bluuebunny/crossref_metadata_2025 huggingface dataset:
The Diversity and Gradient of Sand-Dune Vegetation at Kasatura Bay: A Plant Biodiversity Hotspot in European Türkiye
Authors: Oral Dilek , Kavgacı Ali , Efe Asuman
Published: May 2024

The Kasatura Bay region is one of Türkiye’s Important Plant Areas and plant biodiversity hotspots. In this study, the diversity and gradient of the sand-dune vegetation in Kasatura Bay were studied. Vegetation sampling was carried out by using the Braun-Blanquet method. The vegetation dataset was recorded in the TURBOVEG database management program. For classification, Beta-Flexible clustering (β = −0.25) and the correlation similarity index were used in the PC-ORD program. Diagnostic species of the communities were determined in the JUICE program using the φ-coefficient (higher than 0.30). Detrended Canonical Correspondence analysis was applied to data in the CANOCO program to understand the effect of ecological factors on vegetation diversity. Ellenberg ecological indicator values were used as the ecological variables. As a result, seven different plant communities were identified at the Kasatura Bay sand dunes. A new association of Sileno thymifoliae–Cionuretum erectae ass. nova was identified under the alliance Sileno thymifoliae–Jurineion kilaeae. The sand-dune vegetation represents high diversity, also including endemic plants, some of which are globally threatened. Due to all this diversity, settled on sensitive conditions, conservation strategies need to be developed to protect and ensure the continuity of Kasatura Bay sand-dune vegetation in the face of intense human pressure.
De-Anonymizing Users across Rating Datasets via Record Linkage and Quasi-Identifier Attacks
Authors: Torres Nicolás , Olivares Patricio
Published: May 2024

The widespread availability of pseudonymized user datasets has enabled personalized recommendation systems. However, recent studies have shown that users can be de-anonymized by exploiting the uniqueness of their data patterns, raising significant privacy concerns. This paper presents a novel approach that tackles the challenging task of linking user identities across multiple rating datasets from diverse domains, such as movies, books, and music, by leveraging the consistency of users’ rating patterns as high-dimensional quasi-identifiers. The proposed method combines probabilistic record linkage techniques with quasi-identifier attacks, employing the Fellegi–Sunter model to compute the likelihood of two records referring to the same user based on the similarity of their rating vectors. Through extensive experiments on three publicly available rating datasets, we demonstrate the effectiveness of the proposed approach in achieving high precision and recall in cross-dataset de-anonymization tasks, outperforming existing techniques, with F1-scores ranging from 0.72 to 0.79 for pairwise de-anonymization tasks. The novelty of this research lies in the unique integration of record linkage techniques with quasi-identifier attacks, enabling the effective exploitation of the uniqueness of rating patterns as high-dimensional quasi-identifiers to link user identities across diverse datasets, addressing a limitation of existing methodologies. We thoroughly investigate the impact of various factors, including similarity metrics, dataset combinations, data sparsity, and user demographics, on the de-anonymization performance. This work highlights the potential privacy risks associated with the release of anonymized user data across diverse contexts and underscores the critical need for stronger anonymization techniques and tailored privacy-preserving mechanisms for rating datasets and recommender systems.
Applying the German S2k-Guideline for Diagnosis and Treatment of Spondylodiscitis—A 5-Year Retrospective Evaluation of Patients without Neurological Symptoms
Authors: Kolster Moritz , Hönning Alexander , Käckenmester Wiebke , Goy Janet , Ekkernkamp Axel , Spranger Nikolai
Published: May 2024

Spondylodiscitis is a rather rare condition with an annual incidence of 1–7 per 100,000. Thus, empirical data on the treatment of this disease are limited. In 2020, the first German guideline for the diagnosis and treatment of spondylodiscitis was published. In a 5-year retrospective analysis, we examined the patient collective, the current diagnosis and treatment strategy, and the effect of Magnetic Resonance Imaging (MRI) diagnostics on therapeutic decisions of a consecutive monocentric cohort of 66 patients without neurological symptoms. The majority of the patients were male (55%) with a mean age of 74 years. Non-operative therapy was found to be associated with short-term treatment success in 54 (82%) of the patients. In 12 patients, who underwent surgical therapy, MRI diagnostics and clinical findings were equally important for the decision to perform a surgery. Patients treated operatively stayed for an average of 33.6 (±12.9) days in the hospital and thus significantly longer than non-operatively treated patients with 22.2 (±8.0) days. The in-house standard of care did not essentially deviate from the guideline’s recommendations. Future research should address early detection of the need for surgical therapy, and immediate anti-infective treatment appropriate to the detected pathogen.
Understanding the Dynamics of Inflammatory Cytokines in Endodontic Diagnosis: A Systematic Review
Authors: Barbero-Navarro Ignacio , Irigoyen-Camacho Maria Esther , Zepeda-Zepeda Marco Antonio , Ribas-Perez David , Castaño-Seiquer Antonio , Sofian-Pauliuc Iuliana
Published: May 2024

The primary aim of this literature review is to delineate the key inflammatory cytokines involved in the pathophysiology of pulp inflammation. By elucidating the roles of these cytokines, a deeper comprehension of the distinct stages of inflamed pulp can be attained, thereby facilitating more accurate diagnostic strategies in endodontics. The PRISMA statement and Cochrane handbook were used for the search strategy. The keywords were created based on the review question using the PICO framework. The relevant studies were meticulously assessed according to predefined inclusion and exclusion criteria for this systematic review. A rigorous quality checklist was implemented to evaluate each included study, ensuring scrutiny for both quality and risk-of-bias assessments. The initial pilot search conducted on PubMed, Scopus, Cochrane, and WoS databases yielded 9 pertinent articles. Within these articles, multiple cytokines were identified and discussed as potential candidates for use in endodontic diagnosis, notably including IL-8, IL-6, TNF-α, and IL-2. These cytokines have been highlighted due to their significant roles in the inflammatory processes associated with pulp pathology. The identification of specific inflammatory cytokines holds promise for enhancing endodontic diagnostic procedures and exploring diverse treatment modalities. However, the current body of research in this area remains limited. Further comprehensive studies are warranted to fully elucidate the potential of cytokines in refining diagnostic techniques in endodontics.
Hypertriglyceridemia-Induced Acute Pancreatitis—The Milky Way Constellation—The Seven-Year Experience of a Large Tertiary Centre
Authors: Edu Andrei Vicențiu , Pahomeanu Mihai Radu , Ghiță Andreea Irina , Constantinescu Dalia Ioana , Grigore Daniela Gabriela , Bota Andreea Daniela , Luta-Dumitrașcu Daniela Maria , Țieranu Cristian George , Negreanu Lucian
Published: May 2024

(1) Background: Hypertriglyceridemia (HTG) is a well-known metabolic condition associated with an increased risk of acute pancreatitis. In this study, we tried to establish whether there are any significant disparities concerning recurrence rate, intensive care unit (ICU) admission, hospital (ICU and total) length of stay (LoS), morphology, severity and age between HTG-induced acute pancreatitis and any other known cause of pancreatitis (OAP). (2) Methods: The research was a retrospective unicentric cohort study, using information from the Bucharest Acute Pancreatitis Index (BUC-API) registry, a database of 1855 consecutive cases of acute pancreatitis. (3) Results: We found a weak association between HTG-AP and recurrence. The HTG-AP patients were younger, with a median of 44.5 years, and had a longer ICU stay than the OAP patients. In addition, we identified that the HTG-AP patients were more likely to develop acute peripancreatic fluid collection (APFC), to be admitted in ICU, to have a more severe course of disease and to be cared for in a gastroenterology ward. (4) Conclusions: Hypertriglyceridemia-induced APs have a more severe course. The typical patient with HTG-AP is a middle-aged male, with previous episodes of AP, admitted in the gastroenterology ward, with a longer ICU stay and longer length of hospitalization, more likely to evolve in a severe acute pancreatitis (SAP) and with a higher probability of developing APFC.
Correction: Díaz-Aljaro et al. Quantitative Microvascular Change Analysis Using a Semi-Automated Algorithm in Macula-on Rhegmatogenous Retinal Detachment Assessed by Swept-Source Optical Coherence Tomography Angiography. Diagnostics 2024, 14, 750
Authors: Díaz-Aljaro Pablo , Valldeperas Xavier , Broc-Iturralde Laura , Romanic-Bubalo Nevena , Díaz-Aljaro Ignacio , Chu Zhongdi , Wang Ruikang K. , Zarranz-Ventura Javier
Published: May 2024

In the original publication [...]
Correlation between Peripheric Blood Markers and Surgical Invasiveness during Humeral Shaft Fracture Osteosynthesis in Young and Middle-Aged Patients
Authors: Moldovan Flaviu
Published: May 2024

The treatment for humeral shaft fractures (HSFs) is still controversial, consisting of a wide variety of orthopedic osteosynthesis materials that imply different grades of invasiveness. The aim of this study is to investigate the correlation between inflammatory blood-derived markers and the magnitude of the surgical procedure in young and middle-aged patients who sustained these fractures. Observational, retrospective research was conducted between January 2018 and December 2023. It followed patients diagnosed with recent HFSs (AO/OTA 12−A and B) and followed operative treatment. They were split in two groups, depending on the surgical protocol: group A, operated by closed reduction and internal fixation (CRIF) with intramedullary nails (IMNs), and group B, operated by open reduction and internal fixation (ORIF) with dynamic compression plates (DCPs). Statistically significant differences (p < 0.05) between the two groups could be observed in injury on the basis of surgery durations, surgical times, pre- and postoperative neutrophil-per-lymphocyte ratio (NLR), postoperative platelet-per-lymphocyte ratio (PLR), monocyte-per-lymphocyte ratio (MLR), systemic inflammatory index (SII), systemic inflammatory response index (SIRI) and aggregate inflammatory systemic index (AISI). The multivariate regression model proposed revealed that NLR > 7.99 (p = 0.007), AISI > 1668.58 (p = 0.008), and the surgical times (p < 0.0001) are strongly correlated to the magnitude of the surgical protocol followed. Using receiver operating characteristic (ROC) curve analysis, a balanced reliability was determined for both postoperative NLR > 7.99 (sensitivity 75.0% and specificity 75.6) and AISI > 1668.58 (sensitivity 70.6% and specificity 82.2%). Postoperative NLR and AISI as inflammatory markers are highly associated with the magnitude of surgical trauma sustained during humeral shaft fracture osteosynthesis in a younger population.
Beyond Flight: Enhancing the Internet of Drones with Blockchain Technologies
Authors: Tychola Kyriaki A. , Voulgaridis Konstantinos , Lagkas Thomas
Published: May 2024

The Internet of Drones (IoD) is a decentralized network linking drones’ access to controlled airspace, providing high adaptability to complex scenarios and services to various drone applications, such as package delivery, traffic surveillance, and rescue, including navigation services. Unmanned Aerial Vehicles (UAVs), combined with IoD principles, offer numerous strengths, e.g., high mobility, wireless coverage areas, and the ability to reach inaccessible locations, including significant improvements such as reliability, connectivity, throughput, and decreased delay. Additionally, emerging blockchain solutions integrated within the concept of the IoD enable effective outcomes that surpass traditional security approaches, while enabling decentralized features for smart human-centered applications. Nevertheless, the combination of the IoD and blockchain faces many challenges with emerging open issues that require further investigation. In this work, we thoroughly survey the technological concept of the IoD and fundamental aspects of blockchain, while investigating its contribution to current IoD practices, the impact of novel enabling technologies, and their active role in the combination of the corresponding synergy. Moreover, we promote the combination of the two technologies by researching their collaborative functionality through different use cases and application fields that implement decentralized IoD solutions and highlighting their indicative benefits, while discussing important challenges and future directions on open issues.
Global Semantic-Sense Aggregation Network for Salient Object Detection in Remote Sensing Images
Authors: Li Hongli , Chen Xuhui , Yang Wei , Huang Jian , Sun Kaimin , Wang Ying , Huang Andong , Mei Liye
Published: May 2024

Salient object detection (SOD) aims to accurately identify significant geographical objects in remote sensing images (RSI), providing reliable support and guidance for extensive geographical information analyses and decisions. However, SOD in RSI faces numerous challenges, including shadow interference, inter-class feature confusion, as well as unclear target edge contours. Therefore, we designed an effective Global Semantic-aware Aggregation Network (GSANet) to aggregate salient information in RSI. GSANet computes the information entropy of different regions, prioritizing areas with high information entropy as potential target regions, thereby achieving precise localization and semantic understanding of salient objects in remote sensing imagery. Specifically, we proposed a Semantic Detail Embedding Module (SDEM), which explores the potential connections among multi-level features, adaptively fusing shallow texture details with deep semantic features, efficiently aggregating the information entropy of salient regions, enhancing information content of salient targets. Additionally, we proposed a Semantic Perception Fusion Module (SPFM) to analyze map relationships between contextual information and local details, enhancing the perceptual capability for salient objects while suppressing irrelevant information entropy, thereby addressing the semantic dilution issue of salient objects during the up-sampling process. The experimental results on two publicly available datasets, ORSSD and EORSSD, demonstrated the outstanding performance of our method. The method achieved 93.91% Sα, 98.36% Eξ, and 89.37% Fβ on the EORSSD dataset.
Impact of Combined Modulation of Two Potassium Ion Currents on Spiral Waves and Turbulent States in the Heart
Authors: Bai Jing , Zhang Chunfu , Liang Yanchun , Tavares Adriano , Wang Lidong
Published: May 2024

In the realm of cardiac research, the control of spiral waves and turbulent states has been a persistent focus for scholars. Among various avenues of investigation, the modulation of ion currents represents a crucial direction. It has been proved that the methods involving combined control of currents are superior to singular approaches. While previous studies have proposed some combination strategies, further reinforcement and supplementation are required, particularly in the context of controlling arrhythmias through the combined regulation of two potassium ion currents. This study employs the Luo–Rudy phase I cardiac model, modulating the maximum conductance of the time-dependent potassium current and the time-independent potassium current, to investigate the effects of this combined modulation on spiral waves and turbulent states. Numerical simulation results indicate that, compared to modulating a single current, combining reductions in the conductance of two potassium ion currents can rapidly control spiral waves and turbulent states in a short duration. This implies that employing blockers for both potassium ion currents concurrently represents a more efficient control strategy. The control outcomes of this study represent a novel and effective combination for antiarrhythmic interventions, offering potential avenues for new antiarrhythmic drug targets.
Quantum Authentication Evolution: Novel Approaches for Securing Quantum Key Distribution
Authors: Termos Hassan
Published: May 2024

This study introduces a novel approach to bolstering quantum key distribution (QKD) security by implementing swift classical channel authentication within the SARG04 and BB84 protocols. We propose mono-authentication, a pioneering paradigm employing quantum-resistant signature algorithms—specifically, CRYSTALS-DILITHIUM and RAINBOW—to authenticate solely at the conclusion of communication. Our numerical analysis comprehensively examines the performance of these algorithms across various block sizes (128, 192, and 256 bits) in both block-based and continuous photon transmission scenarios. Through 100 iterations of simulations, we meticulously assess the impact of noise levels on authentication efficacy. Our results notably highlight CRYSTALS-DILITHIUM’s consistent outperformance of RAINBOW, with signature overheads of approximately 0.5% for the QKD-BB84 protocol and 0.4% for the QKD-SARG04 one, when the quantum bit error rate (QBER) is augmented up to 8%. Moreover, our study unveils a correlation between higher security levels and increased authentication times, with CRYSTALS-DILITHIUM maintaining superior efficiency across all key rates up to 10,000 kb/s. These findings underscore the substantial cost and complexity reduction achieved by mono-authentication, particularly in noisy environments, paving the way for more resilient and efficient quantum communication systems.
A Direct Entropic Approach to the Thermal Balance of Spontaneous Chemical Reactions
Authors: D’Anna Michele , Lubini Paolo , Fuchs Hans U. , Corni Federico
Published: May 2024

When working with, and learning about, the thermal balance of a chemical reaction, we need to consider two overlapping but conceptually distinct aspects: one relates to the process of reallocating entropy between reactants and products (because of different specific entropies of the new substances compared to those of the old), and the other to dissipative processes. Together, they determine how much entropy is exchanged between the chemicals and their environment (i.e., in heating and cooling). By making explicit use of (a) the two conjugate pairs chemical amount (i.e., amount of substance) and chemical potential, and entropy and temperature, respectively, (b) the laws of balance of amount of substance on the one hand and entropy on the other, and (c) a generalized approach to the energy principle, it is possible to create both imaginative and formal conceptual tools for modeling thermal balances associated with chemical transformations in general and exothermic and endothermic reactions in particular. In this paper, we outline the concepts and relations needed for a direct approach to chemical and thermal dynamics, create a model of exothermic and endothermic reactions, including numerical examples, and discuss how to relate the direct entropic approach to traditional models of these phenomena.
Extended Regression Analysis for Debye–Einstein Models Describing Low Temperature Heat Capacity Data of Solids
Authors: Gamsjäger Ernst , Wiessner Manfred
Published: May 2024

Heat capacity data of many crystalline solids can be described in a physically sound manner by Debye–Einstein integrals in the temperature range from 0K to 300K. The parameters of the Debye–Einstein approach are either obtained by a Markov chain Monte Carlo (MCMC) global optimization method or by a Levenberg–Marquardt (LM) local optimization routine. In the case of the MCMC approach the model parameters and the coefficients of a function describing the residuals of the measurement points are simultaneously optimized. Thereby, the Bayesian credible interval for the heat capacity function is obtained. Although both regression tools (LM and MCMC) are completely different approaches, not only the values of the Debye–Einstein parameters, but also their standard errors appear to be similar. The calculated model parameters and their associated standard errors are then used to derive the enthalpy, entropy and Gibbs energy as functions of temperature. By direct insertion of the MCMC parameters of all 4·105 computer runs the distributions of the integral quantities enthalpy, entropy and Gibbs energy are determined.
Combining Exergy and Pinch Analysis for the Operating Mode Optimization of a Steam Turbine Cogeneration Plant in Wonji-Shoa, Ethiopia
Authors: Sharew Shumet Sendek , Di Pretoro Alessandro , Yimam Abubeker , Negny Stéphane , Montastruc Ludovic
Published: May 2024

In this research, the simulation of an existing 31.5 MW steam power plant, providing both electricity for the national grid and hot utility for the related sugar factory, was performed by means of ProSimPlus® v. 3.7.6. The purpose of this study is to analyze the steam turbine operating parameters by means of the exergy concept with a pinch-based technique in order to assess the overall energy performance and losses that occur in the power plant. The combined pinch and exergy analysis (CPEA) initially focuses on the depiction of the hot and cold composite curves (HCCCs) of the steam cycle to evaluate the energy and exergy requirements. Based on the minimal approach temperature difference (∆Tlm) required for effective heat transfer, the exergy loss that raises the heat demand (heat duty) for power generation can be quantitatively assessed. The exergy composite curves focus on the potential for fuel saving throughout the cycle with respect to three possible operating modes and evaluates opportunities for heat pumping in the process. Well-established tools, such as balanced exergy composite curves, are used to visualize exergy losses in each process unit and utility heat exchangers. The outcome of the combined exergy–pinch analysis reveals that energy savings of up to 83.44 MW may be realized by lowering exergy destruction in the cogeneration plant according to the operating scenario.
Systems and Methods for Transformation and Degradation Analysis
Authors: Osara Jude A. , Bryant Michael D.
Published: May 2024

Modern concepts in irreversible thermodynamics are applied to system transformation and degradation analyses. Phenomenological entropy generation (PEG) theorem is combined with the Degradation-Entropy Generation (DEG) theorem for instantaneous multi-disciplinary, multi-scale, multi-component system characterization. A transformation-PEG theorem and space materialize with system and process defining elements and dimensions. The near-100% accurate, consistent results and features in recent publications demonstrating and applying the new TPEG methods to frictional wear, grease aging, electrochemical power system cycling—including lithium-ion battery thermal runaway—metal fatigue loading and pump flow are collated herein, demonstrating the practicality of the new and universal PEG theorem and the predictive power of models that combine and utilize both theorems. The methodology is useful for design, analysis, prognostics, diagnostics, maintenance and optimization.
Financial and Oil Market’s Co-Movements by a Regime-Switching Copula
Authors: Soury Manel
Published: May 2024

Over the years, oil prices and financial stock markets have always had a complex relationship. This paper analyzes the interactions and co-movements between the oil market (WTI crude oil) and two major stock markets in Europe and the US (the Euro Stoxx 50 and the SP500) for the period from 1990 to 2023. For that, I use both the time-varying and the Markov copula models. The latter one represents an extension of the former one, where the constant term of the dynamic dependence parameter is driven by a hidden two-state first-order Markov chain. It is also called the dynamic regime-switching (RS) copula model. To estimate the model, I use the inference function for margins (IFM) method together with Kim’s filter for the Markov switching process. The marginals of the returns are modeled by the GARCH and GAS models. Empirical results show that the RS copula model seems adequate to measure and evaluate the time-varying and non-linear dependence structure. Two persistent regimes of high and low dependency have been detected. There was a jump in the co-movements of both pairs during high regimes associated with instability and crises. In addition, the extreme dependence between crude oil and US/European stock markets is time-varying but also asymmetric, as indicated by the SJC copula. The correlation in the lower tail is higher than that in the upper. Hence, oil and stock returns are more closely joined and tend to co-move more closely together in bullish periods than in bearish periods. Finally, the dependence between WTI crude oil and the SP500 stock index seems to be more affected by exogenous shocks and instability than the oil and European stock markets.
Intangible and Tangible Investments and Future Earnings Volatility
Authors: Elkemali Taoufik
Published: May 2024

This study delves into the impact of intangible and tangible investments on future earnings volatility within the European financial market context. Drawing from International Accounting Standards (IAS) 16 and 38, we examine the intricate relationship between fixed assets, expenses, and the uncertainty surrounding forthcoming earnings. Our analysis reveals that intangible assets, often associated with heightened uncertainty and risk, contribute to increased earnings volatility compared to capital expenditures. Furthermore, we find that capitalizing intangible assets serves to alleviate uncertainty, resulting in lower earnings volatility compared to expensing them. Our exploration of industries’ effects further reinforce these findings, with the effect of intangible and tangible investments on earnings volatility being more pronounced in high-tech industries than in low-tech industries. Additionally, our robustness test, utilizing goodwill as a proxy for intangible assets and property, plant, and equipment as a proxy for tangible assets, yields consistent results, further bolstering our findings.
Inclusive Education in Portuguese Higher Education: A Study on the Conceptual (In)Definition of Students in Institutional Documents
Authors: Figueiredo Mariana Calhau de , Coelho Orquídea , Veiga Amélia
Published: May 2024

Over time, inclusive education has evolved in the search for more appropriate designations for its target audience, aiming to promote inclusive language and combat prejudiced and discriminatory behavior. More current paradigms, such as the Universal Design for Learning and the Multilevel Approach, propose completely abandoning the use of designations for students (1). This qualitative study uses a documentary analysis to examine how the statutes of 26 Portuguese public higher education institutions (HEIs) designate students who need academic support and to understand the educational concepts associated with these designations (2). The results revealed that Portuguese HEIs still adopt various student designations, often based on a medical model of classification and diagnosis. Furthermore, these designations do not fully encompass academic diversity, nor do they consider the diverse needs that students may present throughout their academic journey, which consequently limits access for all to inclusive practices in higher education (3). Considering these findings, there is an urgent need to abandon student designations in Portuguese HEIs and promote inclusive practices that cater to everyone, ensuring that students remain in and succeed in higher education, regardless of medical validation (4).
Special Issue Introduction: Making Our Way—Rethinking and Disrupting Teacher Education
Authors: Mawhinney Lynnette , Wingfield Jhanae E.
Published: May 2024

PreK-12 curriculums change and evolve [...]
How Doctoral Students Understand Academic Identity in China: A Qualitative Study Based on the Grounded Theory
Authors: Zhang Huirui , Liu Lingyu , Li Xiaoxiao , Sun Yongzhen
Published: May 2024

The process of doctoral students transitioning from being knowledge learners to being knowledge researchers is beneficial for personal growth and career development. This study explores how doctoral students at Chinese research universities understand academic identity from a psychological perspective based on grounded theory. Understanding academic identity for doctoral students involves three psychological activities: situation recognition, psychological interaction, and reflective positioning. The sense of academic meaning and the sense of academic efficacy shape doctoral students’ understanding of academic identity, resulting in four patterns: Adeptness, Academic Pursuit, Alienation and Powerlessness, and Struggling for Meaning. Academic meaning serves as the internal driving force for developing academic identity, while academic efficacy acts as a psychological condition for maintaining academic identity. Therefore, it is crucial to enhance doctoral students’ recognition of academic meaning and academic efficacy.
Autistic Adults’ Reflections on What Supported Their Transitioning from Secondary School
Authors: White Louise Maree , Simpson Kate , Malone Stephanie , Adams Dawn
Published: May 2024

Identifying ways to improve transition support for autistic students exiting secondary school is an important research priority. However, few studies have consulted with autistic/autism community members to identify how to make this support effective. This co-produced study asked autistic adults to reflect on their own transition, with the aim of identifying what was helpful and effective in supporting their transitioning from secondary school. It also aimed to identify whether there were any differences for students from non-metropolitan areas. Ten autistic adults participated in semi-structured interviews conducted online or via telephone. Interview data were analysed using thematic analysis, which generated two themes: (1) accessing opportunities that supported decision making and (2) being able to develop the “skills of adulthood”. The findings suggest the importance of supporting all autistic students to explore multiple pathways to make well-informed post-school decisions. A holistic approach is needed to support autistic students in developing the logistical skills and independence needed to succeed in post-school life. This approach should include guidance from autistic role models. For autistic young people attending non-metropolitan schools, the impact of local cultures and additional logistical barriers indicate that they may need further tailored support to aid their transition to adulthood.
Deep Learning-Based Causal Inference Architecture and Algorithm between Stock Closing Price and Relevant Factors
Authors: Xing Wanqi , Chen Chi , Xue Lei
Published: May 2024

Numerous studies are based on the correlation among stock factors, which affects the measurement value and interpretability of such studies. Research on the causality among stock factors primarily relies on statistical models and machine learning algorithms, thereby failing to fully exploit the formidable computational capabilities of deep learning models. Moreover, the inference of causal relationships largely depends on the Granger causality test, which is not suitable for non-stationary and non-linear stock factors. Also, most existing studies do not consider the impact of confounding variables or further validation of causal relationships. In response to the current research deficiencies, this paper introduces a deep learning-based algorithm aimed at inferring causal relationships between stock closing prices and relevant factors. To achieve this, causal diagrams from the structural causal model (SCM) were integrated into the analysis of stock data. Subsequently, a sliding window strategy combined with Gated Recurrent Units (GRUs) was employed to predict the potential values of closing prices, and a grouped architecture was constructed inspired by the Potential Outcomes Framework (POF) for controlling confounding variables. The architecture was employed to infer causal relationships between closing price and relevant factors through the non-linear Granger causality test. Finally, comparative experimental results demonstrate a marked enhancement in the accuracy and performance of closing price predictions when causal factors were incorporated into the prediction model. This finding not only validates the correctness of the causal inference, but also strengthens the reliability and validity of the proposed methodology. Consequently, this study has significant practical implications for the analysis of causality in financial time series data and the prediction of stock prices.
Optimizing Mobile Robot Navigation Based on A-Star Algorithm for Obstacle Avoidance in Smart Agriculture
Authors: Chatzisavvas Antonios , Dossis Michael , Dasygenis Minas
Published: May 2024

The A-star algorithm (A*) is a traditional and widely used approach for route planning in various domains, including robotics and automobiles in smart agriculture. However, a notable limitation of the A-star algorithm is its tendency to generate paths that lack the desired smoothness. In response to this challenge, particularly in agricultural operations, this research endeavours to enhance the evaluation of individual nodes within the search procedure and improve the overall smoothness of the resultant path. So, to mitigate the inherent choppiness of A-star-generated paths in agriculture, this work adopts a novel approach. It introduces utilizing Bezier curves as a postprocessing step, thus refining the generated paths and imparting their smoothness. This smoothness is instrumental for real-world applications where continuous and safe motion is imperative. The outcomes of simulations conducted as part of this study affirm the efficiency of the proposed methodology. These results underscore the capability of the enhanced technique to construct smooth pathways. Furthermore, they demonstrate that the generated paths enhance the overall planning performance. However, they are also well suited for deployment in rural conditions, where navigating complex terrains with precision is a critical necessity.
Online Social Network Information Source Identification Algorithm Based on Multi-Attribute Topological Clustering
Authors: Dong Ming , Lu Yujuan , Tan Zhenhua , Zhang Bin
Published: May 2024

This paper focuses on the problem of information source identification in online social networks (OSNs). By analyzing the research situation of source identification problems and challenges (such as the randomness of the information dissemination process and complexity of the underlying network topology), this paper studies the problem of multiple source diffusion and proposes a source identification algorithm based on multi-attribute topological clustering (MaTC). The basic idea of the algorithm is to decompose the multi-source problems into a series of single-source problems by using clustering partitioning to improve accuracy and efficiency. Firstly, it estimates the number of source nodes, which is also the number of network partitions, then characterizes the combination of multiple attribute structures as an attribute index of topological clustering, performs an analysis of the distribution of real source nodes in each partition to evaluate the accuracy of the clustering partition, and finally uses Jordan centrality within each partition for single-source identification. Through comparative experiments, it is verified that the proposed MaTC algorithm is superior to the comparison algorithms in evaluating indicators.
LCV2: A Universal Pretraining-Free Framework for Grounded Visual Question Answering
Authors: Chen Yuhan , Su Lumei , Chen Lihua , Lin Zhiwei
Published: May 2024

Grounded Visual Question Answering systems place heavy reliance on substantial computational power and data resources in pretraining. In response to this challenge, this paper introduces the LCV2 modular approach, which utilizes a frozen large language model (LLM) to bridge the off-the-shelf generic visual question answering (VQA) module with a generic visual grounding (VG) module. It leverages the generalizable knowledge of these expert models, avoiding the need for any large-scale pretraining. Innovatively, within the LCV2 framework, question and predicted answer pairs are transformed into descriptive and referring captions, enhancing the clarity of the visual cues directed by the question text for the VG module’s grounding. This compensates for the limitations of missing intrinsic text–visual coupling in non-end-to-end frameworks. Comprehensive experiments on benchmark datasets, such as GQA, CLEVR, and VizWiz-VQA-Grounding, were conducted to evaluate the method’s performance and compare it with several baseline methods. In particular, it achieved an IoU F1 score of 59.6% on the GQA dataset and an IoU F1 score of 37.4% on the CLEVR dataset, surpassing some baseline results and demonstrating the LCV2’s competitive performance.
A Feature-Reduction Scheme Based on a Two-Sample t-Test to Eliminate Useless Spectrogram Frequency Bands in Acoustic Event Detection Systems
Authors: Hajihashemi Vahid , Gharahbagh Abdorreza Alavi , Hajaboutalebi Narges , Zahraei Mohsen , Machado José J. M. , Tavares João Manuel R. S.
Published: May 2024

Acoustic event detection (AED) systems, combined with video surveillance systems, can enhance urban security and safety by automatically detecting incidents, supporting the smart city concept. AED systems mostly use mel spectrograms as a well-known effective acoustic feature. The spectrogram is a combination of frequency bands. A big challenge is that some of the spectrogram bands may be similar in different events and be useless in AED. Removing useless bands reduces the input feature dimension and is highly desirable. This article proposes a mathematical feature analysis method to identify and eliminate ineffective spectrogram bands and improve AED systems’ efficiency. The proposed approach uses a Student’s t-test to compare frequency bands of the spectrogram from different acoustic events. The similarity between each frequency band among events is calculated using a two-sample t-test, allowing the identification of distinct and similar frequency bands. Removing these bands accelerates the training speed of the used classifier by reducing the number of features, and also enhances the system’s accuracy and efficiency. Based on the obtained results, the proposed method reduces the spectrogram bands by 26.3%. The results showed an average difference of 7.77% in the Jaccard, 4.07% in the Dice, and 5.7% in the Hamming distance between selected bands using train and test datasets. These small values underscore the validity of the obtained results for the test dataset.
Digitally Controlled Fractional-Order Elements Using OTA-C Structures
Authors: Emanovic Edi , Vonic Marijan , Jurisic Drazen , Psychalinos Costas
Published: May 2024

This article presents an active realisation of an electronically controlled FO capacitor or a constant phase element (CPE) and an FO inductor (FOI) in the form of an integrated circuit. The realisation is demonstrated using an OTA-C structure in AMS 0.35 μm C35B4C3 technology. The same core is used for both realisations of CPE and FOI, and the angles can be realised in all four quadrants. The realisation of active constant-phase elements using OTAs with MOS transistors in the saturation region is proposed. The operating frequency is in the high range of 7–350 kHz, with a centre frequency of 50 kHz. A tuning method is proposed using different bias currents of the OTAs, which in turn are digitally controlled to obtain the desired parameters such as impedance and angle of an element. The linearisation of the individual OTAs is achieved by source degeneration. The newly introduced minimax approximation is used to design three non-integer orders of 1/3, 1/2, and 2/3. The integrated circuit was designed with a total area of 710 × 1127 µm2. The power consumption of the entire system is 12.37 mW.
Fault-Tolerant Cooperative Control of Multiple Uncertain Euler-Lagrange Systems with an Uncertain Leader
Authors: Gu Rongrong , Sun Xudong
Published: May 2024

This paper explores the fault-tolerant cooperative control of multiagent systems, which are modeled via an uncertain leader system and multiple uncertain Euler–Lagrange systems with actuator faults. A self-adjusting observer is initially proposed to estimate the signal of the uncertain leader for different followers and compute the observer gain in real time. An adaptive fault-tolerant controller is designed based on the above observer and nonsingular fast terminal sliding mode surface. This controller estimates lumped uncertainty and ensures that tracking errors are ultimately bounded. The controller designed in this paper has the following three advantages. Firstly, the observer can estimate and transmit the leader’s state to each follower even without precise knowledge of the leader’s system matrix. Secondly, the controller is robust to actuator faults, uncertain parameters and external disturbances, the upper bounds of which can be arbitrarily large and unidentified. Thirdly, the controller has a simple structure and is also suitable for situations where the actuator is healthy. Lastly, simulations are provided to demonstrate the effectiveness of both the observer and the controller with or without actuator fault.
Enhancing Diabetic Retinopathy Detection Using Pixel Color Amplification and EfficientNetV2: A Novel Approach for Early Disease Identification
Authors: Kao Yi-Hsuan , Lin Chun-Ling
Published: May 2024

Diabetic retinopathy (DR) is a severe complication of diabetes, causing damage to retinal blood vessels due to high blood sugar levels. Early detection is crucial but often requires significant time and expertise from ophthalmologists. While artificial intelligence (AI) and image recognition hold promise for DR detection, inconsistent image quality poses a challenge. Our study presents a novel technique that integrates pixel color amplification and EfficientNetV2 to enhance fundus image attributes, aiming to address issues related to image quality and achieving superior performance in DR detection. Leveraging EfficientNetV2, an advanced convolutional neural network (CNN) architecture, we achieve 84% multiclass accuracy and 99% binary accuracy, surpassing various other CNN models, including VGG16-fc1, VGG16-fc2, NASNet, Xception, Inception ResNetV2, EfficientNet, InceptionV3, MobileNet, and ResNet50. Our research tackles the critical challenge of early detection of DR, essential for preventing vision loss. This advancement holds the potential to enhance the efficiency and accuracy of DR classification, potentially alleviating the burden on medical professionals and ultimately improving the quality of life for individuals at risk of vision loss.
SolarFlux Predictor: A Novel Deep Learning Approach for Photovoltaic Power Forecasting in South Korea
Authors: Min Hyunsik , Hong Seokjun , Song Jeonghoon , Son Byeoungmin , Noh Byeongjoon , Moon Jihoon
Published: May 2024

We present SolarFlux Predictor, a novel deep-learning model designed to revolutionize photovoltaic (PV) power forecasting in South Korea. This model uses a self-attention-based temporal convolutional network (TCN) to process and predict PV outputs with high precision. We perform meticulous data preprocessing to ensure accurate data normalization and outlier rectification, which are vital for reliable PV power data analysis. The TCN layers are crucial for capturing temporal patterns in PV energy data; we complement them with the teacher forcing technique during the training phase to significantly enhance the sequence prediction accuracy. By optimizing hyperparameters with Optuna, we further improve the model’s performance. Our model incorporates multi-head self-attention mechanisms to focus on the most impactful temporal features, thereby improving forecasting accuracy. In validations against datasets from nine regions in South Korea, SolarFlux outperformed conventional methods. The results indicate that SolarFlux is a robust tool for optimizing PV systems’ management and operational efficiency and can contribute to South Korea’s pursuit of sustainable energy solutions.
Non-Stationary Transformer Architecture: A Versatile Framework for Recommendation Systems
Authors: Liu Yuchen , Li Gangmin , Payne Terry R. , Yue Yong , Man Ka Lok
Published: May 2024

Recommendation systems are crucial in navigating the vast digital market. However, user data’s dynamic and non-stationary nature often hinders their efficacy. Traditional models struggle to adapt to the evolving preferences and behaviours inherent in user interaction data, posing a significant challenge for accurate prediction and personalisation. Addressing this, we propose a novel theoretical framework, the non-stationary transformer, designed to effectively capture and leverage the temporal dynamics within data. This approach enhances the traditional transformer architecture by introducing mechanisms accounting for non-stationary elements, offering a robust and adaptable solution for multi-tasking recommendation systems. Our experimental analysis, encompassing deep learning (DL) and reinforcement learning (RL) paradigms, demonstrates the framework’s superiority over benchmark models. The empirical results confirm our proposed framework’s efficacy, which provides significant performance enhancements, approximately 8% in LogLoss reduction and up to 2% increase in F1 score with other attention-related models. It also underscores its potential applicability across accumulative reward scenarios with pure reinforcement learning models. These findings advocate adopting non-stationary transformer models to tackle the complexities of today’s recommendation tasks.
Efficient Inference Offloading for Mixture-of-Experts Large Language Models in Internet of Medical Things
Authors: Yuan Xiaoming , Kong Weixuan , Luo Zhenyu , Xu Minrui
Published: May 2024

Despite recent significant advancements in large language models (LLMs) for medical services, the deployment difficulties of LLMs in e-healthcare hinder complex medical applications in the Internet of Medical Things (IoMT). People are increasingly concerned about e-healthcare risks and privacy protection. Existing LLMs face difficulties in providing accurate medical questions and answers (Q&As) and meeting the deployment resource demands in the IoMT. To address these challenges, we propose MedMixtral 8x7B, a new medical LLM based on the mixture-of-experts (MoE) architecture with an offloading strategy, enabling deployment on the IoMT, improving the privacy protection for users. Additionally, we find that the significant factors affecting latency include the method of device interconnection, the location of offloading servers, and the speed of the disk.