bluuebunny/crossref_metadata_2025 huggingface dataset:
FIGURE 3 from NRF2 Activation in Trp53;p16-deficient Mice Drives Oral Squamous Cell Carcinoma
Authors: Hamad Samera H. , Sellers Rani S. , Wamsley Nathan , Zolkind Paul , Schrank Travis P. , Major Michael B. , Weissman Bernard E.
Published: February 2024

Characterization of oral squamous cell carcinomas from CPN mice. Invasive OSCC from a CPN mouse stained for H&E (A). B, NRF2. PANCK (C) and vimentin (D). Scale bar = 200 µm. Images were taken using a BX61-Neville microscope.
Data from NRF2 Activation in Trp53;p16-deficient Mice Drives Oral Squamous Cell Carcinoma
Authors: Hamad Samera H. , Sellers Rani S. , Wamsley Nathan , Zolkind Paul , Schrank Travis P. , Major Michael B. , Weissman Bernard E.
Published: February 2024

Abstract
Aberrant activation of the NRF2/NFE2L2 transcription factor commonly occurs in head and neck squamous cell carcinomas (HNSCC). Mouse model studies have shown that NRF2 activation alone does not result in cancer. When combined with classic oncogenes and at the right dose, NRF2 activation promotes tumor initiation and progression. Here we deleted the tumor suppressor genes p16INK4A and p53 (referred to as CP mice), which are commonly lost in human HNSCC, in the presence of a constitutively active NRF2E79Q mutant (CPN mice). NRF2E79Q expression in CPN mice resulted in squamous cell hyperplasia or dysplasia with hyperkeratosis in the esophagus, oropharynx, and forestomach. In addition, CPN mice displayed oral cavity squamous cell carcinoma (OSCC); CP mice bearing wild-type NRF2 expression did not develop oral cavity hyperplasia, dysplasia or OSCC. In both CP and CPN mice, we also observed predominantly abdominal sarcomas and carcinomas. Our data show that in the context of p53 and p16 tumor suppressor loss, NRF2 activation serves oncogenic functions to drive OSCC. CPN mice represent a new model for OSCC that closely reflects the genetics of human HNSCC.
Significance:
Human squamous cancers frequently show constitutive NRF2 activation, associated with poorer outcomes and resistance to multiple therapies. Here, we report the first activated NRF2-driven and human-relevant mouse model of squamous cell carcinoma that develops in the background of p16 and p53 loss. The availability of this model will lead to a clearer understanding of how NRF2 contributes to the initiation, progression, and therapeutic response of OSCC.
Atezolizumab-Induced Acrodermatitis and Pustular Psoriasis in a Patient with Non-Small Cell Lung Cancer: A Rare Case Report
Authors: Fattore Davide , Esposito Gianluca , Carangelo Ludovica , Luciano Maria Antonietta , Megna Matteo
Published: February 2024

Introduction: Immune checkpoint inhibitors are new drugs approved for the treatment of many types of malignancies. Despite their wide use and unquestionable clinical benefits, these agents have also been associated with a unique spectrum of side effects known as immune-related adverse events. In this study, we report the first case of atezolizumab-induced pustular psoriasis and acrodermatitis. Case Presentation: A 61-year-old woman presented to our department with erythematous-desquamative and pustular lesions involving all hands and feet fingers, inguinal region, and trunk, associated to severe psoriatic onychodystrophy. She was affected by non-small-cell lung carcinoma from 12 years, and 7 months before admission, she started a treatment with atezolizumab. Conclusion: Immune checkpoint inhibitors such as atezolizumab are linked to a plethora of adverse events. Identifying and treating certain adverse skin events, particularly in cancer patients, can be a challenge, leading oncologists to discontinue immunotherapy. Our case shows how it is necessary to have a shared therapeutic algorithm in order to manage serious skin reactions in cancer patients and avoid disruption of the oncotherapy.
Public Opinions and Attitudes toward Non-invasive Prenatal Testing on Reddit: Content and Sentiment Analysis
Authors: Xiao Bowen , Yan Joyce , Hayeems Robin Z.
Published: February 2024

Introduction: Non-invasive prenatal testing (NIPT) can be used to detect fetal chromosomal abnormalities early in pregnancy. As eligibility criteria broaden and screening targets expand, gauging public acceptability of NIPT becomes increasingly important. Leveraging social media as a rich source of public discourse, the purpose of this study was to understand public opinions and attitudes toward NIPT on the social media platform Reddit.
Methods: We applied content and natural language processing techniques (i.e., sentiment analysis) to textual data collected from 4 Reddit communities focusing on the NIPT content posted from September 2012 to September 2022 (367 posts and 7822 comments in total).
Results: Content analysis findings indicated that social media users consider NIPT to be worthwhile. Reasons NIPT was perceived to be not worthwhile related to unwanted anxiety, and the fact that NIPT results would not change anything about their approach to pregnancy were also expressed. The sentiment analysis identified more positive than negative emotions; the mean sentiment scores ranged from 0.48 and 1.22, depending on the specific Lexicon used. Specific emotions (i.e., trust, fear) were also identified.
Conclusion: Our novel approach to understanding public perception and attitudes toward NIPT yielded results that are consistent with conventional patient-oriented research methods. These findings may not only contribute to ongoing improvements in prenatal patient care, research, and policy but also indicate that sentiment analysis applied to social media data can serve as a suitable means to assess public acceptability of NIPT, particularly as public dialogue on this topic increases over time.
Is 5-Oxoprolinase Deficiency More than Just a Benign Condition?
Authors: Kasapkara Çiğdem Seher , Kıreker Köylü Oya , Engin Erdal Aysenur , Yürek Burak , Ceylan Nesrin , Ceylaner Serdar
Published: February 2024

Introduction: Inherited 5-oxoprolinase (OPLAH) deficiency is a rare inborn condition characterized by 5-oxoprolinuria. The inherited condition of 5-oxoprolinuria, or pyroglutamic aciduria, is primarily caused by mutations in the genes that encode glutathione synthetase (GSS) and 5-oxoprolinase (OPLAH), which are enzymes involved in the gamma-glutamyl cycle in glutathione metabolism. We report a 3-year-old male patient with epilepsy and speech difficulty diagnosed as primary 5-oxoprolinuria due to a novel OPLAH gene mutation. Case Presentation: A 3-year-old boy who was delivered at full term in an uncomplicated birth to consanguineous parents presented with epilepsy at the age of 2 years. He did not speak fluently. He was using 5–10 words with decreased language fluency. His past medical history revealed postnatal macrocephaly, hydrocephalus, and well-controlled epilepsy with levetiracetam. Progressive cerebral atrophy, hypomyelination, ventriculomegaly, and corpus callosum hypoplasia were striking features in brain MRI. A urine sample was sent for organic acid analysis by gas chromatography-mass spectrometry (GC-MS); quantitation of 5-oxoproline by stable isotope dilution gave a value of 177.9 mmol/mol creatinine (reference values 25.8–92.2). Molecular genetic analysis of the OPLAH gene revealed a novel homozygous variant (OPLAH (NM_017570.5): c.1909C>T p.Arg637Trp). Conclusion: We conclude that inherited 5-oxoprolinase deficiency is not a benign biochemical condition, and patients with 5-oxoprolinuria should be screened for it. The nature of this inherited metabolic disorder must be determined through long-term observation. We wish to emphasize the significance of molecular genetic analysis in symptomatic patients with persistently elevated levels of 5-oxoproline in the urine, as measured by organic acid analysis.
Definite time over-current protection on transmission line using MATLAB/Simulink
Authors: Taha Taha A. , Zaynal Hussein I. , T. Hussain Abadal-Salam , Desa Hazry , Taha Faris Hassan
Published: February 2024

This paper has investigated the application of the definite time over-current (DTOC) which reacts to protect the breaker from damage during the occurrence of over-current in the transmission lines. After a distance relay, this kind of over-current relay is utilized as backup protection. The overcurrent relay will provide a signal after a predetermined amount of time delay, and the breaker will trip if the distance relay does not detect a line failure. As a result, this over-current relay functions with a time delay that is just slightly longer than the combined working times of the distance relay and the breaker. This DTOC is tested for various types of faults which are 3- phase fault occurring at load 1, 3-phase fault occurring at load 2, a 3-phase fault occurring before primary protection, and the behaviour of voltage and current with a failed primary protection. All the results will be obtained using the MATLAB/Simulink software package.
Mathematics for 2D face recognition from real time image data set using deep learning techniques
Authors: G. N. Ambika , Suresh Yeresime
Published: February 2024

The recognition of human faces poses a complex challenge within the domains of computer vision and artificial intelligence. Emotions play a pivotal role in human interaction, serving as a primary means of communication. This manuscript aims to develop a robust recommendation system capable of identifying individual faces from rasterized images, encompassing features such as eyes, nose, cheeks, lips, forehead, and chin. Human faces exhibit a wide array of emotions, with some emotions, including anger, sadness, happiness, surprise, fear, disgust, and neutrality, being universally recognizable. To achieve this objective, deep learning techniques are leveraged to detect objects containing human faces. Every human face exhibits common characteristics known as Haar features, which are employed to extract feature values from images containing multiple elements. The process is executed through three distinct stages, starting with the initial image and involving calculations. Real-time images from popular social media platforms like Facebook are employed as the dataset for this endeavor. The utilization of deep learning techniques offers superior results, owing to their computational demands and intricate design when compared to classical computer vision methods using OpenCV. The implementation of deep learning is carried out using PyTorch, further enhancing the precision and efficiency of face recognition.
Challenges in data representation for efficient execution of encryption operation
Authors: Afendee Mohamed Mohamad , Garba Shawai Yahaya , Almaiah Mohammed Amin , Derahman Mohd Noor , Lutfi Abdalwali , Abu Bakar Khairul Azmi
Published: February 2024

Big number operation has always been a bottleneck to computer system as it imposes high demand on computing power. With a limited power available, operations such as exponentiation and multiplication involving large integer belonging to encryption process requires grave scrutiny. One way to address this issue is by replacing an original complex computation into a sequence of small computations that in the end produces the same results. This paper takes an evolutionary approach to survey numerous articles that have contributed to the advancement of integer representation. Numerous representations were proposed, those that come into play concentrated on reducing non-zero digits and limiting non-zero spacing other than allowing subtraction operation. A comparison was made to distinguish the properties of each method from the others. This detailed outlook can be a guide for identifying the correct representation to be chosen for implementation within specific application.
Elitism-crossover barnacle mating optimization and its application to PID controller design for a buck converter
Authors: Kasruddin Nasir Ahmad Nor , Ahmad Mohd Redzuan , Anwari Roslan Muhammad Almaa
Published: February 2024

This paper presents an elitism-crossover barnacle mating optimization (ECBMO). It is an improvement of barnacle mating optimization (BMO). The original BMO suffers from local optima problem leading to a low accurate solution. A new method of offspring generation is adopted into the original BMO structure. Some features of the best-so-far solution are incorporated into the generated offspring. The accuracy performance of the proposed algorithm is tested on several IEEE functions. A statistical analysis is conducted to compare its performance over the original BMO. It is also applied to optimize proportional integral derivative (PID) parameters for controlling output voltage of a buck converter. Result of benchmark functions test shows the proposed algorithm has attained higher accuracy for all functions compared to BMO algorithm. Application on the real problem shows both algorithms control the converter voltage satisfactorily. However, the ECBMO has achieved more optimal PID parameters and leading to a better output voltage response.
Feature importance for software development effort estimation using multi level ensemble approaches
Authors: Rao K. Eswara , Vital Terlapu Pandu Ranga , Annan Naidu Paidi , Ravi Kumar Tammineni , Murali Pydi Bala
Published: February 2024

Feature importance strategy that substantially impacts software development effort estimation (SDEE) can help lower the dimensionality of dataset size. SDEE models developed to estimate effort, time, and wealth required to accomplish a software product on a limited budget are used more frequently by project managers as decision-support tool effort estimation algorithms trained on a dataset containing essential elements to improve their estimation accuracy. Earlier research worked on creating and testing various estimation methods to get accurate. On the other hand, ensemble produces superior prediction accuracy than single approaches. Therefore, this study aims to identify, develop, and deploy an ensemble approach feasible and practical for forecasting software development activities with limited time and minimum effort. This paper proposed a collaborative system containing a multi-level ensemble approach. The first level grabs the optimal features by adopting boosting techniques that impact the decided target; this subset features forward to the second level developed by a stacked ensemble to compute the product development effort concerning lines of code (LOC) and actual. The proposed model yields high accuracy and is more accurate than distinct models.
A novel modified mountain gazelle optimizer for tuning parameter proportional integral derivative of DC motor
Authors: Aribowo Widi , Abualigah Laith , Oliva Diego , Prapanca Aditya
Published: February 2024

This article presents a modified method of mountain gazelle optimizer (MMGO) as a direct current (DC) motor control. Mountain gazelle optimizer (MGO) is an algorithm inspired by the life of the mountain gazelle animal in nature. This animal concept has five essential steps that are duplicated in mathematical modeling. This article uses two tests to get the performance of the MMGO method. The first test uses a benchmark function test with a comparison method, namely the sine tree seed algorithm (STSA) and the original MGO. The second test is the application of MMGO as a DC motor control. The simulation results show that MMGO can reduce the overshoot of conventional proportional integral derivative (PID) control by 0.447% and has a better integral time square error (ITSE) value of 5.345 than conventional PID control. Thus, the MMGO method shows promising performance.
The detection and classification of acute myeloid leukaemia blood cell images based on different YOLO approaches
Authors: Naing Kaung Myat , Kittichai Veerayuth , Tongloy Teerawat , Chuwongin Santhad , Boonsang Siridech
Published: February 2024

Medical image examination with a deep learning approach is greatly beneficial in the healthcare industry for faster diagnosis and disease monitoring. One of the popular deep learning algorithms such as you only look once (YOLO) developed for object detection is a successful state-ofthe-art algorithm in real-time object detection systems. Although YOLO is continuously improving in the object detection area, there are still questions about how different YOLO versions compare in terms of performance. We utilize eight YOLO versions to classify acute myeloid leukaemia (AML) blood cells in image examinations. We also acquired the publicly available AML dataset from the cancer imaging archive (TCIA) which consists of expert-labeled single cell images. Data augmentation techniques are additionally applied to enhance and balance the training images in the dataset. The overall results indicated that eight types of YOLO approaches have outstanding performances of more than 90% in precision and sensitivity. In comparison, YOLOv4-tiny has a more reliable performance than the other seven approaches. Consistently, the YOLOv4-tiny also achieved the highest AUC score. Therefore, this work can potentially provide a beneficial digital rapid tool in the screening and evaluation of numerous haematological disorders.
Proposed fog computing-enabled conceptual model for semantic interoperability in internet of things
Authors: Nagasundaram Devamekalai , Manickam Selvakumar , Laghari Shams Ul Arfeen , Karuppayah Shankar
Published: February 2024

Semantic interoperability has emerged as a key barrier amidst the major developments and challenges brought about by the rapid expansion of internet of things (IoT) applications. Establishing interoperability is essential for IoT systems to function optimally, especially across diverse organizations. Despite extensive research in achieving semantic interoperability, dynamic interoperability, a vital facet, remains inadequately addressed. This paper addresses this gap by presenting a fog-based conceptual model designed to facilitate dynamic semantic interoperability in IoT. The model incorporates a single-tier fog layer, providing the necessary processing capabilities to achieve this goal. The study conducts a comprehensive literature review on semantic interoperability, emphasizing latency, bandwidth, total cost, and energy consumption. Results demonstrate the proposed double skin façade (DSF) model’s remarkable 88% improvement in service delay over IoT-SIM and Open IoT, attributed to its efficient load-offloading mechanism and optimized fog layer, offering a 50% reduction in service delay, power consumption, and 86% reduction in network usage compared to existing approaches through data redundancy elimination via pre-processing at the fog layer.
Simplifying the electronic wedge brake system model through model order reduction techniques
Authors: Che Hasan Mohd Hanif , Hassan Mohd Khair , Ahmad Fauzi , Marhaban Mohammad Hamiruce , Haris Sharil Izwan , Arasteh Ehsan
Published: February 2024

The electronic wedge brake (EWB) uses self-reinforcement principles to optimise stopping power, but its mathematical model has various actuation angles and system dynamics making controller design complex and computationally burdensome. Therefore, the model order reduction (MOR) is made based on three factors that may have a negligible influence on the EWB system: the motor inductance, lead screw axial damping, and wedge mass. Six reduced order model (ROM) types were proposed when one, two, or all factors were ignored. The ROM accuracy was analysed using the frequency and time domain. The percentage of root means square error (RMSE) response value between the EWB benchmark model, and the predicted response based on the ROM was found to be less than 2%, with ROM size reduced from 5 to 2 orders. It guarantees that the new ROM series will be useful for simpler EWB controller design. The proposed ROM simplifies the original model drastically while retaining accuracy at an adequate level. Even though the simplest EWB model is a 2nd  order linear system, the best ROM vary depending on EWB design parameters.
The development and usability test of an automated fish counting system based on CNN and contrast limited histogram equalization
Authors: Leong Jing Mei , Ahmad Hijazi Mohd Hanafi , Saudi Azali , Kim On Chin , Fui Fui Ching , Haviluddin Haviluddin
Published: February 2024

The aquaculture industry has rapidly grown over the year. One pertinent aspect is the ability of the aquaculture farm management to accurately count the fish populations to provide effective feeding and the control of breeding density. The current practice of counting the fish manually increased the hatchery workers workload and led to inefficiency. The presented work proposed an intelligent, web-based fish counting system to assist hatchery workers in counting fish from images. The methodology consists of two phases. First, an intelligent fish counting engine is developed. The captured image was first enhanced using the contrast limited adaptive histogram equalization. A deep learning architecture in the form of you only look once (YOLO)v5 is used to generate a model to identify and count fish on the image. Second, a web-based application is developed to implement the developed fish counting engine. When applied to the test data, the developed engine recorded a precision of 98.7% and a recall of 65.5%. The system is also evaluated by hatchery workers in the University Malaysia Sabah fish hatchery. The results of the usability and functionality evaluations indicate that the system is acceptable, with some future work suggested based on the feedback received.
Comparative analysis and validation of advanced control modules for standalone renewable micro grid with droop controller
Authors: Swathi Savitri , Kumar Bhaskaruni Suresh , Upendar Jalla
Published: February 2024

A micro grid system with renewable source operation control is a complex part as each source operates at different parameters. This renewable micro grid with multiple sources like solar plants, wind farm, fuel cell, battery backup has to be operated in both grid connected and standalone condition. During grid connection the micro grid, inverter has to inject power to the grid and compensate load in synchronization to the grid voltages. And during standalone condition the inverter is controlled with droop control module which stabilizes the voltage and frequency of the system even during grid disconnection. The droop control module is further updated with new advanced controllers like fuzzy inference system (FIS) and adaptive neuro-fuzzy inference system (ANFIS) replacing the traditional proportional integral derivative (PID) and proportional integral (PI) controllers improving the response rate and for achieving better stabilization. This paper has comparative analysis of the micro grid system with different droop controllers under various operating conditions. Parameters like voltage magnitude (Vmag), frequency (F), load and inverter powers (Pload and Pinv) of the test system are compared with different controllers. A numeric comparison table is given to determine the optimal controller for the inverter operation. The analysis is carried out in MATLAB/Simulink software with graphical and parametric validations.
Squirrel search method for deep learning-based anomaly identification in videos
Authors: Malphedwar Laxmikant , Rajesh Kumar Thevasigamani
Published: February 2024

The monitoring of human behavior and traffic surveillance in various locations has become increasingly important in recent years. However, identifying abnormal activity in real-world settings is a challenging task due to the many different types of worrisome and abnormal actions, including theft, violence, and accidents. To address this issue, this paper proposes a new framework for deep learning-based anomaly identification in videos using the squirrel search algorithm and bidirectional long short-term memory (BiLSTM). The proposed method combines the squirrel search algorithm, an optimization technique inspired by nature, with BiLSTM for anomaly recognition. The framework uses the knowledge gained from a sequence of frames to categorize the video as either typical or abnormal. The proposed method was exhaustively tested in several benchmark datasets for anomaly detection to confirm its functionality in challenging surveillance circumstances. The results show that the proposed framework outperforms existing methods in terms of area under curve (AUC) values, with a test set AUC score of 93.1%. The paper also discusses the importance of feature selection and the benefits of using BiLSTM over traditional unidirectional long short-term memory (LSTM) models for anomaly detection in videos. Overall, the proposed framework provides a highly precise computerization of the system, making it an effective tool for identifying abnormal human behavior in surveillance footage.
Portable smart attendance system on Jetson Nano
Authors: Yose Edward , Victor Victor , Surantha Nico
Published: February 2024

The masked face recognition-based attendance management system is an important biometric-based attendance tracking solution, especially in light of the COVID-19 pandemic. Despite the use of various methods and techniques for face detection and recognition, there currently needs to be a system that can accurately recognize individuals while they are wearing a mask. This system has been designed to overcome the challenges of widespread mask use, impacting the effectiveness of traditional face recognition-based attendance systems. The proposed system uses an innovative method that recognizes individuals even while wearing a mask without the need for removal. With its high compatibility and real-time operation, it can be easily integrated into schools and workplaces through an embedded system like the Jetson Nano or conventional computers executing attendance applications. This innovative approach and its compatibility make it a desirable solution for organizations looking to improve their attendance-tracking process. The Experimental results indicates using maximum resources possible the execution time needed on Jetson Nano is 15 to 22 seconds and 14 to 18 seconds respectively and the average frame capture if there are at least one face detected on Jetson Nano is 3-4 frames.
Mobile application: awareness of the population on the environmental impact
Authors: Andrade-Arenas Laberiano , Giraldo-Retuerto Margarita , Molina-Velarde Pedro , Yactayo-Arias Cesar
Published: February 2024

Nowadays, pollution keeps increasing due to social, political, economic, cultural, and environmental factors. Environmental awareness is close to zero because people prioritize personal activities. In that sense, the objective of this investigation is to raise environmental awareness in the population regarding the impact of pollution and support this through a mobile application (APP) that helps reduce pollution. The methodology used was the cascade, and through its phases, it was developed the prototype design of the mobile APP. The results obtained from this hybrid research were through a survey using ATLAS.ti 22; it concluded that environmental awareness begins at home and is taught by the parents, also it should be promoted from elementary school to high school and even in college. Moreover, in a survey, the users stated by 89% that the use of this mobile APP can help reduce the environmental impact. Also, in the validation through expert judgment, all the attributes were accepted with an average of 81%, that of functionality was the lowest, and the highest was that of consistency and integration with 83%. Finally, environmental education should be a priority policy in any country, as this will benefit its population.
Firefly algorithm tuning of PID position control of DC motor using parameter estimator toolbox
Authors: Jallad Jafar , Badran Ola
Published: February 2024

This paper aims to design an accurate angular position control for DC motors using a proportional integral derivative (PID) controller. The estimated DC motor parameters have been calculated using the parameter estimator toolbox in MATLAB, Arduino Mega 2560 and speed sensor to build an accurate model in MATLAB. The optimized PID coefficients are found for the DC motor model using the firefly algorithm (FA), which aims to make the actual angle match the desired value without overshooting and oscillations. The PIC16F877A microcontroller was used to implement the code based on optimized PID coefficients found in MATLAB/Simulink to generate the suitable pulse width modulation (PWM) output. In this work, step input was tested to analyze the characteristics of the system response in terms of rise time, settling time and overshoot. It was found that the controller output response curve which is produced from FA-based-PID reached the desired position without overshoot and any oscillations. The findings established that closed-loop control of any system using a system identification toolbox and optimized PID technique can be applied in real applications using low-cost controllers and sensors such as PIC16F877A microcontroller and analog rotary position sensor, respectively.
Enhancing the medical diagnosis of COVID-19 with learning based decision support systems
Authors: Berrahal Mohammed , Boukabous Mohammed , Yandouzi Mimoun , Grari Mounir , Idrissi Idriss
Published: February 2024

Since late December 2019, the COVID-19 pandemic has had substantial impact and long-lasting impact on numerous lives. The surge in patients has overwhelmed hospitals and exhausted essential resources such as masks and gloves. However, in response to this crisis, we have developed a robust solution that can ease the burden on emergency services and manage the influx of patients. Our proposed framework comprises deep learning and machine learning models that can predict and manage patient demand with high accuracy. The first model, is specifically designed to classify computed tomography (CT) scan images for COVID or non-COVID cases. We trained multiple convolutional neural network (CNN) models on a large dataset of CT scan images and evaluated their performance on a separate test set. Our evaluation showed that the ResNet50 model was the most effective, achieving an accuracy of 93.28%. The second model uses patient measurements dataset to predict the likelihood of intensive care unit (ICU) admission for COVID-19 patients. We experimented with the XGBoost machine learning algorithm and found that the accuracy score achieved 88.40%.
Reliability analysis in distribution system by deep belief neural network
Authors: Ramalingappa Likhitha , Ekanthaiah Prathibha , Ali MD Irfan , Manjunatha Aswathnarayana
Published: February 2024

Rapid increase in the usage of intermittent renewable energy, ongoing changes in electrical power system structure and operational needs posing growing problems while ensuring adequate service reliability and retaining the quality of power. Power system reliability is a pertinent factor to consider while planning, designing, and operating distribution systems. utilities are obligated to offer their customers uninterrupted electrical service at the least cost while maintaining a satisfactory level of service quality. The important metrics for gauging the effect of distributed renewable energy on distribution networks is reliability analysis. Reliability analysis in distribution systems involves evaluating the performance and robustness of electrical distribution networks. An artificial intelligence approach is implemented in this paper to improve reliability analysis with dispersed generations in distribution network. Deep belief neural networks (DBNNs) are a type of artificial neural network that can be used for various tasks, including analyzing complex data such as those found in power distribution systems. This paper integrated a DBNN using a particle swarm optimization (PSO) technique. The proposed model performance is assessed using mean square error, mean absolute error, root mean square error, and R squared error. The findings reveal that reliability analysis with this novel technique is more accurate.
A comprehensive review on different types of fuel cell and its applications
Authors: Ramasamy Palanisamy , Muruganantham Balakrishnan , Rajasekaran Stanislaus , Durai Babu Babu , Ramkumar Ravindran , Aparna Marthanda Ayyalasomayajula Venugopala , Mohan Sadees
Published: February 2024

This review article provides an overview of various types of fuel cells that are currently being researched and developed. Fuel cells are electrochemical devices that convert chemical energy directly into electrical energy, making them a promising technology for clean and efficient energy production. The review covers the principles of operation and key characteristics of proton exchange membrane fuel cells (PEMFCs), solid oxide fuel cells (SOFCs), alkaline fuel cells (AFCs), direct methanol fuel cells (DMFCs), and microbial fuel cells (MFCs). The article also discusses the advantages and limitations of each type of fuel cell, as well as the current research and development efforts aimed at improving their performance and reducing their costs. Overall, this review provides a comprehensive understanding of the various types of fuel cells and their potential applications in the field of energy production.
Real-time monitoring tool for heart rate and oxygen saturation in young adults
Authors: Fatimah Abdul Razak Siti , Jia Wee Yap , Yogarayan Sumendra , Noor Masidayu Sayed Ismail Sharifah , Fikri Azli Abdullah Mohd
Published: February 2024

Health monitoring is crucial to maintain optimal well-being, especially for young adults. Wearable sensors have become popular for collecting healthcare data, but there are concerns regarding their reliability and safety, particularly with wireless sensors that use radio-frequency (RF) based devices. Researchers have proposed real-time monitoring systems for measuring heart rate beats per minute (BPM) and blood oxygen saturation (SpO2) saturation levels, but more studies are needed to determine the accuracy and user acceptance of these tools among young adults. To address these concerns, this study proposes a real-time monitoring tool that incorporates MAX 30100 sensors to collect heart rate BPM and SpO2 data. The collected data is then connected to a visualization platform, i.e., InfluxDB and Grafana, to provide valuable insights of the body’s physiological state. By testing the feasibility and usability of the tool, we found motivating differences in resting heart rates and changes in heart rate after activity between male and female participants. By developing this real-time monitoring tool and investigating gender-specific differences in heart rate and activity-induced changes, our study contributes to the advancement of health monitoring technologies for young adults, ultimately promoting personalized healthcare and well-being.
Simulation of autonomous navigation of turtlebot robot system based on robot operating system
Authors: Ghazal Mohammed Talal , Al-Ghadhanfari Murtadha , Waisi Najwan Zuhair
Published: February 2024

Complex system science has recently shifted its focus to include modeling, simulation, and behavior control. An effective simulation software built on robot operating system (ROS) is used in robotics development to facilitate the smooth transition between the simulation environment and the hardware testing of control behavior. In this paper, we demonstrate how the simultaneous localization and mapping (SLAM) algorithm can be used to allow a robot to navigate autonomously. The Gazebo is used to simulate the robot, and Rviz is used to visualize the simulated data. The G-mapping package is used to create maps using collected data from a variety of sensors, including laser and odometry. To test and implement autonomous navigation, a Turtlebot was used in a Gazebo-generated simulated environment. In our opinion, additional study on ROS using these important tools might lead to a greater adoption of robotics tests performed, further evaluation automation, and efficient robotic systems.
Reliability evaluation of non-isolated high gain interleaved DC-DC converter
Authors: Ramamurthi Subbulakshmy , Ramasamy Palanisamy
Published: February 2024

A high gain DC-DC converter is the crucial part in renewable energy systems (RES) and in electric vehicular systems. The reliability of those high-gain converters needs to be assessed for the long-term operation of renewable energy systems. This article presents the reliability analysis of non-isolated high gain interleaved DC-DC converter. The analysis primarily relies on calculating the mean time between failures (MTBF). Based on military handbook (MIL-HDBK-217) criteria, the reliability calculation is performed. Stress factors and predicted failure rate for each component of presented converter is evaluated and tabulated. Reliability evaluation is performed for 1.5 kW hardware prototype. Based on reliability evaluation results, a reliable converter with better operating life time has been introduced.
Harmonics mitigation technique for asymmetrical multilevel inverter fed by photovoltaic sources
Authors: Ali Ali Riyadh , Antar Rakan Khalil , Abdulrazzaq Abdulghafoor Abdul Ghani
Published: February 2024

A multilevel inverter is an electrical device that converts a DC voltage into a higher AC voltage by generating a stepped waveform with several voltage levels. Unlike traditional inverters that produce a square wave or a pulse-width modulated (PWM) waveform with only two voltage levels, multilevel inverters can generate waveforms with three or more levels, resulting in reduced harmonic distortion, improved efficiency, and decreased electromagnetic interference. The design and control of multilevel inverters are active research areas that aim to enhance their performance, reliability, and scalability. In this research, a 31-level asymmetric cascaded multilevel inverter is suggested. The proposed multilevel inverter (MLI) system employs four photovoltaic cells as dc sources with structure of (1:2:4:8) Vdc. The system is modeled by MATLAB/Simulink and total harmonic distortion (THD) values of the output voltage and current are 1.106% for resistive load, and 1.35% and 0.403% for inductive load. These outcomes demonstrate the recommended circuit's efficacy and demonstrate its suitability for medium- and high-power applications.
Effective privacy preserving in cloud computing using position aware Merkle tree model
Authors: Gangadharaiah Shruthi , Shrinivasacharya Purohit
Published: February 2024

In this research manuscript, a new protocol is proposed for predicting the available space in the cloud and verifying the security of stored data. The protocol is utilized for learning the available data, and based on this learning, the available storage space is identified, after which the cloud service providers allow for data storage. The Integrity verification separates the private and the public data, which avoids privacy issues. The integration of the private data is done with the help of cloud service providers with respect to the third-party auditing (TPA). Earlier, public key cryptography and bilinear map technologies have been combined by the researchers, but the computation time and costs were high. To secure the integrity of the data storage, the client should execute several computations. Therefore, this research suggests a reliable and effective method called position-aware Merkle tree (PMT), which is implemented for ensuring data integrity. The proposed system uses a PMT that enables the TPA to perform multiple auditing tasks with high efficiency, less computational cost and computation time. Simulation results clearly shows that the developed PMT method consumed 0.00459 milliseconds of computation time, which is limited when compared to the existing models.
Metode Karakterisasi Karbon Nanomaterial
Authors: Setianingsih Tutik
Published: February 2024

Buku ini membahas tentang berbagai macam metode karakterisasi karbon nanomaterial (CNM) secara instrumental yang penting dalam menggambarkan karakter CNM secara fisik maupun kimiawi. Instrumen yang digunakan untuk karakterisasi CNM dalam buku ini meliputi difraktometer sinar-X, Mikroskop Elektron Pemindai (SEM), Mikroskop Elektron Transmisi (TEM), Surface Area Analyzer (SAA), TDS-meter, spektrometer FTIR, spektrofotometer UV-Vis, EDX, dan XRF. Pemilihan jenis instrumen dalam buku ini didasarkan pada pengalaman penulis dalam karakterisasi karbon mesopori pada penelitian untuk disertasi S3, karakterisasi biochar pada proyek penelitian PUPT 2015-2017, karakterisasi karbon aktif pada penelitian DPP SPP 2017, CNM pada HPU 2019, CNS pada penelitian HDLK 2020, DPP-SPP 2021, litdas 1 internal UB 2023 dan HDLK 2023, pengalaman memberi kuliah yang di dalamnya melibatkan karakterissai material dengan berbagai instrumen tsb, antara lain Sintesis Anorganik (S1), Sintesis Anorganik lanjut (S2), Mineralogi (S1), serta Prinsip dan Pemisahan Kimia (S1).
Pengantar Algoritma dan Pemrograman dengan Python
Authors: Anam Syaiful , Widhiatmoko Fery , Yanti Indah , Fitriah Zuraidah , Sa’adah Umu , Guci Abdi Negara
Published: February 2024

Buku ini terdiri dari 8 bab yang setiap babnya dilengkapi dengan latihan soal untuk menguji pemahaman materi yang sudah dijelaskan. BAB 1 Pengantar Ilmu Komputasi dan Penerapannya Bab ini membahas konsep dasar ilmu komputasi; sejarah komputasi pada awal peradapan; dan perkembangan dan penerapan ilmu komputasi pada kehidupan sehari-hari. BAB 2 Pengantar Pemrograman dan Algoritma Bab ini menjelaskan konsep dasar pemrograman dan algoritma yang meliputi: program dan pemrograman; algoritma, flowchart dan pseudocode; struktur sekuensial dan kondisional; struktur perulangan; dan kombinasi struktur perulangan dan kondisional. BAB 3 Pengantar Bahasa Pemrograman Python Bab ini menjelaskan dasar-dasar Bahasa Pemrograman Python yang terdiri beberapa sub bab yaitu: pengantar Bahasa Pemrograman Python; instalasi Bahasa Pemrograman Python; dan elemen dasar Bahasa Pemrograman Python. Bagian ini juga dilengkapi contoh-contoh soal untuk memperjelas teori yang diberikan. BAB 4 Instruksi Kondisional Bab ini membahas dasar instruksi kondisional pada Bahasa Pemrograman Python. Bab ini terbagi menjadi beberapa sub bab yaitu Pernyataan If-Else; Pernyataan If Bertangga; dan Pernyataan If Bersarang. Bab ini juga dilengkapi contoh-contoh soal yang diberikan terkait masalah Matematika sederhana dan masalah sehari-hari. BAB 5 Instruksi Perulangan Bab ini menjelaskan intruksi perulangan yang ada pada Bahasa Pemrograman Python antar lain: perulangan for; perulangan while; perulangan bersarang; dan pernyataan break, continue, pass, dan else. Bab ini dilengkapi contoh penyelesaian masalah matematika sederhana. BAB 6 Fungsi Bab ini menjelaskan bagaimana mendefinisikan fungsi dan memanggil fungsi pada Bahasa Pemrograman Python, scope variabel, fungsi rekursif dan fungsi lambda. Contoh soal juga diberikan untuk meningkatkan pemahaman materi fungsi. . BAB 7 List, Tuple, Dictionary dan Numpy Array Bab ini akan menjelaskan struktur data List, Tuple, Dictionary, dan Numpy Array. Bab ini dilengkapi dengan contoh-contoh soal pada bidang aljabar vektor, aljabar matriks, teori himpunan, statistika dan lain-lain. BAB 8 Module dan Package Bab ini menjelaskan bagaimana membangun program dengan menggunakan konsep module dan Package. Akhir bab akan membahas bagaimana menggunakan Predefine Package. Bab ini juga dilengkapi dengan contoh soal dalam membuat module dan package dan cara memanggilnya.
Inclusive Instructions: Teori dan Praktik di Pendidikan Tinggi
Authors: Lintangsari Alies Poetri , Kusumawardani Irene Nany , Emaliana Ive , Mahalli M , Koeswoyo Rachmawati Ayu , Sujannah Wahyu Diny , Ekatina Meitia
Published: February 2024

Inisiatif Pendidikan inklusi di Perguruan Tinggi telah meningkat sepanjang tahunnya. Namun, tantangan dalam praktik pengajaran masih ditemui oleh pengajar. Buku Inclusive Instructions: Teori dan Praktik di Perguruan Tinggi ini ditulis berdasarkan pengalaman tim penulis dalam menerapkan strategi pembelajaran inklusif di Perguruan Tinggi. Buku ini terdiri dari 5 bab yaitu Bab 1. Pengantar Pendidikan Inklusi, Bab 2. Keragaman Peserta Didik, Bab 3. Aksesibilitas, Bab 4. Inclusive Instructions: Teori dan Praktik and Bab 5. Menyusun Inclusive Instructions. Bab 1 menjelaskan tentang pengantar pendidikan inklusi dilanjutkan dengan Bab 2 yang mengelaborasi keragaman peserta didik mencakup penjelasan karakteristik dari masing-masing keragaman, hambatan yang dialami peserta didik atas keragaman yang ia alami serta tantangan dan solusi dalam pemenuhan kebutuhan belajar peserta didik berdasarkan keragaman yang mereka alami. Praktik pembelajaran inklusi merupakan praktik yang kerap kali melibatkan integrasi teknologi sehingga prinsip aksesibilitas perlu disampaikan untuk memberikan informasi kepada pembaca bahwa aksesibilitas merupakan prinsip sentral dalam implementasi inclusive instructions. Bab 3 secara khusus menyampaikan tentang prinsip aksesibilitas dan akomodasi dalam praktik pembelajaran inklusi. Sebelum memberikan panduan praktis tentang bagaimana merencanakan dan menerapkan inclusive instructions, Bab 4 akan memberikan penjelasan tentang berbagai strategi dalam menerapkan inclusive instructions. Setelah pembaca mendapatkan ilustrasi teoritis tentang pembelajaran inklusi, Bab 5 menyajikan panduan praktis dan praktik baik bagaimana merencanakan dan menerapkan inclusive instructions. Di setiap bab dilengkapi dengan aktivitas “jeda sejenak” yang mengajak pembaca berkontemplasi tentang pengalaman pendidikan dan mengasosiasikan dengan konsep pendidikan inklusi. Tim penulis berharap bahwa buku ini akan memberikan manfaat bagi para pembaca dan lebih lanjut dapat memberikan kesempatan belajar yang nyaman, aman, adil, dan setara bagi semua peserta didik.
Toksikologi Narkotika, Psikotropika, dan Zat Adiktif
Authors: Sulistomo Hikmawan Wahyu , Holipah H , Nugrahenny Dian , Yudhantara Dearisa Surya , Abdullah Taufiq , Nagara Aurick Yudha , Hanif H , Rahmad R , Rakhmatiar Rodhiyan , Fadli M Luqman , Arifin Samsul , Prayata Sahasika , Kastanja Gloria Claudia , Fauzi Izzulhaq Nur , Jiwa Smaraduta Kusuma , Syarifuddin Mohammad Iqbal
Published: February 2024

Penyalahgunaan narkotika, psikotropika, dan zat adiktif (NAPZA) semakin meningkat dan tahun ke tahun. Hal ini diperparah dengan penemuan dan penggunaan NAPZA jenis baru yang peredarannya meluas hingga pelosok negeri. Undang-Undang No 35 tahun 2009 tentang Narkotika dan Peraturan Menteri Kesehatan No. 9 Tahun 2022 Tentang Perubahan Penggolongan Narkotika menjadi salah satu bukti bahwa Pemerintah melalui regulasinya berusaha melindungi rakyatnya dari ancaman penyalahgunaan NAPZA. Usaha lain tentunya berasal dari masyarakat yang harus mampu mendeteksi dan melaporkan jika mengetahui adanya penyalahgunaan NAPZA di lingkungan mereka. Buku ini ditulis oleh pakar kesehatan berbagal bidang spesialisasi untuk mengajarkan masyarakat untuk mengenali gejala dan tanda penyalahguna NAPZA utamanya pada zat yang sering dan banyak disalahgunakan di Indonesia seperti ganja, kokain, amfetamin/ ekstasi/ sabu, morfin, dan obat penenang. Penggiat keilmuan kesehatan dapat pula mempelajari sejarah awal mula bagaimana zat tersebut disalahgunakan serta mekanisme kerja NAPZA baik secara molekuler dan kimiawi sehingga dapat menimbulkan kecanduan. Pengenalan faktor penyebab penyalahgunaan NAPZA, identifikasi awal pemakai NAPZA, dan pelaporan kepada pihak berwajib juga dapat dipelajan dalam pembahasan buku ini. Pada akhirnya kita harus memngkul para korban pemakai NAPZA ini untuk direhabilitasi dan didampingi serta pengenaan sanksi sesuai hukum yang berlaku bagi pengedar NAPZA untuk kemaslahatan bangsa.