bluuebunny/crossref_metadata_2025 huggingface dataset:
BapFL: You can Backdoor Personalized Federated Learning
Authors: Ye Tiandi , Chen Cen , Wang Yinggui , Li Xiang , Gao Ming
Published: February 2024

In federated learning (FL), malicious clients could manipulate the predictions of the trained model through backdoor attacks, posing a significant threat to the security of FL systems. Existing research primarily focuses on backdoor attacks and defenses within the generic federated learning scenario, where all clients collaborate to train a single global model. A recent study conducted by Qin et al. [
24
] marks the initial exploration of backdoor attacks within the personalized federated learning (pFL) scenario, where each client constructs a personalized model based on its local data. Notably, the study demonstrates that pFL methods with
parameter decoupling
can significantly enhance robustness against backdoor attacks. However, in this article, we whistleblow that pFL methods with parameter decoupling are still vulnerable to backdoor attacks. The resistance of pFL methods with parameter decoupling is attributed to the heterogeneous classifiers between malicious clients and benign counterparts. We analyze two direct causes of the heterogeneous classifiers: (1) data heterogeneity inherently exists among clients and (2) poisoning by malicious clients further exacerbates the data heterogeneity. To address these issues, we propose a two-pronged attack method, BapFL, which comprises two simple yet effective strategies: (1) poisoning only the feature encoder while keeping the classifier fixed and (2) diversifying the classifier through noise introduction to simulate that of the benign clients. Extensive experiments on three benchmark datasets under varying conditions demonstrate the effectiveness of our proposed attack. Additionally, we evaluate the effectiveness of six widely used defense methods and find that BapFL still poses a significant threat even in the presence of the best defense, Multi-Krum. We hope to inspire further research on attack and defense strategies in pFL scenarios. The code is available at:
https://github.com/BapFL/code
Thermal-based Zinc-Oxide-Coated Smart Fabric for Thermochromic Applications
Authors: Jabir Mohammed , Gopinath P. Guru , Sathiyamoorthy Suhasini , Jayabal K. , Veluswamy Pandiyarasan
Published: February 2024

The present study focuses on developing a thermochromic device with a flexible substrate through the coating of cotton fabric with ZnO (Zinc oxide) by solvothermal synthesis technique. Here, ZnO is used as the thermochromic layer for the fabrication work, and it is suitable for textile and wearable applications as it is non-toxic to human skin. This device is designed and fabricated in order to gain better insight into the role of ZnO in thermochromic applications. Here, 3D nanostructures of ZnO are grown on the surface of cotton fabric using a simple and cost-effective solvothermal synthesis approach. The coated fabrics are investigated to determine their structure, morphology, composition, electrical, optical and emissivity properties using an X-ray diffraction (XRD), field-emission scanning electron microscope (FE-SEM), current-voltage (I-V) characteristics, ultraviolet protection factor (UPF) etc. From the morphology study, uniformly packed ZnO nanorods with growth in the c-axis direction are observed. The ZnO nanostructures are known to have excellent UPF when exposed to solar radiation and showed UPF value of 112.48. It is found that coated fabrics have increased electrical conductivity under optical excitations and also enhanced the reflectance. Moreover, based on the emissivity analysis coated ZnO cotton fabric showed the emissivity of 0.95, which is higher and has greater radiation protection than that of bare cotton fabric. Hence, the developed thermochromic device has potential for use in the future in textile and wearable based thermochromic application.
A Comparative Study of Seismic Performance Evaluation of Reinforced Concrete Frame Structures Using Chinese and African Seismic Codes
Authors: Suliman Musaab , Lu Liang
Published: February 2024

The evaluation of various earthquake codes, it is one of the significant challenges in the study area of earthquake engineering. However, according to the literature review, most research works have not addressed comparing Chinese and African seismic codes. Therefore, the purpose of this study is to identify each code’s advantages by comparing assessment of the seismic efficacy of moment resistance frame reinforced concrete (MRF‐RC) frames using four different codes: the Ethiopian Building Code Standard (EBCS‐8), the Egyptian Code for Design and Construction of Reinforced Concrete Structures (ECP‐201), the Algerian Seismic Regulations (RPA‐99), and the Chinese Code for Seismic Design of Buildings (GB‐50011), the first three are the major codes used in Africa. The seismic provisions of these codes are compared and evaluated using nonlinear time‐history analysis (NL‐THA) and nonlinear static pushover to validate the results. These analyses are performed on four MRF‐RC frame models with different heights. The results include various parameters that reflect the seismic performance of the structures. The study revealed that the Chinese code is more conservative and overestimates seismic performance compared with African codes. However, the Chinese code can be applied in African projects considering the African soil classifications, and seismic weight are adjusted to meet the African design criteria.
Supplementary Figure S1 from Stromal Signals Dominate Gene Expression Signature Scores That Aim to Describe Cancer Cell–intrinsic Stemness or Mesenchymality Characteristics
Authors: Kreis Julian , Aybey Bogac , Geist Felix , Brors Benedikt , Staub Eike
Published: February 2024

Association of EMT-related gene expression signature scores with tumor purity and fibroblast signatures in TME naïve samples
FIGURE 7 from Stromal Signals Dominate Gene Expression Signature Scores That Aim to Describe Cancer Cell–intrinsic Stemness or Mesenchymality Characteristics
Authors: Kreis Julian , Aybey Bogac , Geist Felix , Brors Benedikt , Staub Eike
Published: February 2024

Univariate Cox proportional hazards analyses for EMT-related signature scores and TCGA outcomes. P values were adjusted for multiple testing within each panel using the Holm adjustment method. Top: Association with DFI, excluding GBM and READ due to short follow-up or small number of events. Bottom: Association with OS, excluding GBM due to missing clinical readouts. ns, not significant; **, P < 0.01; *, P < 0.05; ns, P > 0.05.
FIGURE 4 from Stromal Signals Dominate Gene Expression Signature Scores That Aim to Describe Cancer Cell–intrinsic Stemness or Mesenchymality Characteristics
Authors: Kreis Julian , Aybey Bogac , Geist Felix , Brors Benedikt , Staub Eike
Published: February 2024

Comparison of EMT-related signature scores in eight scRNA-seq datasets. The dot plot shows the module scores of EMT-related signatures and the number of cells expressing them in the different cell types. Each panel displays dot plots to quantify the expression of individual signatures in four different tissues (malignant, endothelial, myofibroblast, and fibroblast) derived from scRNA-seq datasets for BRCA, colorectal cancer (CRC), glioma, and HNSC. X, Unavailable cell type.
Data from Stromal Signals Dominate Gene Expression Signature Scores That Aim to Describe Cancer Cell–intrinsic Stemness or Mesenchymality Characteristics
Authors: Kreis Julian , Aybey Bogac , Geist Felix , Brors Benedikt , Staub Eike
Published: February 2024

Abstract
Epithelial-to-mesenchymal transition (EMT) in cancer cells confers migratory abilities, a crucial aspect in the metastasis of tumors that frequently leads to death. In multiple studies, authors proposed gene expression signatures for EMT, stemness, or mesenchymality of tumors based on bulk tumor expression profiling. However, recent studies suggested that noncancerous cells from the microenvironment or macroenvironment heavily influence such signature profiles. Here, we strengthen these findings by investigating 11 published and frequently referenced gene expression signatures that were proposed to describe EMT-related (EMT, mesenchymal, or stemness) characteristics in various cancer types. By analyses of bulk, single-cell, and pseudobulk expression data, we show that the cell type composition of a tumor sample frequently dominates scores of these EMT-related signatures. A comprehensive, integrated analysis of bulk RNA sequencing (RNA-seq) and single-cell RNA-seq data shows that stromal cells, most often fibroblasts, are the main drivers of EMT-related signature scores. We call attention to the risk of false conclusions about tumor properties when interpreting EMT-related signatures, especially in a clinical setting: high patient scores of EMT-related signatures or calls of “stemness subtypes” often result from low cancer cell content in tumor biopsies rather than cancer cell–specific stemness or mesenchymal/EMT characteristics.
Significance:
Cancer self-renewal and migratory abilities are often characterized via gene module expression profiles, also called EMT or stemness gene expression signatures. Using published clinical tumor samples, cancer cell lines, and single cancer cells, we highlight the dominating influence of noncancer cells in low cancer cell content biopsies on their scores. We caution on their application for low cancer cell content clinical cancer samples with the intent to assign such characteristics or subtypes.
Smith-Lemli-Opitz Syndrome with Biallelic c.1295A>G (p.Tyr432Cys) Variant in the DHCR7 Gene in a 73-Year-Old Woman: Report of the Oldest Patient
Authors: Yılmaz Mustafa , Bebek Ogun , Turkyilmaz Ayberk
Published: February 2024

Introduction: Smith-Lemli-Opitz syndrome (SLOS), a genetic developmental disorder characterized by various congenital anomalies, arises from a loss of normal DHCR7 enzymatic action in cholesterol biosynthesis. This syndrome is typically marked by various congenital anomalies, including microcephaly with cognitive impairments, distinctive facial features, and syndactyly of the toes (2-3 fusion). Case Presentation: A 73-year-old woman, followed up on by the neurology clinic for the last 3 years for amnesia and movement disorders, was referred to our clinic for genetic etiology investigation. Although there were no significant dysmorphic findings on her physical examination, observations included partial syndactyly between the second and third toes of both feet, a wide forehead, and a triangular face. We used the whole-exome sequencing (WES) analysis to evaluate the patient because of their various phenotype, which included dysmorphic features, movement problems, recurrent hip dislocation, mild intellectual impairment. WES analysis revealed a homozygous missense c.1295A>G (p.Tyr432Cys) variation in DHCR7 gene. Discussion: A total of 9 patients with p.Tyr432Cys variant have been reported in the literature so far. The present case is the first patient with biallelic c.1295A>G (p.Tyr432Cys) variation in DHCR7 gene in the current literature. Diagnosing the disorder can be challenging, particularly in its milder manifestations, given the extensive range of clinical presentations. The present case is the oldest patient with SLOS reported in the relevant literature. Mild dysmorphic features, mild intellectual disability, and recurrent hip dislocation, along with the typical finding of syndactyly between the second and third toes in the foot, may indicate mild forms of SLOS.
Whole Genome Sequencing: Applications in Clinical Bacteriology
Authors: Mustafa Abu Salim
Published: February 2024

The success in determining the whole genome sequence of a bacterial pathogen was first achieved in 1995 by determining the complete nucleotide sequence of Haemophilus influenzae Rd using the chain-termination method established by Sanger et al. in 1977 and automated by Hood et al. in 1987. However, this technology was laborious, costly, and time-consuming. Since 2004, high-throughput next-generation sequencing technologies have been developed, which are highly efficient, require less time, and are cost-effective for whole genome sequencing (WGS) of all organisms, including bacterial pathogens. In recent years, the data obtained using WGS technologies coupled with bioinformatics analyses of the sequenced genomes have been projected to revolutionize clinical bacteriology. WGS technologies have been used in the identification of bacterial species, strains, and genotypes from cultured organisms and directly from clinical specimens. WGS has also helped in determining resistance to antibiotics by the detection of antimicrobial resistance genes and point mutations. Furthermore, WGS data have helped in the epidemiological tracking and surveillance of pathogenic bacteria in healthcare settings as well as in communities. This review focuses on the applications of WGS in clinical bacteriology.
Energy efficiency in activated sludge process using adaptive iterative learning control with PI ABAC
Authors: Huja Husin Maimun , Mohd Sabri Mohamad Faizrizwan , Hong Ping Kismet Anak , Bateni Norazlina , Suhaili Shamsiah
Published: February 2024

This paper proposed an iterative learning control (ILC) with a feedback regulator based on proportional integral ammonium-based aeration control (PI ABAC) to improve dissolved oxygen control through data learning of iteration data. The proposed controller's performance is evaluated using benchmark simulation model no. 1. (BSM1). The assessments focused on four main areas: effluent violation, effluent quality, aeration energy, and overall cost index. The proposed ILC PI ABAC controller's effectiveness is evaluated by comparing the performance of the activated sludge process to the BSM1 PI and feedback PI ABAC under three different weather conditions: dry, rain, and storm. The improvement of the proposed method over BSM1 PI is demonstrated by a reduction in aeration energy of up to 24%. In conclusion, if the proposed ILC PI ABAC controller is given enough information, it can be quite successful in achieving energy efficiency.
The fog computing for internet of things: review, characteristics and challenges, and open issues
Authors: Al-Shareeda Mahmood A. , Alsadhan Abeer Abdullah , H. Qasim Hamzah , Manickam Selvakumar
Published: February 2024

The internet of things (IoT) research envisions a world in which common place objects are linked to the internet and trade, store, process, and gather data from their surroundings. Due to their inherent resource limitations, IoT devices are typically unable to directly host application services, despite their increasing importance for facilitating the supply of data to enable electronic services. Since it can survive and work in tandem with centralized cloud systems and extends the latter toward the network edge, fog computing (FC) may be an appropriate paradigm to get around these restrictions. This paper reviews the overview of the IoT in terms of application and design parameters and FC. Meanwhile, this paper presents the architecture of fog computing for IoT (FC-IoT) in terms of communication, security, data quality, sensing and actuation management, codification, analysis, and decision-making. Additionally, this review provides several characteristics and challenges of FC-IoT. Finally, open issues for this paper have been discussed.
Design of passive voltage balancer system for lead acid battery
Authors: Aprillia Bandiyah Sri , Silalahi Desri Kristina , Adam Kharisma Bani , Bhaskara Perteka Yuda Putu Krishna
Published: February 2024

The use of rechargeable batteries for electrical energy storage requires a voltage balancing system. The voltage balancing system requires monitoring in the use of rechargeable batteries so that they can be utilized properly and can improve the electrical energy storage system. In this research, a monitored lead acid battery voltage balancing system was designed so that the management of the battery voltage balance level and the storage of electrical energy in rechargeable batteries can be stored and used optimally. In this research a series of lead acid battery voltage detection and power dissipation circuits were designed. The power dissipation circuit uses the controlled shunt resistor method which is used when the voltage of the lead acid battery being charged exceeds the maximum voltage. This method is easy to implement and can display the value of the lead acid battery voltage and other parameters, so that it can be monitored by the user. The results obtained show that the average voltage error for batteries 1 and 2 is 0.09% and 0.3% respectively. The power dissipation circuit can dissipate lead acid battery power above the reference voltage VREF=±7 V, with a balanced voltage of 6.8 V at 140 minutes and 160 minutes.
Image quality evaluation: evaluation of the image quality of actual images by using machine learning models
Authors: Reddy Shiva Shankar , Maheswara Rao Veeranki V. R. , Sravani Kalidindi , Nrusimhadri Silpa
Published: February 2024

Evaluating image features is a significant step in image processing in applications like number plate detection, vehicle tracking and many image processing-based applications. Image processing-based applications need accurate parts to get the best outcomes. Feature detection is done based on various feature detection techniques. The proposed system aims to get the best feature detector based on the input images by evaluating the image features. For assessing the image features, the proposed system worked on various descriptors like oriented FAST and rotated brief (ORB), learned arrangements of three patch codes (LATCH), binary robust independent elementary features (BRIEF), and binary robust invariant scalable keypoints (BRISK) to extract and evaluate the features using K-nearest neighbor (KNN)-matching and retrieve the inliers of the matching. Each descriptor produces different matching features and inliers; with the matchings and inliers, the inlier ratio calculates to show the analysis. To increase performance, we also examine adding depth information to descriptors.
A review of deep learning models (U-Net architectures) for segmenting brain tumors
Authors: Al-Murshidawy Mawj Abdul-Ameer , Al-Shamma Omran
Published: February 2024

Highly accurate tumor segmentation and classification are required to treat the brain tumor appropriately. Brain tumor segmentation (BTS) approaches can be categorized into manual, semi-automated, and full-automated. The deep learning (DL) approach has been broadly deployed to automate tumor segmentation in therapy, treatment planning, and diagnosing evaluation. It is mainly based on the U-Net model that has recently attained state-of-the-art performances for multimodal BTS. This paper demonstrates a literature review for BTS using U-Net models. Additionally, it represents a common way to design a novel U-Net model for segmenting brain tumors. The steps of this DL way are described to obtain the required model. They include gathering the dataset, pre-processing, augmenting the images (optional), designing/selecting the model architecture, and applying transfer learning (optional). The model architecture and the performance accuracy are the two most important metrics used to review the works of literature. This review concluded that the model accuracy is proportional to its architectural complexity, and the future challenge is to obtain higher accuracy with low-complexity architecture. Challenges, alternatives, and future trends are also presented.
Convolution neural network hyperparameter optimization using modified particle swarm optimization
Authors: Munsarif Muhammad , Sam'an Muhammad , Fahrezi Andrian
Published: February 2024

Based on the literature review, a convolutional neural network (CNN) is one of the deep learning techniques most often used for classification problems, especially image classification. Various approaches have been proposed to improve accuracy performance. In CNN architecture, parameter determination is very influential on accuracy performance. Particle swarm optimization (PSO) is a type of metaheuristic algorithm widely used for hyperparameter optimization. PSO convergence is faster than genetic algorithm (GA) and attracts many researchers for further studies such as genetic algorithms and ant colony. In PSO, determining the value of the weight parameter is very influential on accuracy. Therefore, this paper proposes CNN hyperparameter optimization using modified PSO with linearly decreasing randomized weight. The experiments use the modified National Institute of Standards and Technology (MNIST) dataset. The accuracy of the proposed method is superior, and the execution time is slower to random search. In epoch 1, epoch 3, and epoch 5, the proposed method is superior to baseline CNN, linearly decreasing weight PSO (LDWPSO), and RL-based optimization algorithm (ROA). Meanwhile, the accuracy performance of the proposed method is superior to previous studies, namely LeNet-1, LeNet-2, LeNet-3, PCANet-2, RANDNet-2, CAE1, CAE-2, and bee colony. Otherwise, lost to PSO-CNN, distributed PSO (DPSO), recurrent CNN, and CNN-PSO. However, the four methods have a complex architecture and wasteful execution time.
Predicting lung cancer risk using explainable artificial intelligence
Authors: Shoukat Makubhai Shahin , Pathak Ganesh R. , Chandre Pankaj R.
Published: February 2024

Lung cancer is a lethal disease that claims numerous lives annually, and early detection is essential for improving survival rates. Machine learning has shown promise in predicting lung cancer risk, but the lack of transparency and interpretability in black-box models impedes the understanding of factors that contribute to risk. Explainable artificial intelligence (XAI) can overcome this limitation by providing a clear and understandable approach to machine learning. In this study, we will use a large patient record dataset to train an XAI-based model that considers various patient information, including lifestyle factors, clinical data, and medical history, for predicting lung cancer risk. We will use different XAI techniques, including decision trees, partial dependence plots, and feature importance, to interpret the model’s predictions. These methods will provide healthcare professionals with a transparent and interpretable framework for screening and treatment decisions concerning lung cancer risk.
Exploiting channel state information of WiFi signal for human activity detection: an experimental study
Authors: Boudlal Hicham , Serrhini Mohammed , Tahiri Ahmed
Published: February 2024

Ubiquitous computing aims to seamlessly integrate computing into our daily lives, and requires reliable information on human activities and state for various applications. In this paper, we propose a device-free human activity recognition system that leverages the rich information behind WiFi signals to detect human activities in indoor environments, including walking, sitting, and standing. The key idea of our system is to use the dynamic features of activities, which we carefully examine and analyze through the characteristics of channel state information. We evaluate the impact of location changes on WiFi signal distribution for different activities and design an activity detection system that employs signal processing techniques to extract discriminative features from wireless signals in the frequency and temporal domains. We implement our system on a single off-the-shelf WiFi device connecting to a commercial wireless access point and evaluate it in laboratory and conference room environments. Our experiments demonstrate the feasibility of using WiFi signals for device-free human activity recognition, which could provide a practical and non-intrusive solution for indoor monitoring and ubiquitous computing applications.
Dual-band GPS/LoRa antenna for internet of thing applications
Authors: Yahya Muhammad Sani , Soeung Socheatra , Emmanuel Chinda Francis , Musa Umar , Yunusa Zainab
Published: February 2024

This paper presents the design and characterization of a compact dual-band microstrip antenna for GPS and long range (LoRa) internet of thing (IoT) applications. The antenna operates at 868 MHz and 1.57 GHz and has a gain of 3.35 dBi and 5.08 dBi, respectively. The antenna design is optimized using CST microwave studio software (MWS®), and both simulation and measurement results are in close agreement. The antenna features a directional E-plane and omnidirectional H-plane radiation pattern in each band of operation. The proposed antenna’s compact size and dual-band capability make it suitable for IoT applications that require GPS and LoRa communication in a small form factor. The results presented in this paper demonstrate the feasibility and effectiveness of the proposed antenna design.
Optimized extreme learning machine using genetic algorithm for short-term wind power prediction
Authors: Mansoury Ibtissame , El Bourakadi Dounia , Yahyaouy Ali , Boumhidi Jaouad
Published: February 2024

Through the much defiance facing energy today, it has become necessary to rely on wind energy as a source of unlimited renewable energies. However, energy planning and regulation require wind capacity forecasting, because oscillations of wind speed drastically affect directly power generation. Therefore, several scenarios must be provided to allow for estimating uncertainties. To deal with this problem, this paper exploits the major advantages of the regularized extreme learning machine algorithm (R-ELM) and thus proposes a model for predicting the wind energy generated for the next hour based on the time series of wind speed. The R-ELM is combined with the genetic algorithm which is designed to optimize the most important hyperparameter which is the number of hidden neurons. Thus, the proposed model aims to forecast the average wind power per hour based on the wind speed of the previous hours. The results obtained showed that the proposed method is much better than those reported in the literature concerning the precision of the prediction and the time convergence.
Enhancing Arabic offensive language detection with BERT-BiGRU model
Authors: Bensoltane Rajae , Zaki Taher
Published: February 2024

With the advent of Web 2.0, various platforms and tools have been developed to allow internet users to express their opinions and thoughts on diverse topics and occurrences. Nevertheless, certain users misuse these platforms by sharing hateful and offensive speeches, which has a negative impact on the mental health of internet society. Thus, the detection of offensive language has become an active area of research in the field of natural language processing. Rapidly detecting offensive language on the internet and preventing it from spreading is of great practical significance in reducing cyberbullying and self-harm behaviors. Despite the crucial importance of this task, limited work has been done in this field for nonEnglish languages such as Arabic. Therefore, in this paper, we aim to improve the results of Arabic offensive language detection without the need for laborious preprocessing or feature engineering work. To achieve this, we combine the bidirectional encoder representations from transformers (BERT) model model with a bidirectional gated recurrent unit (BiGRU) layer to further enhance the extracted context and semantic features. The experiments were conducted on the Arabic dataset provided by the SemEval 2020 Task 12. The evaluation results show the effectiveness of our model compared to the baseline and related work models by achieving a macro F1- score of 93.16%.
Golden jackal optimization for economic load dispatch problems with complex constraints
Authors: Ragunathan Ramamoorthi , Ramadoss Balamurugan
Published: February 2024

This research paper uses the golden jackal optimization (GJO), a novel meta-heuristic algorithm, to address power system economic load dispatch (ELD) problems. The GJO emulates the hunting behavior of golden jackals. GJO algorithm uses the cooperative attacking behavior of golden jackals to tackle complicated optimization problems efficaciously. The objective of ELD problem is to distribute power system load requirement to the different generators with a minimum total fuel cost of generation. ELD problems are highly complex, non-linear, and non-convex optimization problems while considering constraints namely valve point loading effect (VPL) and prohibited operating zones (POZs). The proposed GJO algorithm is applied to solve complex, non-linear, and non-convex ELD problems. Six different test systems having 6, 10, 13, 40, and 140 generators with various constraints are used to validate the usefulness of the suggested GJO method. Simulation outcomes of the test system are compared with various algorithms reported in the algorithms such as particle swarm optimization (PSO), ant colony optimization (ACO), and backtracking search algorithm (BSA). Results show that the proposed GJO algorithm produces minimal fuel cost and has good convergence in solving ELD problems of power system engineering.
Empowering hate speech detection: leveraging linguistic richness and deep learning
Authors: Gde Bagus Janardana Abasan I , Setiawan Erwin Budi
Published: February 2024

Social media has become a vital part of most modern human personal life. Twitter is one of the social media that was formed from the development of communication technology. A lot of social media gives users the freedom to express themselves. This facility is misused by users, so hate speech is spread. Designing a system to detect hate speech intelligently is needed. This study uses the hybrid deep learning (HDL) and solo deep learning (SDL) approach with the convolutional neural networks (CNN) and bidirectional gated recurrent unit (Bi-GRU) algorithm. There are 4 models built, namely CNN, Bi-GRU, CNN+Bi-GRU, and Bi-GRU+CNN. Term frequency-inverse document frequency (TF-IDF) is used for feature extraction, which is to get linguistic features to be analyzed and studied. FastText is used to perform feature expansion to minimize mismatched vocabulary. Four scenarios are run. CNN with an accuracy of 87.63%, Bi-GRU produces an accuracy of 87.46%, CNN+Bi-GRU provides an accuracy of 87.47% and Bi-GRU+CNN provides an accuracy of 87.34%. The ability of this approach to understand the context is qualified. HDL outperforms SDL in terms of n-gram type, where HDL can understand sentences broken down by hybrid n-gram types, namely Unigram-Bigram-Trigram which is a complex n-gram hybrid.
Fine-tuning a pre-trained ResNet50 model to detect distributed denial of service attack
Authors: Sanmorino Ahmad , Kesuma Hendra Di
Published: February 2024

Distributed denial-of-service (DDoS) attacks pose a significant risk to the dependability and consistency of network services. The utilization of deep learning (DL) models has displayed encouraging outcomes in the identification of DDoS attacks. Nevertheless, crafting a precise DL model necessitates an extensive volume of labeled data and substantial computational capabilities. Within this piece, we introduce a technique to enhance a pre-trained DL model for the identification of DDoS attacks. Our strategy’s efficacy is showcased on an openly accessible dataset, revealing that the fine-tuned model we propose surpasses both the initial pre-trained model and other cutting-edge approaches in performance. The suggested fine-tuned model attained 95.1% accuracy, surpassing the initial pre-trained model as well as other leading-edge techniques. Please note that the specific evaluation metrics and their values may vary depending on the implementation, hyperparameter settings, number of datasets, or dataset characteristics. The proposed approach has several advantages, including reducing the amount of labeled data required and accelerating the training process. Initiating with a pre-existing ResNet50 model can also enhance the eventual model’s accuracy, given that the pre-trained model has already acquired the ability to extract significant features from unprocessed data.
Perlindungan Data Pribadi dalam Bidang Rekam Medis
Authors: Hadiyantina Shinta , Ayub Zainal Amin , Cahyandari Dewi , Paramitha Amelia Ayu , Ambarwati Sinta Devi , Mustofa Yusuf , Sudjati Xaviera Qatrunnada Djana , Rahmatika Nur Auliya
Published: February 2024

Alhamdulillah, puji syukur kehadirat Allah SWT atas berkat, rahmat serta inayahNya penulis dapat menyelesaikan buku yang berjudul Pelindungan Data Pribadi dalam Bidang Rekam Medis. Pelindungan data rekam medis menjadi aspek penting bagi perkembangan dunia medis yang mengedepakan keamanan data pribadi sehingga perlu dijamin oleh negara. Data pribadi merupakan data yang rentan, karena didalamnya menyangkut akan kehidupan dari seorang individu. Data pribadi merupakan bagian dari hak asasi manusia, sebagaimana diatur pada UndangUndang Dasar Negara Republik Indonesia Tahun 1945. Oleh karena itu, perlindungan data pribadi menjadi kebutuhan mutlak pada manusia modern, karena semakin tingginya penggunaan data, digitalisasi data, dan efisiensi transfer data yang menyebabkan data dapat ditransfer atau diunggah dengan satu kali klik pada sistem. Hal tersebut mengakibatkan perubahan dalam tatanan pengaturan hukum privasi pada beberapa negara dikarenakan adanya kekaburan antar batas yuridiksi.
Optimal Heart Failure Medical Therapy and Mortality in Survivors of Cardiogenic Shock: Insights From the FRENSHOCK Registry
Authors: Matsushita Kensuke , Delmas Clément , Marchandot Benjamin , Roubille François , Lamblin Nicolas , Leurent Guillaume , Levy Bruno , Elbaz Meyer , Champion Sebastien , Lim Pascal , Schneider Francis , Khachab Hadi , Carmona Adrien , Trimaille Antonin , Bourenne Jeremy , Seronde Marie‐France , Schurtz Guillaume , Harbaoui Brahim , Vanzetto Gerald , Biendel Caroline , Labbe Vincent , Combaret Nicolas , Mansourati Jacques , Filippi Emmanuelle , Maizel Julien , Merdji Hamid , Lattuca Benoit , Gerbaud Edouard , Bonnefoy Eric , Puymirat Etienne , Bonello Laurent , Morel Olivier
Published: February 2024

Background

The effects of pharmacological therapy on cardiogenic shock (CS) survivors have not been extensively studied. Thus, this study investigated the association between guideline‐directed heart failure (HF) medical therapy (GDMT) and one‐year survival rate in patients who are post‐CS.

Methods and Results

FRENSHOCK (French Observatory on the Management of Cardiogenic Shock in 2016) registry was a prospective multicenter observational survey, conducted in metropolitan French intensive care units and intensive cardiac care units. Of 772 patients, 535 patients were enrolled in the present analysis following the exclusion of 217 in‐hospital deaths and 20 patients with missing medical records. Patients with triple GDMT (beta‐blockers, renin‐angiotensin system inhibitors, and mineralocorticoid receptor antagonists) at discharge (n=112) were likely to have lower left ventricular ejection fraction on admission and at discharge compared with those without triple GDMT (n=423) (22% versus 28%,
P
<0.001 and 29% versus 37%,
P
<0.001, respectively). In the overall cohort, the one‐year mortality rate was 23%. Triple GDMT prescription was significantly associated with a lower one‐year all‐cause mortality compared with non‐triple GDMT (adjusted hazard ratio 0.44 [95% CI, 0.19–0.80];
P
=0.007). Similarly, 2:1 propensity score matching and inverse probability treatment weighting based on the propensity score demonstrated a lower incidence of one‐year mortality in the triple GDMT group. As the number of HF drugs increased, a stepwise decrease in mortality was observed (log rank;
P
<0.001).

Conclusions

In survivors of CS, the one‐year mortality rate was significantly lower in those with triple GDMT. Therefore, this study suggests that intensive HF therapy should be considered in patients following CS.
Long‐Term Outcomes After Transcatheter Aortic Valve Implantation in Patients With Chronic Inflammatory Disease
Authors: Brunner Stephanie , Covtun Olga , Moccetti Federico , Loretz Lucca , Bossard Matthias , Attinger‐Toller Adrian , Cuculi Florim , Wolfrum Mathias , Kurmann Reto , Toggweiler Stefan
Published: February 2024

Background

Chronic inflammatory disease (CID) accelerates atherosclerosis and the development of aortic stenosis. Data on long‐term outcomes after transcatheter aortic valve implantation (TAVI) in those patients are missing. The aim of this study was to investigate the clinical long‐term outcomes of patients with and without autoimmune‐related CID undergoing TAVI for the treatment of severe aortic stenosis.

Methods and Results

From a prospective registry, consecutive patients with TAVI were included. Baseline clinic and imaging data (echocardiographic and computed tomography) were analyzed. Long‐term (up to 5 years) clinical and echocardiographic outcomes were studied. Of 1000 consecutive patients (mean age 81±6 years, 46% female), 107 (11%) had CID; the most frequent entities included polymyalgia rheumatica (31%) and rheumatoid arthritis (28%). Patients with CID were predominantly female (60% versus 44%,
P
=0.002) and more often had pulmonary disorders (21% versus 13%,
P
=0.046) and atrial fibrillation (32% versus 20%,
P
=0.003). The presence of CID was associated with a higher rate of postinterventional infection (5% versus 1%,
P
=0.007) and further emerged as a risk factor for rehospitalization for bleeding or infection (hazard ratio, 1.93 and 1.62, respectively). Premature valve degeneration, endocarditis, and all‐cause mortality were not increased among patients with CID.

Conclusions

This real‐world analysis found that patients with CID undergoing TAVI were associated with a higher risk of postinterventional infectious complications and rehospitalization due to infection. However, valve durability and survival seem not to differ between patients with TAVI with versus without CID.
Transcatheter Aortic Valve Implantation Wait‐Time Management: Derivation and Validation of the Canadian TAVI Triage Tool (CAN3T)
Authors: Miranda Rafael N. , Qiu Feng , Manoragavan Ragavie , Austin Peter C. , Naimark David M. J. , Fremes Stephen E. , Ko Dennis T. , Madan Mina , Mamas Mamas A. , Sud Maneesh K. , Tam Derrick , Wijeysundera Harindra C.
Published: February 2024

Background

Transcatheter aortic valve implantation (TAVI) has seen indication expansion and thus exponential growth in demand over the past decade. In many jurisdictions, the growing demand has outpaced capacity, increasing wait times and preprocedural adverse events. In this study, we derived prediction models that estimate the risk of adverse events on the waitlist and developed a triage tool to identify patients who should be prioritized for TAVI.

Methods and Results

We included adult patients in Ontario, Canada referred for TAVI and followed up until one of the following events first occurred: death, TAVI procedure, removal from waitlist, or end of the observation period. We used subdistribution hazards models to find significant predictors for each of the following outcomes: (1) all‐cause death while on the waitlist; (2) all‐cause hospitalization while on the waitlist; (3) receipt of urgent TAVI; and (4) a composite outcome. The median predicted risk at 12 weeks was chosen as a threshold for a maximum acceptable risk while on the waitlist and incorporated in the triage tool to recommend individualized wait times. Of 13 128 patients, 586 died while on the waitlist, and 4343 had at least 1 hospitalization. A total of 6854 TAVIs were completed, of which 1135 were urgent procedures. We were able to create parsimonious models for each outcome that included clinically relevant predictors.

Conclusions

The Canadian TAVI Triage Tool (CAN3T) is a triage tool to assist clinicians in the prioritization of patients who should have timely access to TAVI. We anticipate that the CAN3T will be a valuable tool as it may improve equity in access to care, reduce preventable adverse events, and improve system efficiency.
Concord in the Verbal Domain: External Agreement in Nakh-Daghestanian
Authors: Ganenkov Dmitry
Published: February 2024

The article discusses agreeing ablatives in Aqusha Dargwa (Nakh-Daghestanian), arguing that the morphological agreement in gender-number that these constituents display with the clausemate absolutive argument is not an instance of Agree, but should rather be analyzed as the result of Concord. The argument relies on a difference between Agree and Concord with regard to c-command. The article explores the behavior of the ablative complement to the vP-adjoined adverb ‘secretly’ and demonstrates that gender-number agreement on the complement is best predicted by Concord.
Vedic evidence for the verbal-governing dā́ti-vāra- ‘type’
Authors: Jamison Stephanie W.
Published: February 2024

For more than a century, a verbal-governing compound type has featured prominently in Indo-Europeanist discourse on compounding, a type exemplified by Vedic dā́ti-vāra- ‘granting wishes’, with a -ti-stem first member having a transitive relationship to the nominal second member. However, a critical reexamination of the Vedic evidence for this type reveals that almost none of the standard, regularly repeated examples actually mean what it is claimed they mean. The existence of this “type” is therefore seriously called into question and should not be reconstructed for Indo-European on the basis of the Vedic data.
Contributions of Initial Conditions and Meteorological Forecast to Subseasonal-to-Seasonal Hydrological Forecast Skill in Western Tropical South America
Authors: Recalde-Coronel G. Cristina , Zaitchik Benjamin , Pan William K. , Zhou Yifan , Badr Hamada
Published: February 2024

Hydrological predictions at subseasonal-to-seasonal (S2S) time scales can support improved decision-making in climate-dependent sectors like agriculture and hydropower. Here, we present an S2S hydrological forecasting system (S2S-HFS) for western tropical South America (WTSA). The system uses the global NASA Goddard Earth Observing System S2S meteorological forecast system (GEOS-S2S) in combination with the generalized analog regression downscaling algorithm and the NASA Land Information System (LIS). In this implementation study, we evaluate system performance for 3-month hydrological forecasts for the austral autumn season (March–May) using ensemble hindcasts for 2002–17. Results indicate that the S2S-HFS generally offers skill in predictions of monthly precipitation up to 1-month lead, evapotranspiration up to 2 months lead, and soil moisture content up to 3 months lead. Ecoregions with better hindcast performance are located either in the coastal lowlands or in the Amazon lowland forest. We perform dedicated analysis to understand how two important teleconnections affecting the region are represented in the S2S-HFS: El Niño–Southern Oscillation (ENSO) and the Antarctic Oscillation (AAO). We find that forecast skill for all variables at 1-month lead is enhanced during the positive phase of ENSO and the negative phase of AAO. Overall, this study indicates that there is meaningful skill in the S2S-HFS for many ecoregions in WTSA, particularly for long memory variables such as soil moisture. The skill of the precipitation forecast, however, decays rapidly after forecast initialization, a phenomenon that is consistent with S2S meteorological forecasts over much of the world.
Bayesian Analysis of the Detection Performance of the Lightning Imaging Sensors
Authors: Virts Katrina S. , Lang Timothy J. , Buechler Dennis E. , Bitzer Phillip M.
Published: February 2024

Identical Lightning Imaging Sensors (LIS) aboard the Tropical Rainfall Measuring Mission satellite (TRMM LIS, 1998–2015) and International Space Station (ISS LIS, 2017–23) have provided over two decades of lightning observations over the global tropics, with ISS LIS extending coverage into the midlatitudes. Quantifying the detection performance of both LIS sensors is a necessary step toward generating a combined LIS climatological record and accurately combining LIS data with lightning detections from other sensors and networks. We compare lightning observations from both LIS sensors with reference sources including the Geostationary Lightning Mapper (GLM) and ground-based Earth Networks Total Lightning Network (ENTLN), Earth Networks Global Lightning Network (ENGLN), National Lightning Detection Network (NLDN), and Global Lightning Dataset (GLD360). Instead of a relative detection efficiency (DE) approach that assumes the perfect performance of the reference sensor, we employ a Bayesian approach to estimate the upper limit of the absolute DE (ADE) of each system being analyzed. The results of this analysis illustrate the geographical pattern of ADE as well as its diurnal cycle and yearly evolution. Reference network ADE increased by ∼15%–30% during the TRMM era, leading to a decline in TRMM LIS ADE. ISS LIS flash ADE has been relatively consistent at 61%–65%, about 4%–5% lower than TRMM LIS at the end of its lifetime.
Determination of Aminophylline in Human Serum Using Hydrogel Microspheres for Coupled Surface-Enhanced Raman Spectroscopy (SERS) and Solid-Phase Extraction
Authors: Wang Ying , Kong Hongxing , Chen Ruijue , Xu Ziwei , Zhou Pei , Zhan Yaqin , Huang Wenyi , Cheng Hao , Li Lijun , Feng Jun
Published: February 2024

Aminophylline (AMP) is a bronchodilator. The therapeutic and toxic doses are very close. Therefore, therapeutic drug monitoring (TDM) of AMP is essential in clinical practice. Microgels were synthesized by free radical precipitation polymerization. Silver@poly( N-isopropyl acrylamide) (Ag@PNIPAM) hybrid microgels were obtained by loading silver (Ag) nanoparticles into the three-dimensional network of the microgels by in situ reduction. The microgel is a three-dimensional reticular structure with tunable pore size, large specific surface area, and good biocompatibility, which can be used as a sorbent for solid-phase extraction (SPE) of target molecules in complex matrices and as a surface-enhanced Raman spectroscopy (SERS) substrate. We optimized the conditions affecting SERS enhancement, such as silver nitrate (AgNO3) concentration and SPE time, according to the SERS strategy of Ag@PNIPAM hybrid microgels to achieve label-free TDM for trace AMP in human serum. The results showed good linearity between the logarithmic concentration of AMP and its SERS intensity in the range of 1–1.1 × 102 µg/mL, with a correlation coefficient ( R2) of 0.9947 and a low detection limit of 0.61 µg/mL. The assay accuracy was demonstrated by spiking experiments, with recoveries ranging from 93.0 to 101.8%. The method is rapid, sensitive, reproducible, requires simple sample pretreatment, and has good potential for use in clinical treatment drug monitoring.
Women’s Life Strains, Race/Ethnicity, and Intimate Partner Violence Perpetration
Authors: Kaufman Joanne M.
Published: February 2024

While women manifest high rates of Intimate partner violence (IPV) victimization, they may perpetrate violence and levels vary by race/ethnicity. Drawing on racial/ethnic extensions of General Strain Theory (GST), I examine IPV perpetration among Black, Latina, and White women using the Add Health data. I found that life strains, especially financial problems and prior IPV victimization, were associated with IPV perpetration among all women supporting the main argument of GST. Anger was significant for White and Latina women though there was limited evidence for emotion mediation. I found racial/ethnic differences in the experience of strain and the impact of some strains on IPV perpetration which provides support for the racial/ethnic extensions of GST in understanding IPV perpetration.