bluuebunny/crossref_metadata_2025 huggingface dataset:
Machine Learning Approach for Arabic Handwritten Recognition
Authors: Mutawa A.M. , Allaho Mohammad , Al-Hajeri Monirah
Published: September 2024

Text recognition is an important area of the pattern recognition field. Natural language processing (NLP) and pattern recognition have been utilized efficiently in script recognition. Much research has been done on handwritten script recognition. However, the research on the Arabic language for handwritten text recognition received little attention compared with other languages. Therefore, it is crucial to develop a new model that can recognize Arabic handwritten text. Most of the existing models used to acknowledge Arabic text are based on traditional machine learning techniques. Therefore, we implemented a new model using deep machine learning techniques by integrating two deep neural networks. The architecture of the Residual Network (ResNet) model is used to extract features from raw images. Then, the Bidirectional Long Short-Term Memory (BiLSTM) and connectionist temporal classification (CTC) are used for sequence modeling. Our system improved the recognition rate of Arabic handwritten text compared to other models of similar type with a character error rate of 13.2% and character error rate of 27.31%. In conclusion, the domain of Arabic handwritten recognition is advancing swiftly with the use of sophisticated deep learning methods.
Unveiling the Fatigue Behavior of Lateral Piles with Open Cross-Sections in Cohesive Soils
Authors: Pérez José A. , Ponce-Torres Alberto , Ríos José D. , Sánchez-González Estíbaliz
Published: September 2024

The majority of structures supporting solar panels are founded on thin-walled metal piles driven into the ground to optimize costs and construction timelines. These pile foundations are subjected to repetitive lateral loads from various external forces, such as wind, which can compromise the in-tegrity of the pile-soil system. Given that the proper functioning of photovoltaic solar plants is typically expected for 20-30 years, predicting their useful life under fatigue loading becomes crucial. This research investigates the lateral load response of a single pile subjected to different fatigue loads in stiff cohesive soils. Additionally, the effect of loading cycles on the degradation of pile-soil ad-hesion is studied through pull-out field tests. This study reveals that soil fatigue does not occur under repetitive loading, and the soil’s stiffness remains constant once the cycles causing soil compaction have been overcome. Nevertheless, the soil's accumulated plastic deflection steadily increases once soil compaction due to cyclic loading occurs. The implications of these results on the fatigue life of photovoltaic solar panel foundations are discussed.
Gender Differences in Performing an Overhead Drilling Task Using an Exoskeleton- a Cross-Sectional Study
Authors: Wollesen Bettina , Graef Julia Katharina , De Bock Sander , Alfio Eligia , Diaz Maria Alejandra , De Pauw Kevin
Published: September 2024

(1) Exoskeletons offer potential benefits for overhead working tasks, but gender effects or differences are unclear. This study aimed to compare the performance, strain, and comfort of men and women using an upper-body exoskeleton. (2) n=20 female and n=16 male participants performed an overhead drilling task with and without a passive upper-body exoskeleton in a randomized cross-over study. Task performance of different movement phases, perceived exertion, and ease of use were measured to compare gender differences. One and two-way analyses were used to com-pare genders in the different conditions. (3) Gender differences after controlling for BMI in task performance were found for error integrals (p
Demystifying DFT-Based Harmonic Phase Estimation, Transformation, and Synthesis
Authors: Oliveira Marco , Santos Vasco , Saraiva André , Ferreira Aníbal
Published: September 2024

Many natural signals exhibit a quasi-periodic behavior and are conveniently modeled as a combination of several harmonic sinusoids whose relative frequencies, magnitudes and phases vary with time. The waveform shape of those signals reflects important physical phenomena underlying their generation, which requires that those parameters be accurately estimated and modeled. In the literature, accurate phase estimation and modeling has received much less research effort than frequency estimation, or magnitude estimation. First, this paper addresses accurate DFT-based phase estimation of individual sinusoids in six scenarios involving two DFT-based filter banks and three different windows. It is shown that bias in phase estimation is less than 1E-3 radians when the SNR is equal to or larger than 2.5 dB. Taking as a reference the Cramér-Rao Lower Bound, it is shown that one particular window offers a performance of practical interest by approximating better the CRLB when signal conditions are favorable, and by minimizing the performance deviation when signal conditions are adverse. Second, this paper explains how a shift-invariant phase-related feature can be devised that characterizes harmonic phase structure, which motivates a signal processing paradigm that greatly simplifies parametric modeling, transformation and synthesis of harmonics signals, in addition to facilitating the understanding and reverse engineering of the phasegram. Theory and results are discussed in a reproducible perspective using dedicated experiments that are supported with code allowing not only to replicate figures and results in this paper, but also to expand research.
Enhancing Virtual Reality Exposure Therapy for Social Anxiety Disorder Using Generative Adversarial Networks: A Personalized and Adaptive Approach
Authors: Duraivel Samuel
Published: September 2024

Social Anxiety Disorder (SAD) is a common mental health condition characterized by an intense fear of social situations, leading to significant impairments in daily functioning. Traditional exposure therapies, including Cognitive-Behavioral Therapy (CBT), have shown efficacy in treating SAD but are often limited by logistical challenges and patient reluctance. Virtual Reality Exposure Therapy (VRET) has emerged as a promising alternative, providing a controlled and immersive environment for exposure to anxiety-provoking scenarios. This paper explores the integration of Generative Adversarial Networks (GANs) into VRET to enhance the personalization, realism, and adaptability of therapeutic scenarios. GANs, which consist of a generator and a discriminator network, allow for the creation of highly realistic virtual environments tailored to the specific fears and triggers of individual patients. By offering real-time adjustments and personalized exposure scenarios, GAN-enhanced VRET addresses key limitations of traditional therapies, increasing patient engagement and treatment adherence. This review synthesizes findings from key studies on GAN-enhanced VRET, evaluating its efficacy in reducing anxiety symptoms and discussing the potential for broader application across various anxiety disorders. The results indicate that GAN-enhanced VRET offers a significant advancement in the treatment of SAD, providing a more accessible, engaging, and effective approach to exposure therapy. Future directions include expanding accessibility through cost reduction, integrating additional AI techniques, and exploring applications beyond SAD to improve mental health outcomes across diverse patient populations.
Evaluation of Sentinel-3 SYN VGT Products in Comparison to the SPOT/VEGETATION and PROBA-V Archives
Authors: Toté Carolien , Swinnen Else , Henocq Claire
Published: September 2024

Sentinel-3 synergy (SYN) VEGETATION (VGT) products were designed to provide continuity to SPOT/VEGETATION (SPOT VGT). In past years, important changes have been implemented in the SYN VGT processing baseline. Since the PROBA-V mission acted as a gap filler between SPOT VGT and Sentinel-3, and in principle a continuous series of data products from the combined data archives of SPOT VGT (1998 – 2014), PROBA-V (2013-2020) and Sentinel-3 SYN VGT (from 2018 onwards) is available to users, consistency of Sentinel-3 SYN VGT with both the latest SPOT VGT (VGT-C3) and PROBA-V (PV-C2) archives is highly relevant. Spatio-temporal intercomparison of the combined time series of VGT-C3, PV-C2 and Sentinel-3 SYN VGT 10-day NDVI composite products with an external reference from LSA-SAF, and the intercomparison of Sentinel-3 SYN V10 products with a climatology of VGT-C3 resp. PV-C2 for 3 distinct periods with different levels of product quality have shown that the subsequent processing baseline updates have resulted in better quality products. It is therefore essential to reprocess the entire Sentinel-3 SYN VGT archive: a uniform data record of standard SPOT VGT, PROBA-V and Sentinel-3 SYN VGT products, spanning over 25 years, would provide valuable input for a wide range of applications.
A Novel Eu3+ Doped Glasses in the MoO3‐WO3‐La2O5‐ B2O3 System: Preparation, Structure and Photoluminescent Properties
Authors: Aleksandrov Lyubomir , Milanova Margarita , Yordanova Aneliya , Iordanova Reni , Shinozaki Kenji , Honma Tsuyoshi , Komatsu Takayuki
Published: September 2024

A novel multicomponent glasses with nominal compositions of (50-x)MoO3:xWO3:25La2O3:25B2O3, x = 0, 10, 20, 30, 40, 50 mol% doped with 3 mol % Eu2O3 were prepared using a conventional melt quenching method. Their structure, thermal behavior and luminescent properties were investigated by Raman spectroscopy, differential thermal analysis and photoluminescence spectroscopy. Physical parameters as density, molar volume, oxygen molar volume and oxygen packing density were determined. Glasses are characterized with high glass transition temperature. Raman analysis revealed that the glass structure is built up mainly from tetrahedral (MoO4)2- and (WO4)2- units providing Raman bands of around 317 cm-1, 341-352 cm-1, 832-820 cm-1 and 928-935 cm-1. At the same time with the replacement of MoO3 with WO3 some fraction of WO6 octahedra are produced, which number increases with the increasing WO3 content. A strong red emission from the 5D0 level of Eu3+ ions was registered under near UV (395 nm) excitation using the 7F0 →5L6 transition of Eu3+. PL emission gradually increases with increasing WO3 content evidencing that WO3 is more appropriate component than MoO3. The integrated ﬂuorescence intensity ratio R (5D0 →7F2/5D0 →7F1) was calculated to estimate the degree of asymmetry around the active ion, suggesting a location of Eu3+ in non-centrosymmetric sites.
Timed Interpreted Systems as a New Agent-Based Formalism for Verification of Timed Security Protocols
Authors: Zbrzezny Agnieszka M. , Siedlecka-Lamch Olga , Szymoniak Sabina , Zbrzezny Andrzej , Kurkowski Mirosław
Published: September 2024

This article introduces a new method for modelling and verifying the execution of timed security protocols (TSP) and their time-dependent security properties. The method, which is both novel and reliable, uses an extension of interpreted systems, accessible semantics in multi-agent systems, and timed interpreted systems (TIS) with dense time semantics to model TSP executions. We enhance the models of TSPs by incorporating delays and varying lifetimes to capture real-life aspects of protocol executions. To illustrate the method, we model a timed version of the Needham-Schroeder Public Key Authentication Protocol. We have also developed a new SMT-based bounded model checking reachability algorithm for the proposed structures and implemented it with the tool. The method comprises a new procedure for modelling TSP executions, a translation of TSP into TIS, and a translation of TIS's reachability problem into the SMT problem. The paper also includes thorough experimental results for nine protocols modelled by TIS and discusses the findings in detail.
Biomarkers of ‘Late Onset Alzheimer’s Disease ‘, Diagnosis and Role of ’Deep Learning’
Authors: Sarma Manash , Chatterjee Subarna
Published: September 2024

Late-onset Alzheimer’s disease (LOAD) is a dementia category disease that starts after the age of 65. Progressive problems in thinking, behaviour, learning etc. are its characteristics. Due to its progressive nature, early diagnosis is crucial. A complex pathway mechanism with different risk factors is involved in LOAD progression. A comprehensive study is done on factors causing LOAD, biomarkers used for diagnosis in clinical and preclinical stage of the disease, ML (Machine Learning) techniques to identify disease stage. We reviewed research papers on clinical and preclinical diagnosis of LOAD using ML techniques from biomarker data: MRI, PET image, MMSE, Genome / Gene expression, DNA / RNA binding sites with protein, Epigenetic expression /DNA, methylation, Speech and text. We have listed improvement scope after finding the gaps and scope of using Generative AI and SMOTE (Synthetic Minority Oversampling Technique) for data augmentation. At the end, we have elaborated current use of DL (Deep Learning) and CNN (‘Convolution Neural Network’) in Alzheimer’s disease (AD) diagnosis.
Composite Dynamic Double Network Hydrogels with Rapid Self-healing, Stretchable, Moldable and Antibacterial Properties based on PVA/ε-Poly-L-lysine/Hyaluronic Acid
Authors: Sun Na , Liu Xiangnan , Lv Wenqi , Xu Chunlin , Zhang Ailing , Sun Panpan
Published: September 2024

Self-healing, stretchable and moldable hydrogels have a great potential application in tissue engineering and soft robotics. Despite great success in reported hydrogels, it is still a great challenge to construct the moldable hydrogels with an ultrafast self-healing performance. Herein, the dynamic double network (DN) composite hydrogels (PBLH) with ultrafast self-healing, stretchable and moldable were successfully constructed by poly (vinyl alcohol) (PVA), borate, ε-poly-L-lysine (EPL) and hyaluronic acid (HA) based on an efficient one-pot method. Fourier transform infrared spectroscopy, X-Ray diffraction and rheological measurements confirmed the formation of dynamic double networks among PVA, B, EPL and HA through the cross-linking of dynamic borate bonds, electrostatic interaction and hydrogen bonding. Having fabricated the dynamic double network structure, the damage gap of the composite hydrogels can heal within 1 min, presenting an excellent self-healing ability. Simultaneously, the DN hydrogels can be molded to various shapes and the length of the DN hydrogels can be stretched to 15 times than original length. In addition, the DN hydrogels exhibited an excellent antibacterial property against Staphylococcus aureus (S. aureus) and Escherichia coli (E. coli). Our results illustrated that the DN composite hydrogels not only retain the advantages of traditional hydrogels, but also possess ultrafast self-healing, outstanding stretchable and antibacterial properties, presenting a prospective candidate for constructing biomedical materials.
The Intersection of ADHD and Autism: Neurodevelopmental Disorders with Shared Origins and Distinct Trajectories
Authors: Montgomery Richard Murdoch
Published: September 2024

Attention Deficit Hyperactivity Disorder (ADHD) and Autism Spectrum Disorder (ASD) are prevalent neurodevelopmental disorders with distinct diagnostic criteria but significant overlap in symptoms and comorbidity. This article explores the relationship between ADHD and ASD, examining their common neurodevelopmental origins, shared features, and distinct trajectories. Both disorders are highly heritable, with genetic studies identifying shared genetic variants and neurobiological abnormalities. Environmental factors, including prenatal and perinatal exposures, also contribute to their etiology. Despite these commonalities, ADHD and ASD exhibit distinct developmental trajectories, cognitive profiles, and long-term outcomes. Understanding the interplay between these disorders is crucial for developing effective diagnostic and treatment strategies. Future research should focus on elucidating the underlying mechanisms and developing targeted interventions for individuals with comorbid ADHD and ASD.
Plastic-Degrading Enzymes from Marine Microorganisms and Their Potential Value in Recycling Technologies
Authors: Ruginescu Robert , Purcarea Cristina
Published: September 2024

Since the 2005 discovery of the first enzyme capable of depolymerizing polyethylene terephthalate (PET), an aromatic polyester once thought to be non-biodegradable, extensive research has been undertaken to identify and engineer new biocatalysts for plastic degradation. This effort was directed towards developing efficient enzymatic recycling technologies that could overcome the limitations of mechanical and chemical methods. These enzymes are versatile molecules obtained from microorganisms living in various environments, including soil, compost, surface seawater, and extreme habitats such as hot springs, hydrothermal vents, deep-sea regions, and Antarctic seawater. Among various plastics, PET and polylactic acid (PLA) have been the primary focus of enzymatic depolymerization research, greatly enhancing our knowledge of enzymes that degrade these specific polymers. They often display unique catalytic properties that reflect their particular ecological niches. This review explores recent advancements in marine-derived enzymes that can depolymerize synthetic plastic polymers, emphasizing their structural and functional features that influence the efficiency of these catalysts in biorecycling processes. Current status and future perspectives of enzymatic plastic depolymerization are also discussed, with a focus on the underexplored marine enzymatic resources.
Transforming User Experience (UX) Through Artificial Intelligence (AI) in Interactive Media Design
Authors: Liu Yingchia , Xu Yang , Song Runze
Published: September 2024

This paper discusses the application and advantages of artificial intelligence technology in digital media interactive product design. Firstly, the development background of artificial intelligence technology and its promoting effect on design innovation is introduced, and the application of new technologies, such as generative adversarial networks in art creation and design personalization, is definitely analyzed. It then explores in detail the ability of AI to break traditional constraints in interactive design, innovate design, and optimize user experience, especially in digital media. Finally, the case study of Microsoft Cortana shows the method of classifying and processing user queries by machine learning system and its experimental results. The research of this paper provides the theoretical basis and empirical support for the application of artificial intelligence technology in the future interactive design of digital media and has important academic and practical significance.
Identifying High-Risk Patterns in Single-Vehicle, Single-Occupant Road Traffic Accidents: A Novel Pattern Recognition Approach
Authors: Fian Tabea , Hauger Georg
Published: September 2024

Despite various interventions in road safety work, fatal and severe road traffic accidents (RTAs) remain a significant challenge leading to human suffering and economic costs. Understanding the multicausal nature of RTAs, where multiple conditions and factors interact, is crucial for developing effective prevention measures in road safety work. This study investigates the multivariate statistical analysis of co-occurring conditions in RTAs, focusing on single-vehicle accidents with single occupancy and personal injury on Austrian roads outside built-up areas from 2012 to 2019. The aim is to detect recurring combinations of accident-related variables, referred to as blackpatterns (BPs), using the Austrian RTA database. The study proposes Fisher’s exact test to estimate the relationship between an accident-related variable and fatal and severe RTAs (severe casualties). In terms of pattern recognition, the study develops the maximum combination value (MCV) of accident-related variables, a procedure to search through all possible combinations of variables to find the one that has the highest frequency. The accident investigation proceeds with the application of pattern recognition methods, including binomial logistic regression and a newly developed method, the PATTERMAX-method, created to accurately detect and analyse variable-specific BPs in RTA data. Findings indicate significant BPs contributing to severe accidents. The combination of binomial logistic regression and the PATTERMAX-method appears to be a promising approach to investigate severe accidents, providing both insights into detailed variable combinations and their impact on accident severity.
Psychometric Evaluation of the Women´s Knowledge of Obstetric Healthcare Rights and Perception of Resource Scarcity Tools
Authors: Silva-Fernández Claudia Susana , de la Calle María , Suta María A. , Arribas Silvia M. , Garrosa Eva , Ramiro-Cortijo David
Published: September 2024

Background/Objectives: Resources to cope for maternity and women’s participation are essential modulators of maternal well-being. Therefore, it is relevant that the psychosocial factors of woman will be monitored during maternity to promote adequately healthcare. This study consisted in the design and the validation of two new tools that identify women's knowledge of obstetric healthcare rights (MatCODE) and perception of resource scarcity (MatER) during pregnancy, labor and early postpartum; Methods: The content validity was carried out using the Aiken's V coefficient and the content validity index (CVI-i) based on 5 experts. In addition, for the face validity of the pilot cohort was considered the INFLESZ scale. Finally, the questionnaires were applied to 185 women, which allowed to assess the construct validation by factorial and Rasch analysis. The divergent validity was also studied with validated psychological questionnaires; Results: MatCODE and MatER questionnaires received CVI-i and Aiken's V>0.80 values, also the INFLESZ demonstrates acceptable semantic understanding. The analysis confirms the unidimensional of the questionnaires with fit values for MatCODE of RMSEA=0.113 [0.105; 0.122] and MatER of RMSEA=0.067 [0.063; 0.072]. The divergent validity showed significative and consistent correlations with the constructs assessed. In MatCODE was =0.95 and =0.94 and in MatER was =0.79 and =0.78; Conclusions: MatCODE and MatER are new useful tools monitoring maternal healthcare, with adequate psychometric characteristics in Spanish context.
How Government Subsidies Facilitate Digital Transformation of Suppliers
Authors: Hao Xiong-Lei , Chen Xing-Yu , Wang Fu-Tao
Published: September 2024

Resource and capacity constraints are common challenges for suppliers in digital transformation. This study examines the impact of government subsidies on supplier digital transformation from the perspectives of supply chain diffusion and the local government competition, with supply chain relationship data from A-share listed companies spanning 2010 to 2021. The findings indicate that: government subsidies promote suppliers to expand their diversified downstream customer base, which will boost supplier digital transformation. However, the spillover effect within the same region is higher than that across regions. The heterogeneity analysis results indicate that the effect of government subsidies becomes increasingly apparent as the opportunistic risk escalates; conversely, the impact of such subsidies is considerably constrained by heightened systemic risk. The higher the human capital of suppliers and the smaller the digital divide between suppliers and customers, the better the effect of suppliers receiving innovation diffusion. Further analysis reveals that local government subsidies to upstream suppliers reduce the proportion of downstream local customer purchases outside the jurisdiction, thereby hindering cross-regional spillover effects. This article enriches the research on the promotion mechanism of enterprise digital transformation and the efficiency loss of market segmentation, providing some reference for optimizing local government subsidy behavior.
From Black Open Access to Open Access of Color: Accepting the Diversity of Approaches towards Free Science
Authors: Elbakyan Alexandra
Published: September 2024

The aim of this article is to shed some light on ‘black open access’ model, that still remains poorly understood and largely neglected in the literature, despite being widely adopted in practice. I give an overview of the historical development of black OA and its most important projects: Sci-Hub and Library Genesis. Arguments are provided for why the term ‘black OA’ is misleading and the term ‘RGB OA’ (red, green and blue) would better describe a diverse landscape of open access projects that emerged after 2001. While practical approaches towards OA evolved dramatically in the past 20 years, theoretical discussion is still operating the same two-color scheme of ‘green’ and ‘gold’ open access from BOAI declaration of 2001: novel approaches are either not recognized as OA at all or are neglected as ‘black’. A new and more inclusive OA declaration might be needed to account for greater diversity of approaches.
Theranostics of Neuroendocrine Tumors: Bibliometric Study Based on Web of Science Database
Authors: Wang Qi , Librizzi Damiano , Bagheri Shamim , Ebrahimifard Ali , Hojjat Shamami Azimeh , Eilsberger Friederike , Luster Markus , Hooshyar Yousefi Behrooz
Published: September 2024

Compared to agonists, radiolabeled somatostatin receptor (SSTR) antagonists have advantages in neuroendocrine tumors (NETs). Articles on radiolabeled somatostatin receptor agonists or antagonists in NETs were included by searching the Web of Science (WoS) database. The included literature has been subjected to data collection and bibliometric study. A detailed analysis of the included clinical articles was also performed. 123 publications were identified for biblio-metric analysis. The number of publications on radiolabeled somatostatin receptor agonists and antagonists in the theranostics of NETs has increased significantly after 2014, with Germany publishing the most papers (39), followed by the United States (33) and Switzerland (28). The Swiss paper was cited the most, with 1783 quotations, followed by the USA (1338) and Germany (1330). The most common keywords were NETs (89), Peptide receptor-mediated radionuclide therapy (PRRT) (62), and antagonists (53). Antagonists had lower uptake in normal tissue and higher uptake in tumor tissue compared to agonists, with a high tumor-to-liver ratio. Research on radiolabeled somatostatin receptor agonists and antagonists in theranostics of NETs is increasing, with significant contributions from Europe and the United States. Radiolabeled somatostatin receptor antagonists are likely to be a hot topic in NETs theranostics.
One-Dimensional Numerical Cascade Model of Runoff and Soil Loss on Convergent and Divergent Plane Soil Surfaces: Labora-Tory Assessment and Numerical Simulations
Authors: Mujtaba Babar , de Lima João L. M. P. , de Lima M. Isabel P.
Published: September 2024

A one-dimensional numerical overland flow model based on the cascade plane theory was de-veloped to estimate rainfall-induced runoff and soil erosion on converging and diverging plane surfaces. The model includes three components: (i) soil infiltration using Horton’s infiltration equation, (ii) overland flow using the kinematic wave approximation of the one-dimensional Saint-Venant shallow water equations for a cascade of planes, and (iii) soil erosion based on the sediment transport continuity equation. The model’s performance was evaluated by comparing numerical results with laboratory data from experiments using a rainfall simulator and a soil flume. Four independent experiments were conducted on converging and diverging surfaces under var-ying slope and rainfall conditions. Overall, the numerically simulated hydrographs and sediment graphs closely matched the laboratory results, showing the efficiency of the model for the tested controlled laboratory conditions. The model was then used to numerically explore the impact of different plane soil surface geometries on runoff and soil loss. Seven geometries were studied: one rectangular, three diverging, and three converging. Constant soil surface area, rainfall intensity, and slope gradient were maintained in all simulations. Results showed that increasing convergence angles led to higher peak and total soil loss, while decreasing divergence angles reduced them.
Data-Driven Sparse Sensor Placement Optimization on Wings for Flight-by-Feel: Bioinspired Approach and Application
Authors: Hollenbeck Alex , Beachy Atticus , Grandhi Ramana , Pankonien Alexander
Published: September 2024

This research explores the feasibility of a data-driven optimization algorithm for placing sparse arrays of artificial hair-cell airflow velocity sensors to enable agile and robust flight-by-feel control of unmanned aircraft. The Sparse Sensor Placement Optimization for Prediction (SSPOP) algorithm exploits the information-rich features of large flow datasets to find a near-optimal set of locations for flow sensors on a wing. The flow information from these sensors can be used to predict flight state parameters such as the angle of attack, mimicking the fly-by-feel control systems observed in nature. We evaluate the effectiveness of this approach for a blunt-edged 45∘ swept delta wing. Exploiting the information in the complex airflow patterns over this wing, the SSPOP algorithm selects a sensor location design point (DP) which reliably ranks within the top 1% of all possible DPs by root mean square error in angle of attack prediction. This performance was consistent across models of various artificial hair lengths, and one model with variable hair lengths. This bioinspired dimension-reducing method for sensor placement optimization exhibits reliability and flexibility benefits when compared with a conventional greedy search algorithm and gradient-based optimization using auto-differentiation. The successful application of SSPOP in complex 3D flows paves the way for experimental evaluation of data-driven sparse sensor placement optimization for artificial hair-cell airflow sensors and is a major step toward biomimetic flight-by-feel.
Holomics and AI-Driven Precision Oncology for Medullary Thyroid Carcinoma: Addressing Challenges of a Rare and Aggressive Disease
Authors: Luvhengo Thifhelimbilu E , Moeng Maeyane Stephens , Sishuba N , Makgoka Malose , Jonas Lusanda , Mamathuntsha Tshilidzi Godfrey , Mbambo Thandanani , Kagodora Shingirai Brenda , Dlamini Zodwa
Published: September 2024

Medullary thyroid carcinoma (MTC) is a rare yet aggressive form of thyroid cancer, comprising a disproportionate share of thyroid cancer-related mortalities despite its low prevalence. MTC differs from other differentiated thyroid malignancies due to its heterogeneous nature, presenting complexities in both hereditary and sporadic cases. Traditional management guidelines, designed primarily for papillary thyroid carcinoma (PTC), fall short in providing the individualized care required for MTC patients. In recent years, the sheer volume of data generated from clinical evaluations, radiological imaging, pathological assessments, genetic mutations, and immunological profiles has made it humanly impossible for clinicians to simultaneously analyze and integrate these diverse data streams effectively. This data deluge necessitates the adoption of advanced technologies to assist in decision-making processes. Holomics, an integrated approach that combines various omics technologies, along with artificial intelligence (AI), emerges as a powerful solution to address these challenges. AI-driven precision oncology can significantly enhance the diagnostic work-up, staging, risk stratification, management, and follow-up care of MTC patients by processing vast amounts of complex data quickly and accurately. AI algorithms can identify patterns and correlations that may not be apparent to human clinicians, thereby improving the precision of personalized treatment plans. Moreover, the implementation of AI in MTC management enables the collation and synthesis of clinical experiences from across the globe, facilitating a more comprehensive understanding of the disease and its treatment outcomes. This global approach aligns with the United Nations Sustainable Development Goal 3, which aims to ensure healthy lives and promote well-being for all at all ages. By leveraging AI and holomics, we can strive towards making personalized healthcare accessible to every individual, regardless of their economic status, thereby improving overall survival rates and quality of life for MTC patients worldwide. In conclusion, the integration of holomics and AI in the management of medullary thyroid carcinoma represents a significant advancement in precision oncology. This innovative approach not only addresses the complexities of a rare and aggressive disease but also paves the way for global collaboration and equitable healthcare solutions, ultimately transforming the landscape of MTC treatment and care.
Identification of Candidate Genes for Cold Tolerance at Seedling Stage by GWAS in Rice (Oryza sativa L.)
Authors: Song Qingxin , Xu Janlong , Shi Huimin , Zhang Wenyu , Cao Huimin , Zhai Laiyuan
Published: September 2024

Due to global climate change, cold temperatures have significantly impacted rice production, resulting in reduced yield and quality. In this study, we investigated two traits related to cold tolerance (CT) of 1992 diverse rice accessions at the seedling stage. Geng accessions exhibited higher levels of CT compared to xian accessions, with the GJ-tmp subgroup displaying the strongest CT. However, extreme CT accessions were also identified within the xian subspecies. Through GWAS analysis based on survival rate (SR) and leaf score of cold tolerance (SCT), a total of 30 QTLs associated with CT at the seedling stage were identified, among which four QTLs (qSR3.1a, qSR4.1a, qSR11.1x and qSR12.1a) were found to be important. Furthermore, five candidate genes (LOC_Os03g44760, LOC_Os04g06900, LOC_Os04g07260, LOC_Os11g40610 and LOC_Os12g10710) along with their favorable haplotypes were identified through gene function annotation and haplotype analysis. Pyramiding multiple favorable haplotypes resulted in a significant improvement in CT performance. Subsequently, three selected accessions (CX534, B236, and IRIS_313-8565), carrying different superior alleles for CT, were selected and recommended for molecular breeding for CT using marker-assisted selection (MAS). The findings from this study provide valuable resources for enhancing rice's ability for CT while laying a foundation for future cloning of novel genes involved in conferring CT.
Priority Conservation Area of Quercus mongolica Fisch.ex Ledeb. under Climate Change: Application of an Ensemble Modeling
Authors: Liu Lei , Li Fengzi , Hai Long , Sa Rula , Gao Minglong , Wang Zirui , Tie Niu
Published: September 2024

As the primary secondary tree species in Northeast China, Quercus mongolica possesses significant ecological and economic value. This study employed the Biomod2 platform in conjunction with ArcGIS spatial analysis to assess the potential suitable habitat distribution area of Q. mongolica under current climatic conditions. Furthermore, it forecasted the distribution range and niche changes of potentially suitable habitats for Q. mongolica from 2022 to 2090, and pinpointed the key environmental factors influencing its distribution. The findings reveal that the total potential suitable area for Q. mongolica covers 74,994.792 km², predominantly spread across Inner Mongolia, Heilongjiang, Jilin, and other regions. The primary determinants of suitable area distribution were peak temperature of hottest month, lowest temperature of coldest month and altitude. Under future climate scenarios, the potentially suitable habitats of Q. mongolica are anticipated to diminish to varying extents, with the distribution center exhibiting a tendency towards northward migration. Concurrently, the overlap among different climate scenarios is predicted to expand over time. This investigation facilitates a comprehensive understanding of Q. mongolica's adaptation to climate change, enabling informed adjustments and serving as a valuable reference for the preservation and sustainable management of Q. mongolica populations.
Arthroscopic Debridement Enhanced by Intra-articular Antibiotic-Loaded Calcium Sulphate Beads for Septic Arthritis of a Native Knee Following Iatrogenic Joint Injection: A Case Report
Authors: Alongi Simone , Troiano Elisa , Latino Cristina , Colasanti Giovanni Battista , Greco Tommaso , Perisano Carlo , Mosca Massimiliano , Giannotti Stefano , Mondanelli Nicola
Published: September 2024

Septic arthritis (SA) represents an orthopedics urgency and mainly affects the knee joint. Due to its devastating effects on cartilage, immediate management is crucial. SA is characterized by an an-nual incidence of 2 to 10 cases per 100,000 individuals, with mortality rates fluctuating between 0.5% and 15%, with a substantially higher mortality rate observed in older people (15%) in con-trast to younger cohorts (4%). The etiology of septic arthritis is multifactorial: a spectrum of Gram-positive and Gram-negative bacteria can contribute to the development of this condition, especially Staphylococcus aureus. The treatment involves urgent (arthroscopic or arthrotomic) debridement associated with adequate antibiotic therapy. Intra-articular antibiotic carriers can also be used to increase their local concentration and effectiveness. The case of a 67-years-old woman affected by knee SA from methicillin-susceptible S. aureus is presented. She was treated with an arthroscopic debridement enhanced by intra-articular antibiotic-loaded calcium sulphate beads, together with antibiotic therapy. At 2-year follow-up, the infection has been eradicated and the patient fully recovered. This is the first description, to our knowledge, in English Literature of the use of antibiotic-loaded calcium sulphate beads as an adjuvant in the surgical treatment of SA of a native knee joint.
Safeguarding Personal-Identifiable Information (PII) after Smartphone Pairing with Connected Vehicle
Authors: Carlton Jason W , Malik Hafiz
Published: September 2024

The integration of connected autonomous vehicles into the transportation system introduces significant enhancements in driving experience and convenience. Yet, it simultaneously raises important concerns regarding the security and privacy of passenger data. As individuals increasingly depend on these connected vehicles, particularly rental cars with advanced infotainment systems, safeguarding their personal information becomes a paramount imperative, presenting significant challenges that must be addressed to maintain user trust and comply with privacy regulations. This paper investigates risks associated with personal information stored in connected vehicle technology, emphasizing the importance of robust security and privacy practices. In the advent of cyber threats and the need for data protection, we examine a risk management framework (RMF) as suggested by NIST, which aims to address these quintessential concerns preemptively. Our research moves beyond the limitations of manual safeguarding methods, supporting the necessity for an advanced automated technical solution. To address these issues, we introduce and meticulously assess the efficacy of “Vehicle Inactive Profile Removal” (VIPR), an innovative technical solution conceived to actively eliminate the risks and potential oversights that stem from the prevalent tendency of renters to leave personal data unerased in the infotainment systems of rented connected vehicles. We provide a thorough evaluation of VIPR through various scenarios, including vehicle return to a rental depot, subsequent rentals, and ridesharing contexts. Our proof of concept includes an array of experiments to demonstrate how VIPR proficiently and autonomously removes previous renters’ “Inactive” profiles from a simulated infotainment system. The results highlight how VIPR constitutes a critical step towards enhancing the privacy and security of personal data, thereby promoting a safer, more responsible use of connected vehicle technology in society.
Empowering Bangladesh's Financial Infrastructure, Machine Learning, and Deep Learning Perspectives on Banking Cybersecurity
Authors: Biplob Md. Badiuzzaman
Published: September 2024

The rapid expansion of Bangladesh's financial infrastructure, coupled with the increasing digitization of banking services, underscores the critical importance of cybersecurity. Machine learning and deep learning methodologies have emerged as potent tools in combating cyber threats such as intrusion, malware, and fraud. By harnessing artificial intelligence and data-driven analysis, these techniques enable real-time scrutiny of vast banking data, facilitating proactive defense against cyberattacks. Bangladesh's evolving financial landscape, characterized by governmental digitalization initiatives and a national financial inclusion strategy, heightens the imperative for robust cybersecurity measures. This paper explores the application of machine learning and deep learning techniques in bolstering banking cybersecurity within the context of Bangladesh's burgeoning digital economy.
Explainable Pre-Trained Language Models for Sentiment Analysis in Low-Resourced Languages
Authors: Mabokela Ronny , Primus Mpho , Celik Turgay
Published: September 2024

Sentiment analysis is a pivotal tool for gauging the public’s perception and understanding of human communication across digital social media platforms. However, due to linguistic complexities and limited resources, sentiment analysis is not well-represented in many African languages. While benchmark Africa-Centric Pre-trained Language Models (PLMs) have been developed for various Natural Language Processing (NLP) tasks, their applications in eXplainable Artificial Intelligence (XAI) remain unexplored. In this study, we introduce a novel approach that combines Africa-centric PLMs with XAI techniques for sentiment analysis. We demonstrate that applying attention mechanisms and visualisation techniques improves the transformer-based model’s transparency, trustworthiness, and decision-making abilities when making sentiment predictions. We then employ the SAfriSenti—a multilingual sentiment corpus for South African under-resourced languages. We use the corpus to perform various sentiment analysis experiments and also enable comprehensive evaluations, comparing the performance of Africa-centric models against mainstream PLMs. The Afro-XLMR model outperformed all models and achieved an average F1-score performance of 71.04% across the five tested languages and the lowest error rate among the evaluated models. Additionally, we incorporated techniques like Local Interpretive Model-Agnostic Interpretation (LIME) and Shapley Additive Interpretation (SHAP) in the sentiment classifier’s output to enhance the Afro-XLMR model’s interpretability and explainability. As a result, the use of XAI strategies ensures that sentiment predictions are not only accurate and interpretable but also understandable, fostering trust and reliability in the decision-making of AI-driven NLP technologies, particularly in the context of African languages.
Deltopectoral Flap in Head and Neck Reconstruction
Authors: Pecorari Giancarlo , Bissattini Francesco , Gamba Dario , Pizzo Claudia , Motatto Gian Marco , Riva Giuseppe
Published: September 2024

Background and Objectives: The deltopectoral (DP) flap represents a reconstructive option for head and neck. It is a fasciocutaneous flap raised from the anterior chest wall below the clavicle. Its role partially declined with the arise of free flaps. However, it still remains a valid option in patients that could not undergo a reconstruction with free flaps. The aim of this retrospective study was to evaluate the role of the DP flap in head and neck reconstruction, with a focus on surgical outcomes and complications. Materials and Methods: Thirty-one patients who underwent head and neck reconstruction with DP flap were included in the study. The delayed technique was not used in any procedure to harvest the flap beyond the deltopectoral groove. The patients’ characteristics, the recipient site, the closure of the donor site and the flap-related complications were recorded. Results: The median time to autonomization of DP flap was 23 days. Post-operative complications were observed in 10 subjects (32.3%). A partial necrosis was seen in 5 patients (16.1%), while a complete necrosis developed only in one case (3.2%). The diabetes mellitus was the only parameter associated to post-operative complications. In particular, the percentage of necrosis in subjects with or without diabetes was 66.7% and 8.0%, respectively. Conclusions: The DP has a wide range of applications in head and neck reconstruction and a low complication rate can be observed. The delayed technique does not necessarily need to be applied, and the flap can be extended beyond the deltopectoral groove when necessary. However, patients with diabetes mellitus have a higher risk of postoperative necrosis of the distal portion of the flap.
Recovered Foam Impact Absorption Systems
Authors: Marcelino-Sádaba Sara , Benito Hernandez Pablo , Martin Antunes Miguel Angel , Villanueva Roldan Pedro Maria , Veiga Fernando
Published: September 2024

The utilization of foam in contact with individuals often leads to its deterioration, necessitating periodic material changes to prevent degradation and maintain sanitary conditions. Foam, a porous plastic material formed by an aggregation of bubbles, offers lightweight and impact-absorbing properties, yet its porosity and susceptibility to wear pose challenges. The project's overarching goal is to develop a technological application for repurposing spent foam materials and textile elements from leisure facilities into impact absorption systems. This initiative aims to minimize environmental impact by reusing materials and leveraging their technical characteristics, including those of polyurethanes and textiles. By addressing the challenge of foam deterioration, the project seeks to create sustainable solutions. The development of impact absorption systems will involve the utilization of spent foam, either by reintegrating it into the system after its initial use or combining it with waste textiles. This innovative approach not only promotes sustainability but also enhances safety by ensuring the availability of high-quality impact-resistant elements. Through this endeavor, the project aims to contribute to both environmental conservation and the advancement of effective anti-impact measures in leisure facilities.
Lossy Compression of Z-Map Based Shape Models Using Daubechies Wavelet Transform and Quickselect
Authors: Umezu Nobuyuki , Inui Masatomo
Published: September 2024

We propose an algorithm for lossy compression of computer-aided design models in Z-map representation. Our method employs Daubechies wavelet functions, which are smoother than those of the Haar wavelet used in a previous work for the lossy compression of shape models. A significant reduction in the amount of data of the compressed shape model was achieved using the proposed lossy in which the least significant coefficients of the wavelet synopsis were deleted. The nonlinear filtering of coefficients was based on the quickselect algorithm, which was seven to ten times faster than a normal quicksort algorithm and allowed us to accelerate the entire process. We conducted a series of experiments using shape models with 512 × 512–8192 × 8192 resolutions to evaluate our technique using various wavelet functions. The proposed method performed the process in 50–90 ms for the models at 1024 × 1024 resolution and reduced the output binary size by 75%–90% compared with those compressed using a previous method. Some Daubechies wavelets, such as D4 and D6, were found superior in lossy compression using nonlinear filtering based on the order of magnitude of wavelet coefficients.