bluuebunny/crossref_metadata_2025 huggingface dataset:
An optimistic-pessimistic game cross-efficiency method based on a Gibbs entropy model for ranking decision making units
Authors: Thongmual Noppakun , Laoha Chanchai , Wichapa Narong
Published: February 2024

The game cross-efficiency method, a commonly utilized approach for ranking decision-making units in tie-breaking scenarios, is based on secondary goals. However, in certain data envelopment analysis ranking problems, the classical game cross-efficiency method may fail to differentiate all decision-making units effectively. To address this limitation, it is prudent to explore the development of a new method that can enhance the ranking performance of the classical game cross-efficiency approach. In this study, we propose a novel Gibbs entropy linear programming model that integrates both optimistic and pessimistic perspectives of the classical game cross-efficiency method for data envelopment analysis ranking problems. To validate the reliability and utility of our proposed method, we present three examples: the six nursing homes problem, numerical example 2, and an application involving twenty Thai provinces with cash crop data. The reliability of the proposed method is assessed using Spearman’s correlation coefficient (rs) on the numerical examples. The results demonstrate that the rs values for both the proposed method and the classical game crossefficiency method, specifically for the six nursing homes problem, numerical example 2, and the application involving twenty Thai provinces, are determined to be rs=0.998, 0.998, and 0.986 respectively.
A comprehensive survey on several fire management approaches in wireless sensor networks
Authors: Rajendran Swetha , Chenniappan Navaneethan
Published: February 2024

The majority of the fires are activated through environmental reasons although a minority of them are self-activated. To detect fires several safety systems were introduced. There are wired systems, cameras, satellite systems, and bluetooth feasible to provide a complete image of the world but after a long search period. These systems are not perfect since it prevents fire from finding just at the time, the fire initiates. But, recent technological development in wireless sensor networks (WSN) has spread out its fire detection application. A comprehensive survey on several fire management approaches in WSN propose to discuss various fire detection approaches like early fire detection, energy efficient fire detection, mobile agent-based fire detection, unmanned aerial vehicle (UAV)-based fire detection, threshold-based fire detection, machine learning based fire detection and secure fire detection approaches. Moreover, the comprehensive tabular study of the fire management technique is given that will assist in the suitable selection of approaches to be applied for the detection of fire. Furthermore, WSN uses the clustering method to minimize redundant dataandsecure fire detection approaches collect authenticated data related to fire detection. Early fire detection approaches detects the fire early. Machine learning algorithm detects the fire efficiently.
A novel women's ovulation prediction through salivary ferning using the box counting and deep learning
Authors: Pratikno Heri , Zamri Ibrahim Mohd , Jusak Jusak
Published: February 2024

There are several methods to predict a woman's ovulation time, including using a calendar system, basal body temperature, ovulation prediction kit, and OvuScope. This is the first study to predict the time of ovulation in women by calculating the results of detecting the fractal shape of the full ferning (FF) line pattern in salivary using pixel counting, box counting, and deep learning for computer vision methods. The peak of a woman's ovulation every month in her menstrual cycle occurs when the number of ferning lines is the most numerous or dense, and this condition is called FF. In this study, the computational results based on the visualization of the fractal shape of the salivary ferning line pattern from the pixel-counting method have an accuracy of 80%, while the fractal dimensions achieved by the box-counting are 1.474. On the other hand, using the deep learning image classification, we obtain the highest accuracy of 100% with a precision value of 1.00, recall of 1.00, and F1-score 1.00 on the pre-trained network model ResNet-18. Furthermore, visualization of the ResNet-34 model results in the highest number of patches, i.e., 586 patches (equal to 36,352 pixels), by applying fern-like lines pattern detection with windows size 8x8 pixels.
A novel energy-efficient dynamic programming routing protocol in wireless multimedia sensor networks
Authors: Putra Emansa Hasri , Satria Muhammad Haikal , Azwar Hamid , Rianda Rendy , Saputra Muhammad , Darwis Rizadi Sasmita
Published: February 2024

Wireless multimedia sensor networks (WMSNs) have characteristics that may influence the routing decisions, such as limited energy resources, storage and computing capacity. Therefore, a routing optimization needs to be done to match the characteristics of the WMSNs. Existing routing protocols only consider energy efficiency regardless of energy threshold, maximum energy, and link cost collectively as the primary basis of routing. In this work, the energy-efficient dynamic programming (EEDP) protocol is proposed to optimize routing decisions that take into account the energy threshold, the maximum energy, and the link cost. Then, the protocol is compared with the dynamic programming (DP), and the ant colony optimization (ACO) protocol. The simulation results show that the EEDP protocol can improve energy efficiency of nodes and network lifetime of the WMSNs. Then, the EEDP protocol is also implemented into a network topology of 10 NodeMCU ESP32 devices. As a result, the EEDP protocol can work very well by selecting routes based on nodes that have the remaining energy above 50 and has the shortest distance. The average delay in sending data for the entire route for the 10 iterations of sending data is 3.99 seconds.
Solar power forecasting model as a renewable generation source on virtual power plants
Authors: Suwarno Suwarno , Pinayungan Doni
Published: February 2024

This paper describes modeling solar power generation as a renewable energy generator by simulating the analytical approach mean absolute error and root mean square error (MAE and RMSE). This research estimates the error referring to long short-term memory (LSTM) network learning. Related to this, the Indonesian government is currently actively developing solar power plants without ignoring the surrounding environment. The integration of solar power sources without accurate power prediction can hinder the work of the grid and the use of new and renewable generation sources. To overcome this, virtual power plant modeling can be a solution to minimize prediction errors. This study proposes a method for on-site virtual solar power plant efficiency with a research approach using two models, namely RMSE and MAE to account for prediction uncertainty from additional information on power plants using virtual solar power plants. A prediction strategy verified against the output power of photovoltaic (PV) modules and a set based on data from meteorological stations used to simulate the virtual power plants (VPP) model. This forecast prediction refers to the LSTM network and provides forecast errors with other learning methods, where the approach simulated with 12.36% and 11.85% accuracy for MAE and RMSE, respectively.
On the outage performance analysis of α − κ − µ fading channels with non-orthogonal multiple access protocol
Authors: Le Si-Phu , Nguyen Hong-Nhu , Thi Hau Nguyen , Thi Thu Hang Nguyen , Voznak Miroslav
Published: February 2024

In this paper, we consider effective performance transmission under generalized α − κ − µ the fading distribution. The source-user links are assumed to be non-orthogonal multiple access (NOMA) channels through a downlink power do-main. Two users are selected to service in a situation with perfect channel state information (CSI) in accordance with the NOMA protocol. The closed-form ex-pressions of outage probability (OP) and bit error rate (BER) are derived with the effect of power allocation coefficient, target rate, and channel fading param-eters. In addition, we calculate numerical results to demonstrate the asymptotic expansion in the high signal-to-noise ratio (SNR) analysis. Finally, Monte Carlo simulations are provided to validate and assess the accuracy of the analytical framework proposed.
A study on the solution of interval linear fractional programming problem
Authors: Murugan Yamini , Thamaraiselvan Nirmala
Published: February 2024

Interval linear fractional programming problem (ILFPP) approaches uncertain-ties in real-world systems such as business, manufacturing, finance, and eco-nomics. In this study, we propose solving the interval linear fractional pro-gramming (ILFP) problem using interval arithmetic. Further, to construct the problem, a suitable variable transformation is used to form an equivalent ILP problem, and a new algorithm is depicted to obtain the optimal solution with-out converting the problem into its conventional form. This paper compares the range, solutions, and approaches of ILFP with fuzzy linear fractional pro-gramming (FLFP) in solving real-world optimization problems. The illustrated numerical examples show a better range of interval solutions on practical appli-cations of ILFPs and uncertain parameters.
Unmanned aerial vehicle path planning in a 3D environment using a hybrid algorithm
Authors: Kareem Abbas Abdulrazzaq , Mohamed Mohamed Jasim , Oleiwi Bashra Kadhim
Published: February 2024

The optimal unmanned aerial vehicle (UAV) path planning using bio-inspired algorithms requires high computation and low convergence in a complex 3D environment. To solve this problem, a hybrid A*-FPA algorithm was proposed that combines the A* algorithm with a flower pollination algorithm (FPA). The main idea of this algorithm is to balance the high speed of the A* exploration ability with the FPA exploitation ability to find an optimal 3D UAV path. At first, the algorithm starts by finding the locally optimal path based on a grid map, and the result is a set of path nodes. The algorithm will select three discovered nodes and set the FPA's initial population. Finally, the FPA is applied to obtain the optimal path. The proposed algorithm's performance was compared with the A*, FPA, genetic algorithm (GA), and partical swarm optimization (PSO) algorithms, where the comparison is done based on four factors: the best path, mean path, standard deviation, and worst path length. The simulation results showed that the proposed algorithm outperformed all previously mentioned algorithms in finding the optimal path in all scenarios, significantly improving the best path length and mean path length of 79.3% and 147.8%, respectively.
Effects of processing parameters on the leakage current of silicone rubber insulator
Authors: Nazir Ali Nornazurah , Zainuddin Hidayat , Abd Razak Jeefferie , Abd-Rahman Rahisham , Ambo Nur Farhani
Published: February 2024

Silicone rubber (SiR) is known for its exceptional electrical insulation properties. The performance of SiR could be affected by many factors, including processing parameters, particularly mixing speed and time. While these parameters are crucial for ensuring the homogeneity of blended polymeric materials, their electrical impact remains relatively unexplored. This research investigates the effect of varying processing parameters on SiR samples during rapid aging under the incline plane tracking (IPT) test. The study unfolds in three phases, with the final IPT stage revealing the significant influence of different mixing speeds and times on the recorded leakage current (LC) values for each sample. Sample 2, subjected to 70 rpm mixing speed and 10 minutes of mixing time, exhibited great resistance to tracking and erosion. Fourier transform infrared spectroscopy (FTIR) was conducted on the samples before and after the IPT test to further analyze the impact of the varying processing parameters. Once again, sample 2 displayed notable resilience, demonstrating lower reductions in absorbance values for key functional groups. In conclusion, the specific processing parameters of 70 rpm and 10 minutes have been shown to positively influence the performance of SiR, enhancing their resistance to tracking and erosion during rapid aging.
Image dermoscopy skin lesion classification using deep learning method: systematic literature review
Authors: Nugroho Arief Kelik , Wardoyo Retantyo , Wibowo Moh Edi , Soebono Hardyanto
Published: February 2024

Classifying skin lesions poses a significant challenge due to the distinctive characteristics and diverse shapes they can exhibit, particularly in identifying early-stage melanoma. To address the shortcomings of the prior method, a neural network-driven strategy was introduced to differentiate between two types of skin lesions based on dermoscopic images. This new approach comprises four key stages: i) initial image processing, ii) skin lesion segmentation, iii) feature extraction, and iv) classification using deep neural networks (DNNs). Computers can also provide more accurate diagnosis results. In the review process, the articles are analyzed and summarized to contribute to developing methods or application development in skin lesion diagnosis. The stages include defining the relevant theory, input data, methods used (architecture and modules), training process, and model evaluation. This review also explores information based on trends and users, emphasizing the skin lesion segmentation process, skin lesion classification process, and minimal datasets as recommendations for future research.
Maximum allowable hp rating of 3-phase induction motor fed through a stand-alone constant V/f controlled DFIG via RSC
Authors: Sharawy Mohamed , A. Shaltout Adel , Mohammed Youssef Omar El-Sayed , A. Al-Ahmar Mahmoud , Abdel-Rahim Naser , Sutikno Tole
Published: February 2024

This paper presents a scheme to start three-phase induction motors (IMs) directly connected to terminals of constant V/f controlled doubly fed induction generator (DFIG) through the rotor side converter (RSC). The proposed control is achieved by controlling the output voltage and frequency of stand-alone DFIG via controlling an injected voltage into the rotor circuit through the RSC. The control scheme provides a search for maximum rating of the three-phase IM which can be supplied from a DFIG. The search technique is based on using a simplified mathematical model to find the capability limits of the RSC and DFIG. It is found that these parameters depend on the stator frequency and rotor slip. Therefore, an investigation is performed to find the lowest frequency and the corresponding allowable maximum rating for the IM to be safely started. A typical example is provided in the paper for a 15 kW DFIG. It is shown that this generator could supply a three-phase IM with a maximum rating of 1-hp if it operated at nominal outputs, voltage and frequency, during start-up period. While, using the proposed technique, the same generator could start-up a three-phase IM with maximum power rating of 7.25 hp.
The deep convolutional networks for the classification of multi-class arrhythmia
Authors: Akbar Muhamad , Nurmaini Siti , Partan Radiyati Umi
Published: February 2024

An arrhythmia is an irregular heartbeat. Many researchers in the AI field have carried out the automatic classification of arrhythmias, and the issue that has been widely discussed is imbalanced data. A popular technique for overcoming this problem is the synthetic minority oversampling technique (SMOTE) technique. In this paper, the author adds some sampling of data obtained from other datasets into the primary dataset. In this case, the main dataset is the Massachusetts Institute of Technology–Beth Israel Hospital (MIT-BIH) arrhythmia database and an additional dataset from the MIT-BIH supraventricular arrhythmia database. The classification process is carried out with one-dimensional convolutional neural network model (1D-CNN) to perform multiclass and subject-class advancement of medical instrumentation (AAMII) classifications. The results obtained from this study are an accuracy of 99.10% for multiclass and 99.25% for subject-class.
Identifying deoxyribonucleic acids of individuals based on their chromosomes by proposing a special deep learning model
Authors: Omar Al-Nima Raid Rafi , Salahaldeen Al-Kaltakchi Musab Tahseen , A. Abdulla Hasan
Published: February 2024

One of the most significant physiological biometrics is the deoxyribonucleic acid (DNA). It can be found in every human cell as in hair, blood, and skin. In this paper, a special DNA deep learning (SDDL) is proposed as a novel machine learning (ML) model to identify persons depending on their DNAs. The proposed model is designed to collect DNA chromosomes of parents for an individual. It is flexible (can be enlarged or reduced) and it can identify one or both parents of a person, based on the provided chromosomes. The SDDL is so fast in training compared to other traditional deep learning models. Two real datasets from Iraq are utilized called: Real Iraqi Dataset for Kurd (RIDK) and Real Iraqi Dataset for Arab (RIDA). The results yield that the suggested SDDL model achieves 100% testing accuracy for each of the employed datasets.
Sentiment analysis with hotel customer reviews using FNet
Authors: Bhowmik Shovan , Sadik Rifat , Akanda Wahiduzzaman , Pavel Juboraj Roy
Published: February 2024

Recent research has focused on opinion mining from public sentiments using natural language processing (NLP) and machine learning (ML) techniques. Transformer-based models, such as bidirectional encoder representations from transformers (BERT), excel in extracting semantic information but are resourceintensive. Google’s new research, mixing tokens with fourier transform, also known as FNet, replaced BERT’s attention mechanism with a non-parameterized fourier transform, aiming to reduce training time without compromising performance. This study fine-tuned the FNet model with a publicly available Kaggle hotel review dataset and investigated the performance of this dataset in both FNet and BERT architectures along with conventional machine learning models such as long short-term memory (LSTM) and support vector machine (SVM). Results revealed that FNet significantly reduces the training time by almost 20% and memory utilization by nearly 60% compared to BERT. The highest test accuracy observed in this experiment by FNet was 80.27% which is nearly 97.85% of BERT’s performance with identical parameters.
Intelligent agriculture system using low energy and based on the use of the internet of things
Authors: Elhattab Kamal , Abouelmehdi Karim
Published: February 2024

The field of smart agriculture is ranked among the top areas that uses the internet of things (IoT), whose goal is to increase the quantity and quality of agricultural productivity. The aim of this work is to realize a new device that will be cost-effective, reliable, and autonomous using a solar panel to provide electricity in large-scale agricultural fields, ESP32 to interconnect IoT sensors and the long range (LoRa) data transmission protocol to guarantee connectivity in places where there is no internet, whose objective is to monitor and irrigate agricultural fields only when there is a need for water. The data received by the sensors is sent to mobile app users via the Blynk cloud. The performance of our new approach is measured in terms of energy savings. This new model of irrigation and smart monitoring will improve the efficiency of farming techniques.
Exploratory analysis on the natural language processing models for task specific purposes
Authors: Shidaganti Ganeshayya , Shetty Rithvik , Edara Tharun , Srinivas Prashanth , Tammineni Sai Chandu
Published: February 2024

Natural language processing (NLP) is a technology that has become widespread in the area of human language understanding and analysis. A range of text processing tasks such as summarisation, semantic analysis, classification, question-answering, and natural language inference are commonly performed using it. The dilemma of picking a model to help us in our task is still there. It’s becoming an impediment. This is where we are trying to determine which modern NLP models are better suited for the tasks set out above in order to compare them with datasets like SQuAD and GLUE. For comparison, BERT, RoBERTa, distilBERT, BART, ALBERT, and text-to-text transfer transformer (T5) models have been used in this study. The aim is to understand the underlying architecture, its effects on the use case and also to understand where it falls short. Thus, we were able to observe that RoBERTa was more effective against the models ALBERT, distilBERT, and BERT in terms of tasks related to semantic analysis, natural language inference, and question-answering. The reason is due to the dynamic masking present in RoBERTa. For summarisation, even though BART and T5 models have very similar architecture the BART model has performed slightly better than the T5 model.
Miniaturization of antenna using metamaterial loaded with CSRR for wireless applications
Authors: Pande Suyog V. , Patil Dipak P.
Published: February 2024

This paper proposes a compact decagon antenna for wireless applications based on inspired metamaterial (MTM) loaded with a modified complementary split ring resonator (CSRR). A MTM loaded with CSRR is used to achieve a size reduction of 50% when compared to a traditional antenna. The suggested decagon antenna's ground plane has been loaded with CSRR. The antenna was made on an FR4 substrate with a thickness of 1.6 mm and εr=4.4 and has a very small dimension of 0.288 λ_0x0.272 λ_0x0.013 λ_0 (where λ_0 represent center frequency at 2.4 GHz). The given antenna has a 90 MHz bandwidth (2.40-2.50 GHz) with a peak gain of 2.36 dB. The presented design is validated by showing simulated results of the S parameter, VSWR, gain, surface current, and radiation pattern. The proposed antenna is well suited for wireless applications.
A novel cost-effective power supply model for industrial appliances based on triangular magnetic shunt transformer design
Authors: Lahame Mouhcine , Outzguinrimt Hamid , Oumghar Rajaa , Bahani Boubkar , Chraygane Mohammed
Published: February 2024

This paper presents a new design of a magnetic shunt transformer for use in industrial microwave generators. The proposed transformer has a triangular shape and offers several advantages over existing transformer designs, including reduced volume and maintenance costs. We provide a detailed analysis of the transformer's dimensions and an equivalent model of the three-phase high voltage power supply system. The results of this study have significant implications for the field of industrial microwave generator design and could lead to the development of more efficient and costeffective systems. The resulting model is comprised of saturable inductors capable of accounting for the non-linear phenomena of saturation. The power supply is simulated using MATLAB/Simulink with a neuro-fuzzy ANFIS approach. The results are compared to experimental validations of a single-phase reference power supply for a magnetron, validating the proposed power supply. Additionally, the simulation results demonstrate the effectiveness of the proposed design, which outperforms existing transformers in terms of volume, energy efficiency and maintenance costs.
A low-cost Wi-Fi smart home socket using internet of things
Authors: Suffian Ahmad Taufik Ahmad Danish , Abdullah Rina , Jaafar Afiza Nur , Nik Dzulkefli Nik Nur Shaadah , Ismail Syila Izawana
Published: February 2024

With the emergence of smart home appliances, traditional power sockets are becoming less compatible with modern living styles. Furthermore, modern commercialized sockets are expensive and unaffordable. This project presents the development of a low-cost Wi-Fi smart home socket using internet of things (IoT) technology that is user-friendly for smartphone users to control home appliances. Smart home socket devices can turn on and off power outlets automatically from any location if they are linked to the internet and providing the user with more convenience and energy savings. This project uses a node microcontroller unit (NodeMCU) Wi-Fi module (ESP8266) as the main microcontroller unit to connect to a cloud platform. It also uses a mobile phone application to send instructions to the microcontroller for turning on and off household appliances remotely through a smart socket. The switching mechanism is monitored and controlled through the Blynk platform. A 4-channel relay module is used to transition DC current loads to AC current loads in order to activate switching processes. According to the study’s findings, the Wi-Fi smart home socket system is able to save on excessive usage of electrical appliances while also increasing electrical appliance safety.
Performance evaluation of feature selections on some ML approaches for diagnosing the narcissistic personality disorder
Authors: Sulistiani Heni , Syarif Admi , Muludi Kurnia , Warsito Warsito
Published: February 2024

Narcissistic personality disorder (NPD) is a personality disorder that affects various aspects of life, including relationships, employment, school, and finances. Persons with NPD usually feel unhappy and disappointed when no one helps them and is not praised for their achievements. Diagnosing narcissism is generally done using a screening test that consumes time and costs a lot. This research aims to evaluate the performance of several feature selection (FS) approaches on machine learning (ML) techniques (support vector machine (SVM), random forest classifier (RFC), and Naive Bayes). Three scenarios of FS (all features, the information gain technique and the gain ratio (GR) feature technique) are used for each ML method. Several experiments using the benchmark narcissistic disorder dataset have been done. It adopts the k-fold cross-validation (10-fold cross-validation) strategy. We evaluate the method’s performance by measuring its accuracy, error rate, and processing time. It is shown that the RFC GR strategy gives the best performance with an accuracy of 100%.
Development of the NOCSAE Standard to Reduce the Risk of Commotio Cordis
Authors: Dau Nathan , Bir Cynthia , McCalley Elizabeth , Halstead David , Link Mark S.
Published: February 2024

BACKGROUND:

Commotio cordis, sudden cardiac death (SCD) caused by relatively innocent impact to the chest, is one of the leading causes of SCD in sports. Commercial chest protectors have not been demonstrated to mitigate the risk of these SCDs.

METHODS:

To develop a standard to assess chest protectors, 4 phases occurred. A physiological commotio cordis model was utilized to assess variables that predicted for SCD. Next, a surrogate model was developed based on data from the physiological model, and the attenuation in risk was assessed. In the third phase, this model was calibrated and validated. Finally, National Operating Committee on Standards for Athletic Equipment adopted the standard and had an open review process with revision of the standard over 3 years.

RESULTS:

Of all variables, impact force was the most robust at predicting SCD. Chest wall protectors which could reduce the force of impact to under thresholds were predicted to reduce the risk of SCD. The correlation between the experimental model and the mechanical surrogate ranged from 0.783 with a lacrosse ball at 30 mph to 0.898 with a baseball at 50 mph. The standard was licensed to National Operating Committee on Standards for Athletic Equipment which initially adopted the standard in January 2018, and finalized in July 2021.

CONCLUSIONS:

An effective mechanical surrogate based on physiological data from a well-established model of commotio cordis predicts the reduction in SCD with chest protectors. A greater reduction in force provides a great degree of protection from commotio cordis. This new National Operating Committee on Standards for Athletic Equipment standard for chest protectors should result in a significant reduction in the risk of commotio cordis on the playing field.
Pulsed Field Ablation Index–Guided Ablation for Lesion Formation: Impact of Contact Force and Number of Applications in the Ventricular Model
Authors: Di Biase Luigi , Marazzato Jacopo , Govari Assaf , Altman Andreas , Beeckler Christopher , Keyes Joe , Sharma Tushar , Grupposo Vito , Zou Fengwei , Sugawara Masafumi , Ikeda Atsushi , Raissi Farshad , Bhardwaj Rahul , Hsu Jonathan C. , Lee Mark , Banker Rajesh , Mohanty Sanghamitra , Natale Andrea , Chen Qi , Parikh Paras , Zhang Xiaodong , Nakagawa Hiroshi
Published: February 2024

BACKGROUND:

The effect of contact force (CF) on lesion formation is not clear during pulsed field ablation (PFA). The aim of this study was to evaluate the impact of CF, PFA, and their interplay through the PFA index (PF index) formula on the ventricular lesion size in swine.

METHODS:

PFA was delivered through the CF-sensing OMNYPULSE catheter. Predefined PFA applications (×3, ×6, ×9, and ×12) were delivered maintaining low (5–25 g), high (26–50 g), and very high (51–80 g) CFs. First, PFA lesions were evaluated on necropsy in 11 swine to investigate the impact of CF/PFA—and their integration in the PF index equation—on lesion size (study characterization). Then, 3 different PF index thresholds—300, 450, and 600—were tested in 6 swine to appraise the PF index accuracy to predict the ventricular lesion depth (study validation).

RESULTS:

In the study characterization data set, 111 PFA lesions were analyzed. CF was 32±17 g. The average lesion depth and width were 3.5±1.2 and 12.0±3.5 mm, respectively. More than CF and PFA dose alone, it was their combined effect to impact lesion depth through an asymptotically increasing relationship. Likewise, not only was the PF index related to lesion depth in the study validation data set (r
2
=0.66;
P
<0.001) but it also provided a prediction accuracy of the observed depth of ±2 mm in 69/73 lesions (95%).

CONCLUSIONS:

CF and PFA applications play a key role in lesion formation during PFA. Further studies are required to evaluate the best PFA ablation settings to achieve transmural lesions.
Predictive Value of Artificial Intelligence‐Enabled Electrocardiography in Patients With Takotsubo Cardiomyopathy
Authors: Kanaji Yoshihisa , Ozcan Ilke , Tryon David N. , Ahmad Ali , Sara Jaskanwal Deep Singh , Lewis Brad , Friedman Paul , Noseworthy Peter A , Lerman Lilach O. , Kakuta Tsunekazu , Attia Zachi I. , Lerman Amir
Published: February 2024

Background

Recent studies have indicated high rates of future major adverse cardiovascular events in patients with Takotsubo cardiomyopathy (TC), but there is no well‐established tool for risk stratification. This study sought to evaluate the prognostic value of several artificial intelligence‐augmented ECG (AI‐ECG) algorithms in patients with TC.

Methods and Results

This study examined consecutive patients in the prospective and observational Mayo Clinic Takotsubo syndrome registry. Several previously validated AI‐ECG algorithms were used for the estimation of ECG‐ age, probability of low ejection fraction, and probability of atrial fibrillation. Multivariable models were constructed to evaluate the association of AI‐ECG and other clinical characteristics with major adverse cardiac events, defined as cardiovascular death, recurrence of TC, nonfatal myocardial infarction, hospitalization for congestive heart failure, and stroke. In the final analysis, 305 patients with TC were studied over a median follow‐up of 4.8 years. Patients with future major adverse cardiac events were more likely to be older, have a history of hypertension, congestive heart failure, worse renal function, as well as high‐risk AI‐ECG findings compared with those without. Multivariable Cox proportional hazards analysis indicated that the presence of 2 or 3 high‐risk findings detected by AI‐ECG remained a significant predictor of major adverse cardiac events in patients with TC after adjustment by conventional risk factors (hazard ratio, 4.419 [95% CI, 1.833–10.66],
P
=0.001).

Conclusions

The combined use of AI‐ECG algorithms derived from a single 12‐lead ECG might detect subtle underlying patterns associated with worse outcomes in patients with TC. This approach might be beneficial for stratifying high‐risk patients with TC.
Intravascular Imaging Findings After PCI in Patients With Focal and Diffuse Coronary Artery Disease
Authors: Ohashi Hirofumi , Mizukami Takuya , Sonck Jeroen , Bouisset Frederic , Ko Brian , Nørgaard Bjarne L. , Mæng Michael , Jensen Jesper Møller , Sakai Koshiro , Ando Hirohiko , Amano Tetsuya , Amabile Nicolas , Ali Ziad , De Bruyne Bernard , Koo Bon‐Kwon , Otake Hiromasa , Collet Carlos
Published: February 2024

Background

Following percutaneous coronary intervention (PCI), optical coherence tomography provides prognosis information. The pullback pressure gradient is a novel index that discriminates focal from diffuse coronary artery disease based on fractional flow reserve pullbacks. We sought to investigate the association between coronary artery disease patterns, defined by coronary physiology, and optical coherence tomography after stent implantation in stable patients undergoing PCI.

Methods and Results

This multicenter, prospective, single‐arm study was conducted in 5 countries (NCT03782688). Subjects underwent motorized fractional flow reserve pullbacks evaluation followed by optical coherence tomography‐guided PCI. Post‐PCI optical coherence tomography minimum stent area, stent expansion, and the presence of suboptimal findings such as incomplete stent apposition, stent edge dissection, and irregular tissue protrusion were compared between patients with focal versus diffuse disease. Overall, 102 patients (105 vessels) were included. Fractional flow reserve before PCI was 0.65±0.14, pullback pressure gradient was 0.66±0.14, and post‐PCI fractional flow reserve was 0.88±0.06. The mean minimum stent area was 5.69±1.99 mm
2
and was significantly larger in vessels with focal disease (6.18±2.12 mm
2
versus 5.19±1.72 mm
2
,
P
=0.01). After PCI, incomplete stent apposition, stent edge dissection, and irregular tissue protrusion were observed in 27.6%, 10.5%, and 51.4% of the cases, respectively. Vessels with focal disease at baseline had a lower prevalence of incomplete stent apposition (11.3% versus 44.2%,
P
=0.002) and more irregular tissue protrusion (69.8% versus 32.7%,
P
<0.001).

Conclusions

Baseline coronary pathophysiological patterns are associated with suboptimal imaging findings after PCI. Patients with focal disease had larger minimum stent area and a higher incidence of tissue protrusion, whereas stent malapposition was more frequent in patients with diffuse disease.
Association of Cardiovascular Health Metrics and Mortality Among Individuals With and Without Cancer
Authors: Abramov Dmitry , Kobo Ofer , Mamas Mamas A.
Published: February 2024

Background

Although metrics of cardiovascular health have been associated with improved mortality, whether the association remains among individuals with a history of cancer has not been well characterized.

Methods and Results

The National Health and Nutrition Examination Survey data from 2009 to 2018 were used to identify individuals with and without a history of cancer. For each participant, American Heart Association Life's Essential 8 cardiovascular health metrics of health behaviors (diet, physical activity, nicotine exposure, and sleep) and health factors (body mass index, non–high‐density lipoprotein cholesterol, blood glucose, and blood pressure) were obtained. All‐cause, cardiovascular, and cancer‐related mortality were noted. Out of 21 967 individuals, 8% had a history of cancer. In analyses adjusted for age, race and ethnicity, sex, and income among the whole cohort, better Life's Essential 8 cardiovascular health metrics were associated with lower all‐cause (adjusted hazard ratio [aHR ], 0.38 [95% CI, 0.29–0.49];
P
<0.001), cardiovascular (aHR, 0.38 [95% CI, 0.22–0.49];
P
<0.001), and cancer mortality (aHR, 0.50 [95% CI, 0.31–0.79];
P
=0.001). This association was driven by better health behaviors that were associated with lower all‐cause (aHR, 0.30 [95% CI, 0.26–0.35];
P
<0.001), cardiovascular (aHR, 0.39 [95% CI, 0.26–0.52];
P
<0.001), and cancer mortality (aHR, 0.35 [95% CI, 0.26–0.47];
P
<0.001), whereas better health factors were not associated with lower mortality. There were no significant interactions in these associations between individuals with and without cancer.

Conclusions

Better metrics of cardiovascular health, particularly health behaviors, are associated with improved all‐cause, cardiovascular, and cancer mortality to a similar extent in individuals with and without cancer. Attempts to improve cardiovascular health should be prioritized similarly among individuals with and without cancer.
“How to Release or Not Release, That Is the Question.” A Review of Interleukin‐1 Cellular Release Mechanisms in Vascular Inflammation
Authors: Kidder Evan , Gangopadhyay Siddhartha , Francis Sheila , Alfaidi Mabruka
Published: February 2024

Cardiovascular disease remains the leading cause of death worldwide, characterized by atherosclerotic activity within large and medium‐sized arteries. Inflammation has been shown to be a primary driver of atherosclerotic plaque formation, with interleukin‐1 (IL‐1) having a principal role. This review focuses on the current state of knowledge of molecular mechanisms of IL‐1 release from cells in atherosclerotic plaques. A more in‐depth understanding of the process of IL‐1's release into the vascular environment is necessary for the treatment of inflammatory disease processes, as the current selection of medicines being used primarily target IL‐1 after it has been released. IL‐1 is secreted by several heterogenous mechanisms, some of which are cell type–specific and could provide further specialized targets for therapeutic intervention. A major unmet challenge is to understand the mechanism before and leading to IL‐1 release, especially by cells in atherosclerotic plaques, including endothelial cells, vascular smooth muscle cells, and macrophages. Data so far indicate a heterogeneity of IL‐1 release mechanisms that vary according to cell type and are stimulus‐dependent. Unraveling this complexity may reveal new targets to block excess vascular inflammation.
Continuous Theta-Burst Stimulation on the Left Posterior Inferior Frontal Gyrus Perturbs Complex Syntactic Processing Stability in Mandarin Chinese
Authors: Wu Junjie , Cheng Yao , Qu Xingfang , Kang Tianmin , Cai Yimin , Wang Peng , Zaccarella Emiliano , Friederici Angela D. , Hartwigsen Gesa , Chen Luyao
Published: February 2024

The structure of human language is inherently hierarchical. The left posterior inferior frontal gyrus (LpIFG) is proposed to be a core region for constructing syntactic hierarchies. However, it remains unclear whether LpIFG plays a causal role in syntactic processing in Mandarin Chinese and whether its contribution depends on syntactic complexity, working memory, or both. We addressed these questions by applying inhibitory continuous theta-burst stimulation (cTBS) over LpIFG. Thirty-two participants processed sentences containing embedded relative clauses (i.e., complex syntactic processing), syntactically simpler coordinated sentences (i.e., simple syntactic processing), and non-hierarchical word lists (i.e., word list processing) after receiving real or sham cTBS. We found that cTBS significantly increased the coefficient of variation, a representative index of processing stability, in complex syntactic processing (esp., when subject relative clause was embedded) but not in the other two conditions. No significant changes in d′ and reaction time were detected in these conditions. The findings suggest that (a) inhibitory effect of cTBS on the LpIFG might be prominent in perturbing the complex syntactic processing stability but subtle in altering the processing quality; and (b) the causal role of the LpIFG seems to be specific for syntactic processing rather than working memory capacity, further evidencing their separability in LpIFG. Collectively, these results support the notion of the LpIFG as a core region for complex syntactic processing across languages.
Davinci the Dualist: The Mind–Body Divide in Large Language Models and in Human Learners
Authors: Berent Iris , Sansiveri Alexzander
Published: February 2024

A large literature suggests that people are intuitive Dualists—they consider the mind ethereal, distinct from the body. Furthermore, Dualism emerges, in part, via learning (e.g., Barlev & Shtulman, 2021). Human learners, however, are also endowed with innate systems of core knowledge, and recent results suggest that core knowledge begets Dualism (Berent, 2023a; Berent et al., 2022). The resulting question, then, is whether the acquisition of Dualism requires core knowledge, or whether Dualism is learnable from experience alone, via domain-general mechanism. Since human learners are equipped with both systems, the evidence from humans cannot decide this question. Accordingly, here, we probe for a mind–body divide in Davinci—a large language model (LLM) that is devoid of core knowledge. We show that Davinci still leans towards Dualism, and that this bias increases systematically with the learner’s inductive potential. Thus, davinci (which forms part of the GPT-3 suite) exhibits mild Dualist tendencies, whereas its descendent, text-davinci-003 (a GPT-3.5 model), shows a stronger bias. It selectively considers thoughts (epistemic states) as disembodied—as unlikely to show up in the body (in the brain). Unlike humans, GPT 3.5 categorically rejected the persistence of the psyche after death. Still, when probed about life, GPT 3.5 showed robust Dualist tendencies. These results demonstrate that the mind–body divide is partly learnable from experience. While results from LLMs cannot fully determine how humans acquire Dualism, they do place a higher burden of proof on nativist theories that trace Dualism to innate core cognition (Berent, 2023a; Berent et al., 2022).
Keeping a close watch on Innovation Studies: Opening the black box of journal editorships
Authors: Santos Ana Teresa , Mendonça Sandro
Published: February 2024

Editors are journals’ entrepreneurs, managers, and stewards. They have the duty of holding high standards of scholarly quality in terms of end product (articles) and due process (adequate peer-review procedures). Given the importance of journals in the contemporary science “ecosystem,” editors emerge as decisive power brokers. Elite board members are scrutinizers but, paradoxically, they are themselves seldom subject to systematic study. This paper presents a comprehensive portrait of the editorship phenomenon in Innovation Studies by probing the structural features of the boards of 20 leading innovation-oriented journals as of 2019 and conducting an editor survey. We account for 2,440 individual editors in 3,005 different roles based in 53 countries. We uncover the overwhelming dominance of U.S.-affiliated editors and the contrasting rare presence of scholars from the Global South. The gender balance tilts toward men, with a weight above 60%. Enhanced journal achievement is associated with editors on multiple boards, diverse national representation, and increased women on boards. Almost 20% of scholars serve on multiple boards, and no single journal is free from this interlocking editorship phenomenon. The journal Research Policy is the most central in the cross-board network, followed by Industrial and Corporate Change. Finally, the implications of editormetrics for journal governance are discussed.
The use of linear models in quantitative research
Authors: Castro Torres Andrés F. , Akbaritabar Aliakbar
Published: February 2024

The diversity of analysis frameworks used in different fields of quantitative research is understudied. Using bibliometric data from the Web of Science (WoS), we conduct a large-scale and cross-disciplinary assessment of the proportion of articles that use linear models in comparison to other analysis frameworks from 1990 to 2022 and investigate the spatial and citation patterns. We found that, in absolute terms, linear models are widely used across all fields of science. In relative terms, three patterns suggest that linear-model-based research is a dominant analysis framework in Social Sciences. First, almost two-thirds of research articles reporting a statistical analysis framework reported linear models. Second, research articles from underrepresented countries in the WoS data displayed the highest proportions of articles reporting linear models. Third, there was a citation premium to articles reporting linear models in terms of being cited at least once for the entire period, and for the average number of citations until 2012. The confluence of these patterns may not be beneficial to the Social Sciences, as it could marginalize theories incompatible with the linear models’ framework. Our results have implications for quantitative research practices, including teaching and education of the next generations of scholars.
The policy is dead, long live the policy—Revealing science, technology, and innovation policy priorities and government transitions via network analysis
Authors: Cortés Julián D. , Ramírez Cajiao María Catalina
Published: February 2024

Each government has priorities for science, technology, and innovation policies (STIP). How can we identify the changing or reinforced STIP research priorities induced by government transitions? This study aims to unveil the STIP changing structure in the public funding research call contents in Colombia from 2007 to 2022, applying a co-word and network analysis approach to 389 funding research calls. We showed each government’s changing distribution of the betweenness centrality of 334 fields from Health, Life, Physical, and Social Sciences. We found that STIP priorities are getting denser and more diverse in terms of research fields. Despite this complexity, just 14 fields of Life, Physical, and Social Sciences, such as drug discovery, general business, management & accounting, and nature and landscape conservation, maintained their higher strategic relevance despite the government in office. This study maps the short and long-term focus of STIP regardless of the changing political tide inherent in democratic countries.
Do you cite what you tweet? Investigating the relationship between tweeting and citing research articles
Authors: Hare Madelaine , Krause Geoff , MacKnight Keith , Bowman Timothy D. , Costas Rodrigo , Mongeon Philippe
Published: February 2024

The last decade of altmetrics research has demonstrated that altmetrics have a low to moderate correlation with citations, depending on the platform and the discipline, among other factors. Most past studies used academic works as their unit of analysis to determine whether the attention they received on Twitter was a good predictor of academic engagement. Our work revisits the relationship between tweets and citations where the tweet itself is the unit of analysis, and the question is to determine if, at the individual level, the act of tweeting an academic work can shed light on the likelihood of the act of citing that same work. We model this relationship by considering the research activity of the tweeter and its relationship to the tweeted work. The results show that tweeters are more likely to cite works affiliated with their same institution, works published in journals in which they also have published, and works in which they hold authorship. It finds that the older the academic age of a tweeter the less likely they are to cite what they tweet, though there is a positive relationship between citations and the number of works they have published and references they have accumulated over time.