bluuebunny/crossref_metadata_2025 huggingface dataset:
M‐ANCHORO: Mobile Anchor Node‐Based Path Optimization for Full Coverage Path Planning Algorithm
Authors: Tsai Rong-Guei , Zhao Jixin , Yu Yicong , Lin Di , Hong Anxuan , Lin Chengzhu , Chen Xiaolan , Luo Delin , Lin Lin
Published: August 2024

Localization is a key technology in wireless sensor network (WSN) applications. The use of mobile anchor nodes (MANs) to assist in sensor localization is a feasible solution for reducing deployment costs and overcoming geographical limitations. However, real‐world environments often contain obstacles with signal‐blocking characteristics. Random collision methods cannot guarantee that sensors receive sufficient localization information, resulting in the generation of many redundant beacon points by MANs. To achieve complete localization coverage and improve localization accuracy, we propose a path planning and optimization algorithm called M‐ANCHORO. The proposed algorithm divides subregions based on obstacle distribution information and deploys beacon points in equilateral triangles. It then uses the SCAN method to plan a fully localized coverage path and optimizes the number of beacon points using a path simplification procedure to establish a trajectory for full localization coverage. Experimental results showed that M‐ANCHORO outperformed Z‐curve, SLMAT, and SCAN in terms of localization accuracy and coverage.
Remission or Persistence? A Prediction Tool to Identify Women at Risk for Long‐Term Depressive Symptoms Postpartum
Authors: Gidén Karin , Duque Björvang Richelle , White Richard Aubrey , Vinnerljung Lisa , Iliadis Stavros I , Skalkidou Alkistis , Fransson Emma
Published: August 2024

Background. Peripartum depression is a common complication with potential long‐term adverse effects on the woman and her family. Approximately 30%–50% of newly delivered women experience prolonged depressive symptoms at 6–12 months postpartum. Early detection may facilitate preventive and treatment interventions. Aim. To investigate correlates for and create a tool for predicting long‐term symptomatology in women experiencing depressive symptoms at 6 weeks postpartum. Materials and Methods. Data from the Biology, Affect, Stress, Imaging, and Cognition study was used, to identify women who scored high (≥12) on the Edinburgh Postnatal Depression Scale (EPDS) at 6 weeks postpartum (n = 697). Further, we collected data from medical records and included 40 variables based on earlier studies and clinical experience. A total of 654 women were included. Elastic net linear regression analysis was performed to identify predictors of continued symptoms at 6 months postpartum. An equation predicting the EPDS score at 6 months postpartum based on weighted variables was developed. Results. High education level and sleep for more than 6 hr per night in pregnancy week 17 were protective factors. Parity, pregnancy complications, stressful events, attention deficit hyperactivity disorder/attention deficit disorder, history of depression, depressive symptoms, and anxiety during pregnancy were predictive factors of prolonged depressive symptoms. A prediction tool with area under curve 0.73 and positive predictive value of 79%–83% depending on chosen EPDS cutoff was developed for clinical use. Conclusions. Our prediction tool offers a method to identify women at risk for persisting depressive symptoms postnatally, based on their significant depressive symptoms during the first weeks after delivery. Screening in order to identify these women can already start in the antenatal setting.
Supplementary Figures and Tables from The Cancer-Associated ATM R3008H Mutation Reveals the Link between ATM Activation and Its Exchange
Authors: Milanovic Maja , Sprinzen Lisa , Menolfi Demis , Lee Ji-Hoon , Yamamoto Kenta , Li Yang , Lee Brian J. , Xu Jun , Estes Verna M. , Wang Dong , Mckinnon Peter J. , Paull Tanya T. , Zha Shan
Published: August 2024

Sup. Figures
Supplemental Figure Legends from Loss of STK11 Suppresses Lipid Metabolism and Attenuates KRAS-Induced Immunogenicity in Patients with Non–Small Cell Lung Cancer
Authors: Principe Daniel R. , Pasquinelli Mary M. , Nguyen Ryan H. , Munshi Hidayatullah G. , Hulbert Alicia , Aissa Alexandre F. , Weinberg Frank
Published: August 2024

Supplemental Figure Legends
Figure 2 from Loss of STK11 Suppresses Lipid Metabolism and Attenuates KRAS-Induced Immunogenicity in Patients with Non–Small Cell Lung Cancer
Authors: Principe Daniel R. , Pasquinelli Mary M. , Nguyen Ryan H. , Munshi Hidayatullah G. , Hulbert Alicia , Aissa Alexandre F. , Weinberg Frank
Published: August 2024

KRAS-mutated NSCLC tumors display increased PDL1 expression. A and B, Tissue biopsies from 189 treatment-naïve patients with NSCLC were stained with either H&E or via IHC for PDL1. Representative images are shown for low and high expression in LUAD (N = 132) and SCC (N = 57) histologies. C, PDL1 TPS arranged by tumor histology. D, PDL1 TPS plotted against percentile CD274 mRNA expression. E, PDL1 TPS expression for tumor specimens obtained from the primary lung tumor (lung, N = 114), lymph node (LN, N = 31), or distant metastatic site (Met, N = 4). F and G, PDL1 TPS or CD274 mRNA expression for either KRAS-WT (N = 144) or KRAS mutated patients (MT, N = 45). H and I, PDL1 TPS or CD274 mRNA for either KRAS-WT or KRAS-MT patients arranged by tumor histology. J, PDL1 TPS or CD274 mRNA for either KRAS-WT or KRAS-MT patients arranged by tumor site. H&E, hematoxylin and eosin; NS, nonsignificant.
Figure S3 from Characterization of Non-Monotonic Relationships between Tumor Mutational Burden and Clinical Outcomes
Authors: Anaya Jordan , Kung Julia , Baras Alexander S.
Published: August 2024

BPC fits. Cox and neural net model fits were mean normalized and averaged over 10 K-folds. TMB distributions shown as rug plots.
Supplementary Table 1 from KRAS Promotes GLI2-Dependent Transcription during Pancreatic Carcinogenesis
Authors: Sigafoos Ashley N. , Tolosa Ezequiel J. , Carr Ryan M. , Fernandez-Barrena Maite G. , Almada Luciana L. , Pease David R. , Hogenson Tara L. , Raja Arul Glancis L. , Mousavi Fatemeh , Sen Sandhya , Vera Renzo E. , Marks David L. , Flores Luis F. , LaRue-Nolan Kayla C. , Wu Chen , Bamlet William R. , Vrabel Anne M. , Sicotte Hugues , Schenk Erin L. , Smyrk Thomas C. , Zhang Lizhi , Rabe Kari G. , Oberg Ann L. , Zaphiropoulos Peter G. , Chevet Eric , Graham Rondell P. , Hagen Catherine E. , di Magliano Marina P. , Elsawa Sherine F. , Pin Christopher L. , Mao Junhao , McWilliams Robert R. , Fernandez-Zapico Martin E.
Published: August 2024

Supplementary Table 1 describes the characteristics of the patients used in our study.
Supplementary Figure 2 from KRAS Promotes GLI2-Dependent Transcription during Pancreatic Carcinogenesis
Authors: Sigafoos Ashley N. , Tolosa Ezequiel J. , Carr Ryan M. , Fernandez-Barrena Maite G. , Almada Luciana L. , Pease David R. , Hogenson Tara L. , Raja Arul Glancis L. , Mousavi Fatemeh , Sen Sandhya , Vera Renzo E. , Marks David L. , Flores Luis F. , LaRue-Nolan Kayla C. , Wu Chen , Bamlet William R. , Vrabel Anne M. , Sicotte Hugues , Schenk Erin L. , Smyrk Thomas C. , Zhang Lizhi , Rabe Kari G. , Oberg Ann L. , Zaphiropoulos Peter G. , Chevet Eric , Graham Rondell P. , Hagen Catherine E. , di Magliano Marina P. , Elsawa Sherine F. , Pin Christopher L. , Mao Junhao , McWilliams Robert R. , Fernandez-Zapico Martin E.
Published: August 2024

Supplementary Figure 2 describes the characterization of the impact Gli2 overexpression in pancreas development. showing that Gli2 loss has no impact on pancreas development or survival in vivo.
ChatGPT: Großer Einfluss auf ärztliches Handeln, aber limitiert im direkten Patientenkontakt
Authors: Göbel Katja
Published: August 2024

Fragestellung: In den letzten Jahren nimmt die künstliche Intelligenz (KI) als neues Segment der Informatik auch in der Medizin eine immer größere Bedeutung ein. Ziel dieses Projekts war es zu untersuchen, ob die aktuelle Version von ChatGPT (ChatGPT 4.0) in der Lage ist, offene Fragen zu beantworten, die im Rahmen einer deutschen Facharztprüfung in der Augenheilkunde gestellt werden könnten. Methoden: Aus dem Lehrbuch «1000 Fragen Augenheilkunde» (2. Auflage, 2014) wurden nach Ausschluss bildbasierter Fragen jeweils 10 Fragen aus 15 verschiedenen Kapiteln/Themenschwerpunkten ausgewählt. ChatGPT wurde mittels eines sog. Prompt instruiert, die Rolle eines Facharztes für Augenheilkunde einzunehmen und sich im Umfang der Antworten auf das Wesentliche zu konzentrieren. Die Bewertung eines Themengebietes erfolgte durch einen in der Subspezialität langjährig erfahrenen Ophthalmologen, welcher die Antworten hinsichtlich Richtigkeit, Themenrelevanz und innerer Kohärenz beurteilte und die Gesamtleistung mit einer Schulnote bewertete. Ergebnisse: ChatGPT hätte die Facharztprüfung in 12 von 15 Themengebieten bestanden. Allerdings war die Gesamtleistung auf nur 53,3% vollständig korrekte Antworten beschränkt. Während die Korrektheit der Ergebnisse in den unterschiedlichen Themengebieten sehr variabel war («Uveitis» und «Linse/Katarakt» 100%; «Optik und Refraktion» 20%), hatten die Antworten stets eine hohe thematische Passgenauigkeit (70%) und innere Kohärenz (71%). Schlussfolgerung: Die Tatsache, dass ChatGPT 4.0 in 12 von 15 Themengebieten die Facharztprüfung bestanden hätte, ist vor dem Hintergrund bemerkenswert, dass diese KI nicht spezifisch für medizinische Fragestellungen trainiert wurde. Allerdings offenbart sich eine erhebliche Leistungsvarianz zwischen den Themengebieten mit zum Teil gravierenden Mängeln, die einen sicheren Einsatz in der klinischen Praxis derzeit ausschließt.
Influence of the IDOL Gene Variants on Low-Density Lipoprotein Cholesterol Levels in Turkish Patients with Familial Hypercholesterolemia
Authors: Duymus Fahrettin , Kocak Nadir , Özdemir Ebru Marzioğlu , Esin Deniz , Körez Muslu Kazim , Cora Tülün
Published: August 2024

Introduction: The inducible degrader of low-density lipoprotein (IDOL) receptor, an E3 ubiquitin ligase, was recently identified as a regulator of the LDL receptor (LDLR) pathway. Shortly, IDOL stimulates LDLR degradation through ubiquitination. However, the association of IDOL gene variants with plasma lipid levels is controversial. No previous study in the Turkish population has reported the relationship between variants of the IDOL gene and low-density lipoprotein cholesterol (LDL-C) levels. Our study aims to investigate the effects of genetic variants in the human IDOL gene, which may be a therapeutic target in human cholesterol metabolism, on LDL-C levels. Methods: We sequenced all coding, critical intronic, and untranslated regions of the IDOL gene in 125 controls (77 women, 48 men) and 125 patients (64 women, 61 men) with definite or probable familial hypercholesterolemia (FH) according to the criteria of the Dutch Lipid Clinic Network, in whom no pathogenic/likely pathogenic LDLR variants are present. Results: We identified 12 different IDOL gene variants, including the p.(N342S) and p.(G51S), whose association with LDL-C levels has been investigated, and classified them into common and rare variants. A rare variant p.(G51S) was only detected in patients the patient group. We compared the minor allele frequency (MAF) distribution of common variants between patient and control groups and examined the association of their genotypic distribution with plasma LDL-C levels using genetic models (dominant, recessive, overdominant, codominant). There was no statistically significant difference in the parameters of the patient and control groups (p > 0.05). Conclusion: Our findings suggest that the common IDOL variants we identified do not associate with the LDL-C level in the Turkish population. Rare variants that were not found to be statistically significant in our study, should be emphasized, and supported with further research.
Generative AI in Critical Care Nephrology: Applications and Future Prospects
Authors: Cheungpasitporn Wisit , Thongprayoon Charat , Ronco Claudio , Kashani Kianoush B.
Published: August 2024

Background: Generative artificial intelligence (AI) is rapidly transforming various aspects of healthcare, including critical care nephrology. Large language models (LLMs), a key technology in generative AI, show promise in enhancing patient care, streamlining workflows, and advancing research in this field. Summary: This review analyzes the current applications and future prospects of generative AI in critical care nephrology. Recent studies demonstrate the capabilities of LLMs in diagnostic accuracy, clinical reasoning, and continuous renal replacement therapy (CRRT) alarm troubleshooting. As we enter an era of multiagent models and automation, the integration of generative AI into critical care nephrology holds promise for improving patient care, optimizing clinical processes, and accelerating research. However, careful consideration of ethical implications and continued refinement of these technologies are essential for their responsible implementation in clinical practice. This review explores the current and potential applications of generative AI in nephrology, focusing on clinical decision support, patient education, research, and medical education. Additionally, we examine the challenges and limitations of AI implementation, such as privacy concerns, potential bias, and the necessity for human oversight. Key Messages: (i) LLMs have shown potential in enhancing diagnostic accuracy, clinical reasoning, and CRRT alarm troubleshooting in critical care nephrology. (ii) Generative AI offers promising applications in patient education, literature review, and academic writing within the field of nephrology. (iii) The integration of AI into electronic health records and clinical workflows presents both opportunities and challenges for improving patient care and research. (iv) Addressing ethical concerns, ensuring data privacy, and maintaining human oversight are crucial for the responsible implementation of AI in critical care nephrology.
Bee-inspired knowledge transfer: synthesizing data for enhanced deep learning explainability
Authors: Chungnoy Kritanat , Tanantong Tanatorn , Songmuang Pokpong
Published: August 2024

This paper presents the generation method for an explainable model based on the given information of a black box model using a concept of knowledge transfer to synthesize a dataset. The proposed method applies with GAN and Bee algorithm (BA) for data synthesis technique to synthesize a dataset by considering loss value in a knowledge transferring process to inherit the significance of features. The synthesized dataset is used to train for a proxy model as an explainable model. The result of the experiment indicates that knowledge transfer from Bee algo better than generative adversarial network (GAN) in terms of the coefficient of determination R2. In addition, explainable models from the synthesized data of the Bee-based method obtains F1 score superior to those from the GAN-based method in all datasets and settings. The dataset synthesized from the Bee-based method produces the explainable prediction model that has similar top-10 features according to similarity score of 0.6718 using shapley additive explanations (SHAP) feature importance which is higher than those from GAN-based method for 0.4218 in average. Additionally, experimental result to evaluate accuracy shows that F1 score from explainable models from the Bee-based method are closed to F1 score from a model generated from the original dataset.
Sentiment analysis of student evaluation feedback using transformer-based language models
Authors: Daqiqil ID Ibnu , Saputra Hendy , Syamsudhuha Syamsudhuha , Kurniawan Rahmad , Andriyani Yanti
Published: August 2024

This paper proposes an approach to sentiment analysis of student evaluation feedback using transformer-based language models. The primary objective of this study is to conduct an in-depth analysis of sentiment expressed in student evaluation feedback, with a focus on introducing contextual understanding into the sentiment classification process. In this research, four different variants of transformer language models were assessed, namely multilingual bidirectional encoder representations from transformers (MBERT), IndoBERT, RoBERTa Indonesia, and generative pre-trained transformer (GPT-2 Indonesia). Additionally, we also compared the performance of transformer models with two traditional models, namely support vector machine (SVM) and Naive Bayes (NB). The evaluation was conducted using feedback data collected from the Evaluasi Dosen oleh Mahasiswa (EDOM) system at Riau University, which had been categorized as either positive or negative. The outcomes indicate that IndoBERT base uncased exhibits the highest performance, with precision, accuracy, and recall values of 0.858, 0.929, and 0.911, respectively. This observation highlights the effectiveness of transformer-based language models in sentiment analysis of student evaluation feedback and provides insights for improving educational assessment practices.
Enhancing phishing URL detection through comprehensive feature selection: a comparative analysis across diverse datasets
Authors: Preeti Preeti , Sharma Priti
Published: August 2024

Malicious attacks have developed a prominent risk to the safety of online users, with attackers employing increasingly sophisticated systems to deceive unsuspecting victims. This research focuses on the critical aspect of feature selection in optimizing phishing uniform resource locator (URL) detection system. Feature selection boosts machine learning (ML) and deep learning (DL) by picking vital attributes efficiently. This research paper provides a comprehensive examination of feature selection techniques using five diverse datasets. Various methods, including random forest (RF) select from model, SelectKBest with chi-square statistic, principal component analysis (PCA) and recursive feature elimination (RFE), were employed. The experiments, with a particular emphasis on PCA and fourth dataset, revealed that all four models RF, decision trees (DTs), XGBoost, and multilayer perceptron) achieved 100% accuracy in detecting phishing URL attacks. This underscores the efficacy of feature selection methods in enhancing to a deeper understanding of feature selection’s role in bolstering the effectiveness of phishing detection system across diverse datasets, highlighting the importance of leveraging techniques such as PCA for optimal results.
DDoS-attacks prevention using MinE-DT an adaptive security and energy optimization integration of NIPS in wireless sensor networks
Authors: Ramachandra Bharathi , Surekha T. P.
Published: August 2024

Wireless sensor networks (WSNs) have revolutionized data collection in diverse environments, from industrial settings to natural ecosystems. However, their decentralized nature and energy constraints pose unique security and operational challenges. Previous research provided foundational insights into WSN security but lacked comprehensive strategies for real-time intrusion prevention and efficient energy utilization. Our work employs a multi-layered approach, integrating network intrusion prevention systems (NIPS) with WSNs and leveraging machine learning for threat detection. We developed MinE-DT (minimum energy-direct transmission) hybrid routing an integrated WSN model that not only identifies and mitigates distributed denial-of-service (DDoS) attack but also optimizes energy consumption, ensuring prolonged network longevity without compromising security. The proposed model's distinctiveness lies in its fusion of NIPS with energy-saving algorithms, offering a dual advantage of enhanced security and energy efficiency. Utilizing a combination of simulations and theoretical analysis, our methodology yielded promising results, showcasing significant improvements in threat detection rates and energy conservation.
Network routing and scheduling architecture in a fully distributed cloud computing environment
Authors: Kumar S Vijaya , Periyasamy Muthusamy , Radhakrishnan R. , Karuppiah Tamilarasi , Elumalai Thenmozhi
Published: August 2024

Distributed computing has turned into an indispensable application administration because of the colossal development and fame of the internet. However, determining the allocation of various tasks to suitable service nodes is crucial. For the reasons expressed over, an effective booking strategy is expected to work on the framework’s exhibition. As a result, three-layer cloud dispatching (TLCD) design is introduced to further develop mission planning execution. The assignments should be arranged into various sorts in the primary layer in radiance of about their personalities clustering selection algorithm is composed of then recommended in second layer towards dispatch the undertakings to significant help bunches. Likewise, to further develop booking effectiveness, another planning technique for third stage proposes dispatching that job here to system thinking in a central server. As a rule, the proposed TLCD design yields the quickest work finishing time. Moreover, in cloud computing network architecture, load balancing and stability can be achieved.
Optimal size and allocation of wind distributed generation in distribution network using particle swarm optimization
Authors: Sankepally Swathi , Bali Sravana Kumar
Published: August 2024

The aim of this research is to evaluate the performance of the distribution network by connecting wind distributed generation (DG) and determining the optimal location and size using the particle swarm optimization (PSO) technique, once the wind DG is connected at the optimal location, the output of wind turbines is not constant but varies with changes in wind speed. Wind turbines are designed to generate the energy from the wind. As the output of the wind turbines changes, it influences the power flow and voltage levels in the distribution network. The injection of power from the wind turbines can cause variations in voltages within the distribution network. Additionally, the changing power flow may contribute to power losses in the distribution system. In this paper, the voltages and active power losses are evaluated with the change in wind speed for the IEEE 15 Bus system by conducting load flow analysis in MATLAB. The results reveal optimized solutions that contribute to reduced power losses, increased renewable energy generation, and improved voltage profiles. This research underscores the potential of PSO-based optimization in conforming more efficient and sustainable distribution networks.
Characterization of UF-18 cacao pods using Arduino-based load compressor testing machine
Authors: Dayaday Maricel Gamolo , Alucilja Renel M. , T. Cuarteros Ritchell Joy , Lavarias Jeffrey A.
Published: August 2024

Bean damage is one of the primary concerns in the pod-breaking process. Studies for pod-breaking machines are ongoing to ensure that the products made from these machines are of good quality. The objective of the study is to determine the physical and mechanical characteristics of the UF-18 pod. The Arduino-based load compressor testing machine was designed and developed to characterize the UF-18 pod. It was found that the average geometric mean diameter, surface area, and sphericity index of 115.37 mm, 41,899.48 mm², and 0.6372, respectively, and with a variation of ±27.17, ±14538133.04, and ±0.00038 respectively. Furthermore, the cacao pod samples had an average dimension of 181.29 mm, 94.26 mm, 90.01 mm, and 17.44 mm measured for the length, equatorial diameter, intermediate diameter and external thickness, respectively. Different pod sizes and thicknesses require various forces ranging from 36.94 to 92.42 kg (362.38 N to 906.64 N) and time ranging from 6-11 seconds to be able to break the pods. Determining the physical and mechanical properties of cacao pods enables fabricators to design efficient machines, which lessens the force to break and the damage to the beans, thus producing quality beans.
Ultra-miniaturized dual-band implantable antenna for retinal prosthesis
Authors: Bousrout Abdelmouttalib , Khabba Asma , Ibnyaich Saida , Mazri Tomader , Habibi Mohamed , Sutikno Tole
Published: August 2024

This article presents two miniaturized antennas designed for retinal prosthesis devices, aimed at enhancing vision for blind individuals with functional optic nerves. The implantable antenna is 2.2 mm wide, 2.15 mm tall, and 0.78 mm thick. It works in the ISM bands but is small because it uses slot incorporation and high-permittivity substrates. High-frequency structure simulator (HFSS) electromagnetic simulations show great performance, with a 16.66% impedance bandwidth at 2.4 GHz and a 10.34% bandwidth at 5.8 GHz. The peak gain values are -27.76 dB at 2.4 GHz and -16.40 dB at 5.8 GHz. We have also developed an extraocular antenna for telemetry and energy transfer, with dimensions of 36×36×1.6 mm3 . Validation through CST calculation software confirms the efficacy of both antenna designs. Implantable antennas hold significant promise in biomedical antenna research, demonstrating capabilities conducive to retinal implantation and offering potential advancements in vision restoration technology.
Effects of TiO2 in graphene-quantum-dot film on lighting color uniformity of a white light-emitting diodes
Authors: Le Phan Xuan , Cong Pham Hong
Published: August 2024

Improvement in color uniformity of white light-emitting diodes (WLED) is one of the imperative goals for high-quality solid-state illumination. The conventional WLED model with a single yellow phosphor YAG:Ce3+ (TiO2@GD) is proposed to fulfill this goal. The TiO2@GD composites prove to possess excellent biocompatibility, low toxicity, and thermal and chemical stability, holding great potential in high-power WLED production. By maintaining a constant GDs content of 10 wt%, the research explores the impact of varying TiO2 doping concentrations on the lighting performance of the WLEDs via the mean of light scattering. The TiO2@GD layer also induces a red-shift in the emitted light spectrum, contributing to a reduction in color variation. While a decline in luminosity and color rendering performance becomes evident with excessive TiO2 content, the study underscores the potential of TiO2@GD as a viable diffusing layer for LEDs to obtain improved angular uniformity of color distribution.
Exploring the landscape of dysarthric speechrecognition: a survey of literature
Authors: Antony Aneeta S. , Nagapadma Rohini , Abraham Ajish K.
Published: August 2024

Automatic speech recognition (ASR) is a valued tool for individuals with dysarthria, a speech impairment characterized by various pathological traits that differ from healthy speech. However, recognizing dysarthric speech, which is spoken by individuals with speech impairments, poses unique challenges due to its diverse characteristics such as rugged pronunciation, loudness that varies at different intervals, speech that has lot of delays, pauses that are inpredictable, excessive nasal sounds, explosive pronunciation, and airflow noise. The survey reveals the various models for dysarthric speech recognition. Deep learning technologies, unfurls an improved ASR performance leaps and bounds breaking the fluency and pronunciation barriers. Various feature extractions and identification of different types of dysarthria, including spastic, mixed, ataxic, hypokinetic, and hyperkinetic are explored. The performance of contemporary deep learning approaches in dysarthric speaker recognition (DSR) is tested using various datasets to determine accuracy. In conclusion the most effective DSR strategies are identified and areas for future investigation is suggested. However, speaker-dependent difficulties restrict the generalizability of acoustic models, and a lack of speech data impedes training on large datasets. The study throws light on how the effectiveness of ASR for dysarthric speech can be improved and further areas of research in the area are highlighted.
Modified back-line inset feed 1x4 array microstrip antenna for 5.8 GHz frequency band
Authors: Hasan Md Fazlul , Awang Mat Dayang Azra , Sayed Md Abu
Published: August 2024

This paper presents the design of 1x4 array microstrip antenna utilizing modified backline feeding technique at 5.8 GHz frequency band. The antenna, designed on flame retardant (FR-4) substrate with a dielectric constant of 4.4, aims to achieve reduced harmonics and mutual coupling between closely spaced antenna elements. The primary scope of the paper is investigating the performance of a single band microstrip antenna employing the proposed modified backline feeding method. Moreover, developed design came out with the result and critical analysis by various parameters such as, gain, return loss, voltage standing wave ratio (VSWR), and directivity. Therefore, the proposed design of microstrip antenna with backward linefeed (BLF) demonstrates a directivity of 10.29 dBi, return loss of -21.947 dB, and VSWR of 1.173; are significant improvement compared to recent literature shown in this paper. The adoption of proposed back line feeding technique (BLF) represents a promising alternative for addressing poor wireless connectivity issues in terms of antenna design, gain, and direction within microstrip technology.
Microstrip patch antenna for energy harvesting in smart buildings
Authors: E. Brucal Stanley Glenn , M. Africa Aaron Don , P. Chavez Julian Carlos , Devera Nathan H. , A. Escamilla Philip Martin Emmanuel , L. Payuyo John Louie
Published: August 2024

The present study analyzes the microstrip antenna design for wireless power transfer in smart buildings, harnessing the ambient electromagnetic radiation due to common electronic gadgets that energize wireless sensor networks, computing devices, and connected appliances. With the increased number of these devices, so does the potential for health problems caused by electromagnetic radiation. However, these devices also provide a renewable energy source through their emissions. This study suggests the creation of a 5G Microstrip antenna that enhances the absorption of this radiation for the purpose of recharging batteries in smart buildings. The design capitalizes on the inherent low-profile and cost-effective features of microstrip antennas, making them well-suited for incorporation into building infrastructure and 5G wireless technologies. Although each individual device emits a little amount of energy, the combined effect achieved by advanced antenna design and power converters is anticipated to result in a substantial energy production. The antenna designer tool from MATLAB was used to carry out a conceptual simulation of the microstrip antenna. This has set up the framework of a feasible way of predicting the performance with high efficiency and sustainability for a wireless power transfer (WPT) system.
Classification of endometrial adenocarcinoma using histopathology images with extreme learning machine method
Authors: Rulaningtyas Riries , Rahaju Anny Setijo , Dewi Rosa Amalia , Hanifah Ummi , Purwanti Endah , Rahma Osmalina Nur , Katherine Katherine
Published: August 2024

As many as 70-80% of endometrial cancer cases are endometrial adenocarcinoma. Histopathological assessment is based on the degree of differentiation, into well-differentiated, moderate-differentiated, and poorly-differentiated. Management and prognosis differ between grades, so differential diagnosis in determining the degree of tumor differentiation is crucial for appropriate treatment decisions. Histopathological image analysis offers detailed diagnostic results, but manual analysis by a pathologist is very complicated, error-prone, quite tedious, and time-consuming. Therefore, an automatic diagnostic system is needed to assist pathologists in grading the tumor. This research aims to determine the degree of differentiation of endometrial adenocarcinoma based on histopathological images. The extreme learning machine (ELM) method performs image classification with gray level run long matrix (GLRLM) features and a combination of local binary pattern (LBP)-GLRLM features as input. Experimental results show that the ELM model can achieve satisfactory performance. Training accuracy, testing accuracy, and model precision with GLRLM features were 97.13%, 91.33%, and 80% and combined LBPGLRLM features were 91.03%, 71.33%, and 100%. Overall, the model created can determine the degree of tumor differentiation and is useful in providing a second opinion for pathologists.
Mutual information-MOORA based feature weighting on naive bayes classifier for stunting data
Authors: Prabiantissa Citra Nurina , Hakimah Maftahatul , Rozi Nanang Fakhrur , Puspitasari Ira , Yamani Laura Navika , Mahendra Victoria Lucky
Published: August 2024

One effort to reduce stunting rates is to predict stunting status early in toddlers. This study applies Naive Bayes (NB) to build a stunting prediction model because it is simple and easy to use. This study proposes a filter-based feature weighting technique to overcome the NB assumption, which states that each feature has the same contribution to the target. The frequency of an event in a dataset influences the feature weighting using mutual information criteria. This is the gap in the filter-based ranking highlighted in this study. Therefore, this study proposes a feature-weighting method that combines mutual information with the MOORA (MI-MOORA) decision-making method. This technique makes it possible to include external factors as criteria for ranking important features. For stunting cases, the external consideration for ranking purposes is the assessment of nutrition experts based on their experience in dealing with stunted toddlers. The MI-MOORA technique makes the availability of clean water the most influential feature that contributes to the stunting status. In the ten best features, the MI-MOORA ranking results are dominated by family factors. Based on the performance evaluation results of NB and other classifiers, MI-MOORA can improve the performance of stunt prediction models.
Deflection Behavior of Reinforced Concrete Beam Frame System with 3/4 Spacing Effective Height of The Beam
Authors: Huwae David D. M. , Frans Pieter L. , Parera Lydia R.
Published: August 2024

This study aimed to analyze the deflection behavior of reinforced concrete beams with a spacing of 3/4 of the effective beam height. This research is a laboratory experimental study with a design of 6 (six) test objects consisting of 3 (three) normal beams (BN) as control variable beams and 3 (three) reinforcing beams of the frame system with a spacing of 0.75d (BTR75 ) as the independent variable. Data were analyzed using the strength design method. The results showed that the deflection behavior of reinforced concrete beams with a spacing of 3/4 of the effective beam height (BTR75) had better serviceability and increased the flexural capacity of Mu beams up to 4.60% and reduced the amount of deflection of the beam BN.
Perfil epidemiológico dos casos de distúrbios osteomusculares na população brasileira no período de 2018 a 2023
Authors: Gonçalves Beatriz Alves , Ferreira Vinícius Ryan de Melo , Siqueira Francisléia Falcão França Santos , Feitosa Ana Lys Marques
Published: August 2024

Introduction: Work-Related Musculoskeletal Disorders (WMSDs) have emerged as a worrying category of injuries affecting the musculoskeletal system, influenced by laboratory conditions. With the COVID-19 pandemic, there has been a significant shift to remote work, increasing the risks of musculoskeletal disorders. The epidemiological profile of RSI/WMSD cases in the Brazilian population during the period 2018 to 2023 was investigated. Material and Methods: A descriptive and ecological approach with quantitative time series analysis was used to investigate the incidence of Work-Related Musculoskeletal Disorders (RSI/WMSD) among Brazilian workers aged between 20 and 64 using data provided by DATASUS. Results: Brazil recorded 46,307 cases of RSI/WMSD, with a peak in 2023. Men (53.13%) were more affected than women (46.87%). The age group most affected was 40 to 59 (56.1% of the total). The states most affected were Rio de Janeiro, São Paulo, Minas Gerais and Rio Grande do Sul. Conclusion: The specific drop in the 20-34 age group suggests the effectiveness of preventive measures. Disparities by gender, age, race and region highlight the importance of integrated approaches adapted to the diversity of the Brazilian labor scenario.
Desinformação e implicações para a democracia: reflexões a partir dos atentados de 8 de janeiro
Authors: Mendes Conrado Moreira , Sanglard Fernanda Nalon , Costa Verônica Soares da
Published: August 2024

A partir de uma articulação entre Semiótica Discursiva e Sociossemiótica, estudos sobre plataformização e teorias sobre esfera pública e democracia, este artigo busca refletir sobre a noção de desinformação a partir de um episódio específico, o atentado de 8 de janeiro no Brasil, compreendido como tentativa de golpe para destituição de um presidente eleito. Considerado um dos principais ataques à democracia na contemporaneidade, o episódio e seu contexto servem de exemplo reflexivo para a compreensão das implicações da desordem informacional e do uso das plataformas de mídias sociais, considerados atores importantes sem os quais não se pode mais pensar a democracia e as ameaças a ela.
Characterization and Clinical Significance of Hemolysis After Pulsed Field Ablation for Atrial Fibrillation: Results of a Multicenter Analysis
Authors: Popa Miruna A. , Venier Sandrine , Menè Roberto , Della Rocca Domenico Giovanni , Sacher Frédéric , Derval Nicolas , Hocini Mélèze , Dulucq Stéphanie , Caluori Guido , Combes Stéphane , Albenque Jean-Paul , Saitta Federica , Haller Bernhard , Chierchia Gian-Battista , de Asmundis Carlo , Defaye Pascal , Boveda Serge , Jaïs Pierre
Published: August 2024

BACKGROUND:

Pulsed field ablation (PFA) is increasingly used in clinical practice for the treatment of atrial fibrillation. While the susceptibility of erythrocytes to electroporation is well established, the effect of cardiac PFA technologies on hemolysis has remained underreported. The aim of this study was to investigate the incidence, severity, and clinical impact of PFA-induced hemolysis.

METHODS:

We included n=145 patients undergoing atrial fibrillation catheter ablation with a pentaspline PFA catheter (biphasic, bipolar pulses of 2 kV) and n=70 patients receiving radiofrequency ablation (40–90 W) at 4 high-volume European centers. The lesion set comprised pulmonary vein isolation for paroxysmal atrial fibrillation and pulmonary vein isolation±additional lesions for persistent atrial fibrillation. Hemolysis and renal function biomarkers were analyzed in blood samples at baseline, at the end of ablation, and 24 hours after the procedure.

RESULTS:

Baseline characteristics were well balanced between groups (overall mean 65.7±9.4 years; 69.3% men). The ablation procedures comprised a mean of 61.6±27.4 PFA deliveries and 26.3±15.0 minutes RF duration. Hemolysis was detected in 94.3% versus 6.8% of patients after PFA versus radiofrequency ablation (
P
<0.001): PFA was associated with significantly lower haptoglobin levels (0.5±0.4 versus 1.0±0.4 g/L), while free plasma hemoglobin (592.8±330.6 versus 147.8±183.0 mg/L), bilirubin (21.3±11.3 versus 14.8±8.8 µmol/L), and LDH (lactate dehydrogenase, 352.7±115.7 versus 253.2±56.5 U/L) were significantly higher after PFA compared with radiofrequency ablation (all
P
<0.001). Hemolysis correlated with the number of PFA deliveries (r=0.62 [95% CI, 0.33–0.80];
P
<0.001), with the highest severity occurring ≥54 PFA deliveries. After PFA, hemoglobinuria occurred in 36.4%, while creatinine increase was higher in patients with baseline glomerular filtration rate <50 mL/min than with baseline glomerular filtration rate >50 mL/min (Δcrea, 27.0±103.1 versus −0.2±12.1 µmol/L;
P
=0.010).

CONCLUSIONS:

Intravascular hemolysis is a frequent finding after PFA and increases with the number of PFA deliveries. Until the clinical impact of PFA-associated hemolysis is fully elucidated, a careful titration of PFA deliveries during the ablation procedure is warranted.
Prototype Analysis in Hopfield Networks With Hebbian Learning
Authors: McAlister Hayden , Robins Anthony , Szymanski Lech
Published: August 2024

We discuss prototype formation in the Hopfield network. Typically, Hebbian learning with highly correlated states leads to degraded memory performance. We show that this type of learning can lead to prototype formation, where unlearned states emerge as representatives of large correlated subsets of states, alleviating capacity woes. This process has similarities to prototype learning in human cognition. We provide a substantial literature review of prototype learning in associative memories, covering contributions from psychology, statistical physics, and computer science. We analyze prototype formation from a theoretical perspective and derive a stability condition for these states based on the number of examples of the prototype presented for learning, the noise in those examples, and the number of nonexample states presented. The stability condition is used to construct a probability of stability for a prototype state as the factors of stability change. We also note similarities to traditional network analysis, allowing us to find a prototype capacity. We corroborate these expectations of prototype formation with experiments using a simple Hopfield network with standard Hebbian learning. We extend our experiments to a Hopfield network trained on data with multiple prototypes and find the network is capable of stabilizing multiple prototypes concurrently. We measure the basins of attraction of the multiple prototype states, finding attractor strength grows with the number of examples and the agreement of examples. We link the stability and dominance of prototype states to the energy profile of these states, particularly when comparing the profile shape to target states or other spurious states.
Predictive Representations: Building Blocks of Intelligence
Authors: Carvalho Wilka , Tomov Momchil S. , de Cothi William , Barry Caswell , Gershman Samuel J.
Published: August 2024

Adaptive behavior often requires predicting future events. The theory of reinforcement learning prescribes what kinds of predictive representations are useful and how to compute them. This review integrates these theoretical ideas with work on cognition and neuroscience. We pay special attention to the successor representation and its generalizations, which have been widely applied as both engineering tools and models of brain function. This convergence suggests that particular kinds of predictive representations may function as versatile building blocks of intelligence.
The Limitations of Large Language Models for Understanding Human Language and Cognition
Authors: Cuskley Christine , Woods Rebecca , Flaherty Molly
Published: August 2024

Researchers have recently argued that the capabilities of Large Language Models (LLMs) can provide new insights into longstanding debates about the role of learning and/or innateness in the development and evolution of human language. Here, we argue on two grounds that LLMs alone tell us very little about human language and cognition in terms of acquisition and evolution. First, any similarities between human language and the output of LLMs are purely functional. Borrowing the “four questions” framework from ethology, we argue that what LLMs do is superficially similar, but how they do it is not. In contrast to the rich multimodal data humans leverage in interactive language learning, LLMs rely on immersive exposure to vastly greater quantities of unimodal text data, with recent multimodal efforts built upon mappings between images and text. Second, turning to functional similarities between human language and LLM output, we show that human linguistic behavior is much broader. LLMs were designed to imitate the very specific behavior of human writing; while they do this impressively, the underlying mechanisms of these models limit their capacities for meaning and naturalistic interaction, and their potential for dealing with the diversity in human language. We conclude by emphasising that LLMs are not theories of language, but tools that may be used to study language, and that can only be effectively applied with specific hypotheses to motivate research.
Resistance from the Margins: ‘Untouchable’ Women’s Voices vis-à-vis the Gorkha Identity and the Gorkhaland Movement
Authors: Singh Jenisha , Singh Prabudh
Published: August 2024

One of the most conspicuous features of the Gorkhaland movement is the development of a strong sense of community that has subsumed many diverse voices within the dominant discourse of ethnicity. However, such ethno-nationalism is also problematic as it often results in the erasure of intersecting identity markers such as class, caste, and gender. In addition, the exclusive nature of the over-encompassing Nepali or Gorkha identity has led to harmful consequences for marginalized groups, particularly the lower castes. This paper thus brings to the fore alternative voices and perspectives, particularly those of women who come from the margins of caste society or who have been labelled as ideological adversaries. This paper further illustrates their unique experiences and perspectives, which are often ignored in mainstream discussions about the social reality of the Gorkhaland movement.
Exploring disparities of teleconsultation readiness: A comparative analysis of healthcare facilities in Indonesia
Authors: Nugroho Daniel Chriswinanto Adityo , Adisaputro Katherina , Sigilipoe Mitra Andini , Triastuti Ida Ayu , Hutomo Suryani , Septarda Ardhinta Bellano , Yuwono Aryososepti , Yusuf Caraka Randi , Sulistiawan Dedik , Gusbela Balqis , Hsu Jason C , Su Emily Chia-Yu
Published: August 2024

Objectives

To investigate the implementation of teleconsultation and assess the level of readiness for its adoption among various types of healthcare facilities

Methods

This cross-sectional study involved medical doctors working in a public hospital, a private hospital, and community health centers in Yogyakarta, Indonesia. We recruited 29 medical specialists from various departments in two hospitals and 27 heads of community health centers. The readiness items were categorized into sections that encompassed various readiness areas such as core, technological, motivational, learning, work culture, and policy readiness. Data were analyzed using a one-way analysis of variance and the Kruskal-Wallis test to evaluate differences in levels of readiness across healthcare facilities. A logistic regression analysis was conducted to further assess factors predicting the implementation of teleconsultation.

Results

Variations in technological readiness were observed between the community health centers and the public hospital ( p = 0.006) and the private hospital ( p = 0.007). Differences in learning readiness were found between the public hospitals and private hospitals ( p = 0.01). There were also disparities in cultural readiness between the public hospital and the private hospital ( p = 0.04) and between public hospital and community health centers ( p = 0.01). Logistic regression revealed an association between technological readiness and the use of video teleconsultation (OR  = 1.13; p = 0.017). The private hospital was more likely to implement video-based teleconsultation than was the public hospital (OR  = 2.68; p = 0.003) or community health centers (OR  = 3.13; p ≤ 0.001).

Conclusion

Significant differences in technology readiness were identified among community health centers, public hospitals, and private hospitals. Future policy implementation should focus on customizing technology use and providing cultural training to help healthcare institutions with different technological readiness levels.