bluuebunny/crossref_metadata_2025 huggingface dataset:
Non‐ionotropic NMDAR signalling activates Panx1 to induce P2X4R‐dependent long‐term depression in the hippocampus
Authors: Nielsen Allison C. , Anderson Connor L. , Ens Carina , Boyce Andrew K. J. , Thompson Roger J.
Published: December 2024

In recent years, evidence supporting non‐ionotropic signalling by the NMDA receptor (niNMDAR) has emerged, including roles in long‐term depression (LTD). Here, we investigated whether niNMDAR‐pannexin‐1 (Panx1) contributes to LTD at the CA3–CA1 hippocampal synapse. Using whole‐cell, patch clamp electrophysiology in rat hippocampal slices, we show that a low‐frequency stimulation (3 Hz) of the Schaffer collaterals produces LTD that is blocked by continuous but not transient application of the NMDAR competitive antagonist, MK‐801. After transient MK‐801, LTD involved pannexin‐1 and sarcoma (Src) kinase. We show that pannexin‐1 is not permeable to Ca2+, but probably releases ATP to induce LTD via P2X4 purinergic receptors because LTD after transient MK‐801 application was prevented by 5‐BDBD. Thus, we conclude that niNMDAR activation of Panx1 can link glutamatergic and purinergic pathways to produce LTD following low frequency synaptic stimulation when NMDARs are transiently inhibited.

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Key points

Differential effect of short‐term D‐APV and MK‐801 application on long‐term depression (LTD) suggests that the NMDA receptor (niNMDAR) contributes to later phases of synaptic depression.

niNMDAR LTD involved sarcoma (Src) kinase and pannexin‐1 (Panx1), which is a pathway previously identified to be active during excitotoxicity.

Panx1 was not calcium permeable but may contribute to late phase LTD via ATP release. Panx1 blockers prevent LTD, and this was rescued with exogenous ATP application. Inhibition of LTD with 5‐BDBD suggests the downstream involvement of postsynaptic P2X4 receptors.
Multifocal neuromuscular hamartoma with smooth muscle and Schwannian components
Authors: Atchison Thomas Joseph , Ilyas Farhan , Satturwar Swati , Beane Joal D
Published: December 2024

A male in his early 20s with gastro-oesophageal reflux disease and severe weight loss was found to have two intrabdominal masses causing his symptoms: one in the gastro-oesophageal junction and the other occupying the coeliac plexus in the cardiophrenic region. These masses were surgically removed and sent to pathology where they were found to be smooth muscle hamartomas with Schwannian components. These represent a unique presentation of benign smooth muscle tumours that is not typically seen in young adults, especially in the cardiophrenic region involving the coeliac plexus.
Exogenous progesterone-induced acute urticaria in association with in vitro fertilisation
Authors: Clara Joseph Shilpa , S Jayashree , P K Ashwini , Palaksha Shilpa
Published: December 2024

Exogenous progesterone aids in enhancing embryo implantation and consequently pregnancy rates, which makes it crucial for both establishing and sustaining pregnancy during in vitro fertilisation (IVF) treatment. Hypersensitivity reaction following administration of progesterone is a rare adverse drug reaction. We present the case of a woman in her early 30s with primary infertility who presented with a generalised itchy rash since the evening before admission. The patient had generalised wheals on examination. The day before her admission, as part of her IVF procedure, she had taken progesterone. The diagnosis of exogenous progesterone-induced acute urticaria was made by de-challenging and then re-challenging the suspected drug after all other possible explanations had been ruled out. The patient was given symptomatic treatment, which led to the subsiding of the reaction. She was followed up periodically and did not observe a recurrence of the rash. She conceived in this treatment cycle and delivered a healthy child.
Management of refractured mandibular condyle after open reduction and internal fixation
Authors: Khandelwal Garima , Yadav Rahul , Puthukkudiyil Jithin Sasikumar
Published: December 2024

The purpose of the present case is to highlight the surgical management of the refractured condylar segment of the mandible after open reduction and internal fixation. A man in his 20s reported to the department with an alleged history of road traffic injury (RTI). He was diagnosed with a fracture of the right parasymphysis and left subcondylar region of the mandible. He was managed surgically using 2 mini plates of 2.0 mm in relation to the condyle, one 5-hole miniplate in relation to the posterior border of the mandible, one 3-hole miniplate in relation to the anterior border of the condyle. A 2.4 mm 8-hole reconstruction plate was placed in relation to the right parasymphysis region of the mandible. He again reported to the department of oral and maxillofacial surgery after 25 days with an alleged history of RTI. After a complete examination, he was diagnosed with a fracture of the symphysis and left subcondylar region of the mandible. He was managed surgically using 11-hole reconstruction plate 2.4 mm with bicortical screws to fix symphysis fracture of the mandible. A 4-hole high-profile matrix mandible 2.0 mm plate was used to fix the left subcondylar fracture. Now the patient is having good mouth opening, preinjury occlusion and a good range of functional movements of the mandible.

After functionally stable fixation of the condyle and rigid fixation of the anterior mandible, refracture is rarely reported. The anatomy of the condyle is complex, so after the removal of the miniplates, the bone available is less. The purpose of reporting the present case is to highlight the management of refracture of the condyle using matrix mandible plate. The use of a synthes matrix mandibular plate is promising in such cases and can be used effectively.
Challenges in managing an incidentally diagnosed left atrial myxoma during pregnancy
Authors: Rajendran Priyanka , Ramasamy Kokila , Pegu Bhabani , KS Prasad Sreevathsa , Pant Bhagwati Prasad , Dorairajan Gowri , Keepanasseril Anish
Published: December 2024

Atrial myxoma, a rare benign tumour of the heart, predominantly affects women in the reproductive age group. There is no consensus on the monitoring and treatment of atrial myxoma in pregnancy, as the experience is limited to a few cases reported in the literature. We report a case of a pregnant individual referred with suspected left atrial thrombus, finally diagnosed to have pedunculated left atrial myxoma and underwent successful resection along with caesarean section, resulting in good maternal–fetal outcomes.
Renoprotective effects of coenzyme Q10 supplementation in patients with chronic kidney disease: a protocol for a systematic review
Authors: Abe Yoshifusa , Nishiwaki Hiroki , Suzuki Taihei , Noma Hisashi , Watanabe Yoshitaka , Ota Erika , Hasegawa Takeshi
Published: December 2024

Introduction

Coenzyme Q10 (CoQ10) is a fat-soluble vitamin-like quinone. The plasma levels of CoQ10 are reduced in patients with chronic kidney disease (CKD). CoQ10 supplementation can improve mitochondrial function and decrease oxidative stress in these patients. This systematic review will assess the renoprotective effects of CoQ10 supplementation in patients with CKD.

Methods and analysis

We will include the following studies: (1) randomised-controlled trials, (2) participants with CKD and (3) participants treated with CoQ10 as an intervention. The systematic review protocol was prepared in accordance with the Preferred Reporting Items for Systematic Review and Meta-Analysis Protocols. MEDLINE, EMBASE, Cochrane Central Register of Controlled Trials and International Clinical Trials Register databases will be searched for articles without language restrictions in December 2024. The authors will be divided into two groups. Two independent authors will screen the titles and abstracts of all reports extracted via an electronic search. After the initial screening, the authors will independently review the full-text articles and perform a directed content analysis of the extracted data. For outcomes measured using continuous scales of measurement, we will adopt standardised mean differences as the effect measures. We will pool the data using the random‐effects model.

Ethics and dissemination

No human participants will be involved in the study. On completion of the analysis, the manuscript will be prepared for publication in a peer-reviewed journal.

PROSPERO registration number

CRD42021241085.
Protocol for a multi-domain scoping review to identify measures of decision-making ability in an ageing population
Authors: Ho Emily H. , Ece Berivan , Novack Miriam A. , Pila Sarah , Karpouzian-Rogers Tatiana , Mather Molly A , Dworak Elizabeth McManus , Hosseinian Zahra , Bucko Patricia , Kaat Aaron James , Han S. Duke , Lichtenberg Peter , Hanmer Janel , Miller Corinne H , Gershon Richard , Weintraub Sandra
Published: December 2024

Introduction

Deficits in decision-making (DM) can lead to adverse outcomes across multiple domains such as financial management and medical care. By hindering such DM abilities, cognitive impairment (CI) often affects quality of life. Routine screening for CI, however, does not include systematic and comprehensive assessment of DM ability. While there are many DM measures, there is considerable heterogeneity in what constructs are measured and the populations in which they have been validated. This scoping review protocol details the proposed comprehensive examination of published literature on DM measures used in adults aged 45 years and older.

Methods and analysis

Arksey and O’Malley’s (2005) scoping review methodology framework will guide the current scoping review. We will systematically search Embase (Elsevier), MEDLINE (Ovid), PsycINFO (Ebsco), Cochrane Library (Wiley), Web of Science (Clarivate) and Scopus (Elsevier) to identify the studies meeting our eligibility criteria. We will review literature published between January 2018 and November 2023. Following best practice recommendations, a two-stage study selection process will be applied. First, titles and abstracts will be independently reviewed by two reviewers based on our eligibility criteria. Second, full texts will be screened and data will be extracted only from the articles included after the full review. The results of the current scoping review will be reported in adherence to PRISMA-Scr guidelines.

Ethics and dissemination

This study does not require ethics approval as the scoping review methodology involves reviewing results from publicly available published materials. Results of the scoping review will be submitted for publication in a peer-reviewed journal.

Trial registration number

NIA grant number 1U24AG082022-01.
Patients’ perceived involvement in clinical decision-making and associated factors among adult patients admitted at Jimma Medical Center, Oromia, Ethiopia: a cross-sectional study
Authors: Dibera Keneni Dina , Debancho Wadu Wolancho , Bedada Gadisa Bekele , Dugasa Yonas Gurmu
Published: December 2024

Objective

To assess patients’ perceived involvement in clinical decision-making and associated factors among adult patients admitted at Jimma Medical Center, Oromia, Ethiopia, 2022.

Design

An institution-based cross-sectional study was employed.

Setting

A study was conducted at a governmental tertiary teaching and referral hospital located in Jimma Zone, Oromia region, southwestern part of Ethiopia. The study was conducted between 15 July and 7 September 2022.

Participants

A total of 422 randomly selected adult patients admitted at Jimma Medical Center were enrolled, after excluding those who were acutely ill during the study period.

Methods

Data were collected via a structured, interviewer-administered tool, entered into EpiData V.4.6 and exported to SPSS V.26 for analysis. Bivariable and multivariable logistic regression analyses were used to identify factors associated with the outcome variable. Significant factors were declared at p<0.05.

Main outcome measure

Patients’ perceived involvement in clinical decision-making (adequate involvement/poor involvement).

Results

A total of 51.4% (95% CI: 46.4 to 56.3) of respondents had adequate perceived involvement in clinical decision-making. Participants who were aged >60 years (adjusted OR (AOR): 2.01 (95% CI: 1.44 to 5.65)), college and above the level of education (AOR: 4.6 (95% CI: 1.6 to 13.26)), length of hospital stay >15 days (AOR: 2.8 (95% CI: 1.41 to 5.5)) and high health literacy level (AOR: 2.04 (95% CI: 1.34 to 4.21)) were more likely to be associated with adequate perceived involvement in clinical decision-making whereas non-prior hospitalisation (AOR: 0.493 (95% CI: 0.3 to 0.82)) and passive autonomy preference (AOR: 0.22 (95% CI: 0.13 to 0.37)) were less likely to be associated with adequate perceived involvement in clinical decision-making than each of their corresponding counterparts.

Conclusion

Only nearly half of the respondents had adequate perceived involvement in clinical decision-making. Collaborative autonomy preferences, high educational levels, long length of hospital stay, high health literacy level and patients in the older age group were among the factors significantly associated with respondents’ adequate perceived involvement in clinical decision-making. Healthcare professionals have to consider empowering patients’ perceived involvement in clinical decision-making, and hospital management has to reinforce healthcare professionals’ efforts to promote patients’ perceived involvement in clinical decision-making.
Safe, effective and cost-effective oxygen saturation targets for children and adolescents with respiratory distress: protocol for a randomised controlled trial (OxyKids study)
Authors: Louman Sam , van Stralen Karlijn J , Koppelman Gerard , Vaessen-Verberne Anja , Bekhof Jolita , Bosmans J , Brackel Caroline , Kamps Arvid W A , de Kleer Ismé , Balemans Walter , Scheffer Mirjam , Brouwer Marianne , van den Beukel Marije , Andrinopoulou Eleni-Rosalina , Pijnenburg Mariëlle W H , Boehmer Annemie L M
Published: December 2024

Introduction

Little is known about the effectiveness and safety of oxygen saturation (SpO2) thresholds in children admitted with respiratory distress. The current 90%–94% threshold could lead to prolonged administration of supplemental oxygen, increased duration of hospital admissions, distress for children and families, and healthcare costs. To balance reducing unnecessary oxygen administration and preventing hypoxia, a lower SpO2 threshold of 88% for oxygen supplementation in children has been suggested. This trial aims to test the hypothesis that a lower SpO2 threshold of 88% safely reduces the length of hospital stay in children admitted with respiratory distress when compared with a 92% SpO2 threshold and to assess its cost-effectiveness.

Methods and analysis

This is a multicentre, open-label, randomised controlled trial with two parallel arms. A total of 560 children aged 6 weeks to 12 years admitted with a diagnosis of bronchiolitis, viral wheeze or lower respiratory tract infection will be recruited and equally randomised into an intervention or usual care arm. Intervention arm patients will receive supplemental oxygen if SpO2 falls below 88% or above 88% if deemed necessary by clinical staff. Control arm patients will receive supplemental oxygen if SpO2 falls below 92% or above 92% if deemed necessary by staff. The primary outcome is the time from admission to the time when all prespecified discharge criteria are met. Secondary outcomes are length of stay, safety (time to recovery, readmissions and paediatric intensive care admissions), quality of life, parental anxiety and societal costs. Patients are followed up by digital questionnaires up to 90 days postdischarge.

Ethics and dissemination

This study has received approval from the research ethics committee (REC) of Leiden, Den Haag and Delft (EU CT number 2023-504817-56). Written informed consent will be obtained from parents or guardians. Parents of patients and patient representatives are involved in all stages of the study, from design to results, interpretation and dissemination. The results of this trial will be disseminated via lay publications, peer-reviewed scientific journals and academic conferences.

Trial registration number

NCT06016244.
Physical activity and tobacco smoking in the German adult population
Authors: Acar Zeynep , Jackson Sarah , Klosterhalfen Stephanie , Kotz Daniel
Published: December 2024

Background

Physical inactivity and tobacco smoking remain the leading causes of morbidity and mortality worldwide. In Germany, smoking prevalence is high at around 30%, and only 45% achieve the WHO recommendation for physical activity (PA). Understanding how smoking and physical inactivity co-occur can inform interventions targeting these behaviours.

Methods

We analysed data from 4073 adults (≥18 years) participating in a national household survey between April and July 2022. We tested the association between PA level (not=reference/low/medium/very active) and smoking status (never=reference/ex/current smoker). Among current smokers, we tested associations between PA level and cigarettes smoked per day, time spent with and strength of urges to smoke and motivation to stop smoking.

Results

Overall, 29.9% (95% CI=28.5; 31.4) reported no leisure-time PA; among current smokers, it was 39.8% (95% CI=37.3; 42.4). Higher PA levels were associated with lower odds of being a current versus never smoker (OR=0.74, 95% CI=0.69; 0.79). Among current smokers, higher PA levels were associated with smoking fewer cigarettes per day (β=−0.98, 95% CI=−1.39; −0.56), weaker urges to smoke (OR=0.81, 95% CI=0.74; 0.89) and higher motivation to stop smoking (OR=1.13, 95% CI=1.02; 1.24). The association between PA and time spent with urges to smoke was not statistically significant (OR=0.93, 95% CI=0.85; 1.02).

Conclusion

People who are more physically active are less likely to smoke. Current smokers with higher PA levels smoke less, are less dependent and are more motivated to quit. Further research is required to determine whether these associations are causal and, if so, whether interventions to increase PA could help people to quit smoking.
Impact of lateral ankle sprains on physical function, range of motion, isometric strength and balance in professional soccer players
Authors: Wilczyński Bartosz , Cabaj Paulina , Biały Maciej , Zorena Katarzyna
Published: December 2024

Objective

Lateral ankle sprains (LASs) are prevalent in soccer and can affect long-term performance, injury recurrence and risk for chronic ankle instability. This case–control study examined functional impairments associated with LAS in professional soccer players aged 17–21.

Methods

40 players were divided into 2 groups: 21 with a history of LAS and 19 healthy matched controls. Functional assessments included the Foot and Ankle Ability Measure (FAAM), Y Balance Test (YBT), Weight Bearing Lunge Test, Heel Rise Test, Side Hop Test, and ankle and hip isometric strength measurements.

Results

Soccer players with a history of LAS exhibited significant deficits in dynamic balance, particularly in the anterior and composite YBT scores, with moderate effect sizes (p<0.05). FAAM scores also revealed functional limitations in daily activities and sports performance for the LAS group. No significant differences were observed in strength or mobility measures, including the Heel Rise, Side Hop and isometric tests. There were also no differences between injured and non-injured legs in the LAS group in the performance tests.

Conclusions

These findings highlight the persistent deficits in dynamic balance following LAS, emphasising the need for targeted rehabilitation to mitigate the risk of reinjury and enhance performance in soccer players.
Local Linear Wavelet Neural Network–Based Unscented Kalman Filter for Vehicle Collision Estimate Warning System and Ensuring Stable Vehicle‐to‐Infrastructure Communication
Authors: Lema Yonas Kebede , Mishra Satyasis , Gelmecha Demissie J.
Published: December 2024

The accident mortality rates are rapidly increasing due to driver inattention, and traffic accidents become a significant problem on a global scale. For this reason, advanced driver assistance systems (ADASs) are essential to enhance traffic safety measures. However, adverse environmental factors, weather, and light radiation affect the sensors’ accuracy. Furthermore, potential risks may go unreported if they obstruct the sensor’s line of sight or are outside its limited field of view. To overcome these problems, this research presents a vehicle collision estimate warning system that leverages a combined approach of a local linear wavelet neural network (LLWNN) and an unscented Kalman filter (UKF). The system integrates sensor data and vehicle‐to‐everything (V2X) communication to enhance the accuracy and reliability of vehicle state estimation and collision prediction. The LLWNN module is responsible for forecasting the future states of the vehicle based on historical sensor measurements. This powerful time‐series modeling technique allows the system to anticipate the vehicle’s trajectory and potential collision risks. The UKF then optimally fuses the LLWNN predictions with the real‐time sensor data, including information received through V2X communication, to generate accurate, up‐to‐date estimates of the vehicle’s state. The V2X technology enables the seamless exchange of critical safety information between the host vehicle, surrounding vehicles, infrastructure, and other road users. This includes data on vehicle position, speed, acceleration, and intended maneuvers. By incorporating this V2X‐based situational awareness, the system can better perceive the dynamic traffic environment and identify potential collision threats that may be outside the line of sight or detection range of the vehicle’s onboard sensors alone. The LLWNN‐based UKF module then processes this rich, multimodal data to provide timely and pertinent collision alerts to the driver. These alerts can warn the driver of impending collisions with distant objects, enabling them to take appropriate evasive action. By implementing this integrated LLWNN‐UKF approach leveraging sensor data and V2X communication, we aim to reduce the number of collisions caused by reckless driving, which will lead to a decrease in traffic‐related fatalities and injuries.
Immunohistochemical Detection of CD147 Expression in Adenocarcinoma of the Prostate: A Case‐Control Study
Authors: Suliman Sara , Ellaithi Mona
Published: December 2024

Prostate cancer is the most common noncutaneous malignancy among men worldwide, including in Sudan, where it represents a significant public health challenge. CD147, a transmembrane glycoprotein implicated in tumor progression, invasion, and metastasis, has shown potential as a prognostic biomarker in various cancers. This retrospective case‐control study aimed to evaluate CD147 expression in prostate adenocarcinoma among Sudanese men and its association with tumor grade. A total of 80 paraffin‐embedded tissue samples, including 40 cases of prostate adenocarcinoma and 40 benign prostatic hyperplasia (BPH) controls, were analyzed using immunohistochemistry. CD147 expression was observed in 22.5% of adenocarcinoma cases compared to 7% of controls; however, the association was not statistically significant (p = 0.07). Low‐grade tumors were predominant in the cohort, consistent with early‐stage diagnoses. The findings revealed no clear link between CD147 expression and tumor grade, diverging from prior studies that associate CD147 with advanced tumor stages. The nonsignificant results may be attributed to the small sample size, emphasizing the need for future research with larger, more diverse cohorts, advanced molecular techniques, and functional studies to better elucidate the role of CD147 in prostate cancer pathogenesis and its potential as a therapeutic target.
A survey on novel approach to semantic computing for domain specific multi-lingual man-machine interaction
Authors: Bohra Anjali , Barwar Nemi Chand
Published: December 2024

Natural language processing (NLP) helps computational linguists to understand, process, and extract information from natural languages. Linguist Panini signifies ’information coding’ in a language and explains that Karakas are semanticosyntactic relations between nouns and verbs that resemble participant roles of modern case grammar. Computational grammar maps vibhakti (inflections) of nominals and verbs to their participant roles. Karaka’s theory extracts semantic roles in the sentences which act as intermediate steps for various NLP tasks. The survey shows that NLP seeks to bridge the gap for man-machine interaction. The work presents the impact of machine learning on natural language processing with changing trends from traditional to modern scenarios with Panini’s classification scheme for semantic computing facilitating machine understanding. The study presents the significance of Karaka for semantic computing, methodologies for extracting semantic roles, and analysis of various deep learning-based language processing systems for applications like question answering. The survey covered around 50 research articles and 21 Karaka-based NLP systems performing multiple tasks like machine translation, question-answering systems, and text summaries using machine learning tools and frameworks. The work includes surveys from renowned journals, books, and relevant conferences, as well as descriptions of the latest trends and technologies in the machine learning domain.
Enhancing predictive modelling and interpretability in heart failure prediction: a SHAP-based analysis
Authors: Khan Niaz Ashraf , Bin Hafiz Md. Ferdous , Pramanik Md. Aktaruzzaman
Published: December 2024

Predictive modelling plays a crucial role in healthcare, particularly in forecasting mortality due to heart failure. This study focuses on enhancing predictive modelling and interpretability in heart failure prediction through advanced boosting algorithms, ensemble methods, and SHapley Additive exPlanations (SHAP) analysis. Leveraging a dataset of patients diagnosed with cardiovascular diseases (CVD), we employed techniques such as synthetic minority over-sampling technique (SMOTE) and bootstrapping to address class imbalance. Our results demonstrated exceptional predictive performance, with the gradient boosting (GBoost) model achieving the highest accuracy of 91.39%. Ensemble techniques further enhanced performance, with the voting classifier (VC), stacking classifier (SC), and Blending achieving accuracies of 91.00%. SHAP analysis uncovered key features such as time, Serum_creatinine, and Ejection_fraction, significantly impacting mortality prediction. These findings highlight the importance of transparent and interpretable machine learning models in healthcare decision-making processes, facilitating informed interventions and personalized treatment strategies for heart failure patients.
Collaborative singular value decomposition with user-item interaction expansion for first-time user and item recommendations
Authors: Loukili Manal , Messaoudi Fayçal
Published: December 2024

In today's digital landscape, recommendation systems are essential for delivering personalized content and improving user engagement across various platforms. However, a key challenge known as the cold-start problem—where limited user-item interaction data hampers the ability to generate accurate recommendations—remains a significant obstacle, particularly for new users and items. To address this issue, this paper introduces an enhanced methodology combining collaborative singular value decomposition (Co-SVD) with an innovative approach to reduce data sparsity. The objective of this research is to improve recommendation accuracy in sparse data environments by leveraging collaborative information in the user-item interaction matrix. Extensive experiments conducted on an e-commerce dataset validate the superiority of the proposed Enhanced Co-SVD model over traditional Co-SVD, content-based filtering, and random recommendation methods across multiple metrics. Our approach demonstrates particular strength in cold-start scenarios, providing precise recommendations with minimal user interaction data. These findings have important implications for e-marketing, personalized user experiences, and overall business success in online environments.
Enhancing credit card security using RSA encryption and tokenization: a multi-module approach
Authors: Saha Mainak , Basu M. Trinath , Gupta Arpita , Ashrith K. , Vardhan Reddy Chevella Vamshi , Reddy Shashanth , Reddy Rohith
Published: December 2024

The security of credit card information remains a critical challenge, with existing methods often falling short in safeguarding data integrity, confidentiality, and privacy. Traditional approaches frequently transmit sensitive information in unencrypted formats, exposing it to significant risks of unauthorized access and breaches. This study introduces a robust security framework that leverages Rivest-Shamir-Adleman (RSA) encryption and tokenization to protect credit card information during transactions. The proposed solution is structured into three key modules: merchant, tokenization, and token vault. The merchant module works in tandem with the tokenization module to generate transaction validation tokens and securely transmit credit card data. The token vault, maintained on a secure cloud storage platform, acts as a restricted-access database, ensuring that sensitive information is encrypted and inaccessible to unauthorized entities. Through this multi-layered approach, the study demonstrates a significant enhancement in the security of credit card transactions, effectively mitigating the risks of data breaches and unauthorized disclosures. The findings indicate that the proposed method not only addresses existing security vulnerabilities but also offers a scalable and efficient solution for protecting financial transactions.
Enhanced pulse shaping filters for minimizing interference in GFDM signals for 5G cellular networks
Authors: Tadikamalla Sairam Vamsi , Mohanta Harish Chandra , Terlapu Sudheer Kumar , Mandhapati Vamshi Krishna
Published: December 2024

The imminent rise of 5th generation (5G) wireless standards heralds a pivotal era in cellular communication. Among the challenges faced, selecting an optimal multiple access technique stands out as crucial for achieving the desired blend of low latency, high data rates, and throughput. Generalized frequency division multiplexing (GFDM) emerges as a promising can-didate meeting 5G requirements. This study introduces two innovative pulse shaping filters (PSFs) the better than raised cosine filter (BRCF) and modified bartlett hanning filter (MBHF) paired with various modulation schemes such as binary phase shift keying (BPSK), quadrature phase shift keying (QPSK), and quadrature amplitude modulation (QAM) to assess GFDM signal performance. Considering its spectrum efficiency, QAM modulation emerges as the preferred choice. Performance evaluation of the PSFs entails analyzing symbol error rate (SER) against signal to noise ratio (SNR) across different modulation schemes.
Prediction of international rice production using long short-term memory and machine learning models
Authors: Arya Suraj , Anju Anju , Ramli Nor Azuana
Published: December 2024

Rice, a staple food source globally, is in high demand and production across the world. Its consumption varies in different countries, with each nation having its unique way of incorporating rice into its diet. Recognizing the global nature of rice, its production is a crucial aspect of ensuring its availability, agriculture forecasting, economic stability, and food security. By predicting its production, we can develop a global plan for its production and stock, thereby preventing issues like famine. This paper proposes machine learning (ML) and deep learning (DL) models like linear regression, ridge regression, random forest (RF), adaptive boosting (AdaBoost), categorical boosting (CatBoost), extreme gradient boosting (XGBoost), gradient boosting, decision tree, and long short-term memory (LSTM) to predict international rice production. A total of nine ML and one DL models are trained and tested on the international dataset, which contains the rice production details of 192 countries over the last 62 years. Notably, linear regression and the LSTM algorithm predict rice production with the highest percentage of R-squared (R2 ), 98.40% and 98.19%, respectively. These predictions and the developed models can play a vital role in resolving crop-related international problems, uniting the global agricultural community in a common cause.
Planar hexagonal patch multiple input multiple output 4x4 antenna for UWB applications
Authors: Nasrul Nasrul , Firdaus Firdaus , Zahra Nurraudya Tuz , Rachmawati Maulidya
Published: December 2024

The combination of Multiple Input Multiple Output (MIMO) antennas and Ultra-Wideband (UWB) technology offers several advantages, including reduced interference, improved isolation, and optimized dual paths. These benefits extend the range and enhance signal quality. However, designing UWB-MIMO antennas presents challenges, such as achieving low mutual coupling for high isolation and creating small-sized antennas suitable for portable devices while being effective for UWB frequencies in a MIMO configuration. The proposed antenna is a 4x4 planar MIMO antenna with a hexagon-shaped patch, a partial ground plane featuring an inverted L-stub on the left side, and a plus-shaped slot in the centre ground. It has dimensions of 32 x 32 x 1.6 mm³ and is capable of achieving a wide bandwidth of 3-12.5 GHz. The antenna's performance measurements are impressive: return loss below -10 dB at frequencies of 3-12.5 GHz, mutual coupling below -16.5 dB, Envelope Correlation Coefficient (ECC) bellow 0.005, Diversity gain of more than 9.97, Total Active Reflection Coefficient (TARC) below -10 dB. Based on these results, the proposed antenna offers excellent performance for UWB applications, featuring high efficiency, minimal interference between antenna elements, and optimal diversity performance.
An model for structured the NoSQL databases based on machine learning classifiers
Authors: Benmakhlouf Amine
Published: December 2024

Today, the majority of data generated and processed in organizations is unstructured. NoSQL database management systems perform the management of this data. The problem is that these unstructured databases cannot be analyzed by traditional OLAP analytical treatments. The latter are mainly used in structured relational databases. In order to apply OLAP analyses on NoSQL data, the structuring of this data is essential. In this paper, we propose a model for structuring the data of a document-oriented NoSQL database using machine learning (ML). This method is broken down into three steps, first the vectorization of documents, then the learning via different ML algorithms and finally the classification, which guarantees that documents with the same structure will belong to the same collection. Therefore, the modeling of a data warehouse can be carried out in order to create OLAP cubes. Since the models found by learning allow the parallel computation of the classifier, our approach represents an advantage in terms of speed since we will avoid doubly iterative algorithms, which rely on textual comparisons (TC). A comparative study of the performances is carried out in this work in order to detect the most efficient methods to perform this type of classification.
Strategic Deployment of EV Charging Infrastructure: An In-Depth Exploration of Optimal Location Selection and CC-CV Charging Strategies
Authors: Mishra Debani Prasad , Nayak Pranav Swaroop , Kumar Aman , Salkuti Surender Reddy
Published: December 2024

The continued expansion of the electric vehicle (EV) market necessitates strategic planning for the placement of charging stations to ensure efficient access and utilization of electric infrastructure. This paper presents a comprehensive review of the critical factors in optimizing the selection of EV charging station locations, along with the implementation of Constant Current-Constant Voltage (CC-CV) charging models. The study addresses the challenges and opportunities in identifying the most effective locations for charging stations to accommodate the growing demand for sustainable transportation. Furthermore, it examines the benefits of adopting CC-CV charging models to improve the charging process, achieving a balance between charging speed and battery longevity. Through this analysis, the review aims to provide valuable insights to stakeholders involved in the development and expansion of EV charging infrastructure, thereby supporting the transition to a more sustainable and extensive electric mobility ecosystem.
Teaching learning based optimization algorithm for effective analysis of power quality using dynamic voltage restorer
Authors: Das Soumya Ranjan , Salkuti Surender Reddy
Published: December 2024

In this study, the load voltage is dynamically restored utilising the dynamic voltage restorer (DVR) using the voltage injection approach. The injected voltage is generated using a voltage-source inverter (VSI), which is necessary to correct for the utility network's sag and swell characteristics voltage. The restoration process is dependent on the condition and quality of the utility system, i.e., it injects energy into the external system for the duration of voltage sag, and during voltage swell, energy is absorbed by the compensator from the external system, causing an rise in dc link voltage, which is connected across the VSI. In this study two different controllers are employed based on a learning based optimized algorithm. The simulation results are shown using two different controllers and the performance of the proposed controller is found to be a better one.
Symmetrical cryptographic algorithms in the lightweight internet of things
Authors: Dhingra Akshaya , Sindhu Vikas , Sangwan Anil
Published: December 2024

The internet of things (IoT) has emerged as a prominent area of scrutiny. It is being deployed in multiple applications like smart homes, smart agriculture, intelligent surveillance systems, and even innovative industries. Security is a significant issue that needs to be addressed in these types of networks. This paper aims to describe symmetrical lightweight cryptographic algorithms (SLCAs) for lightweight IoT networks. The article focuses on discussing the principal difficulties of using cryptography in lightweight IoT devices, exploring SLCAs and their types based on structure formation throughout the literature survey, and comparing and evaluating different LCAs proposed in recent research. The main goal is to demonstrate how to solve the issues associated with conventional cryptography techniques and how lightweight cryptography algorithms aid limited IoT devices in achieving cybersecurity objectives.
Automatic vehicle accident detection and alerting notification using internet of things
Authors: Savani Vijay , Pandya Viranchi , Senghani Dhairya , Nahta Shreya , Agrawal Risheen
Published: December 2024

Immigrants in developing countries have indirectly encouraged increased automobile use, leading to a strong association between automobile accidents and their victims. However, recent technological developments, especially artificial intelligence and electronics, seem promising in overcoming these risks. This research paper focuses on complex systems developed using internet of things (IoT) technology. The system integrates various components such as micro controller, radio frequency identification (RFID) card reader for license validation, liquid crystal display (LCD), Ultrasonic sensor for interference, measuring device and global positioning system (GPS) unit. Additionally, the system has a simple mail transfer protocol (SMTP) server that can send timely email alerts to emergency responds and log email addresses for real-time emergency detection. This facilitates rapid response and emergency rescue, thereby reduces the risk of accidents and increases overall safety.
Conceptualization of IoT architectures
Authors: Paolone Gaetanino , Paesani Romolo , Camplone Jacopo , Piazza Andrea , Di Felice Paolino
Published: December 2024

Although there is a large interest about internet of things (IoT) architectures, still there is no consensus on their conceptualization in the extant literature. This lack of information in conceptualization is problematic because it hampers the deep understanding of the appeared proposals, as well as the adoption of a shared workflow by the involved architects of these systems. Thus, a concise and agreed-upon conceptualization of IoT architectures is called for. This paper aims at giving a contribution on the topic. We start by reviewing the available standards, then, in light of their suggestions, a workflow to be followed in the definition of the architecture descriptions (ADs) of IoT systems is detailed and, in addition, a sample case study, which implements that workflow, is proposed. The contributions are sufficiently abstract to be applicable also to the description of the architecture of artificial intelligence of things (AIoT) systems.
A hybrid machine learning approach for improved ponzi scheme detection using advanced feature engineering
Authors: Hossain Fahad , Shuvo Mehedi Hasan , Uddin Jia
Published: December 2024

Ponzi schemes deceive investors with promises of high returns, relying on funds from new investors to pay earlier ones, creating a misleading appearance of profitability. These schemes are inherently unsustainable, collapsing when new investments wane, leading to significant financial losses. Many researchers have focused on detecting such schemes, but challenges remain due to their evolving nature. This study proposes a novel hybrid machine-learning approach to enhance Ponzi scheme detection. Initially, we train an XGBoost classifier and extract its features. Meanwhile, we tokenize opcode sequences, train a gated recurrent unit (GRU) model on these sequences, and extract features from the GRU. By concatenating the features from the XGBoost classifier and the GRU, we train a final XGBoost model on this combined feature set. Our methodology, leveraging advanced feature engineering and hybrid modeling, achieves a detection accuracy of 96.57%. This approach demonstrates the efficacy of combining XGBoost and GRU models, along with sophisticated feature engineering, in identifying fraudulent activities in Ethereum smart contracts. The results highlight the potential of this hybrid model to offer more robust and accurate Ponzi scheme detection, addressing the limitations of previous methods.
Efficient blockchain based solution for secure medical record management
Authors: Mishra Debani Prasad , Rajeev B , Mallick Soubhagya Ranjan , Lenka Rakesh Kumar , Salkuti Surender Reddy
Published: December 2024

Electronic medical records (EMRs) have become a key player in the healthcare ecosystem contributing to the assessment of ailments, the choice of the treatment avenue, and the delivery of services. However, there is consideration of EMR storage whereby centralized storage leads to increased security and privacy issues in the patient’s record. In this paper, we proposed a blockchain and interplanetary file system (IPFS) based prototype model for EMR management. It provides a smart contract-enabled decentralized storage platform where healthcare data security, availability, and access management are prioritized. This model also employs cryptographic techniques to protect sensitive healthcare data. Finally, the model is evaluated in a realistic scenario. The experimental results demonstrate that compared to the current systems, the proposed prototype model outperforms them in terms of efficiency, privacy, and security.
Explainable zero-shot learning and transfer learning for real time Indian healthcare
Authors: Saigaonkar Swati , Narawade Vaibhav
Published: December 2024

Clinical note research is globally recognized, but work on real-time data, particularly from India, is still lagging. This study initiated by training models on medical information mart for intensive care (MIMIC) clinical notes, focusing on conditions like chronic kidney disease (CKD), myocardial infarction (MI), and asthma using the structured medical domain bidirectional encoder representations from transformers (SMDBERT) model. Subsequently, these models were applied to an Indian dataset obtained from two hospitals. The key difference between publicly available datasets and real-time data lies in the prevalence of certain diseases. For example, in a real-time setting, tuberculosis may exist, but the MIMIC dataset lacks corresponding clinical notes. Thus, an innovative approach was developed by combining a fine-tuned SMDBERT model with a customized zero-shot learning method to effectively analyze tuberculosis-related clinical notes. Another research gap is the lack of explainability because deep learning (DL) models are inherently black-box. To further strengthen the reliability of the models, local interpretable model-agnostic explanations (LIME) and shapley additive explanations (SHAP) explanations were projected along with narrative explanations which generated explanations in a natural language format. Thus, the research provides a significant contribution with ensemble technique of zero-shot learning and SMDBERT model with an accuracy of 0.92 as against the specialized models like scientific BERT (SCIBERT), biomedical BERT (BIOBERT) and clinical BioBERT.
Performance comparison of indoor navigation and obstacle avoidance methods for low-cost implementation in wheelchairs
Authors: Ashwathnarayan Satish Bhogannahalli , Arsa Deekshitha , Yerriyuru Narasimhaiah Sharath Kumar , Anchan Shreyas , Prasath Giri
Published: December 2024

Wheelchairs are a huge support for the movement of people who have disabilities. The wheelchairs that were traditionally moved using manual effort have given way to powered and smart wheelchairs with various controlling methods. When powered wheelchairs are used indoors, navigation and avoiding obstacles become challenging and tricky for a disabled user. To address these challenges there have been implementations of expensive and high-end systems to make the wheelchair move autonomously but as a result such a wheelchair is not economically viable for many users. Thus, there is a need for an alternative low cost method for users to be able to navigate and move in an indoor environment. The paper reviews low-cost methods for implementing indoor navigation systems, weighing their performances to validate if these methods can be used as a viable alternative to the high-cost systems for autonomous navigation in an indoor environment.
Optimizing resource allocation in job shop production systems with seasonal demand patterns
Authors: Hammedi Salah , Elmeliani Jalloul , Nabli Lotfi
Published: December 2024

Job shop production systems that encounter seasonal demand patterns in the manufacturing industry are the subject of this article's exploration of the complex challenges of resource allocation. A nuanced understanding of each product's unique production processes, resource requirements, and lead times is necessary for the inherent complexity of job shop production, which characterized by diverse product lines. Resource reallocation becomes more complicated due to seasonal demand patterns, which require manufacturers to seamlessly transition resources between products and adjust strategies dynamically throughout the year. This article explores potential optimization techniques by drawing on insights from related studies on reliability monitoring and Petri nets. Strategically managing resource allocation is highlighted due to its significant impact on a company's competitiveness, adaptability to market changes, and overall financial performance. In the paper, there is a proposed architecture for resource allocation that combines data-driven insights, workforce planning, inventory management, machine allocation, lean principles, and technology integration. Effective strategies for reallocating resources are highlighted through the presentation of case studies and best practices, which include accurate demand forecasting and flexible workforce planning. The final section of the article emphasizes the holistic approach required to navigate the complexities of seasonal demand patterns and achieve sustained competitiveness and customer satisfaction.