bluuebunny/crossref_metadata_2025 huggingface dataset:
Web Block Craft: web development for children using Google Blockly
Authors: Gunaratne Madhumini , Weerasekara Senal , Weerakkody Dehemi , Sashmitha Nisal , Zoysa Rivoni De , Kodagoda Nuwan
Published: August 2024

Web Block Craft is an innovative educational application that uses the Google Blockly framework to teach web development to children aged eleven and above. The application serves as a comprehensive learning tool, allowing users to explore both frontend project and backend project development. The frontend project includes HTML, CSS, JavaScript, and DOM manipulation, while the backend project covers server building, web app security, application programming interfaces (APIs), and database management. Web Block Craft's unique block-based interface allows users to easily drag and drop components into a dynamic working environment, resulting in an engaging experience with live output display and simultaneous code presentation. A unique feature of Web Block Craft is the integration of a platform within the application, which allows teachers to create lessons with step-by-step instructions for students. This new feature allows for a more structured learning experience, which improves understanding of web development concepts. To enhance the learning experience, the application provides extensive documentation, serving as a valuable resource for users to grasp the intricacies of web programming. By combining the power of Google Blockly with a creative user interface and educational resources, Web Block Craft provides a comprehensive learning environment that empowers creative web programming with confidence.
Optimizing intrusion detection in 5G networks using dimensionality reduction techniques
Authors: Salah Zaher , Elsoud Esraa , Al-Sit Waleed , Alhenawi Esraa , Alshraiedeh Fuad , Alshdaifat Nawaf
Published: August 2024

The proliferation of internet of things (IoT) technologies has expanded the user base of the internet, but it has also exposed users to increased cyber threats. Intrusion detection systems (IDSs) play a vital role in safeguarding against cybercrimes by enabling early threat response. This research uniquely centers on the critical dimensionality aspects of wireless datasets. This study focuses on the intricate interplay between feature dimensionality and intrusion detection systems. We rely on the renowned IEEE 802.11 security-oriented AWID3 dataset to implement our experiments since AWID was the first dataset created from wireless network traffic and has been developed into AWID3 by capturing and studying traces of a wide variety of attacks sent into the IEEE 802.1X extensible authentication protocol (EAP) environment. This research unfolds in three distinct phases, each strategically designed to enhance the efficacy of our framework, using multi-nominal class, multi-numeric class, and binary class. The best accuracy achieved was 99% in the three phases, while the lowest accuracy was 89.1%, 60%, and 86.7% for the three phases consecutively. These results offer a comprehensive understanding of the intricate relationship between wireless dataset dimensionality and intrusion detection effectiveness.
A cost-effective, reliable and accurate framework for multiple-target tracking by detection approach using deep neural network
Authors: Divyaprabha Divyaprabha , Seebaiah Guruprsad
Published: August 2024

Over the years the area of object tracking and detection has emerged and become ubiquitous owing to its potential contribution towards video surveillance applications. Multiple object tracking (MOT) estimates the trajectory of several objects of interest simultaneously over time in a series of video frames. Even though various research proposals have encouraged the use of machine learning techniques in designing multi-object trackers, the existing solutions need to be more practicable for online tracking due to more complicated algorithms, The study, therefore, introduces a cost-effective tracking solution for multiple–target tracking by detection where it incorporates the you only look once version 4 (YOLOv4) and person re-identification network, which are further integrated with the proposed tracking model, which considers both bounding box and appearance features to handle the motion prediction and data association respectively. The novelty of this approach lies in considering appearance features, which not only help predict tracks through allocations problem solving but also handle the cost of computation problems. Here, the system utilizes a pre-trained association metric with which the occlusion challenges are also handled, whereas the target tracking has taken place even in more extended periods of occlusion, making it suitable with the existing efficient tracking algorithms.
Novel cryptosystem integrating the Vigenere cipher and one Feistel round for color image encryption
Authors: Tabti Hassan , El Bourakkadi Hamid , Chemlal Abdelhakim , Jarjar Abdellatif , Najah Said , Zenkouar Khalid
Published: August 2024

The present research will propose an innovative technique for pixel-level encryption of color images. After isolating the R, G, and B channels and converting them into vector mode, an enhanced Feistel network will be applied at the hexadecimal level, facilitated by integrating a substitution table generated from the employed chaotic maps. This is followed by a binary conversion and a shift ensured by pseudo-random vectors. A diffusion function is applied, incorporating another replacement matrix constructed from commonly used chaotic maps in cryptography. This operation links the cipher pixel to the next pixel, thereby reinforcing the avalanche effect and safeguarding the system against any differential attacks. Simulations conducted using our new system on various color images, arbitrarily selected from multiple databases, have yielded satisfactory and highly promising results.
An internet of things-based healthcare system performing on a prediction approach based on random forest regression
Authors: Shaban Fahad Ahmed , Golshannavaz Sajjad
Published: August 2024

To predict physiological indicators, such as heart rate, blood pressure, and body heat sensors, this study develops an internet of things (IoT)-based healthcare approach performing on random forest regression models and mean square error (MSE). Machine learning approaches such as random forest design is trained to predict factors like age, heart rate, and recorded physiological measures using a dataset generated by sensors with Raspberry Pi. The precision and dependability of the models are assessed by contrasting the predictions with the physiological degrees produced by sensors. IoT-enabled models and sensors are useful for a variety of healthcare monitoring tasks, such as early anomaly detection and quick assistance for medical interventions. It is seen that the proposed model could provide appropriate predictions that are in line with common datasets demonstrated by the results. Moreover, there is strong agreement between the sensor readings and the predicted values for the considered parameters showcasing the outperformance of the proposed healthcare system.
Gaussian filter-based dark channel prior for image dehazing enhancement
Authors: Nurhayati Oky Dwi , Surarso Bayu , Syafei Wahyul Amien , Nugraheni Dinar Mutiara Kusumo
Published: August 2024

The presence of haze in an image is one of the challenges in computer vision tasks, such as remote sensing, object monitoring, and traffic monitoring applications. The hazy image is considered to contain noise and it can interfere with the image analysis process. Thus, image dehazing becomes a necessity as part of image enhancement. Dark channel prior (DCP) is one of the images dehazing methods that works based on a physical degradation model and utilizes low-intensity values from outdoor image characteristics. The DCP method generally consists of some steps, which are finding the dark channel and gradient image, estimating the sky region, atmospherical light, and transmission map, and reconstructing the dehazed image. This study introduces image dehazing by utilizing the Gaussian filter combined with the DCP method to increase the sharpness and accentuate the details of hazy images. Experimental results show that the proposed method could produce dehazed images with a visual quality is 18.94 dB on average or an increase of 11.91% compared to the original hazy image with a similarity index is 66.71% on average or an increase of 8.10%. Therefore, it is expected that this study can contribute to the image dehazing method enrichment based on DCP.
A novel comprehensive investigation for enhancing cluster analysis accuracy through ensemble learning methods
Authors: Lakshmi H. N. , Ramana Thaduri Venkata , K LNC Prakash , Reddy L. Kiran Kumar , Raju Kachapuram Basava
Published: August 2024

Ensemble learning stands out as a widely embraced technique in machine learning. This research explores the application of ensemble learning, including ensemble clustering, to enhance the precision of cluster analysis for datasets with multiple attributes and unclear correlations. Employing a majority voting-based ensemble clustering approach, specific techniques such as k-means clustering, affinity propagation, mean shift, BIRCH clustering, and others are applied to defined datasets, leading to improved clustering results. The study involves a comprehensive comparative analysis, contrasting ensemble clustering outcomes with those of individual techniques. The process of improving cluster identification accuracy encompasses data collection, pre-processing to exclude irrelevant elements, and the application of standard clustering algorithms. The task includes defining the optimal number of groups before comparing clustering models. Additionally, a combined model is constructed by merging BIRCH clustering and mean shift clustering, leveraging their advantages to enhance overall clustering strength and accuracy. This research contributes to advancing ensemble learning and ensemble clustering methodologies, offering improved accuracy, and uncovering hidden patterns in complex datasets.
Detection of elements of personal safety for the prevention of accidents at work with convolutional neural networks
Authors: Bonfante Maria Claudia , Hernandez Ivan , Montes Juan Contreras , Arrieta Rodríguez Eugenia , Cama-Pinto Alejandro
Published: August 2024

The task of recognizing personal protective elements in workplace environments in real time is fundamental to protecting the employees in case of any accidents. This can be achieved by deploying a convolutional neural network (CNN) algorithm that can efficiently detect protective elements through surveillance devices. Therefore, this work proposes the construction of a model, implementing the you only look once (YOLO) detector, whose architecture has been one of the most tested according to literature review. YOLOv5 and YOLOv7 versions were used and a dataset of 2,000 images for four classes considered. This dataset was collection from various sources and labelled by the authors, of which 80% was used for training, 15% for testing and 5% for model validation. The most important metrics are presented, making a comparison between the models, and finally it was identified that YOLOv7 achieved a higher success rate, which could be considered a more complete solution for occupational health and safety management in companies.
Deep learning model for elevating internet of things intrusion detection
Authors: Dash Nitu , Chakravarty Sujata , Rath Amiya Kumar
Published: August 2024

The internet of things (IoT) greatly impacts daily life by enabling efficient data exchange between objects and servers. However, cyber-attacks pose a serious threat to IoT devices. Intrusion detection systems (IDS) are vital for safeguarding networks, and machine learning methods are increasingly used to enhance security. Continuous improvement in accuracy and performance is crucial for effective IoT security. Deep learning not only outshines traditional machine learning methods but also holds untapped potential in fortifying IDS systems. This paper introduces an innovative deep learning framework tailored for anomaly detection within IoT networks, leveraging bidirectional long short-term memory (BiLSTM) and gated recurrent unit (GRU) architectures. The hyper parameters of the proposed model are optimized using the JAYA optimization technique. These models are validated using IoT-23 and MQTTset datasets. Several performance metrics including accuracy, precision, recall, f-score, true negative rate (TNR), false positive rate (FPR), and false negative rate (FNR), have been selected to assess the effectiveness of the suggested model. The empirical results are scrutinized and juxtaposed with prevailing approaches in the realm of intrusion detection for IoT. Notably, the proposed method emerges as showcasing superior accuracy when contrasted with existing methods.
Fortifying network security: machine learning-powered intrusion detection systems and classifier performance analysis
Authors: Tawil Arar Al , Al-Shboul Lara , Almazaydeh Laiali , Alshinwan Mohammad
Published: August 2024

Intrusion detection systems (IDS) protect networks from threats; they actively monitor network activity to identify and prevent malicious actions. This study investigates the application of machine learning methods to strengthen IDS, explicitly emphasizing the comprehensive CICIDS 2017 dataset. The dataset was refined by implementing stringent preprocessing methods such as feature normalization, class imbalance management, feature reduction, and feature selection to ensure its quality and lay the foundation for developing robust models. The performance evaluation of three classifiers-support vector machine (SVM), extreme gradient boosting (XGBoost), and naive Bayes was highly impressive. Vital accuracy, precision, recall, and F1-score values of 0.984389, 0.984479, 0.984375, and 0.984304, respectively, were achieved by SVM. Notably, XGBoost demonstrated exceptional performance across all metrics, attaining flawless scores of 1.0. naive Bayes demonstrated noteworthy accuracy, precision, recall, and F1-score performance, which were recorded as 0.877392, 0.907171, 0.877007, and 0.876986, respectively. The results of this study emphasize the critical importance of preparation methods in improving the effectiveness of IDS via machine learning. This further demonstrates the potential of particular classifiers to detect and prevent network intrusions efficiently, thereby substantially contributing to cybersecurity measures.
Enhancing internet of things security: evaluating machine learning classifiers for attack prediction
Authors: Arabiat Areen , Altayeb Muneera
Published: August 2024

The internet of things (IoT) has contributed to improving the quality of service and operational efficiency in many areas, such as smart cities, but this technology has faced a major dilemma: the problem of cyber-attacks of various types. In this study, we relied on the use of machine learning (ML) and deep learning (DL) techniques to present a proposed model of an intrusion detection system (IDS) for detecting different types of IoT attacks that include ARP_poisoning, DOS_SYN_Hping, MQTT_Publish, NMAP_FIN_SCAN, NMAP_OS_DETECTION, and Thing_Speak. However, the proposed model is built using Orange3 data mining tools. The model consists of random forest (RF), artificial neural network (ANN), logistic regression (LR), and support vector machine (SVM) classifiers. On the other hand, the data set that is used was obtained from the Kaggle platform's real-time IoT infrastructure data set, called RT-IoT2022. The data set consists of a huge number of records, which are processed and then reduced to 7,481 records using linear discriminant analysis. In the next stage, the data set is fed to the Orange3 data mining tool, which is divided into 70% of the training dataset and 30% of the test dataset, in addition to using fold-cross validation to increase accuracy and avoid overfitting. Thus, the experimental results showed the superiority of RF with a classification accuracy of (99.9%), while the accuracy in ANN reached (99.8%), (97.8%) in LR, and finally, for SVM, the accuracy reached (92.9%).
Binge Alcohol Consumption Elevates Sympathetic Transduction to Blood Pressure: A Randomized Controlled Trial
Authors: Bigalke Jeremy A. , Greenlund Ian M. , Solis-Montenegro Tatiana X. , Durocher John J. , Joyner Michael J. , Carter Jason R.
Published: August 2024

BACKGROUND:

Alcohol consumption is associated with cardiovascular disease, and the sympathetic nervous system is a suspected mediator. The present study investigated sympathetic transduction of muscle sympathetic nerve activity to blood pressure at rest and in response to cold pressor test following evening binge alcohol or fluid control, with the hypothesis that sympathetic transduction would be elevated the morning after binge alcohol consumption.

METHODS:

Using a randomized, fluid-controlled (FC) crossover design, 26 healthy adults (12 male, 14 female, 25±6 years, 27±4 kg/m
2
) received an evening binge alcohol dose and a FC. All participants underwent next-morning autonomic-cardiovascular testing consisting of muscle sympathetic nerve activity, beat-to-beat blood pressure, and heart rate during a 10-minute rest period and a 2-minute cold pressor test. Sympathetic transduction was assessed at rest and during the cold pressor test in both experimental conditions.

RESULTS:

Evening alcohol increased heart rate (FC: 60±9 versus alcohol: 64±9 bpm;
P
=0.010) but did not alter resting mean arterial pressure (FC: 80±6 versus alcohol: 80±7 mm Hg;
P
=0.857) or muscle sympathetic nerve activity (FC: 18±9 versus alcohol: 20±8 bursts/min;
P
=0.283). Sympathetic transduction to mean arterial pressure (time×condition;
P
=0.003), diastolic blood pressure (time×condition;
P
=0.010), and total vascular conductance (time×condition;
P
=0.004) was augmented after alcohol at rest. Sympathetic transduction during the cold pressor test was also elevated after evening binge alcohol consumption (
P
=0.002).

CONCLUSIONS:

These findings suggest that evening binge alcohol consumption leads to augmented morning-after sympathetic transduction of muscle sympathetic nerve activity to blood pressure, highlighting a new mechanism whereby chronic or excessive alcohol consumption contributes to cardiovascular disease progression via altered end-organ responsiveness to sympathetic neural outflow.

REGISTRATION:

URL:
https://clinicaltrials.gov/study/NCT03567434
; Unique identifier: NCT03567434.
Impact of Procedural Success Definitions on Long‐Term Outcomes in Patients With Rheumatic Mitral Stenosis Treated With Percutaneous Balloon Mitral Valvuloplasty: A Multicenter, Retrospective Cohort Study
Authors: Manoret Premanan , Thonghong Tasalak , Meemook Krissada , Kosallavat Seththawut , Aroonsiriwattana Suchart , Songsangjinda Thammarak , Suwanugsorn Saranyou , Nilmoje Thanapon , Cheewatanakornkul Sirichai , Wisaratapong Treechada , Limumpornpetch Sunti , Lohawijarn Watchara , Thungthienthong Metus , Chamnarnphol Noppadol , Chandavimol Mann , Suwannasom Pannipa , Jintapakorn Woravut , Chichareon Ply
Published: August 2024

Background

It is uncertain which percutaneous balloon mitral valvuloplasty (PBMV) success definitions should be used because there are no studies comparing the effects of these definitions on subsequent outcomes. We evaluated the association between 3 success definitions and long‐term clinical outcomes in patients with rheumatic mitral stenosis who underwent PBMV.

Methods and Results

This multicenter retrospective study included patients with severe rheumatic mitral stenosis who underwent PBMV. Three definitions were used as follows: (A) post‐PBMV mitral valve area (MVA) ≥1.5 cm
2
or ≥50% increase in MVA with MR <3+; (B) post‐PBMV MVA ≥1.5 cm
2
and MR ≤2+; and (C) post‐PBMV MVA ≥1.5 cm
2
or ≥50% increase in MVA, with no more than 1‐grade increment in MR. Multivariable Cox regression analyses were performed to evaluate the associations between PBMV success and the composite of all‐cause mortality, mitral surgery, and repeat PBMV. Successful PBMV, according to definitions A, B, and C was associated with a lower risk of the composite outcomes (definition A—hazard ratio [HR], 0.55 [95% CI, 0.43–0.69], definition B—HR, 0.55 [95% CI, 0.43–0.69], definition C—HR, 0.55 [95% CI, 0.44–0.69]). Patients meeting all 3 success definitions had the lowest risk compared with those who did not meet any definition or met 1 or 2 definitions.

Conclusions

All 3 success definitions had a prognostic impact on outcomes. Patients who achieved post‐PBMV MVA ≥1.5 cm
2
, irrespective of the percentage increase in MVA, and MR ≤grade 2, with no more than a 1‐grade increment in MR, had the most favorable outcomes.
Prediction for Perioperative Stroke Using Intraoperative Parameters
Authors: Oh Mi‐Young , Jung Young Mi , Kim Won‐Pyo , Lee Hyung‐Chul , Kim Tae Kyong , Ko Sang‐Bae , Lim Jaehyun , Lee Seung Mi
Published: August 2024

Background

Perioperative stroke is a severe complication following surgery. To identify patients at risk for perioperative stroke, several prediction models based on the preoperative factors were suggested. Prediction models often focus on preoperative patient characteristics to assess stroke risk. However, most existing models primarily base their predictions on the patient's baseline characteristics before surgery. We aimed to develop a machine‐learning model incorporating both pre‐ and intraoperative variables to predict perioperative stroke.

Methods and Results

This study included patients who underwent noncardiac surgery at 2 hospitals with the data of 15 752 patients from Seoul National University Hospital used for development and temporal internal validation, and the data of 449 patients from Boramae Medical Center used for external validation. Perioperative stroke was defined as a newly developed ischemic lesion on diffusion‐weighted imaging within 30 days of surgery. We developed a prediction model composed of pre‐ and intraoperative factors (integrated model) and compared it with a model consisting of preoperative features alone (preoperative model). Perioperative stroke developed in 109 (0.69%) patients in the Seoul National University Hospital group and 11 patients (2.45%) in the Boramae Medical Center group. The integrated model demonstrated superior predictive performance with area under the curve values of 0.824 (95% CI, 0.762–0.880) versus 0.584 (95% CI, 0.499–0.667;
P
<0.001) in the internal validation; and 0.716 (95% CI, 0.560–0.859) versus 0.505 (95% CI, 0.343–0.654;
P
=0.018) in the external validation, compared to the preoperative model.

Conclusions

We suggest that incorporating intraoperative factors into perioperative stroke prediction models can improve their accuracy.
Technological Advances Are Associated With Better Clinical Outcomes of Percutaneous Coronary Intervention in Patients With Unprotected Left Main Disease
Authors: Zhang Dong , Yan Ruohua , Wang Hao‐Yu , Zhang Rui , Zhao Zhiyong , Gao Guofeng , Yang Min , Wang Hao , Liu Shuai , Fu Rui , Yin Dong , Zhu Chenggang , Feng Lei , Yang Yuejin , Dou Kefei
Published: August 2024

Background

Few large‐scale studies have evaluated the effectiveness of percutaneous coronary intervention (PCI) technological advances in the treatment of patients with unprotected left main coronary artery disease (LM‐CAD). We aim to identify independent factors that affect the prognosis of PCI in patients with unprotected LM‐CAD and to assess the impact of PCI technological advances on long‐term clinical outcomes.

Methods and Results

A total of 4512 consecutive patients who underwent unprotected LM‐CAD PCI at Fuwai Hospital from 2004 to 2016 were enrolled. Multivariable Cox proportional hazards model was used to identify which techniques can independently affect the incidence of major adverse cardiac events (MACEs; a composite of cardiac death, myocardial infarction, or target vessel revascularization). The incidence of 3‐year MACEs was 9.0% (406/4512). Four new PCI techniques were identified as the independent protective factors of MACEs, including second‐generation drug‐eluting stents (hazard ratio [HR], 0.61 [95% CI, 0.37–0.99]), postdilatation (HR, 0.75 [95% CI, 0.59–0.94]), final kissing balloon inflation (HR, 0.78 [95% CI, 0.62–0.99]), and using intravascular ultrasound (HR, 0.78 [95% CI, 0.63–0.97]). The relative hazard of 3‐year MACEs was reduced by ≈50% with use of all 4 techniques compared with no technique use (HR, 0.53 [95% CI, 0.32–0.87]).

Conclusions

PCI technological advances including postdilatation, second‐generation drug‐eluting stent, final kissing balloon inflation, and intravascular ultrasound guidance were associated with improved clinical outcomes in patients who underwent unprotected LM‐CAD PCI.
Risk of Major Bleeding, Stroke/Systemic Embolism, and Death Associated With Different Oral Anticoagulants in Patients With Atrial Fibrillation and Severe Chronic Kidney Disease
Authors: Xu Yunwen , Ballew Shoshana H. , Chang Alexander R. , Inker Lesley A. , Grams Morgan E. , Shin Jung‐Im
Published: August 2024

Background

Patients with atrial fibrillation and severe chronic kidney disease have higher risks of bleeding, thromboembolism, and mortality. However, optimal anticoagulant choice in these high‐risk patients remains unclear.

Methods and Results

Using deidentified electronic health records from the Optum Labs Data Warehouse, adults with atrial fibrillation and severe chronic kidney disease (estimated glomerular filtration rate <30 mL/min per 1.73 m
2
) initiating warfarin, apixaban, or rivaroxaban between 2011 and 2021 were included. Using inverse probability of treatment weighting, adjusted risks of major bleeding, stroke/systemic embolism, and death were compared among agents. A total of 6794 patients were included (mean age, 78.5 years; mean estimated glomerular filtration rate, 24.7 mL/min per 1.73 m
2
; 51% women). Apixaban versus warfarin was associated with a lower risk of major bleeding (incidence rate, 1.5 versus 2.9 per 100 person‐years; subdistribution hazard ratio [sub‐HR], 0.53 [95% CI, 0.39–0.70]), and similar risks for stroke/systemic embolism (incidence rate, 1.9 versus 2.4 per 100 person‐years; sub‐HR, 0.80 [95% CI, 0.59–1.09]) and death (incidence rate, 4.6 versus 4.5 per 100 person‐years; HR, 1.03 [95% CI, 0.82–1.29]). Rivaroxaban versus warfarin was associated with a higher risk of major bleeding (incidence rate, 4.9 versus 2.9 per 100 person‐years; sub‐HR, 1.65 [95% CI, 1.10–2.48]), with no difference in risks for stroke/systemic embolism and death. Apixaban versus rivaroxaban was associated with a lower risk of major bleeding (sub‐HR, 0.53 [95% CI, 0.36–0.78]).

Conclusions

These real‐world findings are consistent with potential safety advantages of apixaban over warfarin and rivaroxaban for patients with atrial fibrillation and severe chronic kidney disease. Further randomized trials comparing individual oral anticoagulants are warranted.
Mendelian Randomization and Bayesian Colocalization Analysis Implicate Glycoprotein VI as a Potential Drug Target for Cardioembolic Stroke in South Asian Populations
Authors: Wu Siwei , Meena Devendra , Yarmolinsky James , Gill Dipender , Smith Alexander , Dib Marie‐Joe , Chauhan Ganesh , Rohatgi Anand , Dehghan Abbas , Tzoulaki Ioanna
Published: August 2024

Background

Circulating plasma proteins are clinically useful biomarkers for stroke risk. We examined the causal links between plasma proteins and stroke risk in individuals of South Asian ancestry.

Methods and Results

We applied proteome‐wide Mendelian randomization and colocalization approaches to understand causality of 2922 plasma proteins on stroke risk in individuals of South Asian ancestry. We obtained genetic instruments (proxies) for plasma proteins from the UK Biobank (N=920). Genome‐wide association studies summary data for strokes (N≤11 312) were sourced from GIGASTROKE consortium. Our primary approach involved the Wald ratio or inverse‐variance‐weighted methods, with statistical significance set at false discovery rate <0.1. Additionally, a Bayesian colocalization approach assessed shared causal variants among proteome, transcriptome, and stroke phenotypes to minimize bias from linkage disequilibrium. We found evidence of a potential causal effect of plasma GP6 (glycoprotein VI) levels on cardioembolic stroke (odds ratio [OR]
Wald ratio
=2.53 [95% CI, 1.59–4.03];
P
=9.2×10
−5
, false discovery rate=0.059). Generalized Mendelian randomization accounting for correlated single nucleotide polymorphisms (SNPs), with the
P
value threshold at
P
<5×10
−8
and clumped at
r
2
=0.3, showed consistent direction of effect of GP6 on cardioembolic stroke (OR
generalized inverse‐variance‐weighted
=2.21 [95% CI, 1.46–3.33];
P
=1.6×10
−4
). Colocalization analysis indicated that plasma GP6 levels colocalize with cardioembolic stroke (posterior probability=91.4%). Multitrait colocalization combining transcriptome, proteome, and cardioembolic stroke showed moderate to strong evidence that these 2 traits colocalize with
GP6
expression in the coronary artery and brain tissues (multitrait posterior probability>50%). The potential causal effect of GP6 on cardioembolic stroke was not significant in European populations (OR
inverse‐variance‐weighted
=1.08 [95% CI, 0.93–1.26];
P
=0.29).

Conclusions

Our joint Mendelian randomization and colocalization analyses suggest that genetically predicted GP6 is potentially causally associated with cardioembolic stroke risk in individuals of South Asian ancestry. As genetic data on individuals of South Asian ancestry increase, future Mendelian randomization studies with larger sample size for plasma GP6 levels should be implemented to further validate our findings. Additionally, clinical studies will be necessary to verify GP6 as a therapeutic target for cardioembolic stroke in South Asians.
Neighborhood Socioeconomic Disadvantage and 30‐Day Outcomes for Common Cardiovascular Conditions
Authors: Lusk Jay B. , Blass Beau , Mahoney Hannah , Hoffman Molly N. , Clark Amy G. , Bae Jonathan , Mentz Robert J. , Wang Tracy Y. , Patel Manesh , Hammill Bradley G.
Published: August 2024

Background

Understanding the relationship between neighborhood environment and cardiovascular outcomes is important to achieve health equity and implement effective quality strategies. We conducted a population‐based cohort study to determine the association of neighborhood socioeconomic deprivation and 30‐day mortality and readmission rate for patients admitted with common cardiovascular conditions.

Methods and Results

We examined claims data from fee‐for‐service Medicare beneficiaries aged ≥65 years between 2017 and 2019 admitted for heart failure, valvular heart disease, ischemic heart disease, or cardiac arrhythmias. The primary exposure was the Area Deprivation Index; outcomes were 30‐day all‐cause death and unplanned readmission. More than 2 million admissions were included. After sequential adjustment for patient characteristics (demographics, dual eligibility, comorbidities), area health care resources (primary care clinicians, specialists, and hospital beds per capita), and admitting hospital characteristics (ownership, size, teaching status), there was a dose‐dependent association between neighborhood socioeconomic deprivation and 30‐day mortality rate for all conditions. In the fully adjusted model for death, estimated effect sizes of residence in the most disadvantaged versus least disadvantaged neighborhoods ranged from adjusted odds ratio 1.29 (95% CI, 1.22–1.36) for the heart failure group to adjusted odds ratio 1.63 (95% CI, 1.36–1.95) for the valvular heart disease group. Neighborhood deprivation was associated with increased adjusted 30‐day readmission rates, with estimated effect sizes from adjusted odds ratio 1.09 (95% CI, 1.05–1.14) for heart failure to adjusted odds ratio 1.19 (95% CI, 1.13–1.26) for arrhythmia.

Conclusions

Neighborhood socioeconomic disadvantage was associated with 30‐day mortality rate and readmission for patients admitted with common cardiovascular conditions independent of individual demographics, socioeconomic status, medical risk, care access, or admitting hospital characteristics.
Evolutionary history of the Balkan endemic genus Delminichthys (Teleostei, Leuciscidae) with an emphasis on the population structure of Southern Dalmatian minnow
Authors: Lukač Milica , Ludoški Jasmina , Dekić Radoslav , Milankov Vesna
Published: August 2024

The genus Delminichthys consists of allopatric species restricted to ephemeral watercourses in the karst fields of the left-bank (D. ghetaldii) and the right-bank (D. adspersus and D. jadovensis) Neretva River basin as well as in the Una River drainage (D. krbavensis). The first aim addressed questions of interspecies variation and phylogenetic relationships among Delminichthys based on time-calibrated analysis of cyt b mtDNA (cytochrome b) and COI mtDNA (cytochrome c subunit I) mitochondrial DNA genes. Species-specific haplogroups supported two mtDNA genes as suitable barcoding markers, while we additionally proposed character-based and genetic distance threshold-based delimitation of species. We confirmed that the Pliocene and Pleistocene refugium for freshwater species was the Neretva basin, promoting the diversification of the genus Delminichthys. Our second aim was to test the population structure of D. ghetaldii (Southern Dalmatian minnow) by performing an integrative study using molecular mtDNA data, linear and geometric morphometric data. We consider that the spatial aspects of the population genealogical structure of D. ghetaldii mirror the Pleistocene paleodrainages. The differentiated COI mtDNA clusters corresponding to the populations from Fatničko Polje+Dabarsko Polje and Ljubomirsko Polje+Konavosko Polje represent geographically discrete genetic entities, which should be addressed through conservation management.
Morphology and DNA barcodes of four species of Bradysia hilaris group from China (Diptera, Sciaridae)
Authors: DU HONG , LIU JIAWEI , HELLER KAI , SHAH BISMILLAH , WANG QINGYUN , HUANG JUNHAO
Published: August 2024

Four morphologically allied species of the Bradysia hilaris group were studied from China. In a DNA metabarcoding based dipteran diversity study in Zhejiang, eastern China, a hyper-abundant sciarid species was discovered. It was further recognized in this study to be new to science, Bradysia tianmuensis Du & Huang sp. nov., as well as a morphologically similar species, Bradysia curvula Du & Huang sp. nov. Both new species were found to be fairly similar morphologically to the holotype of Bradysia noduspina Yang, Zhang & Yang, 1993 from Guizhou in western China. However, the paratype of B. noduspina appeared to be different from the holotype and determined to be new to science, Bradysia chikunae Du & Huang sp. nov. A phylogenetic tree of all the available 31 COI sequences of the Bradysia hilaris group was provided. Molecular work conducted in the current study also supports Bradysia tianmuensis Du & Huang sp. nov. and Bradysia curvula Du & Huang sp. nov. as new to science thus the four species were described or redescribed accompanied by detailed imagery of habitus and other characters useful for determination.
Impacts of Sampling and Storm-Motion Estimates on RUC/RAP-Based Discriminations of Nontornadic and Tornadic Supercell Environments
Authors: Coniglio Michael C. , Thompson Richard L.
Published: August 2024

This study explores reasons for differences in discriminations of nontornadic and tornadic supercell environments between a recent study of field project (FP) radiosonde observations and RUC/RAP-based studies. Two differences are explored: 1) differences in the relative skill between near-ground and deeper-layer storm-relative helicity (SRH) and 2) differences in skill for storm-relative winds (SRWs) seen in observed soundings that are not seen in RUC/RAP-based analyses. Results show that RUC/RAP-derived near-ground SRH continues to show larger skill than deeper-layer SRH for springtime, afternoon/evening cases over the plains (the “FP” domain), although 0–1-km SRH becomes more skillful than 0–500-m SRH. The skill of kinematic variables decreases over the FP domain, as the skill of mixed-layer convective available potential energy (MLCAPE) and the percent of the low-level horizontal vorticity that is streamwise increase for significant tornadoes. Large skill is found for mean ground-relative winds (GRWs) over all layers tested, but the skill of SRWs using Bunkers motion is relatively small. The field project dataset is shown to be biased toward particularly high-end nontornadic supercells, with more tornado-favorable mixed-layer lifted condensation levels (MLLCLs), lapse rates, and low-level shear/SRH compared to the nontornadic cases in the RUC/RAP dataset over the FP domain. The skill of deeper-layer SRH, GRWs, SRWs, and MLCAPE is unusually large in the field project sample, which highlights variables that may increase the likelihood of tornadoes when other variables that relate to the supercell tornado production (low-level shear/SRH and MLLCLs) are already in a tornado-favorable range. The skill of deeper-layer kinematic variables is particularly evident when observed storm motions are used instead of Bunkers motion.

Significance Statement

Researchers continue to explore reasons why some storms produce tornadoes and others do not. Some recent studies show conflicting results, including the best layers to analyze the environmental wind shear and the ability of wind speeds relative to the storm to distinguish nontornadic from tornadic storms. One study is rooted in observations taken for field projects that targeted tornadic storms and the others in model analyses. Restricting model analyses to a spatiotemporal region that emulates field projects does not explain the disagreement but reveals how the skill of some other variables changes. The tendency for field projects to target particularly strong storms and the use of actual storm motions versus estimates of storm motions both contribute to the conflicting results.
Evolving Landscape of Inferior Vena Cava Filter Utilization: A Comprehensive Bibliometric Analysis
Authors: Liao Jianyu , Tan Zhimin , Wu Zhoupeng
Published: August 2024

This bibliometric analysis scrutinizes the evolution and current challenges in the use of Inferior Vena Cava (IVC) filters, focusing on trends from 2004 to 2023. Analyzing 2470 records, we report the United States’ dominant role, with over half of the studies, and a significant shift towards retrievable filters. Despite technological advancements, controversies persist regarding efficacy, safety, and retrieval issues. Our findings point to the need for refined clinical guidelines and enhanced management strategies to navigate the complex landscape of IVC filter utilization effectively.
Revolutionaries for Railways
Authors: Ji Chengyuan , Ma Xiao
Published: August 2024

This study explores the sources of regional favoritism in government-invested infrastructure projects. We built an original county-level dataset that matches the biographies of 1614 retired communist revolutionaries with information on the expansion of China’s state-directed high-speed railway program. Our findings indicate that a surviving revolutionary makes his birth county significantly more likely to receive the central government’s approval for railway investment. This pattern is robust after accounting for a wide range of alternative explanations and a natural experiment design that exploits variations in the timings of revolutionaries’ natural deaths. Additional evidence suggests that the empowering effect of the retired revolutionaries stems most likely from their assistance in their birth counties’ bottom-up lobbying of the central government. Their moral authority as the founders of the regime helps boost local requests for investment in the eyes of central policymakers. Our findings highlight a bottom-up intergovernmental dynamic that translates personal influence into policy benefits.
Social Change and Women’s Left Vote. The Role of Employment, Education, and Marriage in the Gender Vote Gap
Authors: van Ditmars Mathilde M. , Shorrocks Rosalind
Published: August 2024

The “modern” gender vote gap – where women are generally more supportive of left parties than men – is established in many Western democracies. Whilst it is linked to societal changes, and in particular the transformation of gender roles and relations, scholars still grapple with its underlying mechanisms. This paper tests one mechanism currently untested in existing accounts: that women’s specific experiences in less traditional social statuses – in employment, education, or out of marriage – drive their support for the left. Analyses using German, Swiss, and English panel data do show differences in left party support between men and women, and amongst women, according to these social statuses. However, we do not find evidence that these occur because of these experiences directly. Rather, our findings indicate that left-leaning women self-select into certain life trajectories. This suggests that women’s shifting political views due to societal change have corresponding changes in individual life choices.
Professional Development Experiences Aligned to the Ideal Self: A Positive Psychology Perspective on Individual Development
Authors: Dhar Udayan
Published: August 2024

The purpose of this study is to use a positive psychology lens to broaden and refine our understanding of professional development experiences, and to construct a practical, but evidence-based framework for a developmental human resource management (HRM) system. Study 1 uses focus group interviews with 18 full-time professionals enrolled in a management education program to explore experiences that are likely to strengthen and clarify one's aspirational future professional identity, or the ideal self. In study 2, two separate professional samples ( N = 204, 271) are used for measurement development and hypotheses testing. Overall, the two studies show that 15 specific development experiences are particularly helpful in the formation of a clear and motivating ideal self for professionals in a wide variety of industries. As a practical application of the findings, a framework for a Developmental HRM System is proposed.
3D printable regolith filled shape memory vitrimer composite for extraterrestrial application
Authors: Gyabaah Kingsley Yeboah , Konlan John , Tetteh Obed , Jahan Maryam , Jackson Enrique , Mensah Patrick , Li Guoqiang
Published: August 2024

This study investigates a neoteric approach in manufacturing lunar regolith-filled shape memory vitrimer (SMV) composites for extraterrestrial applications. A SMV with robust mechanical properties was combined with locally available lunar regolith to form a composite material. Fourier Transfer Infrared Spectroscopy (FTIR), Scanning Electron Microscope (SEM), Thermogravimetric Analysis (TGA), and X-ray fluorescence (XRF) were used to characterize the resin, the regolith simulant, and the prepared SMV-regolith composites. We explored conventional synthesis as well as 3D printing methods for manufacturing the composite. Glass fabric-reinforced laminated composites were also prepared to evaluate the impact tolerance and damage healing efficiency. Compressive strength, flexural strength, and impact resistance of the composite were tested at both room and elevated temperatures. A compressive strength of 96.0 MPa and 5.4 MPa were recorded for composite with 40 wt% regolith ratio at room and elevated temperatures, respectively. The glass fabric reinforced SMV-regolith laminate exhibited a bending strength of 232.7 MPa, good impact tolerance under low-velocity impact test, and good healing efficiency up to two damage healing cycles. The 3D printed SMV-regolith composite using a liquid crystal display (LCD)-based printer exhibited a good thermomechanical property with a compressive and tensile strength of 139.16 MPa and 13.99 MPa, respectively, and a good shape memory effect. However, the LCD-based printing using vat-photopolymerization limits the size of the printed samples. Nonetheless, this study shows that utilization of regolith to form advanced composite is possible. SMV regolith composite is a promising material for lunar base applications due to its simple manufacturing process, excellent mechanical properties, and low energy consumption.
AI–Human Hybrids for Marketing Research: Leveraging Large Language Models (LLMs) as Collaborators
Authors: Arora Neeraj , Chakraborty Ishita , Nishimura Yohei
Published: August 2024

The authors’ central premise is that a human–LLM (large language model) hybrid approach leads to efficiency and effectiveness gains in the marketing research process. In qualitative research, they show that LLMs can assist in both data generation and analysis; LLMs effectively create sample characteristics, generate synthetic respondents, and conduct and moderate in-depth interviews. The AI–human hybrid generates information-rich, coherent data that surpasses human-only data in depth and insightfulness and matches human performance in data analysis tasks of generating themes and summaries. Evidence from expert judges shows that humans and LLMs possess complementary skills; the human–LLM hybrid outperforms its human-only or LLM-only counterpart. For quantitative research, the LLM correctly picks the answer direction and valence, with the quality of synthetic data significantly improving through few-shot learning and retrieval-augmented generation. The authors demonstrate the value of the AI–human hybrid by collaborating with a Fortune 500 food company and replicating a 2019 qualitative and quantitative study using GPT-4. For their empirical investigation, the authors design the system architecture and prompts to create personas, ask questions, and obtain responses from synthetic respondents. They provide road maps for integrating LLMs into qualitative and quantitative marketing research and conclude that LLMs serve as valuable collaborators in the insight generation process.
Driving Mobile App User Engagement Through Gamification
Authors: Paschmann Jens W. , Bruno Hernán A. , van Heerde Harald J. , Völckner Franziska , Klein Kristina
Published: August 2024

Many mobile app providers offer their apps for free and base their business models on user engagement. However, declining usage over time threatens apps’ ability to add business value. To keep users engaged, app providers use gamification—that is, they use game elements (e.g., levels, points)—in their nongame apps. Complementing traditional loyalty strategies that reward value-added activities (e.g., purchases) through value rewards, gamification rewards ongoing engagement through game elements. Thus, reward architectures of many apps have become hybrid, with value- and game-reward pursuit simultaneously driving engagement. However, it is unclear to what extent gamification helps drive user engagement and add business value. To address this question, the authors study unique data from a gamified market research app comprising daily individual-level app usage observations of 18,952 users. The findings show that game rewards increase engagement significantly over and above value rewards, leading to a lift in business value, especially when users are in closer proximity to both types of rewards. However, the analysis also shows a dark side of gamification: When users enter a state of flow in the game, game engagement has a weaker effect on value-added engagement. The authors discuss implications for gamified reward architectures.