impact of the involved physical parameters is performed in graphical and tabular forms. Some special cases of the current work are compared with published studies, and an excellent agreement is obtained. The main results of the present work indicate that the higher values of the Casson parameter cause an increase in both the shear stress and heat flux, but a decrease in the mass flux. Also, it is noted that the chemical reaction, the nanoparticles’ volume, and the permeability factor enhance the effect the of Casson parameter on both the shear stress and heat flux, while the variable thickness and thermal radiation field reduce it; on the other hand, the variable thickness and nanoparticles’ volume enforce the influence of the Casson parameter on mass flux, but thermal radiation, the permeability factor, and chemical reaction decrease it. The present study has important applications in mechanical engineering and natural sciences. In addition, it has significant applications in devices used for blood transfusion, dialysis and cancer therapy.
Selection of a Particulate Filter for a Gasoline-Powered Vehicle Engine in Static and Dynamic Conditions
Authors: Pielecha Jacek , Merkisz Jerzy
Published: November 2023

Current challenges in terms of exhaust emission limits are related to the reduction of the particle numbers in spark ignition direct injection engines. The article concerns the analysis of the thermodynamic parameters of engine operation, allowing the selection of the particulate filter configuration and its technical parameters. The designed system consisting of an internal combustion engine and an exhaust system with an exhaust gas treatment system should be sufficient to meet ecological requirements in the form of reducing particulate matter emissions. The analysis of particulate matter emissions for the system without a filter and with a filter installed in the engine exhaust system was carried out for the mass, number and dimensional distribution of particulate matter. The result was an assessment of filtration efficiency for the entire spectrum of particulate diameters in the identified engine operating ranges. As a result, it was found that the particulate filter used in the engine exhaust system effectively reduces the particle number due to the greater filtration efficiency of large particles. The summary of the work related to the analysis of the ecological parameters of a spark ignition engine with direct fuel injection was a simulation of road tests of a vehicle with the proposed modified vehicle exhaust system equipped with a particulate filter. For this configuration, the analysis of particulate number emissions in the parameterized engine operating areas showed that it is possible to meet the particulate number emission limits, and the obtained road emission results are fully acceptable in terms of the obtained absolute values.
Effect of Hydration under High Temperature and Pressure on the Stress Thresholds of Shale
Authors: Wu Jianfa , Guo Yintong , Huang Haoyong , Zhao Guokai , Gou Qiyong , Gui Junchuan , Xu Ersi
Published: November 2023

The stress threshold of deep reservoir shale subjected to fracturing fluid immersion is an important factor affecting fracture initiation and propagation during fracturing. However, little information has been reported on the effect on shale of soaking at high temperature and high pressure (HTHP). In this study, immersion tests and triaxial compression tests were carried out at reservoir temperature and in-situ stress on the downhole cores with different mineral compositions. The characteristics of stress thresholds, i.e., crack initiation stress (σci), crack damage stress (σcd), and peak deviator stress (σp), of shale affected by the different times of soaking with low-viscosity fracturing fluid (a) and the different viscosity fracturing fluids (a, b, and c) were investigated. The results show that hydration at HTHP has a significant softening effect on the stress thresholds (σci, σcd, σp) of reservoir shale, but the softening rate varies for samples with different mineral compositions. The crack initiation stresses of quartz-rich and clay-rich shales treated with different soaking times and different soaking media remain almost unchanged in the range of 47 to 54% of the corresponding peak strength, while the crack initiation stresses of carbonate-rich shales are significantly affected. The ratio σcd/σp of quartz-rich shale is significantly affected by the different viscosity fracturing fluids (a, b) and the different times of soaking with low-viscosity fracturing fluid (a), while clay- and carbonate-rich shales are less affected. The results of this study can provide a reference for the fracturing design of deep shale gas development.
Identification of Key Factors for the Development of Agricultural Biogas Plants in Poland
Authors: Ginda Grzegorz , Szyba Marta
Published: November 2023

Agricultural biogas plants are a renewable source of energy and at the same time enable the disposal of biodegradable waste generated in agriculture and the food industry. In Poland, a program aimed at constructing agricultural biogas plants has been in operation since 2010 with the goal of producing 1.7 billion Nm3 of biogas, which has not been achieved. Factors that could influence the development of this energy source were identified based on a register of agricultural biogas producers from the National Agriculture Support Center and data from the Energy Regulatory Office. These factors are technology, substrates, state energy policy, profitability, population density in the commune and the spatial arrangement of the commune resulting from spatial development plans. A pairwise comparison analysis using the DEMATEL method was conducted for these factors. It allowed us to conclude that they are population density and the lack of local spatial development plans in most rural areas. The situation may be improved by the obligation to develop such plans for the entire area of each commune and by locating biogas plants near livestock farms and agri-food processing plants. The selected DEMATEL method is mature and comprehensively verified. It enables research to be carried out in other contexts, taking into account the correlations between factors. It is a universal method, and after collecting expert opinions, research can be expanded. The obtained results of the analysis will allow for further research by collecting the opinions of experts such as biogas plant users, local communities, local government officials and other stakeholders. In addition, further analysis of key factors will be carried out using the DEMATEL method for several scenarios. The PESTEL method will be used to identify key factors.
Exploring Time Series Models for Wind Speed Forecasting: A Comparative Analysis
Authors: Li Xiangqian , Li Keke , Shen Siqi , Tian Yaxin
Published: November 2023

The sustainability and efficiency of the wind energy industry rely significantly on the accuracy and reliability of wind speed forecasting, a crucial concern for optimal planning and operation of wind power generation. In this study, we comprehensively evaluate the performance of eight wind speed prediction models, spanning statistical, traditional machine learning, and deep learning methods, to provide insights into the field of wind energy forecasting. These models include statistical models such as ARIMA (AutoRegressive Integrated Moving Average) and GM (Grey Model), traditional machine learning models like LR (Linear Regression), RF (random forest), and SVR (Support Vector Regression), as well as deep learning models comprising ANN (Artificial Neural Network), LSTM (Long Short-Term Memory), and CNN (Convolutional Neural Network). Utilizing five common model evaluation metrics, we derive valuable conclusions regarding their effectiveness. Our findings highlight the exceptional performance of deep learning models, particularly the Convolutional Neural Network (CNN) model, in wind speed prediction. The CNN model stands out for its remarkable accuracy and stability, achieving the lowest mean squared error (MSE), root mean squared error (RMSE), mean absolute error (MAE), mean absolute percentage error (MAPE), and the higher coefficient of determination (R2). This underscores the CNN model’s outstanding capability to capture complex wind speed patterns, thereby enhancing the sustainability and reliability of the renewable energy industry. Furthermore, we emphasized the impact of model parameter tuning and external factors, highlighting their potential to further improve wind speed prediction accuracy. These findings hold significant implications for the future development of the wind energy industry.
High Penetration of Renewable Energy Sources and Power Market Formation for Countries in Energy Transition: Assessment via Price Analysis and Energy Forecasting
Authors: Koeva Dimitrina , Kutkarska Ralena , Zinoviev Vladimir
Published: November 2023

Climate change as a challenge we all are facing, varying degree of economic development as a result of COVID-19, the volatility in energy prices and political as well as other factors, most countries have restructured their electricity markets in order to facilitate the use of green renewable energy. The right energy mix in a period of energy transformation is the best strategy for achieving reduction of carbon emissions. Bulgaria is a special case because it has expanded the use of solar and wind energy exponentially, without conducting an adequate preliminary forecast analysis and formulating a parallel strategy for the development and expansion of the energy storage infrastructure. In this regard, the article is focused on how the power energy market is structured with the increasingly large-scale and global penetration of renewable energy sources as primary energy sources, observing several key factors influencing the energy transition. Due to the cyclical nature of energy production and the necessity for a smooth and efficient transition, a long-term seasonal storage plan should be considered. Furthermore, solar energy production facilities have a greater share of installed power, but wind power facilities generate a roughly equivalent amount of electric energy over the course of a year. One of the aims of this research is to discover an appropriate model for predicting the electricity output of wind and solar facilities located in Bulgaria that can be used to ease the transition process. Based on thorough data analysis of energy production over the past 11 years and 5 months, our findings suggest that a SARIMA model might be appropriate, as it takes into account the seasonal cycles in the production process.
Power Distribution Systems’ Vulnerability by Regions Caused by Electrical Discharges
Authors: Santos Andréia S. , Faria Lucas Teles , Lopes Mara Lúcia M. , Minussi Carlos R.
Published: November 2023

Energy supply interruptions or blackouts caused by faults in power distribution feeders entail several damages to power utilities and consumer units: financial losses, damage to power distribution reliability, power quality deterioration, etc. Most studies in the specialized literature concerning faults in power distribution systems present methodologies for detecting, classifying, and locating faults after their occurrence. In contrast, the main aim of this study is to prevent faults by estimating the city regions whose power grid is most vulnerable to them. In this sense, this work incorporates a geographical-space study via a spatial data analysis using the local variable electrical discharge density that can increase fault risks. A geographically weighted spatial analysis is applied to data aggregated by regions to produce thematic maps with the city regions whose feeders are more vulnerable to failures. The spatial data analysis is implemented in QGIS and R programming environments. It is applied to the real data of faults in distribution power grid transformers and electrical discharges in a medium-sized city with approximately 200,000 inhabitants. In this study, we highlight a moderate positive correlation between electrical discharge density and the percentage of faults in transformers by regions in the central and western areas of the city under study.
Breakdown Characteristics of Schottky Barrier Diodes Used as Bypass Diodes in Photovoltaic Modules under Lightning Surges
Authors: Hamada Toshiyuki , Nanno Ikuo , Ishikura Norio , Fujii Masayuki , Oke Shinichiro
Published: November 2023

Damage to photovoltaic power-generation systems by lightning causes the failure of bypass diodes (BPDs) in solar cell modules. Bypass diodes damaged by lightning experience high-resistance open- or short-circuit failures. When a bypass diode experiences short-circuit failure due to indirect lightning, the damage may not be immediately visible. When solar radiation is subsequently received, the current circulating in the closed circuit formed by the cell string and short-circuited bypass diode flows, resulting in overheating and burnout of the short-circuited bypass diode. The authors’ research group previously reported that when a bypass diode fails within a range of approximately 10−1 Ω to 10 Ω, the heat generated by the failed bypass diode is high, posing the risk of burnout. However, the detailed failure characteristics of the bypass diode that fail because of indirect lightning surges are not clear. In this study, we performed indirect lightning fracture tests and clarified the dielectric breakdown characteristics of Schottky barrier diodes (SBDs) contained in the bypass diodes of photovoltaic solar cell modules, which are subjected to indirect lightning surges. Furthermore, we attempted to determine the conditions of indirect lightning that resulted in a higher risk of heat and ignition. As a result, short-circuit failures occurred in all the Schottky barrier diodes that were destroyed in the forward or reverse direction because of the indirect lightning surges. Moreover, the fault resistance decreased as the indirect lightning surge charge increased. These results indicate that the risks of heat generation and burnout increase when the Schottky barrier diode fails with a relatively low electric charge from an indirect lightning surge. In addition, we observed that for a forward breakdown of the Schottky barrier diode, the range of the indirect lightning surge that results in a fault condition with a higher risk of heat generation and burnout is wider than that for a reverse breakdown.