bluuebunny/crossref_metadata_2025 huggingface dataset:
Pengaruh Brand Image dan Service Quality Terhadap Keputusan Konsumen dalam Menggunakan Jasa Pengiriman PT JMS Logistics Kota Palembang
Authors: Febrianto M. Asyraf Rally , Roswaty  , Kurniawan DP Muhammad
Published: November 2023

Penelitian ini bertujuan untuk mengetahui pengaruh brand image dan service quality terhadap keputusan konsumen dalam menggunakan jasa pengiriman PT JMS Logistics Kota Palembang. Penelitian ini menggunakan metode kuantitatif. Populasi yang diteliti adalah konsumen yang menggunakan jasa pengiriman PT JMS Logistics Kota Palembang. Teknik pengambilan sampel yang digunakan teknik accidental sampling dengan total 55 responden. Teknik analisis data yang digunakan adalah uji validitas, uji reliabilitas, uji asumsi klasik, analisis linear berganda, uji t, uji F dan koefisien determinasi dengan menggunakan SPSS versi 24. Hasil dari penelitian dan hipotesis menunjukkan bahwa: 1) brand image secara parsial berpengaruh positif dan signifikan terhadap keputusan konsumen dengan nilai sig. 0,032 dan nilai thitung = 2,205, 2) service quality secara parsial berpengaruh positif dan signifikan terhadap keputusan konsumen dengan nilai sig. 0,000 dan nilai thitung = 6,035, 3) brand image dan service quality secara simultan berpengaruh positif dan signifikan terhadap keputusan konsumen dengan nilai sig. 0,000 dan nilai Fhitung = 26,658, 4) koefisien determinasi (R²) menunjukkan bahwa variabel brand image dan service quality mampu menjelaskan terhadap variabel keputusan konsumen sebesar 50,6%, sedangkan sisanya sebesar 49,4% dijelaskan atau dipengaruhi oleh variabel lain diluar penelitian.
Penggunaan Model ICARE dengan Media Make and Match untuk Meningkatkan Hasil Belajar pada Pelajaran PAI
Authors: Hasyim Muhammad
Published: November 2023

Model ICARE is a framework that is used as a guideline in carrying out learning activities. To improve the learning outcomes as desired, the researcher applies Make and Match media as supporting model to improve student learning outcomes. Make and Match media itself is a learning media that prioritizes the cultivation of social skills, especially the ability to work in team, interact with people, and the ability to think fast and critical through the game of finding a partner with the help of cards. After implementing the model ICARE with Make and Match Media, students improved their learning skill. In the pre-action learning activities, the number of students who got a score above the KKM was 2 students or 6.7% with a score of 80. In the first cycle of action the number of students who completed it was 13 students or 43.3% with a score of 80. And in the second cycle of action the number of students who got a score above the KKM totaling 30 students or 100% with a score of 80 as many as 21 students or 70%, 6 students got a score of 90 or 20% and students who got a score of 100 were 3 students or 10%.
HAK-HAK POLITIK WANITA DALAM ISLAM
Authors: NURHIKMAH NURHIKMAH
Published: November 2023

Islam is a religion that elevates the degree of women in various positions. The position and the honor of women which is equal with men is justified by Islam. One of them is the right of women in politics, such as women's rights in the election, nomination for political office, as well as the right to participate in public affairs. This assumption is the thesis of an understanding of the general arguments contained in the Qur'an and Sunnah, for both men and women. Unless the distinction of their fitrah in the household, one male and one female. Women have their own fitrah such as menstruation, nifas, istihadhah, pregnancy, giving a birth, feeding, parenting and so forth. While men have level of 'qawamah', leadership, responsibility towards their family, and also the right to provide food for the family.
PER-SATU-TUBUH-AN SEBAGAI SIMBOL POLA RELASI LAKI-LAKI DAN PEREMPUAN (Sebuah Konsep Kesetaraan Gender dalam Al-Quran)
Authors: Widodo Aris
Published: November 2023

Since the last decades of twentieth century, gender discourse has become a hot issue among Indonesian Muslim intellectuals. This phenomenon cannot be detached from international socio-intellectual constellation. In 1970’s, gender issue became a significant discourse in England. From then on, the discourse spread to many countries, including Indonesia. The result is that many Western perspectives on the issue inspired Muslim intellectuals. One of the influences is the using of terms such as “domination”, “sub-ordination”, and “equality”. This article tries to describe how the perceptions of domination, sub-ordination, and equality emerged in Western culture. This is followed by examples of Muslim intellectuals’ thoughts inspired by the perspective. Finally, the writer will provide a concept of relationship of man and woman based on an understanding on Qur’anic verses. It is argued that man and woman are equal in terms of quality. Yet, man and woman may differ if quantity is concerned.
Discovering Premature Replacements in Predictive Maintenance Time-to-Event Data
Authors: Abdallah Abdallah , Rognvaldsson Thorsteinn , Fan Yuantao , Pashami Sepideh , Ohlsson Mattias
Published: November 2023

Time-To-Event (TTE) modeling using survival analysis in industrial settings faces the challenge of premature replacements of machine components, which leads to bias and errors in survival prediction. Typically, TTE survival data contains information about components and if they had failed or not up to a certain time. For failed components, the time is noted, and a failure is referred to as an event. A component that has not failed is denoted as censored. In industrial settings, in contrast to medical settings, there can be considerable uncertainty in an event; a component can be replaced before it fails to prevent operation stops or because maintenance staff believe that the component is faulty. This shows up as “no fault found” in warranty studies, where a significant proportion of replaced components may appear fault-free when tested or inspected after replacement.
In this work, we propose an expectation-maximization-like method for discovering such premature replacements in survival data. The method is a two-phase iterative algorithm employing a genetic algorithm in the maximization phase to learn better event assignments on a validation set. The learned labels through iterations are accumulated and averaged to be used to initialize the following expectation phase. The assumption is that the more often the event is selected, the more likely it is to be an actual failure and not a “no fault found”.
Experiments on synthesized and simulated data show that the proposed method can correctly detect a significant percentage of premature replacement cases.
Semi-supervised machine learning for motor eccentricity fault diagnosis
Authors: Wang Bingnan , Zhang Shen , Inoue Hiroshi , Kanemaru Makoto
Published: November 2023

Eccentricity is one major indicator of mechanical faults in electric machines and needs to be detected early to avoid machine failures. Data-driven techniques based on machine learning and deep learning algorithms have been proposed in recent years for motor fault detection. However, majority of these methods use supervised learning algorithms and require large, labelled datasets, which can be challenging to obtain. In this paper, we propose a semi-supervised learning method based on a deep generative model using variational auto-encoder for eccentricity fault quantification. Good prediction accuracy can be achieved when only a small subset of training data has labels.
Simple Remaining Useful Life Algorithm Using the Quadratic
Authors: Bechhoefer Eric , Fujiki Nobuhiro , Kitsutaka Yusuke , Tsukamoto Sotaro , Usui Akio
Published: November 2023

The goal of predictive maintenance (PdM) is to facilitate on-condition maintenance or reduce/eliminate unscheduled maintenance events. For critical systems such as aircraft,PdM improves safety while increasing operational readiness. Aircraft operators can order the parts and ensure the correct skills and tools are available to avoid unplanned downtime.An enabler for PdM is the need to estimate the remaining useful life (RUL). For RUL to be accurate, there needs to be an assessment of the current component health, a thresholdfor when it is appropriate to do maintenance, and a degradation model. This model could be based on some physical processes, such as high-cycle fatigue failure.However, often the exact fatigue process is unknown. In this paper, a quadratic RUL model is used to calculate RUL using a state estimator. The proposed process allows for modelvalidation of the RUL state estimator itself. This is demonstrated using a bearing fault, a gear fault, and oil debris example.
Comparative Study of K-Means Clustering and a Novel Ranking Algorithm for Risk Priority Number Analysis in FMECA
Authors: Cheng Jiaxiang , Cho Sungin , Tan Yap Peng , Hu Guoqiang
Published: November 2023

Failure mode, effects, and criticality analysis (FMECA) has become a fundamental tool for identifying critical failure modes and prioritizing maintenance activities. As part of the analysis, the risk priority number (RPN), a numeric assessment of the risk, has received much attention as it is computed using severity (S), occurrence (O), and detectability (D), which serve as the main criteria for criticality analysis in many practical FMECA cases. In this paper, we assemble and present a data set containing RPN evaluations from 20 real-world cases. We then apply K-Means clustering to identify the most critical failure modes and propose a novel ranking algorithm that prioritizes mitigation actions based on specific criteria for each failure mode. Our experimental results suggest that both clustering and ranking methods can provide effective prioritization for critical failure modes under given assumptions, while our novel ranking algorithm can adapt to general scenarios and provide more accurate prioritization that can help develop effective maintenance strategies to minimize equipment failure risk and optimize maintenance costs.
Detection and Diagnostic with Random Forest Classifier (RFC) to Improve the Maintenance Management System in Steam Boiler of Power Plant
Authors: Ashfania Ghiffari Awliya Muhammad , Warsokusumo Tarwaji , Prahasto Toni , Widodo Achmad
Published: November 2023

Industrial internet of things (IIoT), digital twin, and connected devices can continue to use smart equipment and improve access to data. While the data collected by sensors has been an invaluable asset to companies, the ability to understand and use this data to drive new insights. The development of Condition Monitoring (CM) technology and Computerized Maintenance Management System (CMMS) in power generation systems provides a validated set of operation and maintenance data with abundant event data. Maintenance decision-making is primarily based on equipment reliability and performance-based features for diagnosing equipment failure. The most critical asset and often reduces the reliability and availability of a Coal Fired Steam Power Plant (CFSPP) with the most frequency of disturbances is the steam boiler. As a departure from the idea of creating a digital twin, this article will focus on analyzing equipment health conditions and finding causes of failure of the tools, utilizing data for diagnostic purposes. Real-case used in this research are steam boilers, which are important assets in power plant generation. The online and Failure Mode and Effect Analysis (FMEA) module data will be combined to realize the concept of anomaly diagnosis which is driven by hybrid data. Hoping that accurate diagnosis result with the Random Forest Classifier (RFC) Algorithm can be obtained and be used to analyze the causes of failure and decrease in equipment performance resulting by a decrease of energy efficiency performance. The analytical approaches are carried out to have the goal of generating detection models and diagnostic insights of event data based on operational data and FMEA.
Prognosis using Bayesian Method by Incorporating Physical Constraints
Authors: Park Hyung Jun , Kim Nam Ho , Choi Joo-Ho
Published: November 2023

Accurately predicting the remaining useful life (RUL) of industrial machinery is crucial for ensuring their reliability and safety. Prognostic methods that rely on Bayesian inference, such as Bayesian method (BM), Kalman and Particle filter (KF, PF), have been extensively studied for the RUL prognosis. However, these algorithms can be affected by noise when training data is limited, and the uncertainty associated with empirical models that are used in place of expensive physics models. As a result, this can lead to significant prediction errors or even infeasible RUL prediction in some cases. To overcome this challenge, three different approaches are proposed to guide the Bayesian framework by incorporating low-fidelity physical information. The proposed approaches embed inequality constraints to reduce sensitivity to local observations and achieve robust prediction. To determine an appropriate approach and its advantageous features, performance is evaluated by both numerical example and real case study for drone motor degradation.
Deep Metric Learning for Abnormal Rotation Detection of Satellites from Irregularly Sampled Light Curve
Authors: Yoshida Jun , Togawa Ryosuke , Sano Taichiro
Published: November 2023

In recent years, satellites have become an indispensable infrastructure in our lives. The number of satellites is increasing yearly and becoming increasingly active. To use satellites safely, it is crucial to manage them and detect the anomaly of satellites as much as possible. However, it currently takes skilled operators to detect an anomaly, and it is difficult for even skilled operators to detect the anomaly early without the telemetry data in cases such as an abnormal rotation. To address these challenges, we tested the feasibility of using deep metric learning for early anomaly detection from the irregularly sampled light curve. One of the characteristics of a light curve is unequally spaced because the optical sensor on the ground can only observe the subject at night and not when the weather is terrible. Given an irregularly sampled light curve, our model employs a long short-term memory (LSTM) unit of encoding the temporal dynamics and learns the embedding on the feature space using triplet loss. Then, an anomaly score is calculated based on pairwise distances between segments from the learned embedding in the feature space. With actual data from the satellite being operated, we showed the effectiveness of our model and the feasibility of early anomaly detection. Also, by exploring learned embedding in the feature space, we show that our model could capture the continuous state of the satellite.
Comprehensive Failure Diagnosis Model with Degradation Indicators of Multiple Sensors
Authors: Tominaga Jun , Kamiaka Shoya , Kuroda Kohei
Published: November 2023

Although monitoring system can detect abnormality of sensor reading in air-conditioning equipment, the root cause of the abnormality may not be sensor failure but other failures such as gas shortage. We propose new method that estimates the cause by the following steps. Firstly, regression model predicts the normal readings of multiple sensors (e.g., thermistor) for a given operational condition. Secondly, the gap between measured and predicted values is calculated for each parameter as a degradation indicator. Finally, our failure diagnosis model estimates the cause by considering degradation indicators of multiple sensors. Our evaluation verifies the effectiveness of our method.
Analysis of Statistical Data Heterogeneity in Federated Fault Identification
Authors: Taghiyarrenani Zahra , Nowaczyk Slawomir , Pashami Sepideh
Published: November 2023

Federated Learning (FL) is a setting where different clients collaboratively train a Machine Learning model in a privacy-preserving manner, i.e., without the requirement to share data. Given the importance of security and privacy in real-world applications, FL is gaining popularity in many areas, including predictive maintenance. For example, it allows independent companies to construct a model collaboratively. However, since different companies operate in different environments, their working conditions may differ, resulting in heterogeneity among their data distributions. This paper considers the fault identification problem and simulates different scenarios of data heterogeneity. Such a setting remains challenging for popular FL algorithms, and thus we demonstrate the considerations to be taken into account when designing federated predictive maintenance solutions.
Fuzzy-membership-based labeling: a new labeling method for both classification task and regression task
Authors: Ruan Diwang , Li Zhaorong , Wu Yuheng , Yan Jianping , Gühmann Clemens
Published: November 2023

In the machine learning and deep learning field, there are two main kinds of tasks: classification and regression. The label for the former is discrete, while for the latter is continuous. Due to the big gaps in labels, these two tasks are generally re solved separately, bringing low training efficiency and waste of computing resources. To this end, this paper proposes a new labeling method based on fuzzy membership. The main idea is to build an intermediate variable, which behaves between continuous and discrete variables. Then, the relation between the intermediate variable and the discrete label can be identified with fuzzy membership. Finally, the fuzzy membership is adopted for building labels to train the source model. After training, the source model can be transferred to achieve both classification and regression tasks. To validate the new labeling method, two typical tasks in the PHM field, aging stage classification and RUL prediction, are selected as the representative for classification and regression tasks, respectively. Furthermore, LSTM with two dense layers is chosen as the benchmark source model. With the C-MAPSS dataset, the superiority of the proposed fuzzy-membership based labeling to improve the network’s task transfer learning performance has been verified.
Unsupervised Anomaly Detection in Marine Diesel Engines using Transformer Neural Networks and Residual Analysis
Authors: Liang Qin , Knutsen Knut Erik , Vanem Erik , Zhang Houxiang , Æsøy Vilmar
Published: November 2023

This paper presents a novel unsupervised approach for detecting anomalies in marine diesel engines using a Transformer Neural Network based autoencoder (TAE) and residual analysis with Sequential Probability Ratio Test (SPRT) and Sum of Squares of Normalized Residuals (SSNR). The proposed method can capture temporal dependencies in normal timeseries data without the need for labeled failure data. To assess the effectiveness of the proposed approach, a dataset of faulty data is generated under the same operational profile as the normal training data. The model is trained using normal data, and the faulty data is reconstructed using the trained model. SPRT and SSNR are then used to analyze the residuals from the observed and reconstructed faulty data, with significant deviations exceeding a predefined threshold being identified as anomalous behavior. The experimental results demonstrate that the proposed approach can accurately and efficiently detect anomalies in marine diesel engines. Therefore, this approach can be considered as a promising solution for early anomaly detection, leading to timely maintenance and repair, and preventing costly downtime.
Method for Executing Digital System Models and Digital Twins at Scale to Enrich Fleet Health Management
Authors: Newman Daniel
Published: November 2023

The digitization of the aviation industry brings the promise of increased transparency into the engineering design across aircraft lifecycles. With an increased focus on Model-Based Engineering, design engineers are constructing interoperable, standardized simulation models which can be leveraged across the Digital Thread. This advancement presents an opportunity to improve health management and prognostics by directly comparing these models against in-service data. This paper proposes a method for leveraging engineering Digital System Models and Digital Twins at-scale to facilitate and enrich health management activities. Supporting considerations include specifying model standards and ingesting fleet-level data using cloud computing.
Proposal of a Time Series Anomaly Detection Method Using Image Encoding Techniques
Authors: Sakurai Ryo , Yairi Takehisa
Published: November 2023

Time series anomaly detection is considered to play a major role in many areas of society. Models using RNN, which are well suited for time series data, have been studied. However, models using RNN have the problem of high cost; image en- coding approaches combining Gramian Angular Fields(GAF) and Autoencoder are less expensive than RNN. However, the accuracy in existing studies is not as good as RNN. In this paper, we propose a time-series anomaly detection frame- work that first focuses on the structural issues of GAF and the reconstruction accuracy of Autoencoder. Experiments were conducted to verify the effectiveness of the framework. The results showed that the approach focusing on the structural is- sues of GAF achieved a significant improvement in accuracy, while the approach focusing on improving the reconstruction accuracy of the Autoencoder network decreased the anomaly detection accuracy. The reason for the lower accuracy was found to be that the networks with higher reconstruction accuracy accurately reconstructed even the anomaly images, making anomaly detection based on L1 errors impossible. These results indicate that an approach that focuses on the structural problems of GAF is effective, while an approach that improves the reconstruction accuracy of Autoencoder is not necessarily effective.
PHM for Spacecraft Propulsion Systems: Similarity-Based Model and Physics-Inspired Features
Authors: Minami Takanobu , Lee Jay
Published: November 2023

This paper presents a methodology designed for the Prognostics and Health Management (PHM) Asia-Pacific 2023 Conference Data Challenge. In particular, this study targets the health assessment of spacecraft propulsion systems. The challenge involved analyzing and categorizing a simulation-generated dataset that included four unique spacecraft and multiple health conditions, such as normal operation, bubble anomalies, and solenoid valve faults in various system locations. The proposed approach uses a two- step process. First, a model based on similarity measures is employed to classify the data into one of four health states. Then, a model incorporating physics-inspired features is utilized in solenoid valve faults to identify the fault location and estimate the valve opening ratio. The validity of the model is confirmed through cross-validation with the training dataset, which achieved a flawless total score across all permutations. Our method effectively categorizes the test data, including cases from a spacecraft not covered in the training, thereby securing a top position in the competition. The findings highlight the strength of our proposed model, which uses physics-inspired features to predict valve opening ratios, proving useful in managing and interpreting complex, unfamiliar spacecraft health data.
Predictive modelling for airline technical operations
Authors: Reiswich Dimitri
Published: November 2023

The digital platform AVIATAR leverages aircraft and maintenance data as well as advanced algorithms to optimize technical operations. Optimization is achieved by utilizing predictive maintenance algorithms which lead to a reduction of costs and operational incidences. The rise of AI algorithms combined with an increase of the available data to feed those algorithms provides both an opportunity as well as challenge to the traditional approaches which were in use in the aviation industry for decades. We demonstrate how AVIATAR’s data science team develops state of the art predictive maintenance models by combining advanced statistical models and AI with the unique engineering know how of Lufthansa Technik. We will show the immense value of full flight data in order to achieve this and provide an outlook into the potential future role of algorithms in the aviation industry.
Change Point-based Spatio-temporal Process Modeling of Image Degradation for Manufacturing Process
Authors: Lim Munwon , Bae Suk Joo
Published: November 2023

As an advent of smart factory technology, data-driven condition-based maintenance (CBM) is developed to automatically control the production process in engineering field. CBM usually focuses on diagnosing the production status based on real-time data from the sensors. In general manufacturing field, the performance of production equipment gradually decreases due to the wear or deterioration of equipment. To determine if the process is in-control, degradation modeling of observed data from the equipment and its statistical inference is conducted. In this paper, we propose image-based degradation modeling and change-point detection using spatio-temporal process (STP). To describe the deteriorating patterns of image observation, degradation based on spatial and temporal relationship is conducted. At the same time, change-point is estimated to distinguish the degradation under normal and abnormal production status. Through the application to the image stream in real industry, the proposed monitoring scheme `vely conduct the bi-phase representation providing the change-point of manufacturing processes.
Dataset Generation Based on 1D-CAE Modeling for Fault Diagnostics in a Spacecraft Propulsion System
Authors: Tominaga Kohji , Daimon Yu , Toyama Masao , Adachi Kazushi , Tsutsumi Seiji , Omata Noriyasu , Nagata Taiichi
Published: November 2023

The objective of this study is to generate a classified dataset of valve faults and bubble contamination anomalies in the propellant supply pipe of spacecraft propulsion systems. The dataset is available in PHMAP23, and the paper intends to describe its characteristics. The dataset includes time and pressure information and has been generated through numerical simulations using SimlationX, a 1D-CAE software. The condition of the propulsion system is reflected by the characteristics of the pressure dynamic response generated by the water hammer in the supply pipe caused by the rapid opening and closing of the downstream solenoid valve. Therefore, accurate classification of anomalies and faults can be achieved by extracting characteristics from the pressure dynamic response waveform.
Millimeter wave MPA using Metamaterial-substrate Antenna array for Gain Enhancement
Authors: Komatineni Rishitha , Hruday Chinta Samson , Vutukuru Varun Sharma , Dasari Vaishnavi
Published: November 2023

This paper presents a Millimeter wave Microstrip Patch antenna(MPA) with a metaplate which consists of Split Ring resonators(SRR) design. The gain and bandwidth of MPA are improved by using 4×3 array unit cells printed on both the sides of the metaplate. Simulation results show that the Gain of the antenna was increased by 4.82 dBi and 4.53 dBi, bandwidth was increased by 2.25% and 6.21% in CST and HFSS softwares respectively using the Metaplate along with the MPA. The center frequency of the proposed antenna is 28.5 GHz. Thus the proposed antenna has a very small size of 18×22 mm2 and is suitable for Millimeter wave applications  
Directional Modulation for Enhanced Privacy in Smart Watch Devices
Authors: Zandamela Abel , Marchetti Nicola , Narbudowicz Adam
Published: November 2023

This article studies security aspects of data transmission in on-body Internet of Things (IoT) devices through the use of Directional Modulation (DM) from a smart watch integrated antenna. DM is a beamsteering-based Physical Layer Security (PLS) method used to transmit baseband constellations to known secure locations, while simultaneously scrambling them in other directions. However, in the state-of-the-art DM is implemented using large arrays, which are out-sized for many on-body IoT devices. To overcome this challenge, this study proposes multimodal ultra-thin compact antennas suitable for integration into smart watches. These antennas present advanced beamsteering capabilities that allow the implementation of DM from a low-thickness structure of up to 0.57 mm or 1/100th of wavelength at the center operating frequency. Two different DM implementations

(simultaneous multiport transmission and an energy-efficient single-port transmission) are tested using a multi-layer phantom. Overall, secure steerable transmissions are realized, and experimental verifications are carried out to validate the proposed concept. Ultimately, this article demonstrates the feasibility of applying new energy-efficient and low-cost security techniques to enhance the privacy of resource-constrained IoT devices. 
A noise analysis of 4D RADAR: robust sensing for automotive?
Authors: Chan Pak Hung , Shahbeigi Roudposhti Sepeedeh , Ye Xinyi , Donzella Valentina
Published: November 2023

The sensor suite for assisted and automated driving functions vehicle is critical to the function of a vehicle, but also the first and most important limitation to the level of automation that the system can achieve. The advancement of 4D RADARs, providing better resolution in both azimuth and elevation compared to traditional RADAR, can assist to achieve more robust situational awareness, whilst also providing more data for perception algorithms and sensor fusion. However, like all perception sensors, 4D RADAR is also affected by numerous noise factors. To explore the sources of noise, this work identifies, classifies, and analyses automotive 4D RADAR noise factors. Overall, 22 noise factors have been considered, in combination with their effect on six 4D RADAR outputs. Finally, this work also presents and applies, for the first time, a dissimilarity metric to collected 4D RADAR data in the presence of rain with different intensities. The proposed metric is used to assess the effect of noise on the variability of the measured data, in addition it can be used to compare any 4D RADAR data. The metric, combined with other pointcloud evaluations, shows that as rainrate intensifies, the size of the pointcloud decreases, but also the variation in the measurements increases. This work presents the importance of evaluating, companding, and quantifying noise for 4D RADAR, and can pave the way for more in depth analysis of its modelling and testing of 4D RADAR for assisted and automated driving functions.
Design of Switchable SHG and THG using Metasurface-based Frequency Mixing System
Authors: Li Mengmeng , Ramaccia Davide , Toscano Alessandro , Bilotti Filiberto
Published: November 2023

In this letter, we propose a nonlinear reflective metasurface-based frequency mixing system able to perform second-harmonic generation (SHG) and third-harmonic generation (THG). The metasurface consists of a planar bidimensional alignment of unit-cells, each of which encompasses a receiving element at fundamental frequency, and two transmitting elements operating at double and triple the fundamental frequency, respectively. The TX/RX elements within each unit-cell are connected via a microwave network to a RF mixing circuit that performs the conversion, assisted by an RF amplifier that improves the overall conversion efficiency. Efficient switchable SHG and THG frequency conversions are demonstrated by both full-wave simulations and experiments. For the input fundamental frequency of 3 GHz, the conversion efficiencies for SHG and THG are 55.5% and 42.6%, respectively.
Real-time Ultrasound Imaging Based Muscle Feedback To Inform Afferent Stimulation For Tremor Suppression
Authors: Sharma Nitin
Published: November 2023

 Tremors due to Essential Tremor (ET) and Parkinson’s Disease (PD) often result in involuntary oscillations of human limbs that impact daily activities such as eating, drinking, and writing. Recently, afferent stimulation of peripheral nerves has emerged as a new non-invasive approach to tremor suppression. To increase its efficacy there is a desire to develop sensing and control strategies the complement the emerging suppression approach. Motivated by technical challenges to deploy prevalent sensing methods for tremor characterization we investigated real-time ultrasound (US)-imaging derived signal to cue afferent stimulation times. To achieve this, we first derived a real-time US-image based metric to characterize tremor in participants with ET and PD. We further determined an effective afferent stimulation frequency for each participant that achieved the best tremor suppression. Finally for the first time, we present results from the implementation of real-time US-imaging that triggers afferent stimulation (on or off) on the onset or subsidence of the wrist tremor in three participants with PD and ET .
Distributed Repairing Multiple Erasures in Reed-Solomon Codes
Authors: Lin Xing
Published: November 2023

The application of Reed-Solomon (RS) codes in distributed storage systems is widespread. A lot of literature have proposed repair schemes for RS codes with one or multiple failed symbols, which can reduce communication cost during repair (i.e. repair bandwidth).

 However, for repair schemes of RS codes with multiple failed symbols,  the existing distributed repair schemes can only repair $e=2,3$ erasures. In this paper, by intelligently dividing the set of evaluation points corresponding to failed symbols, a distributed repair scheme for RS codes with $e\geq 4$ erasures are proposed.
Data clustering with improved expectation maximization for multiomics data integration
Authors: Pateras Joseph , Lodi Musaddiq , Rana Pratip , Ghosh Preetam
Published: November 2023

For studying cancer biology, multiomics data integration has become an invaluable tool. With the amount of -omics data, its availability and the pace of its acquisition increasing rapidly, multiomics data integration is even more pivotal. This work employs a novel adaptation of the expectation maximization routine for joint latent variable modeling of multiomics patient profiles. Along with traditional methods of biological feature selection, the data-centric approach toward latent distribution optimization can adequately cluster patients from well-studied cancer types and does so with lower computational expense. Crucially, this work modifies the optimization subroutines in the relatively standard joint latent variable -omics workflow for improved survival analysis and run-time performance. This work also provides a framework that identifies distinctions between cancer subtypes and proposes potential biomarkers for breast cancer.
Semantic Communication: A Survey on Research Landscape, Challenges, and Future Directions
Authors: Getu Tilahun , Kaddoum Georges , Bennis Mehdi
Published: November 2023

 Amid the global rollout of fifth-generation (5G) services, researchers in academia, industry, and national labo?ratories have been developing proposals for the sixth generation (6G). Despite the many 6G proposals, the materialization of 6G as presently envisaged is fraught with many fundamental interdisciplinary, multidisciplinary, and transdisciplinary (IMT) challenges. To alleviate some of these challenges, semantic com?munication (SemCom) has emerged as promising 6G technology enabler. SemCom is designed to transmit only semantically?relevant information and hence help to minimize power usage, bandwidth consumption, and transmission delay. Thus, SemCom embodies a paradigm shift that can change the status quo that wireless connectivity is an opaque data pipe carrying messages whose context-dependent meaning have been ignored. On the other hand, 6G is critical for the materialization of major SemCom use cases. These paradigms of 6G for SemCom and SemCom for 6G call for a tighter integration and marriage of 6G and SemCom. To facilitate this integration and marriage, this comprehensive survey paper first provides the fundamentals of semantics and semantic information, semantic representation, semantic information, and semantic entropy. It then builds on this understanding and details the state-of-the-art research landscape of SemCom; exposes the fundamental and major challenges of SemCom; and offers promising future research directions for SemCom theories, algorithms, and realization. Accordingly, this survey article stimulates major streams of research on SemCom theories, algorithms, and implementation. 
PENGARUH MOTIVASI BELAJAR TERHADAP HASIL BELAJAR AL-QUR’AN HADITS MI MUHAMMADIYAH SIDOBASUKI TEGINENENG PESAWARAN
Authors: SILVIA ALFI VIOLITA WIJAYA , Khoironi Khoironi , Mardiana Mardiana
Published: November 2023

AbstrakPenelitian ini ini membahas mengenai pengaruh motivasi belajar terhadap hasil belajar Al-Quran Hadits. Pada aktivitas pembelajaran di kelas IV MI Muhammadiyah Sidobasuki Tegineneng terdapat permasalahan di dalam kelas, yaitu menurunnya pencapaian hasil belajar siswa. Adanya siswa yang masih cenderung pasif dalam mengikuti pembelajaran serta kurang kesadaran ketika mengerjakan tugas yang dikasih guru. Ada juga yang beranggapan banyaknya hafalan dan tulisan-tulisan arab yang sulit dipahami siswa, hingga membuat siswa merasa bosan, kurang semangat dan minat untuk belajar sehingga berdampak pada hasil belajar. Hal ini terpengaruh oleh motivasi dari diri siswa dan motivasi dari luar diri siswa.Tujuan dari penelitian ini yakni untuk mengukur pengaruh motivasi belajar terhadap hasil belajar Al-Quran Hadits. Dengan kerangka berfikir dari motivasi belajar yaitu dukungan yang ada di dalam diri sendiri atau dari orang lain yang meliputi : cita-cita siswa, kemampuan siswa, keadaan siswa, kondisi lingkungan siswa, dan upaya guru dalam membelajarkan siswa. Dan hasil belajar meliputi aspek kognitif afektif dan psikomotorik. Hipotesis dalam penelitian ini terdapat adanya pengaruh motivasi belajar terhadap hasil belajar Al-Quran Hadits siswa kelas IV di MI Muhammadiyah Sidobasuki Tegineneng.Jenis penelitian ini adalah penelitian kuantitatif menggunakan teknik analisis regresi linear sederhana yang menggunakan alat bantu program SPSS Versi 16.00. Variabel bebas dalam penelitian ini yaitu motivasi belajar, sedangkan variabel terikat yaitu hasil belajar Al-Quran Hadits. Populasi penelitian ini berjumah 30 siswa kelas IV MI Muhammadiyah Sidobasuki. Sedangkan sampel penelitian adalah seluruh populasi yakni siswa kelas IV MI Muhammadiyah Sidobasuki Tegineneng.Hasil analisis menunjukkan bahwa besarnya nilai korelasi (R) 0,499 yang berarti korelasi sedang atau cukup. Dan persamaan regresi di peroleh Y= 22,372 + 0,396X. Dari hasil uji signifikan diperoleh nilai signifikan sebesar  0,005 dimana nilai signifikan  < 0,05 (0,005 > 0,05), dengan demikian regresi dapat diterima. Kesimpulannya bahwa ada pengaruh motivasi belajar terhadap hasil belajar siswa kelas IV MI Muhammadiyah Sidobasuki Tegineneng sebesar 24,9%.Kata kunci : Motivasi Belajar, Hasil Belajar
Solusi Efektif Pencegahan Hawa Nafsu Pada Kisah Nabi Yusuf dalam Al-Qur'an
Authors: Ibnuansyah Rahmat , Baihaqi Yusuf , Shomad Bukhori Abdul
Published: November 2023

Perkembangan zaman dari masa ke masa semakin bertambah pesat, pola kehidupan masyarakat juga semakin berkembang dengan pesatnya. Permasalahan-permasalahan yang timbul dari pola perkembangan kehidupan masyarakat semakin banyak dan salah satunya terkait permasalahan hawa nafsu. Tujuan dari penelitian ini adalah untuk mengetahui bagaimanakah penafsiran mufassir tentang hawa nafsu dan solusi efektif pencegahan hawa nafsu pada kisah nabi Yusuf dalam Al-Qur’an. Penulis memilih pembahasan ini karena permasalahan-permasalahan yang timbul dari pola perkembangan kehidupan masyarakat semakin banyak dan salah satunya terkait permasalahan hawa nafsu. Banyak masyarakat yang kurang memahami hawa nafsu, sehingga banyak yang terjerumus dalam hal-hal yang menyangkut hawa nafsu. Kurangnya pemahaman dan ilmu terhadap ayat-ayat Al-Qur’an dan Sunnah yang berkaitan hawa nafsu, menyebabkan manusia di kuasai oleh hawa nafsunya yang semakin liar dan sulit di kendalikan.Penelitian ini bersifat kualitatif yaitu, menguraikan pembahasan dan hasil penelitian atau penulisan berbentuk deskripsi pembelajaran antara tentang hawa nafsu dan solusi efektif pencegahan hawa nafsu pada kisah nabi Yusuf dalam Al-Qur’an sehingga menjadi gambaran yang utuh. Hasil dari penelitian ini ialah, Pertama penafsiran mufassir tentang hawa nafsu yang ada pada kisah nabi Yusuf AS. yaitu: senantiasa  menyuruh kepada kejahatan, condong kepada syahwat dan mengajak kepada kesenangan hawa nafsu. Keduasolusi efektif dalam mengatasi hawa nafsu yang bisa kita teladani dari kisah nabi Yusuf AS. yaitu dengan memegang teguh ketaqwaan. Dengan ketaqwaan inilah nabi Yusuf AS.  bisa menghadapi berbagai macam cobaan dan godaan hawa nafsu yang mengajak kepada kejahatan.
The Impact of Perceived Surveillance on Continuance Usage Intention of Voice Assistants
Authors: Abdussami Roja , Candiwan
Published: November 2023

Voice assistants (VAs) are devices that utilize AI, machine learning, and NLP to facilitate users to perform diverse tasks verbally. VAs also have a unique feature that allows them to be “always on” so that every sound generated in their background can be analyzed and start interacting with users when they recognize their wake-up command, for instance, “Hey Siri” or “Okay Google”, which implies that VAs have to be listening to users at all time. This raises the issue of privacy in the form of perceived surveillance. This study aims to assess how perceived surveillance affects the continuance usage intention of VAs in Indonesia with the addition of personal information disclosure as a mediator. Surveillance effect model was utilized to measure perceived surveillance. The model was calculated using PLS-SEM based on online survey data (N=222) distributed over social media. It was revealed that perceived surveillance affects the continuance usage intention of VAs negatively and is partially mediated by personal information disclosure. The result also affirmed that trust, perceived risk, and prior negative experiences are predictors of perceived surveillance. Therefore, VA companies should be mindful of how their customers’ continuance usage intention is affected by how much perceived surveillance they feel.
CATHETER DIRECTED THROMBOLYSIS IN ACUTE LIMB ISCHEMIA PATIENTS. A SINGLE CENTER’S EXPERIENCE
Authors: Hupalo Yurii M. , Makivchuk Dmytro A.
Published: November 2023

The aim: This study evaluates catheter-directed thrombolysis (CDT) outcomes in patients with acute lower limb arterial thrombosis and acute limb ischemia.
Materials and methods: 53 patients (17 females, 36 males, aged 53-76) were studied. 57% had femoropopliteal and below-the-knee (BTK) thromboocclusion, 43% had BTK thromboocclusion. Symptoms included pain, pallor, edema, and cyanosis.
Exclusions criteria: contracture, recent surgeries, bleeding.
Results: In 29 (97%) patients regression of lower-limb ischemia rate by 1-2 stages according to the Rutherford classification were observed. One patient (3%) did not exhibit any regression in the degree of lower-limb ischemia, experiencing increasing pain and decreased sensitivity in the lower limb, leading to the development of contracture in the ankle joint and subsequent lower limb amputation over 7 days. Among 12 (40%) patients, after performing follow-up arteriography of the lower limb, angioplasty was performed on the diagnosed steno-occlusive lesions in the revascularized segment with secondary angioplasty. Within a year, one (3%) patient experienced recurrent thrombosis of the lower limb arteries with subsequent revascularization.
Conclusions: CDT is recommended for ALI Patients with arterial thrombooclusion.
HEMODYNAMIC FEATURES IN THE PATIENTS AFTER STROKE SUFFERED FROM CHRONIC PAIN
Authors: Kovalenko Olha Ye. , Rubanista Maryna Ye. , Lytvyn Olena V. , Ovodiuk Nataliia M.
Published: November 2023

The aim: To study the hemodynamic parameters in patients with post-stroke pain syndromes.
Materials and methods: We examined the hemodynamic parametrs of 74 patients who were treated at the Departments of General Neurology, Vascular Neurology and Pain Center in the Clinical Hospital «Feofania» State Administrative Department. Main group (44 patients) had stroke in anamnesis and chronic pain (headache, back pain, joint pain). The control group (30 patients) suffered from pain but had no stroke in anamnesis. Relationships between hemodynamic parameters and quantitative characteristics of chronic pain (visual analogue scale – VAS) were studied with help of linear Pearson correlation in different stroke periods and between the patients of Main and Control group.
Results: Strong correlations were found between decreasing in a.vertebralis blood flow and intensity of post-stroke pain. Strong correlations were found between intensivity of joint pain and a.vertebralis hemodynamic parameters in the patients in residual period up to 10 years. In the residual period over 10 years, in addition to the connections with hemodynamics in the VB, there was a correlation with the linear rapidly existing parameters of a.cerebri media, a.carotis communis.
Conclusions: Studying of hemodynamic parameters in patients with post-stroke pain syndromes can serve as a basis for a deeper understanding of pain mechanisms and the development of effective preventive and curative measures.
MICROCIRCULATORY ALTERATIONS IN STABLE CORONARY ARTERY DISEASE PATIENTS WITH CONCOMITANT COVID-19
Authors: Netiazhenko Vasyl Z. , Mostovyi Serhii I. , Safonova Olga M. , Mikhaliev Kyrylo O.
Published: November 2023

The aim: To evaluate the alterations in microcirculation of stable coronary artery disease (SCAD) patients with concomitant COVID-19.
Materials and methods: The cross-sectional study analyzed the data from 80 patients, being subdivided as follows: group 1 (G1) – SCAD without COVID-19 (n=30); group 2 (G2) – SCAD with concomitant COVID-19 (n=25); group 3 (G3) – COVID-19 without SCAD (n=25). The control group included 30 relatively healthy volunteers. The state of microcirculation was assessed by nailfold videocapillaroscopy (NVC) and laser Doppler flowmetry (LDF).
Results: NVC data from G2 revealed the sings of capillary bed remodeling, along with the most pronounced decrease in capillary (arteriolar part of the loop) blood flow velocity (vs. G1 and G3). LDF data from G2 were evident for the alterations in both endothelium-dependent and -independent mechanisms of microvascular flow regulation. The 72 % of G2 constituted the cases of microcirculatory hemodynamic «congestion-stasis» (MHCS) type (characterized by the decreased laser Doppler perfusion index and reduced endothelium-dependent microvascular reactivity [MVR]), and the cases of mixed type with reduced MVR. The pooled hyporeactive profile (of both MHCS type and a mixed type with reduced MVR) demonstrated the higher frequency of G2 patients (40 %), as against 11 % in the pooled alternative hemodynamic group (p<0,001) (included 80 % of cases with preserved MVR).
Conclusions: G2 profile demonstrated the predomination of patients, possessing a MHCS type or a mixed type with reduced MVR. The pooled microcirculatory hyporeactive profile was presented with G2 cases to a greater extent, than in the pooled profile with predominantly preserved MVR.
PREDICTIVE VALUE OF PLATELET COUNT AND PLATELET INDICES IN CERVICAL CANCER PATIENTS WITH EXTERNAL RADIATION THERAPY
Authors: Marcelina Vanessa Mutiara , Widyastiti Nyoman Suci , Bramantyo Dion Firli , Kristiawan Endy Cahyono , Nawangsih Prihharsanti Christina Hari
Published: November 2023

The aim: This study aims to assess the role of platelets and platelet indices as a cost-effective predictive marker of the cervical cancer patient’s response before, 15th, and 25th external radiation.
Materials and methods: A total of 54 cervical cancer patients at Dr. Kariadi Hospital were divided into good and poor therapeutic response groups. Measurements of platelet count and indices including PDW, P-LCR, and PCT were carried out before, 15th, and 25th external radiation.
Results: There was no difference in platelet counts, MPV, PDW, P-LCR, and PCT in both groups, but patients with decreased MPV and P-LCR values on 15th-25th external radiation had a better therapeutic response (p= 0.005 and 0.007).
Conclusions: MPV and P-LCR values at 15th to 25th external radiation appeared to decrease further in the group that responded better to therapy.