bluuebunny/crossref_metadata_2025 huggingface dataset:
Machine Learning in Small-Molecule Mass Spectrometry
Authors: Hong Yuhui , Ye Yuzhen , Tang Haixu
Published: February 2025

Tandem mass spectrometry (MS/MS) is crucial for small-molecule analysis; however, traditional computational methods are limited by incomplete reference libraries and complex data processing. Machine learning (ML) is transforming small-molecule mass spectrometry in three key directions: (a) predicting MS/MS spectra and related physicochemical properties to expand reference libraries, (b) improving spectral matching through automated pattern extraction, and (c) predicting molecular structures of compounds directly from their MS/MS spectra. We review ML approaches for molecular representations [descriptors, simplified molecular-input line-entry (SMILE) strings, and graphs] and MS/MS spectra representations (using binned vectors and peak lists) along with recent advances in spectra prediction, retention time, collision cross sections, and spectral matching. Finally, we discuss ML-integrated workflows for chemical formula identification. By addressing the limitations of current methods for compound identification, these ML approaches can greatly enhance the understanding of biological processes and the development of diagnostic and therapeutic tools.
Nuclear Cerebrospinal Fluid Imaging: Guide to Procedures and Interpretation
Authors: Mercer Megan K. , Blacklock Lisa C. , Revels Jonathan W. , Parisi Marguerite T. , Lewis David H. , Zuckier Lionel S. , Graham Michael M. , Elman Shana , Matesan Manuela C. , Elojeimy Saeed
Published: February 2025

Nuclear medicine cisternograms and shuntograms can be useful for diagnosing pathologic disruptions of cerebrospinal fluid (CSF) flow, including CSF leaks, spontaneous intracranial hypotension, normal-pressure hydrocephalus, and CSF shunt malfunction.
Transfer of Inhomogeneous Reaction to Counter Electrode Analyzed by X-Ray Absorption Fine Structure Imaging
Authors: Katayama Misaki , Kato Kazuo
Published: February 2025

Ensuring the safe and efficient use of lithium-ion batteries requires understanding the heterogeneous reaction distributions at the electrodes. This study investigates the effect of inhomogeneous reaction distributions on the counter electrode during charge and discharge cycles. Batteries with LiFePO4 and LiCoO2 electrodes facing each other were analyzed using two-dimensional X-ray absorption fine structure imaging at the Fe K and Co K absorption edges to monitor the in-plane reaction distribution. Our results show that an inhomogeneous reaction pattern at the LiFePO4 electrode, generated during the pretreatment process, significantly influenced the electrochemical reaction at the LiCoO2 electrode. Specifically, the reaction distribution at LiFePO4 induced localized variations at the LiCoO2 electrode, affecting the overall reaction behavior. Additionally, regions of the LiFePO4 electrode that had initially remained unreacted showed partial reaction due to the broad reaction distribution at the LiCoO2 electrode, leading to a complex interaction between the reaction patterns at both electrodes throughout the cycle. These findings provide new insights into the coupling of reactions at both electrodes and underscore the importance of understanding inhomogeneous reaction distributions for optimizing lithium-ion battery performance and safety.
Counterregulatory responses of healthy cats to insulin- induced-hypoglycemia
Authors: Mott Jocelyn , Adin Christopher , Crews Chiquitha , Salute Marc Evan , Tardo Antonio Maria , Porter Lauren , Berg Alisa , Gilor Chen
Published: February 2025

In health, insulin-induced-hypoglycemia (IIH) activates counterregulatory hormones responses and parasympathetic (PS) and sympathoadrenal systems which leads to increased glucagon secretion. In diabetes mellitus, these responses are impaired, resulting in greater severity and delayed recovery from hypoglycemia. These counterregulatory responses in health and disease have been documented in humans, rodents, and dogs, but not yet in cats. The aim of this study was to describe and quantify glucagon, PS and cortisol responses in healthy purpose bred cats at increasing levels of IIH. Glucagon, cortisol, and pancreatic polypeptide (PP) were measured at euglycemia and during stepped hyperinsulinemic-hypoglycemic clamps at two levels of glycemia: moderate hypoglycemia (blood glucose [BG] ~60 mg/dL) and severe hypoglycemia (BG ~45 mg/dL). At moderate hypoglycemia, a cortisol response occurred. At moderate hypoglycemia there was no change in PP and glucagon from baseline. With severe hypoglycemia, both glucagon and PP concentrations decreased from baseline and were not supportive of glucagon and parasympathetic activation in response to IIH. While cortisol increases during IIH, the counterregulatory response to IIH in healthy cats differs from other species, in that glucagon and PS responses were not detected. Moreover, in face of constant inhibition by IIH, glucagon secretion seems dependent on glucose, decreasing when glucose infusion rates were decreased. Understanding counterregulatory responses to hypoglycemia in healthy cats is the first step to exploring how diabetes might impair these responses in cats, as seen in other species.
Proteomic Analysis of Urinary Extracellular Vesicles from Patients with ADTKD-HNF1β identifies roles for Cilia-related Proteins and Serpins
Authors: Kahlman Eveline J.E.M. , Van Heugten Martijn H. , Tholen Lotte E. , Verploegen Maartje F.A. , Spruijt Cornelia G. , Jansen Pascal W.T.C. , Vermeulen Michiel , Hoenderop Joost G.J. , Hoorn Ewout J. , Nijenhuis Tom , de Baaij Jeroen H. F.
Published: February 2025

Autosomal dominant tubulointerstitial kidney disease-subtype hepatocyte nuclear factor 1β (ADTKD-HNF1β) is caused by pathogenic variants in or deletions of the gene encoding transcription factor HNF1β. Patients with the same mutation have variable renal and extrarenal phenotypes, including renal cysts, diabetes, and electrolyte disturbances. The aim of this exploratory study was to provide insight whether pathophysiological effects in the kidney of ADTKD-HNF1β patients are visible by analysing their urinary extracellular vesicle (uEV) proteome. We isolated uEVs collected from patients with ADTKD-HNF1β and included patients with ADPKD and patients with CKD as controls. Subsequent LC-MS/MS proteomics, and differential and pathway enrichment analyses were performed. Transcriptional targets of HNF1β were selected with ChIP-sequencing to study changes in protein abundance due to loss of HNF1β, and correlation analyses with clinical features were performed. We found differential enrichment of five proteins, enrichment of pathways involved in cilia and cell-cell adhesion, and depletion of several Serpins in patients with ADTKD-HNF1β and ADPKD, compared to patients with CKD. We identified differential enrichment of nine HNF1β transcriptional targets between patients with ADTKD-HNF1β and patients with CKD, and we demonstrated that Serpin abundance negatively correlated with eGFR in patients with ADTKD-HNF1β (R = -0.52). The uEV proteome of patients with ADTKD-HNF1β shows an enrichment in proteins involved in renal cysts development, with resemblance to ADPKD. These changes provide new insight in the pathophysiology of ADTKD-HNF1β. Their onset and association with cyst development and kidney function decline warrants further study.
Sex differences in the physiological and perceptual responses to face masks during exercise
Authors: Ferguson O. N. , Flynn M. R. , Mitchell R. A. , Hind A. S. , Arnold J. I. , Dhillon S. S. , Dominelli P. B. , Molgat-Seon Y. , Guenette Jordan A.
Published: February 2025

Despite consistent evidence that face masks (FMs) increase dyspnea during exercise, few studies have examined the sex differences in the physiological and perceptual responses to FMs. In a randomized, cross-over design, 32 healthy individuals (16 female;23±3yr) completed incremental cycling tests on two visits with either no mask or a surgical FM. Dyspnea intensity and unpleasantness were assessed using the 0-10 category-ratio Borg scale. Diaphragmatic electromyography, esophageal pressure (Peso), and transdiaphragmatic pressure (Pdi) were measured using a nasogastric catheter to estimate neural inspiratory drive and respiratory muscle effort. Surface EMG was measured on the sternocleidomastoid and scalene muscles. FMs resulted in a steeper increase in dyspnea unpleasantness in males compared to females across the 0-100% work rate (estimate=1.47CR10, P=0.005), with no significant difference in the effect of the FM on dyspnea intensity between sexes ( P>0.05). Males had a greater increase in Peso and Pdi with FMs compared to females across work rate (3.77cmH2O, P=0.009;4.74cmH2O, P=0.011, respectively) and a greater increase in sternocleidomastoid activation from 40-60% WR (all P<0.05). Additionally, moisture accumulation pre- vs. post-exercise ( P=0.01) was significantly greater in males compared to females. FMs resulted in a similar absolute reduction in exercise time in both sexes although it was only statistically significant in females ( P=0.006). This study highlights that males likely experience greater dyspnea unpleasantness with FMs due to higher flows and ventilations, which increase mask resistance and, combined with greater moisture accumulation, elevate respiratory muscle effort and accessory muscle activation. Nonetheless, the absolute impact on exercise duration appears similar between sexes.
Divergent Multiomic Acute Exercise Responses Reveal the Impact of Sex as a Biological Variable
Authors: Lavin Kaleen M , O'Bryan Samia M , Pathak Khyatiben V , Garcia-Mansfield Krystine , Graham Zachary A , McAdam Jeremy S , Drummer Devin J , Bell Margaret B. , Kelley Christian J , Lixandrão Manoel E , Peoples Brandon , Seay Regina S , Torres Anakaren R , Reiman Rebecca , Alsop Eric , Hutchins Elizabeth , Bonfitto Anna , Antone Jerry , Palade Joanna , Van Keuren-Jensen Kendall , Huentelman Matthew J. , Pirrotte Patrick , Broderick Timothy , Bamman Marcas M
Published: February 2025

The majority of exercise physiology research has been conducted in males, resulting in a skewed biological representation of how exercise impacts the physiological system. Extrapolating male-centric physiological findings to females is not universally appropriate and may even be detrimental. Thus, addressing this imbalance and taking into consideration sex as a biological variable is mandatory for optimization of precision exercise interventions and/or regimens. Our present analysis focused on establishing multiomic profiles in young, exercise-naïve males (n=23) and females (n=17) at rest and following acute exercise. Sex differences were characterized at baseline and following exercise using skeletal muscle and extracellular vesicle transcriptomics, whole blood methylomics, and serum metabolomics. Sex-by-time analysis of the acute exercise response revealed notable overlap, as well as divergent molecular responses between males and females. An exploratory comparison of two combined exercise regimens (high-intensity: HITT and traditional: TRAD) was then performed using singular value decomposition, revealing latent data structures that suggest a complex dose-by-sex interaction response to exercise. These findings lay the groundwork for an understanding of key differences in responses to acute exercise exposure between sexes. This may be leveraged in designing optimal training strategies, understanding common and divergent molecular interplay guiding exercise responses, and elucidating the role of sex hormones and/or other sex-specific attributes in responses to acute and chronic exercise.
Reinfusion of Malignant Ascites through an Extracorporeal Peritoneal Venous Shunt to Avoid Complications and Assess the Safety of a Denver Shunt: A Case Report
Authors: Baba Keisuke , Tanie Tomoki , Matsubara Yasuo , Hirata Yoshihiro , Ikematsu Hiroaki , Boku Narikazu
Published: February 2025

Introduction: Malignant ascites due to peritoneal metastasis of gastric cancer is challenging to manage, especially in frail and elderly patients. Traditional treatments like diuretics and paracentesis offer limited relief and can lead to complications such as malnutrition and dehydration. The Denver shunt, a type of peritoneal venous shunt (PVS), can alleviate symptoms but carries risks of severe complications like acute heart failure and disseminated intravascular coagulation (DIC). Assessing patient tolerance before Denver shunt insertion is crucial to prevent life-threatening events. Case Presentation: An 82-year-old woman with advanced gastric cancer developed refractory malignant ascites unresponsive to diuretics and cell-free and concentrated ascites reinfusion therapy (CART). Given her age and frailty, along with the small amount of blood in the ascites, there were concerns about the risks associated with a Denver shunt. An extracorporeal PVS was employed to reinfuse ascites at a controlled rate using an infusion pump. The infusion started at 40 mL/h and was carefully monitored. When the patient experienced paroxysmal supraventricular tachycardia at 60 mL/h, the rate was reduced, and β-blocker therapy was initiated. No signs of heart failure, infusion reactions, or DIC were observed during the 8-day extracorporeal reinfusion. After confirming stable laboratory tests including D-dimer levels which elevated slightly on day 3 and decreased on day 7 without intervention, a Denver shunt was safely inserted without severe complications. Thereafter, patient’s ascites was effectively managed, not deteriorating her quality of life, until her passing away 2 months later. Conclusion: This case suggests that extracorporeal PVS, in which controlled reinfusion of ascites for several days can prevent acute complication and monitor potential adverse events, can be a valuable prior treatment before Denver shunt insertion in patients with malignant ascites, especially for frail and elderly patients.
Recurrent Urticaria : A Rare Cryopyrin Associated Periodic Syndrome- Muckle Wells Syndrome
Authors: Richa  , Gawhale Siddhi
Published: February 2025

Muckle Wells syndrome is a rare Cryopyrin-Associated Periodic Syndrome (CAPS), an auto-inflammatory disorder due to a loss of function mutation in the NACHT domain of the NLRP3 gene. The loss of cryopyrin activity brought on by this deficiency eventually causes dysregulated inflammation and increased release of the proinflammatory cytokine interleukin (IL)-1 beta. It has an autosomal dominant inheritance. The other two variants of CAPS are neonatal-onset multi-system inflammatory disorder (NOMID) and familial cold auto-inflammatory syndrome (FCAS). MWS mainly presents with recurrent fever, recurrent urticarial rash, sensorineural hearing loss, and secondary amyloidosis, which doesn't respond to the anti-histaminic drugs and steroids. However, anti-IL-1 medications like anakinra, rilonacept, and canakinumab have shown to be highly effective in managing MWS. The etiology and phenotypic presentation of MWS are covered in this case study, followed by clinical perspectives on the illness. With the availability of appropriate medications, future complications can be prevented, and the prognosis is improved.
The Impact of Cilostazol in Refractory Vasospastic Angina
Authors: Lin Richard Z. , Tavella Rosanna , Shakib Sepehr , Beltrame John F.
Published: February 2025

Introduction
Refractory vasospastic angina (VSA) includes patients with disabling angina despite maximally tolerated calcium channel blocker and nitrate therapy. Randomised clinical trial evidence confirms the efficacy of cilostazol in refractory VSA, yet its use in real-world clinical practice is limited. This study evaluated the impact of cilostazol therapy on patient-reported outcomes in patients with refractory VSA.

Methods
Between June 2016 and May 2022, 15 consecutive refractory VSA patients were initiated on cilostazol (50mg twice daily), with baseline and 3-month responses assessed via the Seattle Angina Questionnaire (SAQ). The primary outcome was a clinically significant reduction in angina frequency (ie >10-point improvement in SAQ angina frequency score) at 3 months.

Results
A clinically significant reduction in angina frequency was reported in 13 patients (86%) at 3-months, with 3 (20%) becoming angina free. Moreover, over 3 months, median SAQ scores improved for angina frequency (25 [IQR 15,46] to 75 [30,82]), physical limitation (53 [44,67] to 83 [56,92]), and quality-of-life (17 [4,29] to 50 [35,58]). Additionally, a 54% reduction in angina-related emergency department presentations and 50% reduction in angina-related hospital admissions was noted. Minor medication-related adverse effects were experienced by 3 patients, with no serious adverse effects noted. Cilostazol was continued in 14 patients (93%) beyond the 3-month follow-up period.

Conclusions
In patients with refractory VSA, cilostazol is well tolerated, improves patient-reported outcomes, reduces healthcare utilisation, and thus is an effective therapy in real-world clinical practice.
Data mining and cardiac health: predicting heart attack risks
Authors: Paucar Inoc Rubio , Andrade-Arenas Laberiano
Published: February 2025

In a context where heart attacks continue to be a global health concern, the lack of precision in predicting who is at higher risk poses a critical challenge due to the variability of risk factors and complex interactions among them. The research aims to develop predictive models for heart attack risks using data mining techniques, employing the knowledge discovery in databases methodology (KDD) and the k-means algorithm with RapidMiner studio. The primary objective is to identify patterns and risk profiles, allowing for early identification of at-risk individuals, considering factors like obesity, diabetes, alcoholism, and stress, to reduce preventable deaths and improve cardiac healthcare. This innovative approach combines cardiac health, data mining, and KDD methodology to address the challenge of predicting heart attack risks and has the potential to enhance medical care and save lives. The predominant results obtained were that cluster 1 with a fraction of 0.312 and a percentage of 31.2% of the attribute diabetes was one of the most prevalent causes of cardiac risk. Finally, the research concluded that people with diabetes are more likely to have cardiac risk associated with dietary factors or consumption of other substances.
A hybrid learning model to detect cardiovascular disease from electrocardiogram
Authors: Lakshmi G. V. Rajya , Rao S. Krishna , Rao K. Venkata
Published: February 2025

Cardiovascular diseases (CVDs) continue to be the world’s most significant cause of morbidity and mortality. This paper introduces a unique hybrid learning model for CVD detection using advanced deep learning (DL) methods. The proposed method combines the potent feature extraction powers of the EfficientNet pre-trained model with attention mechanisms and graph convolutional networks (GCNs) for improved performance. First, rich representations from cardiovascular electrocardiogram (ECG) data extract using the EfficientNet architecture as a feature extractor. Using a large dataset of cardiovascular ECG images, you can fine-tune the pre-trained EfficientNet model with Pipeline to make it more suitable for disease identification. Including attention techniques that allow the network to focus on informative regions within the input, ECG images enhanced the model’s discriminative capacity. The model can attend to the salient areas selectively linked with CVD path physiology through dynamic attention processes. More accurate predictions result from this attention-based refining, strengthening the model’s ability to identify significant patterns suggestive of cardiovascular problems. GCN aims to link the natural structure in cardiovascular data. It can efficiently capture complex interactions and dependencies among various data pieces by expressing medical data as graphs, where nodes correspond to image regions, and edges imply spatial connections. Combining GCN into the proposed hybrid learning architecture facilitates extracting contextual information from local and global sources, augmenting the model’s accuracy.
Android malware detection through opcode sequences using deep learning LSTM and GRU networks
Authors: Lakshmanarao Annemneedi , Mantena Jeevana Sujitha , Thota Krishna Kishore , Chandaka Pavan Sathish , Murali Krishna Chinta Venkata , Jetty Madhan Kumar
Published: February 2025

Android malware detection was a complex task due to the intricate structure of Android applications, which consisted of numerous Java methods and classes. Effective detection required the extraction of meaningful features and the application of advanced machine learning (ML) or deep learning (DL) algorithms. This paper presented a novel approach to detecting Android malware by leveraging opcode sequences extracted from Android applications. These opcode sequences, which differed between malicious and benign apps, formed the basis of the detection model. The methodology involved extracting opcode sequences from decompiled Android APK files using the “Androguard” tool and applying recurrent neural networks (RNN) with long short-term memory (LSTM), Bi-LSTM, and gated recurrent unit (GRU) architectures to classify the apps as either malware or benign. The combination of these advanced DL techniques allowed for capturing temporal dependencies in opcode sequences, resulting in a significant improvement in detection capabilities. This work underscored the potential of using opcode sequences in conjunction with RNN, LSTM, and GRU for robust and accurate malware detection, while also highlighting the importance of further exploring additional features for comprehensive classification.
Exploring parents’ perceptions of sex education pedagogy in Moroccan schools using an association rules mining-based algorithm
Authors: Ben Azza Chaymae , El Hamdani Sara , Bennani Mohamed Taj , El Fahssi Khalid , Lamrini Mohamed , Elfar Mohamed
Published: February 2025

Sex education is vital for promoting healthy relationships and preventing sexual exploitation by teaching boundaries, consent and abuse recognition. Customized strategies are needed for children, balancing age-appropriate content with parental and community perspectives. Our study assessed Moroccan parents’ views on sex education’s adoption in schools. Conducted in Taza city, the survey targeted 1946 parents of students over 7 years old. Using association rule mining (ARM), we analyzed their responses. Therefore, Apriori algorithm was implemented to discover strong association rules within parents’ selected responses. Results showed that 74.53% of parents aged 19-30 support sexual education, citing its absence as a factor in child abuse. Meanwhile, 60.48% of those aged 31-59 with university education believe psychological disorders contribute to assaults. While some fathers (32.48%) and some mothers (67.52%) support sexual education, others don’t, but all agree on restricting children’s internet use until age 16 to avoid harmful content. These findings can inform comparative studies, aid decision-makers and enhance AI-based EdTech systems by offering insights into sex education perceptions.
A review of the impacts of linked open data on cross-domain recommender systems for individual and groups
Authors: Xuan Yui Chee , Mat Nawi Rosmamalmi , Mohd Noah Shahrul Azman
Published: February 2025

As users' viewpoints on information searching change from information seeking to information receiving, new search paradigms are continuously emerging. Utilizing a recommender system (RS) is one of the modern ways to get information. The RS has succeeded in various traditional domains, including tourism, health, and books. However, some scenarios are more suitable to recommend to a group of users than an individual, such as listening to music at the same place and group traveling. The limited and incomplete number of user-item ratings triggers the challenges of the group and individual RSs. The data sparsity problem emerges because of this incompleteness. The quality of recommendations offered to individuals and groups suffers when there is data sparsity. Using knowledge gained from a source domain, cross-domain RSs can enhance recommendations in target domain. Cross-domain and linked open data approaches are two ways to increase recommendation systems' performance. The impacts of the two aforementioned approaches on individual and group RSs have been discussed. Furthermore, we highlighted various domains employed in cross-domain RSs for individuals and groups, examined diverse methodologies and algorithms, outlined current issues, and suggested future directions for cross-domain RSs research for groups leveraging linked open data technology.
Textual and numerical data fusion for depression detection: a machine learning framework
Authors: Aziz Mohammad Tarek , Mahmud Tanjim , Abdul Aziz Md Faisal Bin , Siddick Md Abu Bakar , Sharif Md. Maskat , Hossain Mohammad Shahadat , Andersson Karl
Published: February 2025

Depression, a widespread mood disorder, significantly affects global mental health. To mitigate the risk of recurrence, early detection is crucial. This study explores socioeconomic factors contributing to depression and proposes a novel machine learning (ML)-based framework for its detection. We develop a tailored questionnaire to collect textual and numerical data, followed by rigorous feature selection using methods like backward removal and Pearson’s chi-squared test. A variety of ML algorithms, including random forest (RF), support vector machine (SVM), and logistic regression (LR), are employed to create a predictive classifier. The RF model achieves the highest accuracy of 96.85%, highlighting its effectiveness in identifying depression risk factors. This research advances depression detection by integrating socioeconomic analysis with ML, offering a robust tool for enhancing predictive accuracy and enabling proactive mental health interventions.
A deep learning approach to detect DDoS flooding attacks on SDN controller
Authors: Bahashwan Abdullah Ahmed , Anbar Mohammed , Manickam Selvakumar , Al-Amiedy Taief Alaa , Hasbullah Iznan H.
Published: February 2025

Software-defined networking (SDN), integrated into technologies like internet of things (IoT), cloud computing, and big data, is a key component of the fourth industrial revolution. However, its deployment introduces security challenges that can undermine its effectiveness. This highlights the urgent need for security-focused SDN solutions, driving advancements in SDN technology. The absence of inherent security countermeasures in the SDN controller makes it vulnerable to distributed denial of service (DDoS) attacks, which pose a significant and pervasive threat. These attacks specifically target the controller, disrupting services for legitimate users and depleting its resources, including bandwidth, memory, and processing power. This research aims to develop an effective deep learning (DL) approach to detect such attacks, ensuring the availability, integrity, and consistency of SDN network functions. The proposed DL detection approach achieves 98.068% accuracy, 98.085% precision, 98.067% recall, 98.057% F1-score, 1.34% false positive rate (FPR), and 1.713% detection time.
Enhanced vegetation encroachment detection along power transmission corridors using random forest algorithm
Authors: Somasundaram Deepa , Sivaraj Nivetha , Shalinirajan Shalinirajan , Karuppiah Santhi , Rajendran Sudha
Published: February 2025

Vegetation encroachment along power transmission corridors poses significant risks to infrastructure safety and reliability, necessitating effective monitoring and management strategies. This study introduces an innovative methodology for detecting vegetation encroachment using a combination of manual and automatic processes integrated with the random forest algorithm. The issue of vegetation encroachment is critical as it can lead to power interruptions and safety hazards if not addressed promptly. The objective of this research is to develop a scalable and cost-effective solution for vegetation management in power infrastructure maintenance. The methodology involves manual patch extraction and labeling to ensure the accuracy of the training dataset, combined with automatic feature extraction techniques to capture relevant information from satellite imagery. Leveraging the random forest algorithm, the model constructs an ensemble of decision trees based on the extracted features, achieving robust classification accuracy. Findings from this study demonstrate that the proposed approach enables consistent and timely identification of vegetation encroachment in new satellite imagery. Stored model parameters facilitate efficient testing, enhancing the system's ability to provide proactive interventions. This scalable solution significantly reduces reliance on manual labor and offers a cost-effective method for continuous monitoring, ultimately contributing to the resilience and safety of power transmission infrastructure.
Chebyshev distance-embedded twin support vector machine for skewed classification problems
Authors: Balasubramanian Sai Lakshmi , Ganesan Gajendran
Published: February 2025

Support vector machine (SVM) is a pivotal classification algorithm, and its evolutionary counterpart, the twin SVM (TWSVM), has gained acclaim for its advanced generalization capabilities, particularly in handling imbalanced data. TWSVMs achieve swift training by explicitly exploring a pair of non-parallel hyperplanes, yet selecting numerical values for hyperparameters poses a challenge due to the uncertainty introduced by random preferences. This paper presents a novel approach, the Chebyshev distance-based TWSVM, specifically designed for hyperparameter tuning in imbalanced binary classification. This innovative model mitigates the uncertainty of hyperparameter selection by leveraging Chebyshev distance, thereby enhancing the generalization capabilities of the TWSVM. To evaluate its efficacy, computational tests were conducted on publicly accessible real-world benchmark datasets across various domains, including non-linear cases. The results demonstrate that the Chebyshev distance-based TWSVM outperforms several existing methods, achieving superior performance with reduced computational time and setting a new benchmark in the field.
A variant of particle swarm optimization in cloud computing environment for scheduling workflow applications
Authors: Tripathi Ashish , Singh Rajnesh , Moudgil Suveg , Gupta Pragati , Sondhi Nitin , Kumar Tarun , Srivastava Arun Pratap
Published: February 2025

Cloud computing offers on-demand access to shared resources, with user costs based on resource usage and execution time. To attract users, cloud providers need efficient schedulers that minimize these costs. Achieving cost minimization is challenging due to the need to consider both execution and data transfer costs. Existing scheduling techniques often fail to balance these costs effectively. This study proposes a variant of the particle swarm optimization algorithm (VPSO) for scheduling workflow applications in a cloud computing environment. The approach aims to reduce both execution and communication costs. We compared VPSO with several PSO variants, including Inertia-weighted PSO, gaussian disturbed particle swarm optimization (GDPSO), dynamic-PSO, and dynamic adaptive particle swarm optimization with self-supervised learning (DAPSO-SSL). Results indicate that VPSO generally offers significant cost reductions and efficient workload distribution across resources, although there are specific scenarios where other algorithms perform better. VPSO provides a robust and cost-effective solution for cloud workflow scheduling, enhancing task-resource mapping and reducing costs compared to existing methods. Future research will explore further enhancements and additional PSO variants to optimize cloud resource management.
Credit card fraud detection using CNN and LSTM
Authors: Upadhyay Nishant , Bansal Nidhi , Rastogi Divya , Chaturvedi Rekha , Asim Mohammad , Malik Suraj , Jayant Khel Prakash , Vajpayee Abhay Kumar
Published: February 2025

Credit card fraud is an evolving problem with the fraudsters developing new technologies to perform fraud. Fraudsters have found diverse ways to make a fraud transaction to the card holder. Thus, detecting suspicious behavior of a card is critical for preventing fraudulent transactions to happen. Artificial intelligence techniques, in particular deep learning algorithms can tackle these credit card fraud attacks by identifying patterns that predict transactions as fraud or legitimate. One-dimensional convolutional neural network (1D CNN) and long short-term memory (LSTM) both performs well on the sequential data especially on transactions data, yet there are not many studies done on combining these two algorithms to make an effective fraud detection approach. However, the dataset is highly imbalanced containing only 492 fraud transaction out of two lacs transactions. In this experimental study, firstly datasets will get prepared by using different sampling techniques along with their hybrid techniques secondly, observing the performance of individual CNN and LSTM on the datasets, finally on those datasets in which CNN and LSTM are performing well, by implementing ensemble on those data. The performance of the ensembles is observed using the performance metrics namely accuracy, F1-score, precision and recall. In the proposed experimental study, getting the F1-score of 99.96% and 99.89% in ensemble: early fusion and ensemble: late fusion respectively.
An efficient hardware implementation of number theoretic transform for CRYSTALS-Kyber post-quantum cryptography
Authors: Hoang Trang , Anh Duong Tu Dinh , Do Thinh Quang
Published: February 2025

CRYSTALS-Kyber was chosen to be the standardized key encapsulation mechanisms (KEMs) out of the finalists in the third round of the National Institute of Standards and Technology (NIST) post-quantum cryptography (PQC) standardization program. Since the number theoretic transform (NTT) was used to reduce the computational complexity of polynomial multiplication, it has always been a crucial arithmetic component in CRYSTALS-Kyber design. In this paper, a simple and efficient architecture for NTT is presented where we easily archived the functionality of polynomial multiplication with efficient computation time. Only 857 Look-Up Tables and 744 flip-flops were utilized in our NTT design, which consisted of two processing elements (PEs) and two butterfly cores within each PE.
High-efficiency multimode charging interface for Li-Ion battery with renewable energy sources in 180 nm CMOS
Authors: Mamouni Hajjar , El Khadiri Karim , El Affar Anass , Jamil Mohammed Ouazzani , Qjidaa Hassan
Published: February 2025

The high-efficiency multi-source Lithium-Ion battery charger with multiple renewable energy sources described in the present paper is based on supply voltage management and a variable current source. The goal of charging the battery in a constant current (CC) mode and controlling the supply voltage of the charging circuit are both made achievable using a variable current source, which may improve the battery charger’s energy efficiency. The battery must be charged with a degraded current by switching from the CC state for the constant voltage (CV) state to prevent harming the Li-Ion battery. The Cadence Virtuoso simulator was utilized to obtain simulation results for the charging circuit, which is constructed in 0.18 μm CMOS technology. The simulation results obtained using the Cadence Virtuoso simulator, provide a holding current trickle charge (TC) of approximately 250 mA, a maximum charging current (LC) of approximately 1.3 A and a maximum battery voltage of 4.2 V, and takes only 29 minutes to charge.
Performance of rocket data communication system using wire rope isolator on sounding rocket RX
Authors: Rahardiyanti Kandi , Laksono Shandi Prio , Hakim Khaula Nurul , Nugroho Yuniarto Wimbo , Adi Andreas Prasetya , Salman Salman , Kurdianto Kurdianto
Published: February 2025

The rocket experiment (RX) ballistic rocket requires a reliable data communication system capable of withstanding intense vibrations and shocks during flight. This study investigates the application of wire rope isolators (WRI) to damper mechanical disturbances and protect the rocket's communication system. Installation of WRI position and direction in this experiment with compression position. A series of vibration tests were conducted using 4 WRI installed in the rocket’s 30 kg data communication compartment, vibration test results frequency between 4 Hz and 1500 Hz with acceleration of 8.37 g to 20.37 g, higher "g" readings on the test object sensor compared to vibration machine readings are usually caused by phenomena such as resonance, differences in dynamic response, non-linear behavior, sensor placement location, and swing effects when the vibration machine oscillates. This is a natural mechanical response to external vibrations during testing. While the results of flight tests rocket RX has an acceleration of 8 g to 9.3 g. The results showed that the WRI dampers are effective in protecting the data communication system and ensuring the uninterrupted transmission of flight data to the ground control station (GCS).
Optimizing photovoltaic system performance through MPPT synergetic adaptive control
Authors: Hadjadj Kamel , Attoui Hadjira
Published: February 2025

This paper investigates enhancement of energy conversion through the implementation of new MPPT control strategy based on synergetic adaptive control (SAC) for a photovoltaic system. The architecture of this system encompasses a photovoltaic module, a DC-DC boost converter, a resistive load, and an MPPT controller. The controller amalgamates two distinct methodologies: the initial algorithm deduces the peak power current through a perturbation and observation (P&O) method, which serves as the reference point for the subsequent algorithm founded on synergetic adaptive control. The parameters for the latter are refined through the particle swarm optimization (PSO) technique This innovative method is employed to ascertain the optimal power output across varying weather conditions, aiming to enhance power transmission performance irrespective of meteorological variations. The efficacy of this strategy was affirmed through a comparative study with the conventional P&O method using MATLAB/Simulink simulations, which verified the superior performance of the proposed algorithm.
Intrusion detection in clustering wireless network by applying extreme learning machine with deep neural network algorithm
Authors: Parvathy Palaniraj Rajidurai , Sekar Satheeshkumar , Tidke Bharat , Jyothi Rudraraju Leela , Sujatha Venugopal , Shanmugathai Madappa , Murugan Subbiah
Published: February 2025

Nowadays, intrusion detection systems (IDSs) have growingly come to be considered as an important method owing to their possible to expand into a key factor, which is crucial for the security of wireless networks. In wireless network, when there is a thousand times more traffic, the effectiveness of normal IDS to identify hostile network intrusions is decreased by an average factor. This is because of the exponential growth in network traffic. This is due to the decreased number of possibilities to discover the intrusions. This is because there are fewer opportunities to see possible risks. We intend an extreme learning machine with deep neural network (DNN) algorithm-based intrusion detection in clustering (EIDC) wireless network. The main objective of this article is to detect the intrusion efficiently and minimize the false alarm rate. This mechanism utilizes the extreme learning machine (ELM) with a deep neural network algorithm for optimizing the weights of input and hidden node biases to deduce the network output weights. Simulation outcomes illustrate that the EIDC mechanism not only assures a better accuracy for detection, considerably minimizes an intrusion detection time, and shortens the false alarm rate.
2025 ACC/AHA/ACEP/NAEMSP/SCAI Guideline for the Management of Patients With Acute Coronary Syndromes: A Report of the American College of Cardiology/American Heart Association Joint Committee on Clinical Practice Guidelines
Authors: Rao Sunil V. , O’Donoghue Michelle L. , Ruel Marc , Rab Tanveer , Tamis-Holland Jaqueline E. , Alexander John H. , Baber Usman , Baker Heather , Cohen Mauricio G. , Cruz-Ruiz Mercedes , Davis Leslie L. , de Lemos James A. , DeWald Tracy A. , Elgendy Islam Y. , Feldman Dmitriy N. , Goyal Abhinav , Isiadinso Ijeoma , Menon Venu , Morrow David A. , Mukherjee Debabrata , Platz Elke , Promes Susan B. , Sandner Sigrid , Sandoval Yader , Schunder Rachel , Shah Binita , Stopyra Jason P. , Talbot Amy W. , Taub Pam R. , Williams Marlene S.
Published: February 2025

Aim:

The “2025 ACC/AHA/ACEP/NAEMSP/SCAI Guideline for the Management of Patients With Acute Coronary Syndromes” incorporates new evidence since the “2013 ACCF/AHA Guideline for the Management of ST-Elevation Myocardial Infarction” and the corresponding “2014 AHA/ACC Guideline for the Management of Patients With Non–ST-Elevation Acute Coronary Syndromes” and the “2015 ACC/AHA/SCAI Focused Update on Primary Percutaneous Coronary Intervention for Patients With ST-Elevation Myocardial Infarction.” The “2025 ACC/AHA/ACEP/NAEMSP/SCAI Guideline for the Management of Patients With Acute Coronary Syndromes” and the “2021 ACC/AHA/SCAI Guideline for Coronary Artery Revascularization” retire and replace, respectively, the “2016 ACC/AHA Guideline Focused Update on Duration of Dual Antiplatelet Therapy in Patients With Coronary Artery Disease.”

Methods:

A comprehensive literature search was conducted from July 2023 to April 2024. Clinical studies, systematic reviews and meta-analyses, and other evidence conducted on human participants were identified that were published in English from MEDLINE (through PubMed), EMBASE, the Cochrane Library, Agency for Healthcare Research and Quality, and other selected databases relevant to this guideline.

Structure:

Many recommendations from previously published guidelines have been updated with new evidence, and new recommendations have been created when supported by published data.
A new species and the first description of the female of Fiaponeura Lu et al. (Neuroptera: Psychopsoidea) from Cretaceous Kachin amber
Authors: ASSMAR ALICE CARVALHO , JOSÉ PIRES MACHADO RENATO , LIU XING-YUE , GILLUNG JESSICA PAULA
Published: February 2025

Lacewings (Insecta: Neuroptera) are a remarkable group of insects displaying astonishing diversity during the Jurassic and Cretaceous. Neuroptera is an order of holometabolous insects, which, together with Megaloptera and Raphidioptera, comprise the superorder Neuropterida (Engel et al., 2018). The morphologically diverse larvae of Neuroptera are known for their specialized sucking mouthparts for fluid feeding, as well as for their distinct life histories, such as trap builders (antlions), freshwater parasites (spongillaflies), and termite predators (beaded lacewings) (Oswald & Machado, 2018). The adults are usually predators, with some exceptions, and exhibit greatly reticulated wings, with variable sizes, shapes and coloration patterns. The order comprises ca. 6,000 extant species in 15 families, and the fossil record impressively adds 15 and 1,078 extinct families and species, respectively (Winterton et al., 2018; Oswald, 2024). During the past decades, extinct lacewings with long proboscides have been discovered in fossil records and placed in the superfamily Psychopsoidea. Investigating the role of these insects in pollination and examining the comparative morphological structure of mouthparts is paramount to our understanding of lacewing diversity, evolution, and function (Lu et al., 2016).
Supplement to the Burmese (Myanmar) amber checklist and bibliography, 2024
Authors: ROSS ANDREW J.
Published: February 2025

This is a supplement to the Burmese (Myanmar) amber checklist and bibliography covering taxa described or recorded during 2024. Up to the end of 2024, 2,989 species have been recorded from Kachin amber (excluding trace fossils and marine encrusters), of which 226 were named in 2024. Additionally, 21 species have been recorded from older Hkamti amber (three named in 2024) and two species from younger Tilin amber (both named in 2024). Another three species were named in 2024, though it is uncertain whether they are in Kachin, Hkamti or Tilin amber. In total, 235 species (including one trace fossil) were named from Cretaceous amber from Myanmar during 2024.
A new moth lacewing genus (Insecta: Neuroptera: Ithonidae) with proliferated and specialized wing venation from the mid-Cretaceous of northern Myanmar
Authors: LIU XING-YUE , LU XIU-MEI , WANG JING-LEI , XU CHUN-PENG , ZHUO DE
Published: February 2025

The neuropteran family Ithonidae is a relict group among extant lacewings, with less species diversity and disjunctive global distribution, but represented by a more diverse palaeofauna since the Late Triassic. However, most fossil ithonids are preserved in compression rocks, while those from amber inclusion are particularly rare. Here we report a new genus and species of Ithonidae, Phyllithone dongshengi gen. et sp. nov., from the mid-Cretaceous Kachin amber of northern Myanmar, representing the second ithonid genus and species from this deposit. The new genus can be assigned to the extinct Principiala genus-group by the specialized MP and CuA in both fore- and hind wings, which is shared by many groups of Myrmeleontoidea as a possibly convergently evolved trait. The new finding highlights the morphological diversity and evolution in early ithonids.
A new buprestid beetle from Eocene Baltic amber (Coleoptera: Buprestidae: Melanophilini)
Authors: KWAST TOM , ALEKSEEV VITALII
Published: February 2025

Phaenops gutowskii sp. nov. is described, diagnosed, and illustrated based on a well-preserved specimen from Baltic amber. This new extinct jewel beetle is compared with the Phaenops aerea species group in the Palaearctic as well as closely related congeners in the Nearctic Realm. Trophic association of the new Eocene buprestid with pine trees and development of its larvae in phloem of Pinus sp. is supposed.
Comments on “Marathon versus Sprint: Two Modes of Tropical Cyclone Rapid Intensification in a Global Convection-Permitting Simulation”
Authors: Montgomery Michael T. , Smith Roger K.
Published: February 2025

In this comment, we offer remarks to highlight important dynamical elements that are intrinsic to the two proposed modes of tropical cyclone rapid intensification.
Quantifying the uncertainty in the Weather Research and Forecasting Model under sea breeze and low-level jet conditions in the New York Bight: Importance to offshore wind energy
Authors: McCabe Elizabeth J. , Freedman Jeffrey M.
Published: February 2025

As development of offshore wind energy continues in the New York Bight (NYB; defined as the region of ocean waters south of Long Island and east of New Jersey), understanding mesoscale circulations such as the sea breeze and accompanying low-level jet (LLJ) is increasingly important. With offshore wind turbines continuing to increase in size and hub heights extending up to 150 m or more above the ocean surface, there is a need for long-term atmospheric measurements within the rotor plane. Limited access to publicly available and long-term wind profiles in the NYB make it necessary to rely on numerical weather prediction models to fully understand the dynamics of the offshore wind field. While models can provide a four-dimensional picture of the sea breeze circulation and extent of the LLJ (defined here as a wind speed maximum 150–300 m above the ocean surface), they exhibit poor performance in reproducing these features.

Using the Weather Research and Forecasting Model (WRF), this study performs sensitivity analyses to determine the best configuration for reproducing the temporal and spatial dynamics of the NYB sea breeze and LLJ. We test seven planetary boundary layer (PBL) schemes, two land surface schemes, sea surface temperature fields, and an urban surface physics parameterization to examine model performance and accuracy. Sensitivity analysis testing 18 different WRF configurations determines that the Mellor–Yamada–Janjić (MYJ) PBL scheme, combined with Noah land surface model and the urban parametrization turned off, is best suited to model these mesoscale summertime phenomena.