bluuebunny/crossref_metadata_2025 huggingface dataset:
Predictors of Climate Change Activism Communication in Social Networks
Authors: Latkin Carl A. , Dayton Lauren , Parker Kelsie , Rimal Rajiv
Published: November 2024

It is critical to understand the determinants of climate change activism (CCA) and CCA communications (CCAC). Such information can help organizations that are committed to addressing climate understand and predict who will engage in CCA, identify barriers to CCA, and develop programs to address these barriers to diffuse climate change activism messages and behaviors through social networks and to mobilize action. This study longitudinally investigates psychosocial predictors of CCAC. Study participants were drawn from a randomized clinical trial of US adults (N = 622). Participants completed baseline and follow-up surveys between August to September 2022. Logistic regression models assessed psychosocial factors and implementation intention factors that predicted CCAC at follow-up. The multivariate logistic regression model baseline factors of positive social network norms related to CCAC (aOR: 1.25, 95% CI: 1.10–1.43), comfort encouraging others to engage in CCAC (aOR: 1.74, 95% CI: 1.01–2.88), and following a climate change social media account (aOR: 2.65, 95% CI: 1.74–4.02) were significantly associated with CCAC at follow-up. In a sub-analysis, plans on talking within a week and having in-person conversations versus texting/email were positively associated with CCAC. These findings suggest that strategies to improve comfort talking about CCA and implementation intentions may increase interpersonal CCAC.
Novel Advance Image Caption Generation Utilizing Vision Transformer and Generative Adversarial Networks
Authors: Tyagi Shourya , Oki Olukayode Ayodele , Verma Vineet , Gupta Swati , Vijarania Meenu , Awotunde Joseph Bamidele , Babatunde Abdulrauph Olanrewaju
Published: November 2024

In this paper, we propose a novel method for producing image captions through the utilization of Generative Adversarial Networks (GANs) and Vision Transformers (ViTs) using our proposed Image Captioning Utilizing Transformer and GAN (ICTGAN) model. Here we use the efficient representation learning of the ViTs to improve the realistic image production of the GAN. Using textual features from the LSTM-based language model, our proposed model combines salient information extracted from images using ViTs. This merging of features is made possible using a self-attention mechanism, which enables the model to efficiently take in and process data from both textual and visual sources using the self-attention properties of the self-attention mechanism. We perform various tests on the MS COCO dataset as well as the Flickr30k dataset, which are popular benchmark datasets for image-captioning tasks, to verify the effectiveness of our proposed model. The outcomes represent that, on this dataset, our algorithm outperforms other approaches in terms of relevance, diversity, and caption quality. With this, our model is robust to changes in the content and style of the images, demonstrating its excellent generalization skills. We also explain the benefits of our method, which include better visual–textual alignment, better caption coherence, and better handling of complicated scenarios. All things considered, our work represents a significant step forward in the field of picture caption creation, offering a complete solution that leverages the complementary advantages of GANs and ViT-based self-attention models. This work pushes the limits of what is currently possible in image caption generation, creating a new standard in the industry.
Shear Strengthening with a Fiber-Reinforced Cementitious Matrix of Reinforced Concrete Elements Under Different Levels of Loads: An Experimental Investigation
Authors: Vegera Pavlo , Borzovic Viktor , Blikharskyi Zinovii , Grynyova Iryna , Baran Jaroslav
Published: November 2024

This article explores the impact of strengthening reinforced concrete beams under different load levels, focusing on the use of polyphenylene benzobisoxazole (P.B.O.) fibers in a stabilized inorganic matrix to enhance the shear capacity. This research examines the interaction between modern composite materials and existing reinforced concrete structures, highlighting the practical challenges when the full unloading of structures is impossible. The experiments demonstrate that strengthening significantly increases the shear strength, with a maximum enhancement of 25%. However, the effect decreases as the load applied during strengthening increases, dropping to 16% at 70% of the ultimate load. This research also highlights the importance of refining current calculation methods due to the complex stress–strain state of beams and the unpredictable nature of shear failures. It concludes that composite materials, especially fiber-reinforced cementitious matrix (FRCM) systems, provide a practical solution for enhancing structural performance while maintaining the integrity and safety of concrete elements. This article emphasizes that the strengthening efficiency should be adjusted based on the applied load, suggesting a 5% reduction in effectiveness for every 10% increase in the initial load level. The findings support the empirical hypothesis that the shear strength improvement diminishes linearly with higher load levels during strengthening.
Klotho Deficiency in Severe COVID-19: A Unifying Hypothesis
Authors: Campos-Obando Natalia , Zillikens M. Carola , Macaya Roman F.
Published: November 2024

COVID-19 is characterized by a striking similarity to premature aging. Its clinical manifestations range from asymptomatic to critical illness. No single central agent has been demonstrated so far. We present Klotho, an antiaging protein, as a key factor in COVID-19 pathophysiology. There is epidemiological evidence that both acute and chronic uses of Klotho agonists have a beneficial effect in reducing COVID-19 severity and mortality. A review of the PubMed epidemiological, clinical, and mechanistic evidence supports a role for Klotho deficit as a central determinant of severe COVID-19. Clinical data support the idea that chronic use of Klotho agonists protects against severe COVID-19 and that its acute use may be beneficial. We propose a unifying hypothesis that low Klotho levels play a key role in severe COVID-19, while increasing Klotho levels can have a beneficial effect through the prevention of acute kidney injury (AKI) and potential antiviral effects. Further research is needed.
Application of Serial Crystallography for Merging Incomplete Macromolecular Crystallography Datasets
Authors: Nam Ki Hyun
Published: November 2024

In macromolecular crystallography (MX), a complete diffraction dataset is essential for determining the three-dimensional structure. However, collecting a complete experimental dataset using a single crystal is frequently unsuccessful due to poor crystal quality or radiation damage, resulting in the collection of multiple incomplete datasets. This issue can be solved by merging incomplete diffraction datasets to generate a complete dataset. This study introduced a new approach for merging incomplete datasets from MX to generate a complete dataset using serial crystallography (SX). Six incomplete diffraction datasets of β-glucosidase from Thermoanaerobacterium saccharolyticum (TsaBgl) were processed using CrystFEL, an SX program. The statistics of the merged data, such as completeness, CC, CC*, Rsplit, Rwork, and Rfree, demonstrated a complete dataset, indicating improved quality compared with the incomplete datasets and enabling structural determination. Also, the merging of the incomplete datasets was processed using four different indexing algorithms, and their statistics were compared. In conclusion, this approach for generating a complete dataset using SX will provide a new opportunity for determining the crystal structure of macromolecules using multiple incomplete MX datasets.
Telecom O-Band Quantum Dots Fabricated by Droplet Etching
Authors: Spitzer Nikolai , Kersting Elias , Grell Meret , Kohminaei Danial , Schmidt Marcel , Bart Nikolai , Wieck Andreas D. , Ludwig Arne
Published: November 2024

We present a novel growth technique for fabricating low-density InAs/GaAs quantum dots that emit in the telecom O-band. This method combines local droplet etching on GaAs surfaces using gallium with Stranski–Krastanov growth initiated by InAs deposition. Quantum dots nucleate directly within nanoholes, avoiding the critical layer thickness typical of standard InAs Stranski–Krastanov growth, resulting in larger, low-density quantum dots. InGaAs strain reduction layers further redshift the emission into and beyond the telecom O-band. Photoluminescence spectra show a small energy difference between ground and excited states, while capacitance-voltage spectroscopy reveal small Coulomb blockade energy. Atomic force microscopy analysis indicates that quantum dots formed within nanoholes exhibit a larger volume compared to standard quantum dots. Additionally, these nanohole nucleated quantum dots require less indium to achieve O-band emission and demonstrate comparable or even better homogeneity, as indicated by the full-width at half-maximum. This improved homogeneity, low density, and increased size make these quantum dots particularly suitable for single-photon sources in quantum communication applications.
Termite (Blattodea: Isoptera) Distribution Along an Elevational Gradient in Northwestern African Mountains
Authors: Najjari Aya , Hernández-Teixidor David , El Ajjouri Bilal , Benyahia Yousra , Taheri Ahmed
Published: November 2024

We examined the distribution of termites along an elevational gradient in the western Rif Mountains of northern Morocco. Sampling followed standardized protocols at nine forest sites (125, 325, 488, 715, 850, 1150, 1316, 1544, and 1700 masl), all except the lowest site within the Intercontinental Mediterranean Biosphere Reserve. We found a total of three termite species, with Reticulitermes grassei (Rhinotermitidae) being the most abundant, followed by Kalotermes flavicollis (Kalotermitinae) and Amitermes sp. (Terminidae). Comparing microhabitat preferences, we found R. grassei and Amitermes sp. exclusively in buried wood and under rocks, and K. flavicollis exclusively in dead tree trunks and surface wood. The findings reveal the distribution of microhabitats along an elevation gradient and the corresponding preferences of termite species across these microhabitats. We found that total termite density decreased significantly with elevation (rs = −0.40; p < 0.001), but with distinct differences among species. Kalotermes flavicollis and Amitermes sp. showed a strong negative correlation between density and elevation. In contrast, for R. grassei, the most common termite, we found no significant correlation. Termite density also varied among forest types, with a non-significant trend (p = 0.075) of higher density in the cork oak forest (hits/m2 = 0.06) compared to holm oak (0.04) and fir–cedar (0.02) forests. This research contributes to the limited knowledge of termite diversity and ecology in northern Africa and western Mediterranean forests.
Breast Cancer Treatment: The Potential of Organic and Inorganic Nanocarriers in Targeted Drug Delivery
Authors: Alamelu Saravanan , Venkatesan Kamalesh Balakumar , Shagirtha Kalist , Srinivasan Manoj Kumar , Panneerselvam Chellasamy , Aziz Al Thabiani , Ali Alshehri Mohammed , Seyed Mohamed Ali , Pachaiappan Pugalendhi
Published: November 2024

Breast cancer (BC) is the most prevalent form of malignancy among women on a global scale, ranking alongside lung cancer. Presently, conventional approaches to cancer treatment include surgical procedures followed by chemotherapy or radiotherapy. Nonetheless, the efficacy of these treatments in battling BC is often compromised due to the adverse effects they inflict on healthy tissues and organs. In recent times, a range of nanoparticles (NPs) has emerged, exhibiting the potential to specifically target malignant cells while sparing normal cells and organs from harm. This has paved the way for the development of nanoparticle-mediated targeted drug delivery systems, holding great promise as a technique for addressing BC. To increase the efficacy of this new method, several nanocarriers including inorganic NPs (such as magnetic NPs, silica NPs, etc.) and organic NPs (e.g., dendrimers, liposomes, micelles, and polymeric NPs) have been used. Herein, we discuss the mechanism of NP-targeted drug delivery and the recent advancement of therapeutic strategies of organic and inorganic nanocarriers for anticancer drug delivery in BC. We also discuss the future prospects and challenges of nanoparticle-based therapies for BC.
The Relationship Between Dental Anxiety and Oral Health-Related Quality of Life in Patients with Periodontitis
Authors: Padilla-Fonseca Nicole , Araya-Castillo Agatha , Arias-Campos María Paula , Solís-Rivera Ana Paula , Jiménez-Matarrita Jeniffer , Ramírez Karol
Published: November 2024

Objectives: This study aimed to (1) investigate whether dental anxiety (DA) and oral health-related quality of life (OHRQoL) differed between patients diagnosed with periodontitis and individuals with periodontal health, (2) examine associations and correlations between these patient-reported measures, and (3) analyze demographic and clinical parameters. Methods: Ninety-six patients diagnosed with periodontitis and age- and sex-matched periodontally healthy controls were included. Participants’ demographic characteristics, smoking status, current dental pain, dental pain during the last month, the Modified Corah’s Scale (MDAS), and the Oral Health Impact Profile (OHIP-14) were determined. Results: The mean age of participants was 48.51 years ± 11.41. Patients with periodontitis experienced higher pain in the last month compared to controls (p = 0.003). Patients with periodontitis exhibited significantly higher MDAS total and sub-scores (p < 0.001). Compared to controls, the periodontitis group indicated extreme DA (1.04% vs. 7.79%, p = 0.034). Patients with periodontitis feared having a foreign object in the mouth compared to controls (p = 0.004). The periodontitis group exhibited worse OHIP-14 global and sub-scores (all Ps < 0.001). Positive associations and correlations of MDAS total and sub-scores with OHIP-14 global and domain scores were found for the periodontitis group, but not for controls. Patients with periodontitis who reported “moderate and extreme anxiety” had poorer OHRQoL compared to controls (p = 0.001). The minimal importance difference for this finding indicates a large effect size and a moderate standardized response mean between groups. Conclusions: Patients with periodontitis had higher levels of DA and worse OHRQoL compared to controls. Our study highlights the importance of providing a comprehensive approach, including psychosocial well-being, when diagnosing and treating periodontal disease.
Correlation and Agreement Between the SCORE2 and PREVENT 10-Year Atherosclerotic Cardiovascular Disease Risk Scores: Insights from Coronary Computed Tomography Angiography
Authors: Ozpelit Mehmet Emre , Colak Ayse , Uzumcu Hatice Irem , Kumral Zeynep , Ozpelit Ebru
Published: November 2024

Background: We aimed to evaluate the correlation and agreement between the Systemic Coronary Risk Estimation 2 (SCORE2) and Predicting Risk of CVD EVENTs (PREVENT) 10-year ASCVD risk scores by incorporating computed tomographic (CT) data to assess differences between the scoring systems. Methods: The PREVENT risk score was calculated for 171 patients, while the SCORE2 and SCORE2 Older Persons (OP) risk scores were calculated for 113 patients. Coronary artery calcium (CAC) scores were calculated, and the grading of coronary artery disease (CAD) was assessed according to these scores. Results: According to the PREVENT risk category, 79 patients (46.2%) were in the low-risk category, 32 (18.7%) were in the borderline-risk category, and 51 (29.8%) were in the intermediate-risk category. In contrast, the SCORE2 systems placed 32 patients (28.3%) in the low- to moderate-risk categories. Only 9 patients (5.3%) were classified as being at high risk by PREVENT, while SCORE2 categorized 39 patients (34.5%) as being at high risk and 42 patients (37.2%) as being at very high risk. There was a strong correlation between the scores (r = 85, p < 0.001), with a Bland–Altman plot analysis showing a bias of −3.71 points and the limits of agreement ranging from −16.06 to 8.64. The total CAC score and CAD grading were significantly different across the PREVENT risk groups (p < 0.001 for all) but were similar across the SCORE2 groups (p = 0.3 and p = 0.051, respectively). Conclusions: There is a strong correlation and agreement between the two risk scores. However, SCORE2 tends to categorize more patients as high-risk than PREVENT does. Additionally, the PREVENT risk categories are more effective than SCORE2 in determining the likelihood of CAD based on CT results.
Association of the Naples Prognostic Score with Long-Term Adverse Events in Chronic Limb-Threatening Ischemia After Below-the-Knee Endovascular Revascularization
Authors: Dervis Emir , Hakgor Aykun , Goksu Muhammed Mert , Yakut Idris , Konte Hasan Can , Panc Cafer , Gurbak Ismail , Kalkan Ali Kemal , Pusuroglu Hamdi , Yalcin Ahmet Arif , Erturk Mehmet
Published: November 2024

Objectives: Chronic limb-threatening ischemia (CLTI) is the most severe manifestation of peripheral artery disease (PAD) and is associated with high morbidity and mortality. The Naples prognostic score (NPS), a composite marker incorporating serum albumin, total cholesterol, neutrophil-to-lymphocyte ratio (NLR), and lymphocyte-to-monocyte ratio (LMR), has shown prognostic value in various cardiovascular conditions. This study aimed to evaluate the prognostic significance of the NPS in predicting all-cause mortality and any kind of amputation in patients with CLTI undergoing endovascular treatment (EVT) for below-the-knee (BTK) lesions. Methods: In this retrospective analysis, 191 patients diagnosed with CLTI and treated with EVT for BTK lesions between 2017 and 2023 were stratified into three groups based on the NPS: low (0–1), intermediate (2), and high (3–4). The primary endpoint was all-cause mortality, while the secondary endpoint was any kind of amputation. Results: A higher NPS was significantly associated with increased all-cause mortality (hazard ratio: 3.66; 95% confidence interval: 1.72–7.78; p < 0.001), while no significant association was observed between the NPS and major amputation. Independent predictors of mortality included a high NPS, reduced left ventricular ejection fraction, and impaired renal function. Conclusions: The NPS is an independent predictor of long-term mortality in CLTI patients undergoing EVT for BTK lesions.
Diagnostic Value of Nuclear Hybrid Imaging in Malignant Struma Ovarii: A Systematic Review of Case Reports
Authors: Peștean Claudiu , Piciu Doina
Published: November 2024

Background: Struma ovarii is a rare tumor, a type of ovarian mature teratoma consisting over 50% of its mass in thyroid ectopic tissue; 5% to 10% of cases, as described in the literature, are malignant and well known as malignant struma ovarii or thyroid cancer from struma ovarii. Due to the limited number of malignant struma ovarii cases, the diagnostic and therapeutic approach of malignant struma ovarii lacks in standardization. Methods: We performed a comprehensive search on the English language PubMed and Google Scholar. We used specific controlled keywords “PET CT”, “SPECT CT”, “PET MRI”, “malignant struma ovarii”, “hybrid imaging” and “mature ovarian teratoma”. Upon the retrieval of potential articles, we analyzed them for their eligibility. The inclusion criteria were: articles discussing the role of PET/CT and SPECT/CT hybrid imaging in malignant struma ovarii, full-text articles on the topic of interest and English publications. The exclusion criteria were articles not directly related to the hybrid imaging and not discussing the subject of malignant struma ovarii. Results: A total of 64 articles were screened, 35 duplicates were eliminated, 15 articles excluded and a total number of 14 articles were included for this systematic review, 13 of them being case reports and one being a case report with a systematic review. F-18 FDG PET/CT contributed in seven cases (50%), I-131 NaI SPECT/CT in seven cases (50%) and I-124 NaI PET/CT in two cases (14.29%). In two cases, 131 NaI SPECT/CT and F-18 FDG PET/CT were used as complementary investigation tools. The hybrid imaging methods used as a part of the diagnostic strategy were accompanied by several diagnostic alternatives: ultrasounds, CT, MRI, I-131 NaI WBS and I-123 NaI WBS. Conclusions: There is no consistent or standardized diagnostic and therapeutic approach for malignant struma ovarii. Hybrid imaging methods may be of great value in initial diagnostic and the association of F-18 FDG PET/CT and I-131 NaI SPECT/CT is a successful diagnostic approach. The association of hybrid imaging with other diagnostic imaging alternatives in initial diagnostic and follow up is essential.
Transforming Cancer Classification: The Role of Advanced Gene Selection
Authors: Yaqoob Abrar , Mir Mushtaq Ahmad , Jagannadha Rao G. V. V. , Tejani Ghanshyam G.
Published: November 2024

Background/Objectives: Accurate classification in cancer research is vital for devising effective treatment strategies. Precise cancer classification depends significantly on selecting the most informative genes from high-dimensional datasets, a task made complex by the extensive data involved. This study introduces the Two-stage MI-PSA Gene Selection algorithm, a novel approach designed to enhance cancer classification accuracy through robust gene selection methods. Methods: The proposed method integrates Mutual Information (MI) and Particle Swarm Optimization (PSO) for gene selection. In the first stage, MI acts as an initial filter, identifying genes rich in cancer-related information. In the second stage, PSO refines this selection to pinpoint an optimal subset of genes for accurate classification. Results: The experimental findings reveal that the MI-PSA method achieves a best classification accuracy of 99.01% with a selected subset of 19 genes, substantially outperforming the MI and SVM methods, which attain best accuracies of 93.44% and 91.26%, respectively, for the same gene count. Furthermore, MI-PSA demonstrates superior performance in terms of average and worst-case accuracy, underscoring its robustness and reliability. Conclusions: The MI-PSA algorithm presents a powerful approach for identifying critical genes essential for precise cancer classification, advancing both our understanding and management of this complex disease.
Should the Faecal Microbiota Composition Be Determined to Certify a Faecal Donor?
Authors: Morales Celia , Ballestero Luna , del Río Patricia , Barbero-Herranz Raquel , Olavarrieta Leticia , Gómez-Artíguez Leticia , Galeano Javier , Avendaño-Ortiz José , Basterra Juan , del Campo Rosa
Published: November 2024

Background/Objectives: Faecal microbiota transplantation (FMT) is considered a safe and effective therapy for recurrent Clostridioides difficile infection. It is the only current clinical indication for this technique, although numerous clinical research studies and trials propose its potential usefulness for treating other pathologies. Donor selection is a very rigorous process, based on a personal lifestyle interview and the absence of known pathogens in faeces and serum, leading to only a few volunteers finally achieving the corresponding certification. However, despite the high amount of data generated from the ongoing research studies relating microbiota and health, there is not yet a consensus defining what is a “healthy” microbiota. To date, knowledge of the composition of the microbiota is not a requirement to be a faecal donor. The aim of this work was to evaluate whether the analysis of the composition of the microbiota by massive sequencing of 16S rDNA could be useful in the selection of the faecal donors. Methods: Samples from 10 certified donors from Mikrobiomik Healthcare Company were collected and sequenced using 16S rDNA in a MiSeq (Illumina) platform. Alpha (Chao1 and Shannon indices) and beta diversity (Bray–Curtis) were performed using the bioinformatic web server Microbiome Analyst. The differences in microbial composition at the genera and phyla levels among the donors were evaluated. Results: The microbial diversity metric by alpha diversity indexes showed that most donors exhibited a similar microbial diversity and richness, whereas beta diversity by 16S rDNA sequencing revealed significant inter-donor differences, with a more stable microbial composition over time in some donors. The phyla Bacillota and Bacteroidota were predominant in all donors, while the density of other phyla, such as Actinomycota and Pseudomonota, varied among individuals. Each donor exhibited a characteristic genera distribution pattern; however, it was possible to define a microbiome core consisting of the genera Agathobacter, Eubacterium, Bacteroides, Clostridia UCG-014 and Akkermansia. Conclusions: The results suggest that donor certification does not need to rely exclusively on their microbiota composition, as it is unique to each donor. While one donor showed greater microbial diversity and richness, clear criteria for microbial normality and health have yet to be established. Therefore, donor certification should focus more on clinical and lifestyle aspects.
Evaluating the Accessibility and Inclusiveness of Community Playgrounds in Australia for Children with Disabilities
Authors: Glass Courtney , Flemmer Claire
Published: November 2024

Play is vital to the wellbeing of children, but playgrounds can be inaccessible to children with disabilities and limit their physical and social participation. This study evaluated the accessibility/inclusivity of twenty-five free-entry playgrounds in two Australian states and investigated whether location, construction age, and regional socioeconomic status affected accessibility/inclusivity. The New South Wales Government’s Everyone Can Play: Playspace Evaluation Checklist was used as the assessment tool with a maximum score of 156 points. Playground scores ranged from 29% to 96% of available points. The average score for destination playgrounds was significantly higher (78.0% ± 16.6%) than that for neighborhood playgrounds (54.5% ± 20.7%). Modern playgrounds generally scored higher than older playgrounds. Regional socioeconomic status did not correlate with playground score. The audit tool inadequately addresses the needs of children with disability, allocating just 24 points (15.4%) to features they need and mostly considering mobility impairment. More inclusive audit tools that capture the needs of a wide range of impairment should be developed. Main areas for improving playground accessibility include wayfinding, layout, signage (including braille, auditory, and pictorial information), and sensory and cognitive play opportunities. These findings are useful for stakeholders involved in the design, provision, and maintenance of community playgrounds.
The Two-Spin Enigma: From the Helium Atom to Quantum Ontology
Authors: Grangier Philippe , Auffèves Alexia , Farouki Nayla , Van Den Bossche Mathias , Ezratty Olivier
Published: November 2024

The purpose of this article is to provide a novel approach and justification of the idea that classical physics and quantum physics can neither function nor even be conceived without the other—in line with ideas attributed to, e.g., Niels Bohr or Lev Landau. Though this point of view may contradict current common wisdom, we will show that it perfectly fits with empirical evidence, and can be maintained without giving up physical realism. In order to place our arguments in a convenient historical perspective, we will proceed as if we were following the path of a scientific investigation about the demise, or vanishing, of some valuable properties of the two electrons in the helium atom. We will start from experimentally based evidence in order to analyze and explain the physical facts, moving cautiously from a classical to a quantum description, without mixing them up. The overall picture will be that the physical properties of microscopic systems are quantized, as initially shown by Planck and Einstein, and that they are also contextual, i.e., they can be given a physical sense only by embedding a microscopic system within a macroscopic measurement context.
Control Power in Continuous Variable Controlled Quantum Teleportation
Authors: Tian Yuehan , Cai Dunbo , Gong Nengfei , Li Yining , Qian Ling , Zhang Runqing , Huang Zhiguo , Wang Tiejun
Published: November 2024

Controlled quantum teleportation is an important extension of multipartite quantum teleportation, which plays an indispensable role in building quantum networks. Compared with discrete variable counterparts, continuous variable controlled quantum teleportation can generate entanglement deterministically and exhibit higher superiority of the supervisor’s authority. Here, we define a measure to quantify the control power in continuous variable controlled quantum teleportation via Greenberger–Horne–Zeilinger-type entangled coherent state channels. Our results show that control power in continuous variable controlled quantum teleportation increases with the mean photon number of coherent states. Its upper bound is 1/2, which exceeds the upper bound in discrete variable controlled quantum teleportation (1/3). The robustness of the protocol is analyzed with photon absorption. The results show that the improving ability of the control power will descend by the increasing photon loss, with the upper bound unchanged and robust. Our results illuminate the role of control power in multipartite continuous variable quantum information processing and provide a criterion for evaluating the quality of quantum communication networks.
Conceptions Connected to Early Academic Studies in Computer Science: Focusing on Students, Lecturers, and Parents
Authors: Miller-Markovitz Lior , Landau Gad M. , Haran Daphna , Leikin Roza
Published: November 2024

The goal of this study was to identify conceptions towards early academic studies in computer science. We focus on a program which offers high school students the unique opportunity to earn a B.Sc. degree in parallel to their studies, resulting in them holding a prestigious degree at an early age. Activity theory framed the design of this study. Fifteen voluntary participants representing three distinct research groups participated in this study: students, parents, and lecturers. The data were collected using a qualitative research paradigm through semi-structured interviews. The findings demonstrated that the research groups mostly held distinctive conceptions. Little similarity may be detected. We argue that high school students are more likely to succeed in early academic programs when they have a rigorous curriculum, an on-staff educational consultant, and lessons that are exclusively attended by other students in their peer group. These types of programs, in our opinion, are well positioned to develop exceptional and gifted individuals’ educational potential.
EiGC: An Event-Induced Graph with Constraints for Event Causality Identification
Authors: Zeng Xi , Bai Zhixin , Qin Ke , Luo Guangchun
Published: November 2024

Event causality identification (ECI) focuses on detecting causal relationships between events within a document. Existing approaches typically treat each event-mention pair independently, overlooking the relational dynamics and potential conflicts among event causalities. To tackle this challenge, we propose the Event-induced Graph with Constraints (EiGC), which models the complex event-level causal structures in a more realistic manner, facilitating comprehensive causal relation identification. To be more specific, we construct a graph based on diverse event-driven knowledge sources, such as coreference and co-occurrence relations. A graph convolutional network (GCN) is then employed to encode these structural features, effectively capturing both local and global dependencies between nodes. Additionally, we implement event-aware constraints through integer linear programming, incorporating the principles of uniqueness, non-reflexivity, and coreference consistency in event-causal relationships. This approach ensures logical consistency and prevents conflicts in the prediction outcomes. Experimental results on three widely used datasets illustrate that our proposed EiGC approach achieves excellent performance among all the baseline models.
A Receiver-Driven Named Data Networking (NDN) Congestion Control Method Based on Reinforcement Learning
Authors: Zheng Ruijuan , Zhang Bohan , Zhao Xuhui , Wang Lin , Wu Qingtao
Published: November 2024

Named data networking (NDN) is a novel networking paradigm characterized by in-network caching, receiver-driven communication, and multi-source, multi-path data retrieval, which poses new challenges for congestion control. Existing work has largely focused on receiver-driven mechanisms. Due to delays in obtaining network control information (timeouts, NACKs) within NDN, consumers are unable to access the network congestion status from this information in a timely manner. To address the issues above, this paper combines the Q-learning algorithm with the NDN architecture, proposing Q-NDN. In Q-NDN, consumers can dynamically adjust the congestion window (cwnd) through the real-time monitoring of network status, leveraging the Q-learning algorithm, achieving automatic congestion control for the NDN architecture. Additionally, this paper introduces content popularity-based traffic scheduling for multi-user scenarioswhich adjusts the transmission rates of content with different popularity levels to maintain a dynamic balance in the network. The experimental results show that Q-NDN can converge quickly, make full use of bandwidth resources, and keep the packet loss rate to 0 in the basic network topology. In competing network topologies, Q-NDN can rapidly address conflict issues, efficiently utilize bandwidth resources, and maintain a relatively low packet loss rate.
MSTrans: Multi-Scale Transformer for Building Extraction from HR Remote Sensing Images
Authors: Yang Fei , Jiang Fenlong , Li Jianzhao , Lu Lei
Published: November 2024

Buildings are one of the most important goals of human transformation of the Earth’s surface. Therefore, building extraction (BE), such as in urban resource management and planning, is a task that is meaningful to actual production and life. Computational intelligence techniques based on convolutional neural networks (CNNs) and Transformers have begun to be of interest in BE, and have made some progress. However, the BE methods based on CNNs are limited by the difficulty in capturing global long-range relationships, while Transformer-based methods are often not detailed enough for pixel-level annotation tasks because they focus on global information. To conquer the limitations, a multi-scale Transformer (MSTrans) is proposed for BE from high-resolution remote sensing images. In the proposed MSTrans, we develop a plug-and-play multi-scale Transformer (MST) module based on atrous spatial pyramid pooling (ASPP). The MST module can effectively capture tokens of different scales through the Transformer encoder and Transformer decoder. This can enhance multi-scale feature extraction of buildings, thereby improving the BE performance. Experiments on three real and challenging BE datasets verify the effectiveness of the proposed MSTrans. While the proposed approach may not achieve the highest Precision and Recall accuracies compared with the seven benchmark methods, it improves the overall metrics F1 and mIoU by 0.4% and 1.67%, respectively.
MultiTagging: A Vulnerable Smart Contract Labeling and Evaluation Framework
Authors: Alsunaidi Shikah J. , Aljamaan Hamoud , Hammoudeh Mohammad
Published: November 2024

Identifying vulnerabilities in Smart Contracts (SCs) is crucial, as they can lead to significant financial losses if exploited. Although various SC vulnerability identification methods exist, selecting the most effective approach remains challenging. This article examines these challenges and introduces solutions to enhance SC vulnerability identification. It introduces MultiTagging, a modular SC multi-labeling framework designed to overcome limitations in existing SC vulnerability identification approaches. MultiTagging automates SC vulnerability tagging by parsing analysis reports and mapping tool-specific tags to standardized labels, including SC Weakness Classification (SWC) codes and Decentralized Application Security Project (DASP) ranks. Its mapping strategy and the proposed vulnerability taxonomy resolve tool-level labeling inconsistencies, where different tools use distinct labels for identical vulnerabilities. The framework integrates an evaluation module to assess SC vulnerability identification methods. MultiTagging enables both tool-based and vote-based SC vulnerability labeling. To improve labeling accuracy, the article proposes Power-based voting, a method that systematically defines voter roles and voting thresholds for each vulnerability. MultiTagging is used to evaluate labeling across six tools: MAIAN, Mythril, Semgrep, Slither, Solhint, and VeriSmart. The results reveal high coverage for Mythril, Slither, and Solhint, which identified eight, seven, and six DASP classes, respectively. Tool performance varied, underscoring the impracticality of relying on a single tool to identify all vulnerability classes. A comparative evaluation of Power-based voting and two threshold-based methods—AtLeastOne and Majority voting—shows that while voting methods can increase vulnerability identification coverage, they may also reduce detection performance. Power-based voting proved more effective than pure threshold-based methods across all vulnerability classes.
Short-Term Wind Power Forecasting Based on OMNIC and Adaptive Fractional Order Generalized Pareto Motion Model
Authors: Cai Fan , Chen Dongdong , Jiang Yuesong , Zhu Tongbo
Published: November 2024

With the rapid development of renewable energy, accurately forecasting wind power is crucial for the stable operation of power systems and effective energy management. This paper proposes a short-term wind power forecasting method based on the Orthogonalized Maximal Information Coefficient (OMNIC) combined with an Adaptive fractional Generalized Pareto motion (fGPm) model. The method quantifies the influence of meteorological factors on wind power prediction and identifies the optimal set and number of influencing factors. The model accounts for long-range dependence (LRD) in time series data and constructs an uncertainty model using the properties and parameters of the fractional generalized Pareto distribution (GPD), significantly improving prediction accuracy under nonlinear conditions. The proposed approach was validated using a real dataset from a wind farm in northwest China and compared with other models such as Convolutional Neural Network-Long Short-Term Memory (CNN-LSTM) and Convolutional Neural Network-Gated Recurrent Unit (CNN-GRU). Results show that the adaptive fGPm model reduces RMSE by 0.448 MW and 0.466 MW, MAPE by 6.936% and 9.702%, and achieves an average R2 of 0.9826 compared to CNN-GRU and CNN-LSTM. The improvement is due to the dynamic adjustment to data trends and effective use of LRD features. This method provides practical value in improving wind power prediction accuracy and addressing grid integration and regulation challenges.
Dynamic Prediction of Proton-Exchange Membrane Fuel Cell Degradation Based on Gated Recurrent Unit and Grey Wolf Optimization
Authors: Wang Xiangdong , Huang Zerong , Zhang Daxing , Yuan Haoyu , Cai Bingzi , Liu Hanlin , Wang Chunsheng , Cao Yuan , Zhou Xinyao , Dong Yaolin
Published: November 2024

This paper addresses the challenge of degradation prediction in proton-exchange membrane fuel cells (PEMFCs). Traditional methods often struggle to balance accuracy and complexity, particularly under dynamic operational conditions. To overcome these limitations, this study proposes a data-driven approach based on the gated recurrent unit (GRU) neural network, optimized by the grey wolf optimizer (GWO). The integration of the GWO automates the hyperparameter tuning process, enhancing the predictive performance of the GRU network. The proposed GWO-GRU method was validated utilizing actual PEMFC data under dynamic load conditions. The results demonstrate that the GWO-GRU method achieves superior accuracy compared to other standard methods. The method offers a practical solution for online PEMFC degradation prediction, providing stable and accurate forecasting for PEMFC systems in dynamic environments.
Building the Future: Integrating Phase Change Materials in Network of Nanogrids (NoN)
Authors: Kalair Ali , Jamei Elmira , Seyedmahmoudian Mehdi , Mekhilef Saad , Abas Naeem
Published: November 2024

Buildings consume 10% of global energy and 50% of global electricity for heating and cooling. Transitioning to energy-efficient buildings is essential to address the global energy challenge and meet sustainable development goals (SDGs) to limit global temperature rise below 1.5 °C. The shift from traditional to smart grids has led to the development of micro, milli, and nanogrids, which share energy resources symbiotically and balance heating/cooling demands dealing with acute doldrums (dunkelflaute). This scoping review explores the methods by which phase change materials (PCMs) can be used in residential buildings to form a nanogrid. This review examines the components and concepts that promote the seamless integration of PCMs in residential houses. It also discusses the key challenges (e.g., scalability, stability, and economic feasibility in high summer temperatures), proposing the community-scale network of nanogrids (NoN) and the potential of thermochromic and photochromic materials. The findings of this review highlight the importance of latent heat storage methods and ingenious grid architectures such as nanogrids to construct resilient and sustainable houses in the future and thereby offer practical insights for policymakers and industries in the energy sector.
Power System Transient Stability Assessment Based on Intelligent Enhanced Transient Energy Function Method
Authors: Mo Tianxiao , Liu Jun , Liu Jiacheng , Wang Guangyao , Li Yuting , Lin Kaiwei
Published: November 2024

The development of power systems puts forward higher requirements for transient stability evaluations of power systems. The accuracy and timeliness of transient stability assessment are of great significance to the safe and stable operation of power systems. Traditional mechanistic judgment methods and criteria have strong interpretability, but they also have great limitations. They are still difficult to apply to complex power systems and are in urgent need of improvement. Artificial intelligence methods have high accuracy in stability judgment, but they have problems such as poor interpretability, and their stability judgment results are often difficult to explain. Based on the transient stability judgment mechanism of the response-driven transient energy function, this paper proposes a transient energy function stability judgment method based on a two-machine equivalent model and enhanced by a convolutional neural network. Firstly, the ST-kmeans method is used to cluster the generator sets, and the S-transformation is performed on the power angle changes of the generator sets to extract features. Then, the principal component analysis method is used to reduce the dimension of the feature data. Based on the k-means clustering method, the IEEE-39 node system generator synchronization units are grouped according to the power angle change trend of each generator after the fault. On the basis of the above methods, a two-machine equivalent model of the IEEE-39 node system is established, and the transient energy function of the two-machine system is derived. Based on the convolutional neural network, the critical energy is enhanced, and the fixed critical energy threshold is replaced by the corrected critical energy. The example results show that the transient stability prediction framework proposed in this paper can improve the scope of the application of mechanism discrimination and enhance the interpretability of the results of the intelligent method.
Cybersecurity in Smart Grids: Detecting False Data Injection Attacks Utilizing Supervised Machine Learning Techniques
Authors: Shees Anwer , Tariq Mohd , Sarwat Arif I.
Published: November 2024

By integrating advanced technologies and data-driven systems in smart grids, there has been a significant revolution in the energy distribution sector, bringing a new era of efficiency and sustainability. Nevertheless, with this advancement comes vulnerability, particularly in the form of cyber threats, which have the potential to damage critical infrastructure. False data injection attacks are among the threats to the cyber–physical layer of smart grids. False data injection attacks pose a significant risk, manipulating the data in the control system layer to compromise the grid’s integrity. An early detection and mitigation of such cyberattacks are crucial to ensuring the smart grid operates securely and reliably. In this research paper, we demonstrate different machine learning classification models for detecting false data injection attacks, including the Extra Tree, Random Forest, Extreme Gradient Boosting, Logistic Regression, Decision Tree, and Bagging Classifiers, to secure the integrity of smart grids. A comprehensive dataset of various attack scenarios provides insights to explore and develop effective detection models. Results show that the Extra Tree, Random Forest, and Extreme Gradient Boosting models’ accuracy in detecting the attack outperformed the existing literature, an achieving accuracy of 98%, 97%, and 97%, respectively.
Intelligent Energy Management Systems in Industry 5.0: Cybersecurity Applications in Examples
Authors: Wyrzykowska Barbara , Szczepaniuk Hubert , Szczepaniuk Edyta Karolina , Rytko Anna , Kacprzak Marzena
Published: November 2024

The article examines modern approaches to energy management in the context of the development of Industry 5.0 with a particular focus on cybersecurity. Key tenets of Industry 5.0 are discussed, including the integration of advanced technologies with intelligent energy management systems (IEMSs) and the growing need to protect data in the face of increasing cyber threats. The challenges faced by small and medium-sized enterprises (SMEs) using solutions based on renewable energy sources, such as photovoltaic farms, are also analyzed. The article presents examples of IEMS applications and discusses methods for securing these systems, offering an overview of cyber threat protection tools in the context of modern energy management. The analysis carried out provided information that will help businesses make rational decisions and contribute to shaping the state’s macroeconomic policy on cybersecurity and energy savings. The results of this research can also help develop more effective strategies for managing technology and IT infrastructure, which is crucial in the digital age of Industry 5.0.