bluuebunny/crossref_metadata_2025_split huggingface dataset:
A Neural Network Based Recursive Least Square Multilateration Technique for Indoor Positioning
Authors: Adhikari Bhagawat , Fernando Xavier 
Published: November 2024

<p> Fast emerging wireless indoor positioning enables many location based services. However, fluctuation receive signal strength due to multipath propagation is a major challenge. Different Machine Learning (ML) approaches are attempted to improve the accuracy in indoor positioning. In this paper, a hybrid technique combining Recursive Least Square (RLS) with Artificial Neural Network (ANN) has been presented to solve a multilateration problem with different sets of anchor nodes. Instead of using directly measured distances, estimated distances using ANN are used in RLS implementation. Results from the hybrid RLS-ANN are compared with pure RLS, pure least square (LS) and LS-ANN approaches. Hybrid RLS-ANN provides the least Root Mean Square Error (RMSE) among all the techniques and improves the accuracy up to 80% compared to the pure LS multilateration technique. Complexity of the proposed technique is relatively low with good accuracy </p>
An Energy Harvesting MAC Protocol for Cognitive Wireless Sensor Networks
Authors: Obaid Arif , Jaseemuddin Muhammad , Fernando Xavier 
Published: November 2024

<p> Cognitive Wireless Sensor Networks (CWSN) are known to be energy constrained because they are required to perform cognitive functions in addition to sensing, which reduces the lifetime of CWSN. RF Wireless Energy Harvesting (RFWEH) is known to improve network lifetime by compensating energy losses through harvesting gain without any additional hardware. However, the allocation of time slots for energy harvesting results in degradation of network parameters such as latency and throughput. In this paper, we propose an energy harvesting MAC protocol (R-MAC) for CWSN that balances network energy losses with harvested energy gains, thereby resulting in a self sustainable CWSN. This is done such that both latency and throughput remain close to predefined target values. R-MAC employs TDMA and relies on clustering for creation and dissemination of the schedule. We also propose an algorithm to add harvesting slots based on a recursive regression technique to achieve the energy balance. Using simulation we compared its performance with the well known cognitive KoNMAC protocol, after incorporating energy harvesting in it. The simulation results show that the proposed protocol increases the lifetime of the CWSN nodes substantially, resulting in an energy self sufficient network while maintaining throughput and latency within acceptable limits. </p>
Hyperscale Data Center Networks with Interconnected Transparent Island Architecture
Authors: Nooruzzaman Md , Fernando Xavier 
Published: November 2024

<p>A large number of daily Internet users generates enormous quantities of new data, driving the sensational escalation of Internet giants, such as Google, Facebook, Amazon, IBM, Dropbox, Alibaba, Microsoft, and so on. New data from Synergy Research Group shows that the total number of hyperscale data centres increased to 504 at the end of 2019 [1]. This number was 338 in 2016 and will reach to 628 by the end of 2021 [2]. In order to cope with this massive demand, the size of the data centres is expanding, number of data centres is growing and a technological change in the data centres has been accelerated remarkably over the past decade. Since we are approaching towards the era where many services must meet strict latency requirements. Therefore, the distance (number of hops) between users and servers as well as between server and server must be reduced. In this paper, we propose a new hyperscale DCN called interconnected transparent island (ITI) to achieve reduced number of hops. The proposed ITI architecture is scalable horizontally while the number of hops remains constant as the data centre grows. </p>
Machine Learning Models for Predicting Thyroid Cancer Recurrence: A Comparative Analysis
Authors: Shah Alam  , Mohammad Abir Hider  , Abdullah Al Mukaddim  , Farhana Rahman Anonna  , Md Sazzad Hossain  , Md khalilor Rahman  , Md Nasiruddin  
Published: November 2024

Thyroid cancer is considered the most common malignancy of the endocrine system and encompasses a broad category of diseases that involve abnormal growth in thyroid cells. Thyroid carcinoma accounts for about 3% of the total cases of cancer diagnosis in the USA. The chief objective of the research project is to design and compare the performance of machine learning models in the prediction of thyroid cancer recurrence to overcome the limitations observed in the current predictive tools. This study aims to develop and compare Machine Learning models. In particular, this study considered different machine-learning algorithms to identify which model can effectively forecast the recurrence of thyroid cancer. The dataset used for the analysis was from Kaggle, the ‘Thyroid Gland Dataset.’ This source had a very elaborate dataset, containing records of patients who were diagnosed with thyroid issues, including demographic data on variables that would be needed to see the recurrence of any disease. Besides, it contained demographic information about the patients, which would serve to comprehend population trends in the patients; examples are age, gender, and ethnicity. The clinical history data included size, histological subtype, lymph node involvement, and staging at diagnosis. This comparative analysis mounted a variety of machine learning algorithms, each of which was chosen based on its capabilities to face structured medical datasets for robust predictions. Each model was chosen based on their different strengths that correspond to characteristics in the dataset and the general goals of the prediction problem. Performance metrics used for the models included overall accuracy, precision, recall, and the F1 score. Logistic Regression performed slightly better than the random forest and the support vector machines. However, this difference in accuracy was minimal and all three can make quite accurate predictions on this data. Logistic Regression provides transparency and interpretability, Random Forest provides high versatility and robustness, while SVM offers precision for complex relationship modeling. The integration of machine learning predictive models into clinical practice has great potential to transform decision-making, particularly in the management of thyroid cancer and the risk of recurrence. These models will greatly assist clinicians by consequently advising them on which patients have a high chance of recurrence, so early intervention might be considered and follow-up care given as need sets in.
Escherichia coli, the Predominant Uropathogen in Women and it’s Antimicrobial Susceptibility
Authors: Mawla Nurun Nahar , Sultana Shahin , Hassan Munir 
Published: November 2024

Urinary tract infection (UTI) is one of the most common bacterial infection in humans and a major cause of morbidity. Most commonly, members of Enterobacteriaceae family, particularly the uropathogenic strains of Escherichia coli is the primary causative organism of UTIs in different parts of the world. The aim of this retrospective study was to determine the frequency of uropathogens in different age groups of both sex and to evaluate Escherichia coli as predominant bacteria for urinary tract infection in women with their antimicrobial sensitivity pattern. A total of 1616 urine samples were collected during January to December, 2012, from patients attending at Delta Medical College & Hospital, Dhaka and analyzed for different uropathogens. Urine cultures were carried out and the isolates were identified by standard procedures as needed. Antimicrobial susceptibility testing was performed by disk diffusion method according to ‘The Clinical Laboratory Standard Institute’ (CLSI) guidelines. In this present study, prevalence of uropathogens were higher in female patients (77.2%) than male patients (22.8%). The isolates were Escherichia coli (80%), Klebsiella spp. (7.9%), Enterococcus spp. (4.9%), Staphylococcus saprophyticus (2%), Proteus (1.4%), Pseudomonas spp. and Staphylococcus aureus (1.2%). The rate of Escherichia coli infection in females of reproductive age group; 15-50 years was higher (61%) and in that only a particular age group (21-30) was more prone to this pathogen (22.9%). Among the drugs used for testing the antimicrobial susceptibility, Imipenem, Amikacin, Nitrofurantoin and Gentamycin were found to be effective in the treatment of urinary tract infection. As drug resistance to commonly used antimicrobials is increasing, improvement in overall sanitary condition, proper knowledge on personal hygiene would play an important role in reducing the incidence and occurrence of urinary tract infection.
J. Dhaka National Med. Coll. Hos. 2013; 19 (02): 13-18
Methimazole-induced congenital hypothyroidism affects gonocytes differentiation and arrests meiosis: role of Sertoli cells
Authors: Gómez-Zúñiga Andrea , Landero-Huerta Daniel Adrián , Rojas-Castañeda Julio César , Sánchez-Huerta Karla , Contreras-García Itzel Jatziri , Reynoso-Robles Rafael , Arteaga-Silva Marcela , Vigueras-Villaseñor Rosa María 
Published: November 2024

BackgroundCongenital hypothyroidism (CH) is a pathology that affects various organs, including the testicles. The mechanisms by which this condition alters fertility is unknown. This study aimed at determining if experimental CH affects gonocyte differentiation and arrests meiosis; and the possible role of the Sertoli cell (SC) in this condition.Material and MethodsTwo groups of rats consisting of Control group and Methimazole (MMI) induced CH rats were formed. The induction of CH was achieved by the administration of MMI starting on day 16 postcoitum and continued until euthanized. Euthanasia was performed at 5, 8, 10, 16 and 64 days of age. Following this, the testicular tissue of each animal was extracted and processed for histopathological and ultrastructural analysis. In addition, the tissue was used for the determination of proteins and their transcriptions, events which are characteristics of gonocyte differentiation. The SC functionality proteins was determined immunohistochemically, while sperm parameters of the cauda epididymis were verified.ResultsCH caused a delay in the gonocyte differentiation, and arrested meiosis and spermiogenesis. These events had long-term repercussions on the quality of the seminiferous epithelium. The results show that CH induces alterations in the functional state of SCs that may have led to the deficiency in the synthesis and/or in the release of molecules necessary for gonocyte differentiation; as well as disorders in the process of meiosis that resulted in sperm absence.ConclusionThese results suggest that CH affects gonocyte differentiation and arrests meiosis, possibly through altering the functional status of SCs.
Nanoparticle-based biomolecules in cancer diagnosis, therapy, drug delivery and prognosis
Authors: SV Sowmya , Augustine Dominic , Hosmani Jagdish , Pagnoni Francesco , Reda Rodolfo , Testarelli Luca , Patil Shankargouda 
Published: November 2024

IntroductionNanoparticles have orchestrated a paradigm shift in the landscape of cancer diagnosis and therapy, presenting a multifaceted approach to tackle the intricacies of malignancies. This comprehensive exposition delves deep into the forefront of nanomedicine, elucidating pivotal strategies and innovations primed to metamorphose the domain of cancer management.MethodologyNanoparticles transcend traditional boundaries, enabling meticulous, site-specific drug release while minimizing systemic toxicity. Intricately designed activation mechanisms, encompassing pH and enzymatic responsivity, along with concentration-dependent strategies, exploit the distinctive attributes of cancer cells, heralding an era characterized by unprecedented therapeutic precision. The pervasive influence of nanotechnology extends to diagnostics, unlocking the realm of early disease detection and personalized treatment. These versatile agents bestow empowering capabilities upon sensitive imaging modalities, affording real-time monitoring and theranostic potential.ResultsThis exposition showcases the evolution of cutting-edge nanoplatforms, bridging the chasm between diagnosis and therapy, thereby redefining the confines of cancer care. This review elucidates strategies to combat drug resistance, a perennial challenge within cancer management. By targeting efflux transporters, modulating apoptotic pathways, and countering hypoxia-induced resistance, nanoparticles stand at the vanguard of therapeutic innovation, poised to reinvigorate treatment efficacy.Discussion &amp; ConclusionMoreover, this exposé underscores the imminent clinical translation of nanoparticle-based drugs, accentuating their potential to metamorphose the landscape of cancer management. Liposomal vaccines, nano-pharmaceuticals, and nanochemodrugs, currently navigating the crucible of clinical trials, bear immense promise in advancing the realm of precision medicine. In this epoch of precision medicine, nanoparticle-fueled innovations stand poised to propel cancer diagnosis and therapy to unprecedented peaks.
Tick population dynamics in the city of Košice (Eastern Slovakia): a public health study
Authors: Hajdová Blažena , Cellengová Zuzana , Peťko Branislav , Ondrejková Anna , Lipinský Jakub , Drážovská Monika 
Published: November 2024

IntroductionUrban habitats, including green spaces, host diverse ecosystems where ticks and their hosts thrive, posing public health risks. Understanding the presence of ticks in urban areas is crucial for the effective management of those parasites.MethodsA three-year study (2021 - 2023) on the presence of ticks in urban green spaces was conducted in the city of Košice, Slovakia. A total of 3443 ticks were collected in woodland and scrubby vegetation areas located in the town by applying the flagging methods.ResultsThree different species of ticks – Ixodes ricinus, Dermacentor reticulatus, and Dermacentor marginatus, were found in the urban spaces, while I. ricinus was the most prevalent tick species (64.7%), followed by D. reticulatus (35.2%). Dermacentor reticulatus, typically found in rural habitats, was detected right in the city centre, where its typical habitat is absent. Significant differences in tick abundance were observed between the scrubby areas (55.9%) and the woodland areas (44.5%), with D. reticulatus being predominantly found in the former. Monthly tick density varied across years, with D. reticulatus activity starting early in February.DiscussionThe findings of the study highlight the importance of considering geographical and ecological factors in tick distribution studies, especially in urban settings. Effective public health management strategies should incorporate efforts aimed at understanding the presence and behaviour of ticks in urban environments, emphasising the need for monitoring those species and taking necessary measures, such as vegetation maintenance, in order to mitigate the tick-related risks in urban areas.
Modulation of the hepatic RANK-RANKL-OPG axis by combined C5 and CD14 inhibition in a long-term polytrauma model
Authors: Li Yang , Horst Klemens , Greven Johannes , Mert Ümit , Lupu Ludmila , Palmer Annette , Doerfer Lena , Zhao Qun , Zhang Xing , Halbgebauer Rebecca , Ignatius Anita , Marzi Ingo , van Griensven Martijn , Balmayor Elizabeth , Hildebrand Frank , Mollnes Tom Eirik , Huber-Lang Markus 
Published: November 2024

BackgroundPolytrauma and hemorrhagic shock can lead to direct and indirect liver damage involving intricate pathophysiologic mechanisms. While hepatic function has been frequently highlighted, there is minimal research on how the receptor activator of the NF-κB (RANK)/RANK ligand (RANKL)/osteoprotegerin (OPG) system is regulated in the liver following trauma. Furthermore, cross-talking complement and toll-like-receptor (TLR) systems can contribute to the posttraumatic response. Therefore, we investigated the hepatic consequences of polytrauma focusing on the RANK-RANKL-OPG axis, and evaluated the effects of a dual blockade of complement factor C5 and TLR-cofactor CD14 on hepatic features.MethodsThe established pig model of polytrauma (PT) and hemorrhagic shock included pulmonary contusion, hepatic dissection, and bilateral femur fractures, surgically addressed either by external fixation (Fix ex) or intramedullary nailing (Nail). Four groups were investigated: 1) sham animals; 2) PT treated by Fix ex (Fix ex); 3) PT by Nail (Nail); or 4) PT by Nail plus combined C5/CD14 inhibition (Nail+Therapy). Serum samples were obtained between 0 - 72 h, and liver samples at 72 h after PT. Liver tissues were histologically scored and subjected to RT-qPCR-analyses, immunohistochemistry and ELISAs to evaluate the posttraumatic hepatic response with a focus on the RANK-RANKL-OPG system.ResultsFollowing PT, the liver injury score of the Nail+Therapy group was significantly lower than in the Fix ex or Nail group without immunomodulation (p&lt;0.05). Similarly, the degree of necrosis, lobular stasis, and inflammation were significantly reduced when treated with C5/CD14-inhibitors. Compared to the Nail group, AST serum concentrations were significantly decreased in the Nail+Therapy group after 72 h (p&lt;0.05). PCR analyses indicated that RANK, RANKL, and OPG levels in the liver were increased after PT in the Nail group compared to lower levels in the Nail+Therapy group. Furthermore, liver tissue analyses revealed increased RANK protein levels and cellular immunostaining for RANK in the Nail group, both of which were significantly reduced in the case of C5/CD14-inhibition (p&lt;0.05).ConclusionFollowing experimental PT, dual inhibition of C5/CD14 resulted in altered, mainly reduced hepatic synthesis of proteins relevant to bone repair. However, a comprehensive investigation of the subsequent effects on the liver-bone axis are needed.
Long-term COVID-19 vaccine- and Omicron infection-induced humoral and cell-mediated immunity
Authors: Belik Milja , Reinholm Arttu , Kolehmainen Pekka , Heroum Jemna , Maljanen Sari , Altan Eda , Österlund Pamela , Laine Larissa , Ritvos Olli , Pasternack Arja , Naves Rauno A. , Iakubovskaia Alina , Barkoff Alex-Mikael , He Qiushui , Lempainen Johanna , Tähtinen Paula A. , Ivaska Lauri , Jalkanen Pinja , Julkunen Ilkka , Kakkola Laura 
Published: November 2024

IntroductionMutations occurring in the spike (S) protein of SARS-CoV-2 enables the virus to evade COVID-19 vaccine- and infection-induced immunity.MethodsHere we provide a comprehensive analysis of humoral and cell-mediated immunity in 111 healthcare workers who received three or four vaccine doses and were followed up to 12 and 6 months, respectively, after the last vaccine dose. Omicron breakthrough infection occurred in 71% of the vaccinees, enabling evaluation of vaccine- and vaccine/infection-induced hybrid immunity.ResultsNeutralizing antibodies were the highest against the ancestral D614G and were sequentially reduced against the Omicron variants BA.2, BA.5 and XBB.1.5. S1-specific IgG and neutralizing antibody levels were significantly higher in infected than in uninfected vaccinees, and the fourth vaccine dose in combination with a breakthrough infection resulted in high neutralizing antibody levels against all variants. T cell-mediated immunity, instead, was well retained already after two vaccine doses, and was not significantly strengthened by additional booster vaccine doses or Omicron breakthrough infections.DiscussionWhile humoral immunity is sensitive to mutations in the S protein and thus declined rapidly, the cell-mediated immunity is durable to antigenic variation, which may explain the good efficacy of COVID-19 vaccines against a severe disease.
Bridging Natural Language Processing and psycholinguistics: computationally grounded semantic similarity datasets for Basque and Spanish
Authors: Goikoetxea Josu , San Martin Itziar , Arantzeta Miren 
Published: November 2024

IntroductionSemantic relations are crucial in various cognitive processes, highlighting the need to understand concept interactions and how such relations are represented in the brain. Psycholinguistics research requires computationally grounded datasets that include word similarity measures controlled for the variables that play a significant role in lexical processing. This work presents a dataset for noun pairs in Basque and European Spanish based on two well-known Natural Language Processing resources: text corpora and knowledge bases.MethodsThe dataset creation consisted of three steps, (1) computing four key psycholinguistic features for each noun; concreteness, frequency, semantic, and phonological neighborhood density; (2) pairing nouns across these four variables; (3) for each noun pair, assigning three types of word similarity measurements, computed out of text, Wordnet and hybrid embeddings.ResultsA dataset of noun pairs in Basque and Spanish involving three types of word similarity measurements, along with four lexical features for each of the nouns in the pair, namely, word frequency, concreteness, and semantic and phonological neighbors. The selection of the nouns for each pair was controlled by the mentioned variables, which play a significant role in lexical processing. The dataset includes three similarity measurements, based on their embedding computation: semantic relatedness from text-based embeddings, pure similarity from Wordnet-based embeddings and both categorical and associative relations from hybrid embeddings.DiscussionThe present work covers an existent gap in Basque and Spanish in terms of the lack of datasets that include both word similarity and detailed lexical properties, which provides a more useful resource for psycholinguistics research in those languages.
MusicARLtrans Net: a multimodal agent interactive music education system driven via reinforcement learning
Authors: Chang Jie , Wang Zhenmeng , Yan Chao 
Published: November 2024

IntroductionIn recent years, with the rapid development of artificial intelligence technology, the field of music education has begun to explore new teaching models. Traditional music education research methods have primarily focused on single-modal studies such as note recognition and instrument performance techniques, often overlooking the importance of multimodal data integration and interactive teaching. Existing methods often struggle with handling multimodal data effectively, unable to fully utilize visual, auditory, and textual information for comprehensive analysis, which limits the effectiveness of teaching.MethodsTo address these challenges, this project introduces MusicARLtrans Net, a multimodal interactive music education agent system driven by reinforcement learning. The system integrates Speech-to-Text (STT) technology to achieve accurate transcription of user voice commands, utilizes the ALBEF (Align Before Fuse) model for aligning and integrating multimodal data, and applies reinforcement learning to optimize teaching strategies.Results and discussionThis approach provides a personalized and real-time feedback interactive learning experience by effectively combining auditory, visual, and textual information. The system collects and annotates multimodal data related to music education, trains and integrates various modules, and ultimately delivers an efficient and intelligent music education agent. Experimental results demonstrate that MusicARLtrans Net significantly outperforms traditional methods, achieving an accuracy of 96.77% on the LibriSpeech dataset and 97.55% on the MS COCO dataset, with marked improvements in recall, F1 score, and AUC metrics. These results highlight the system's superiority in speech recognition accuracy, multimodal data understanding, and teaching strategy optimization, which together lead to enhanced learning outcomes and user satisfaction. The findings hold substantial academic and practical significance, demonstrating the potential of advanced AI-driven systems in revolutionizing music education.