bluuebunny/crossref_metadata_2025 huggingface dataset:
Oxidative Damages of Two Neonicotinoid Pesticides to Arthrospira platensis (Gomont)
Authors: TUNCA Hatice , KÖÇKAR Feray , DOĞRU Ali , GÜZEL Uğur , DİNÇ Tarık , ONGUN SEVİNDİK Tuğba
Published: April 2023

In this study, chlorophyll-a amount, OD 560 and antioxidant parameters (total SOD, APX, GR, MDA, H2O2 and Proline) were determined in order to understand the effects of Thiacloprid and Imidacloprid on Arthrospira platensis Gomont. Both Imidacloprid and Thiacloprid applications showed significant reductions in growth rate and chlorophyll-a content of A. platensis cultures with dose-dependent manner when the days and concentrations were compared each other. SOD activity significantly decreased in the Imidacloprid application while Thiacloprid caused a significant increase only at 75 µg mL-1 concentration. APX activity significantly increased in the Imidacloprid and Thiacloprid applications at 50 µg mL-1 and 35 µg mL-1 concentrations, respectively. Imidacloprid treatment increased GR activity at 20 and 30 µg mL-1 concentrations while GR activity increased at 15, 25 and 35 µg mL-1 Thiacloprid concentrations. The MDA content of A. platensis cultures did not change with Imidacloprid or Thiacloprid applications. The H2O2 content did not change at all different Imidacloprid concentrations. However, the H2O2 content decreased at 15 µg mL-1 and increased at 45 and 75 µg mL-1 Thiacloprid concentrations. Free proline content increased in the Imidacloprid and Thiacloprid applications at 100 µg mL-1 and 75 µg mL-1 concentrations, respectively. These neonicotinoid pesticides cause oxidative stress in A. platensis cells.
Cost of a Renewable Micro Grid in Moorea v1
Authors: not provided clairewiegand
Published: April 2023

Moorea is an island in French Polynesia that depends on imported fossil fuels to meet the community's energy demands, which leads to high costs and high carbon dioxide emissions. The implementation of a renewable energy microgrid may provide a solution to reducing Moorea’s carbon footprint and energy bills. This protocol aims to assess the technical and economic feasibility of implementing a microgrid in Moorea and estimate the corresponding costs.
: Revitalizing Deaf Education Systems via Anarchism
Authors: Skyer Michael , Scott Jessica A. , O'Brien Dai
Published: April 2023

Deaf education is an incoherent macrosystem whose sub‐systems—e.g., biomedical vs. sociocultural institutions—contradict. Unreconciled tensions cause stagnation, not regeneration, and harmful dissensus in deaf educational sub‐systems. To revitalize deaf education, address these contradictions, and eliminate incoherence, we posit that a deafled systemic transformation of deaf education is necessary; furthermore, we argue it may best be realized through theories and actions constitutive of anarchism. To this end, we synthesize four thematic loci where anarchism overtly aligns with constructs immanent in deaf communities. First, collectivism is necessary for survival in anarchist and deaf communities toward shared goals including equity in education, social labor, and politics. Second, mutual aid is integral—like anarchists who work arm‐in‐arm, deaf individuals and groups exhibit uncanny solidarity across political, cultural, technological, linguistic, and geographical boundaries. Third, direct action tactics overlap in both groups: When facing internal or external threats, both communities effectively rally local mechanisms to affect change. Finally, both groups exhibit a stubborn, existential refusal to be subdued or ruled by outsiders. Reframing systemic dilemmas in deaf education via anarchism is a novel, beneficial praxis that’s only been tangentially explored. Centering anarchism in deaf education also generates succor for ongoing struggles about sign language in deaf communities. Toward the horizon of radical equality, our staunchly anarchist analysis of deaf education argues that to guide deaf‐positive system change neoliberalism is inert and neo‐fascism anathema.
Moulded RSA and DES (MRDES) Algorithm for Data Security
Authors: Jenifa Sabeena S. , Antelin Vijila S.
Published: April 2023

In the recent days transmission of large amount of data through online is very prominent. Security is necessary while transmitting large amount of data. Since the data may belost or hacked at some point of transmission. Normally there are three important factors interms of security. They are key generation, encryption and decryption. There are two types of crypto system namely symmetric cryptosystem and asymmetric cryptosystem. There are many publicly available cryptosystems. It may lead the intruders to view the original message sent by the sender using all the possible keys. In order to provide secure transmission of data, a novel encryption algorithm is proposed by analyzing all the existing algorithms. The existing Rivest–Shamir–Adleman (RSA) and Data encryption standard (DES) algorithm are moulded together rto produce the proposed MRDES encryption algorithm. The performance of the proposed Moulded RSA and DES is higher than the existing encryption algorithms and provides higher data  security.
A Framework for Providing Augmented Reality as a Service Provided by Cloud Computing for E-Learning
Authors: Al-jammaz Rashid Abdullah , Rawash Usama Abo , Kashef Nermeen Mahmoud , Ibrahim Elkholy Mohamed
Published: April 2023

The main objective of the proposed study is to develop an e-learning system using augmented reality technology one of the main problems faces using AR in education is the huge computational power needed to transfer 2D animation to enrich learning facilities. Such problem increases when using smart mobile devices that suffer from hardware limitation. A promising framework is used to utilize cloud services to support augmented reality applications on the cloud. Such method significantly reduces consumption of memory and processing units when dealing with large size videos or images. Hence the augmented reality processing is speeded up to meet the requirements of E-learning systems.

The proposed work was conducted on 100 students from different academic levels in the first semester of the year 2022. Three experiments were conducted for different fields of education including two-dimensional images using Unity Program (3D Software) to draw 3D objects and Vufoira software development kit. The experimental results showed promising results as the application has the flexibility to work on different platforms. Moreover the consumed memory to run the application is reduced significantly. The results also showed high performance for the application when drawing complex 3D images and when dealing with different animations.

The study supported with a detailed questioner that proofs the importance of AR in the field of E-learning.
Improved DASH Architecture for Quality Cloud Video Streaming in Automated Systems
Authors: Vijayalakshmi S. , Vishnupriya S. , Sarala B. , Karthik Ch. Bhuvan , Dhanalakshmi R. , Hephzipah J. Jasmine , Pavaiyarkarasi R.
Published: April 2023

In modern times, multimedia streaming systems that transmit video across a channel primarily use HTTP services as a delivery component. Encoding the video for all quality levels is avoided thanks to fuzzy based encoders' ability to react to network changes. Additionally, the system frequently uses packet priority assignment utilising a linear error model to enhance the dynamic nature of DASH without buffering. Based on a fuzzy encoder, the decision of video quality is made in consideration of the bandwidth available. This is a component of the MPEG DASH encoder. The Fuzzy DASH system seeks to increase the scalability of online video streaming, making it suitable for live video broadcasts through mobile and other devices.
A Novel Approach for Integrated Shortest Path Finding Algorithm (ISPSA) Using Mesh Topologies and Networks-on-Chip (NOC)
Authors: Karthick S. , Shankar P. Vijay , Jayakumar T. , Suba G. Merlin , Quadir Munleef , Paul Roy A. Thomas
Published: April 2023

A novel data dispatching or communication technique based on circulating networks of any network IP is suggested for multi data transmission in multiprocessor systems using Networks-On-Chip (NoC). In wireless communication network management have some negatives have heavy data losses and traffic of data sending data while packet scheduling and low performance in the varied network due to workloads. To overcome the drawbacks, in this method proposed system is Integrated Shortest Path Search Algorithm (ISPSA) using mesh topologies. The message is sent to IP (Internet Protocol) in the network until the specified bus accepts it. Integrated Shortest Path Search Algorithm for communication between two nodes is possible at any one moment. On-chip wireless communications operating at specific frequencies are the most capable option for overcoming metal interconnects multi-hop delay and excessive power consumption in Network-on-Chip (NoC) devices. Each node can be indicated by a pair of coordinates (level, position), where the level is the tree's vertical level and the view point is its horizontal arrangement in the sequence of left to right. The output gateway node's n nodes are linked to two nodes in the following level, with all resource nodes located at the bottommost vertical level and the constraint of this topology is its narrow bisection area. The software Xilinx 14.5 tool by using that overall performance analysis of mesh topology, each method are reduced data losses with better accuracy although the productivity of the delay is decreased by 21 % was evaluated and calculated..
Machine Learning-Based IOT Air Quality and Pollution Detection
Authors: Krishna K. Siva , Satish Thatavarti , Mishra Jyotirmaya
Published: April 2023

In India, gas leakage from the different factories harmful to human surveying in the last fifty years is very low. However, there is a lack of prior detection of the chemical gases detection system in the situation raised. So, In this regard, there is a gap identification of chemical gases intensity detection needed. In this work, the main objective is to identify chemical gases intensity and maintain the stream data in the database from different locations. To fill this gap, that is identifying the high-intensity chemical gases from the chemical gas disaster areas. The first step needs to identify the different chemical gases and natural gas compositions. In this regard in this work for design internet-based gases in the air system. So, the sensors  MQ2(Ethanol i-Butane Methane Alcohol Gas Sensor Sensor), MQ3(Sensitivity Alcohol Detector), MQ4 (Methane and Natural Gas (CNG)), MQ-5 ( LPG GAS SENSOR), MQ-7 (CO Gas Sensor Module Test Carbon Monoxide Detector), MQ-8 (hydrogen Gas Sensor), MQ-9 (carbon monoxide), MQ-135 Sensor(Air Quality Sensor Hazardous Gas Detector) and DHT11 Digital Temperature Humidity Sensors.  These sensors are interfacing with the micro-control STM 32 board. It is also called one Pollution identification terminal by using it to pull the sensor stream data from location to centralized data. This stream data transportation is a service to pull the data. For this Data pulling, design an algorithm store into a cloud database. In this research work, design the electronic terminal with a wifi circuit using IoT technologies.Moreover, getting these attributes as data. Data need to apply the preprocessing techniques and extracted feature techniques also. This paper discusses mainly designing the terminal for pollution attributes , cleaning the data, and applying the Machine Learning based Feature extraction techniques.
A VOS analysis of LSTM Learners Classification for Recommendation System
Authors: Pasha M. Jahir , Rao Channapragada Rama Seshagiri , Geetha A. , Fernandez Terrance Frederick , Bhargavi Y. Krishna
Published: April 2023

In response to the growing popularity of social web apps, much research has gone into analyzing and developing an AI-based responsive suggestion system. Machine learning and neural networks come in many forms that help online students choose the best texts for their studies. However, when training recommendation models to deal with massive amounts of data, traditional machine learning approaches require additional training models. As a result, they are deemed inappropriate for the personalized recommender generation of learning systems. In this paper, we examine LSTM-based strategies in order to make useful recommendations for future research.
Design and Implementation of Deep Learning Method for Disease Identification in Plant Leaf
Authors: Allauddin Mulla Rais , Eknath Pawar Mahendra , S. Banait Satish , N. Ajani Samir , Pravin Borawake Madhuri , Hundekari Sheela
Published: April 2023

In the whole agriculture plays a very important in country’s economic condition specially in Indian agriculture has a crucial role for raising the Indian economic structure and its level. India’s frequent changing climatic situation, various bacterial disease is much normal that drastically decreases the productivity of crop productivity. Most of the researcher is moving towards into this topic to find the early detection technique to identify the disease in small green leaves plants. A single, micro bacterial infectious disease can destroy all the agricultural small green leaves plants get damaged overnight and hence must be prevented and cured as earliest as possible so that agriculture production. In this research work, we had tried to developed a green small green leaves plants bacterial disease early detection system based on the deep learning network system which will detect the disease at very earlier state of symptoms observed. Deep learning technique is has various algorithms to detect the earliest stage of any of the procedural processing of any bacterial infections or disease. This paper consists of investigations and analysis of latest deep learning techniques. Initially we will explore the deep learning architecture, its various source of data and different types of image processing method that can be used for processing the images captured of leaf for data processing. Different DL architectures with various data visualization’s tools has recently developed to determine symptoms and classifications of different type of plant-based disease. We had observed some issue that was un identified in previous research work during our literature survey and their technique to resolve that issue in order to handle the functional auto-detection system for identifying the certain plant disease in the field where massive growth of green small green leaves plants production is mostly done. Recently various enhancement has been done in techniques in CNN (convolution neural network) that generates much accurate images classification of any object. Our research work is based on deep learning network that will observe and identifies the symptoms generated in leaflet of plant and identifies the type of bacterial infection in progress in that with the help of plant classification stated in the plant dataset. Our research work represents the implementation DCGAN and Hybrid Net Model using Deep learning algorithm for early-stage identification of green plant leaves disease in various environmental condition. Our result obtained shows that it has DCGAN accuracy 96.90% when compared withHybrid Net model disease detection methodologies.
Computer-Aided Detection of Skin Cancer Detection from Lesion Images via Deep-Learning Techniques
Authors: Yanamadni Venkata Rao , Seetha J. , Kumar T. Sathish , Kannaiah Sathish Kumar , J Balajee , Brahmaiah Madamanchi
Published: April 2023

More and more genetic and metabolic abnormalities are now known to cause cancer, which is typically fatal. Any particular body part may become infected by cancerous cells, which can be fatal. One of the most prevalent types of cancer is skin cancer, which is spreading worldwide.The primary subtypes of skin cancer are squamous and basal cell carcinomas, as well as melanoma, which is clinically aggressive and accounts for the majority of fatalities. Screening for skin cancer is so crucial.Deep Learning is one of the best options to quickly and precisely diagnose skin cancer (DL).This study used the Convolution Neural Network (CNN) deep learning technique to distinguish between the two primary types of cancers, malignant and benign, using the ISIC2018 dataset. The 3533 skin lesions in this dataset range from benign to malignant, and nonmelanocytic to melanocytic malignancies. The images were initially enhanced and edited using ESRGAN. The preprocessing stage involved resizing, normalising, and augmenting the images. By combining the results of numerous repetitions, the CNN approach might be used to categorise images of skin lesions. Several transfer learning models, such as Resnet50, InceptionV3, and Inception Resnet, were then used for fine-tuning. The uniqueness and contribution of this study are the preprocessing stages using ESRGAN and the testing of various models (including the intended CNN, Resnet50, InceptionV3, and Inception Resnet). Results from the model we developed matched those from the pretrained model exactly. The efficiency of the suggested strategy was proved by simulations using the ISIC 2018 skin lesion dataset. In terms of accuracy, the CNN model performed better than the Resnet50 (83.7%), InceptionV3 (85.8%), and Inception Resnet (84%) models.
Prototype of a Mobile Application for the Detection of Car Accidents on the Roads of Peru
Authors: Huamaní Enrique Lee , Leon-Ayala Ricardo , Alva-Mantari Alicia , Meneses-Claudio Brian
Published: April 2023

According to studies of the World Health Organization at least around 1. 35 million deaths and between 20 to 50 million injuries are caused due to car accidents, due to this fact the present research work seeks to mitigate this figure before the problem of car accidents on the roads of Lima, that is why a mobile application was designed whose objective is to detect and report accidents, when performing this action the driver sends his contact information (ID, phone, email, family number) and location in real time to the nearby authorities such as police stations or hospitals and likewise the message comes as a notification to their families. For its development we used the agile methodology Scrum whose structure was very useful to display the information proposed, also design tools such as Balsamiq and Marvel App were used to create the interface. Finally, it is concluded that car accidents is a problem that negatively affects our society, also with the application it was possible to design an interactive application that will be very useful for drivers if it is ever implemented, on the other hand, this research seeks to encourage researchers to use technological means to improve the quality of life of people and thus continue to advance as a socie.
Privacy Preserving and Time Series Analysis of Medical Dataset using Deep Feature Selection
Authors: Rose J. Dafni , R Vinston Raja , Lakshmi D. , Saranya S. , Mohanaprakash T. A.
Published: April 2023

A significant category of medical data that includes rich temporal and spatial information is time-series medical imaging. Since then, experts in a variety of domains, including clinical picture analysis, have been actively participating in the rapidly emerging subject of profound learning. This paper discusses profound learning processes and their applicability to clinical picture examination and mainly focused common machine learning techniques in the field of computer vision and how deep learning has transformed ML, ML models for deep learning and applications of deep learning to clinical image analysis. In fact, even before the term "deep learning" was coined, a variety of clinical picture investigation concerns, including harm and non-harm grouping, harm type characterisation, harm or organ division, and injury recognition, were addressed using picture input machine learning (PIML). Deep learning is predicted to be the key innovation for clinical picture examination in the upcoming few years. Picture input ML, including profound learning, is an exceptionally powerful, flexible, higher-throughput innovation that can raise the current level of execution in clinical picture examination. "Profound learning" or picture input ML, in clinical picture examination is a quickly developing, promising field. Picture input ML is supposed to turn into a significant field in clinical picture examination in the following couple of many years.
An Intelligent Optimal Secure Framework for Malicious Events Prevention in IOT Cloud Networks
Authors: Babu Majjaru Chandra , K Senthilkumar
Published: April 2023

The intrusion is considered a significant problematic parameter in Cloud networks. Thus, an efficient mechanism is required to avoid intrusion and provide more security to the cloud system. Therefore, the novel Artificial Bee-based Elman Neural Security Framework (ABENSF) is developed in this article. The developed model rescales the raw dataset using the pre-processing function. Moreover, the artificial bee's optimal fitness function is integrated into the feature extraction phase to track and extract the attack features. In addition, the monitoring mechanism in the developed model provides high security to the network by preventing attacks. Thus, the tracking and monitoring functions avoid intrusion by eliminating known and unknown attacks. The presented work was designed and validated with an NSL-KDD dataset in python software. Finally, the performance parameters of the presented work are estimated and verified with the existing techniques in a comparative analysis. The comparative performance shows that the developed model has earned better outcomes than others.
Potable Water Identification with Machine Learning: An Exploration of Water Quality Parameters
Authors: Mohan B R , M Dileep , Bhuria Vijay , Gadde Sai Sudha , M Kumarasamy , N Achyutha Prasad
Published: April 2023

In this research, we aim to determine the water potability using three machine learning classification algorithms: decision tree, gradient boosting and bagging classifier. These algorithms were trained and tested on a dataset of water quality measurements. The outcomes of the experiment showed that the gradient boosting algorithm achieved the highest F1-score of 0.78 among all the algorithms. This indicates that the gradient boosting algorithm was most effective in correctly identifying both the safe and contaminated water samples. The results of this study demonstrate that gradient boosting is a promising approach for determining water potability and can be used as a reliable method for water quality assessment.
Deep Convolutional Neural Network Classifier for Effective Knee Osteoarthritis Classification
Authors: Kumar M Ganesh , Goswami Agam Das
Published: April 2023

Millions of people are affected by the disease Knee Osteoarthritis, and the prevalence of the condition is steadily increasing. Knee osteoarthritis has a significant impact on people's lives by generating increased worry, mental health disorders, and physical problems. Early detection of knee osteoarthritis is critical for decreasing disease consequences, and numerous studies are being conducted to classify knee osteoarthritis. In this study, the deep CNN classifier is used to classify knee osteoarthritis, which effectively extracts the features required for disease classification more efficiently. The preprocessing of the data, which is done in three processes such as Circular Fourier Transform, Multivariate Linear Function, and Histogram Equalization, is particularly important in this research since it aids in obtaining more efficient information about the image. The deep CNN classifier's weights and bias deliver better and desired classification results while spending less time and storage. The proposed deep CNN classifier attained the Accuracy of 94.244%, F1 measure of 94.059%, Precision of 94.059%, Recall of 93.586%.
Potential of Artificial Intelligence in Boosting Employee Retention in the Human Resource Industry
Authors: Paigude Supriya , Pangarkar Smita C. , Hundekari Sheela , Mali Manisha , Wanjale Kirti , Dongre Yashwant
Published: April 2023

Artificial intelligence (AI) has the potential to transform the human resource (HR) industry by automating routine tasks, improving decision-making, and enhancing employee engagement and retention. In this paper, we explore the use of machine learning and deep learning techniques to boost employee retention in the HR industry. We review the current state of the art in AI for HR, including the use of predictive analytics, natural language processing, and chatbots for talent management and employee development. We also discuss the challenges and ethical considerations of using AI in HR, including issues of bias and the need for transparent and explainable algorithms. Finally, we present case studies of successful AI-powered HR initiatives that have demonstrated improvements in employee retention and engagement. Our findings suggest that AI has the potential to significantly enhance employee retention in the HR industry, but its implementation requires careful planning and consideration of potential risks and ethical issues.
Three Step Authentication of Brain Tumour Segmentation Using Hybrid Active Contour Model and Discrete Wavelet Transform
Authors: Ramasamy Jayaraj , Doshi Ruchi , Hiran Kamal Kant
Published: April 2023

An innovative imaging research is expected in the medical field due to the challenges and inaccuracies in diagnosing the life-threatened harmful tumours. Brain tumor diagnosis is one of the most difficult areas of study in diagnostic imaging, with the maximum fine for a small glitch given the patients survival rate. Conventionally, biopsy method is used to identify the tumour tissues from the brain's soft tissues by the medical researchers (or) practitioners and it is unproductive due to: (i) it requires more time, and (ii) it may have errors. This paper presents the three-stage authentication-based hybrid brain tumour segmentation process and it makes the detection more accrual. Primarily, tumour area is segmented from a magnetic resonance image and after that when comparing a differentiated segment of an image to the actual image, an improved active contour model is employed to achieve a good match. In addition, discrete wavelet transform is used for the features extraction which leads to improve the accuracy and robustness in the tumour diagnosis. Finally, RELM classifier is used for precise classification of brain tumours. The most effective section of our method is checking the status of the tumour through finding the tumour region. The results are evaluated through new dataset, and it demonstrates that the suggested approach is more efficient than the alternatives as well as provides 96.25% accuracy.
A Novel Marine Predators Optimization based Deep Neural Network for Quality and Shelf-Life Prediction of Shrimp
Authors: Prema K. , J Visumathi
Published: April 2023

Consumer satisfaction and food safety are prime concerns for seafood retailers and wholesalers. Shrimp and its products are popular all over the world and play a significant part in maintaining a healthy diet by delivering a variety of nutrients and health benefits. Fresh shrimp and shrimp products, on the other hand, are very perishable and vulnerable to the rapid formation of disagreeable scents and tastes, as well as a rapid decay process. Shrimp freshness has previously been determined using a variety of techniques, but earlier techniques lacked adequate precision. To overcome this issue, in this manuscript, a Hybrid Convolutional Neural Network (Hyb-CNN) and Support Vector Machine (SVM) optimized with Marine Predators Algorithm (MPA) for shrimp freshness detection (SFD-Hyb-CNN-SVM-MPA) is proposed for classifying the freshness shrimp and non-freshness shrimp. The real time dataset is given to mean curvature Flow (MCF) filtering method and its pre-processed images are given to the Hybrid Convolutional Neural Network and Support Vector Machine classifier for classifying the freshness shrimp and non-freshness shrimp. Generally, The Hyb-CNN- does not demonstrate the implementation of any optimization techniques for identifying the ideal parameters and assuring correct classification. The proposed Marine Predators Algorithm (MPA) is considered for optimizing the hyper parameter of Hyb-CNN and SVM which is executed in MATLAB and certain performance measures are used to assess the effectiveness of the proposed approach such as precision, recall, f-measure, accuracy, computation time. The proposed method attains lower computational time39.89%, 43.78%, and 52.67%, higher accuracy 21.35%, 18.56%, and 13.56% compared with the existing methods, like shrimp freshness detection using Deep shrimp Net (SFD-D-SHNet), shrimp freshness detection using artificial neural network and k-neighbour network (SFD-ANN-KNN), shrimp freshness detection using convolutional neural network (SVM-FCCD) respectively.
E2BNAR: Energy Efficient Backup Node Assisted Routing for Wireless Sensor Networks
Authors: Bhat Akshatha Hari , H V Balachandra Achar
Published: April 2023

In Wireless Sensor Networks (WSNs), each sensor node can only use so much power before recharging. If energy is depleted too quickly, nodes will fail one by one, bringing down the network as a whole. To this end, a design is needed to reduce the burden on the sensor nodes' power supplies while extending the network's useful life. This paper proposes a new approach, called Energy Efficient Backup Node Assisted Routing, to accomplish this (E2BNAR). Each primary node in the network has a group of backup nodes to ensure the network continues functioning. Assuming that the sensor nodes are capable of energy harvesting, E2BNAR finds the best backup node by analyzing the statistical relationship between energy harvesting and consumption rates. Periodically, residual energy is used to analyze the current energy consumption rate. When evaluating performance, several different indicators are taken into account. These include the Packet Delivery Ratio, Throughput, Average Energy Consumption, and Number of Awakened Sensor Nodes. Through analysis and experimentation in several settings, the proposed method's efficacy has been established.
Critical Analysis on Detection and Mitigation of Security Vulnerabilities in Virtualization Data Centers
Authors: Manikandan J. , Uppalapati Sri Lakshmi
Published: April 2023

There is an increasing demand for IT resources in growing business enterprises. Data center virtualization helps to meet this increasing demand by driving higher server utilization and utilizing un-used CPU cycles without causes much increase in new servers. Reduction in infrastructure complexities, Optimization of cost of IT system management, power and cooling are some of the additional benefits of virtualization. Virtualization also brings various security vulnerabilities. They are prone to attacks like hyperjacking, intrusion, data thefts, denial of service attacks on virtualized servers and web facing applications etc. This works identifies the security challenges in virtualization. A critical analysis on existing state of art works on detection and mitigation of various vulnerabilities is presented. The aim is to identify the open issues and propose prospective solutions in brief for these open issues.
Foster Parents' Trauma Knowledge and Understanding of Emotions as Correlates of Placement Stability
Authors:   , Dilks Katherine Ellane , Krauthamer-Ewing E. Stephanie
Published: April 2023

The primary objective of the current study was to investigate parental trauma knowledge, empathic response style, and emotional intelligence as correlates of placement stability in foster homes. The study was a retrospective survey study. Foster parents completed a series of surveys, including demographic questions, self-report of understanding of emotions/emotional intelligence, empathic response patterns, and trauma knowledge. Foster parents also reported on child demographics, length of stay for each child placed in their care, and children's externalizing behaviors. Higher levels of foster parent emotional intelligence (EI) trended toward a significant relationship to higher rates of child placement stability. In the subset of parents who had ever requested any removals, foster parental empathic perspective taking correlated with higher rates of child placement stability as did parental trauma knowledge. Foster parent EI significantly moderated the relationship between child externalizing behavior and placement stability that placement stability was highest when parental EI was high and child externalizing behavior was low, while all other combinations of parent EI and child externalizing behavior levels showed higher rates of child removal requests. Results contribute to knowledge of parent/family characteristics that may be important correlates of placement stability in foster care and suggest that parental empathic perspective taking, emotional intelligence/understanding of emotions and trauma knowledge may be important protective factors for the placement stability of children in foster care. While parental strengths like emotional intelligence may be protective against placement disruption when children have relatively low levels of externalizing behavior, future studies should attempt to identify parental resources and contextual factors that may be protective when children have higher levels of externalizing behavior, a common risk factor for placement disruption.
Pengembangan E-book Bahasa Mandarin “学汉语” Berbasis Kvisoft Flipbook Maker untuk Siswa Kelas XI SMA Islam
Authors: Utami Tri Endri , Retnantiti Sawitri
Published: April 2023

Abstract: This research and development aimed to produce a product, namely an e-book in Mandarin according to the needs of the 11th-grade students of Islamic Senior High School in Kepanjen Malang. The product was developed using the Kvisoft flipbook maker application. The research and development process used a model from Sugiyono which has been modified into seven stages, namely: 1) potential and problems; 2) data collection; 3) product design; 4) design validation; 5) design revision; 6) product testing; and 7) product revision. The e-book contains the starting page, table of contents (theme of each chapter, learning objectives and apperception), materials, practice questions, and closing pages. The developed e-book is categorized as valid with a score of 88 percent from the material expert and as very valid with a score of 88.8 percent from the field material expert, and categorized as valid with a score of 84.7 percent from the media expert. The e-book is also categorized as good through product trials with a percentage of 80.2 percent. Based on trials, it was revealed that the Mandarin e-book product 学汉语 has matched the learning needs of the 11th-grade students of Islamic High School Kepanjen Malang.
Keywords: e-book; Chinese; Kvisoft flipbook maker
Abstrak: Penelitian dan pengembangan ini bertujuan untuk menghasilkan produk yaitu e-book bahasa Mandarin sesuai kebutuhan bahan ajar kelas XI Semester 2 SMA Islam Kepanjen Malang. E-book bahasa Mandarin dikembangkan menggunakan bantuan aplikasi yaitu Kvisoft flipbook maker. Proses penelitian dan pengembangan menggunakan model dari Sugiyono yang sudah dimodifikasi menjadi tujuh tahapan yaitu: 1) potensi dan masalah; 2) pengumpulan data; 3) desain produk; 4) validasi desain; 5) revisi desain; 6) uji coba produk; dan 7) revisi produk. E-book berisi halaman awal, daftar isi (tema setiap bab, tujuan pembelajaran dan apersepsi), materi, latihan soal, dan halaman penutup. E-book yang dihasilkan dikategorikan valid dengan nilai sebesar 88 persen dari ahli materi, sangat valid dengan nilai sebesar 88,8 persen dari ahli materi lapangan, dan valid dengan nilai sebesar 84,7 persen dari ahli media. E-book 学汉语 uga dikategorikan baik melalui uji coba produk dengan persentase sebesar 80,2 persen. Berdasarkan uji coba di lapangan menunjukkan bahwa produk e-book bahasa Mandarin 学汉语 yang telah dikembangkan sesuai kebutuhan pembelajaran di kelas bahasa Mandarin SMA Islam Kepanjen Malang.
Kata kunci: e-book; bahasa Mandarin; Kvisoft flipbook maker
Perancangan Identitas Visual Usaha Dessert Pufflicious
Authors: Amina Zahra Fithri , Irwanto Rudi , Firmansyah Novian Wahyu
Published: April 2023

Pufflicious merupakan sebuah usaha rumahan perseorangan yang bergerak di bidang industri food and beverage, lebih tepatnya hidangan manis atau dessert. Usaha tersebut merupakan usaha baru yang dirintis pada bulan desember 2021 lalu dan beroperasi di Kota Bogor. Sebagai sebuah usaha, Pufflicious memiliki sebuah visi yaitu untuk mengenalkan dan menjual produk dessert dengan bahan yang berkualitas premium yang lezat dan halal untuk dikonsumsi. Namun, sebagai bisnis pendatang baru, belum banyak upaya yang dilakukan dalam mengenalkan produknya pada masyarakat ter-utama dari segi keunikan identitas visual. Hal tersebut menjadi suatu kekurangan bagi bisnis baru terutama pada era digital seperti sekarang ini. Tantangan yang dihadapi dalam persaingan antar bis-nis adalah bagaimana mendapatkan perhatian online di tengah banyaknya persaingan dengan brand yang serupa. Maka dari itu, Pufflicious membutuhkan perancangan identitas visual agar dapat lebih menonjol di antara kompetitornya. Metode yang digunakan dalam perancangan ini adalah metode kualitatif dan menggunakan model perancangan prosedural milik Alina Wheeler dengan pengum-pulan data yang didapatkan melalui wawancara dan observasi. Hasil data kemudian dianalisis me-nggunakan analisis SWOT. Hasil dari perancangan identitas visual ini meliputi logo, graphic standard manual dan implementasinya pada berbagai media.
Kata kunci: identitas visual; disain Pufflicious; Alina Wheeler.
Visual Identity Planning of Pufflicious Dessert Business
Pufflicious is a home-based business engaged in the food and beverage industry, more specifically in sweet dishes or desserts. This is a new business that was started in December 2021 and operates in the city of Bogor. As a business, Pufflicious has a vision, namely to introduce and sell dessert products with premium quality ingredients that are delicious and halal for consumption. However, as a new-comer in the business industry, not much effort has been made in introducing its products to the public in terms of visual identity. This is a drawback for new businesses, especially in the digital era as it is today. The challenge faced in competition between businesses is how to get online attention amidst lots of competition with similar brands. Therefore, Pufflicious needs to design a visual identity so that it can stand out more among its competitors. The method used in this design is a qualitative method and uses Alina Wheeler's procedural design model by collecting data obtained through interviews and observation. The results of the data were then analyzed using SWOT analysis. The result of this visual identity design include logos, graphic standard manuals and their implementation in various media.
Keywords: visual identity; desain Pufflicious; Alina Wheeler
Pengembangan Materi Pembelajaran Nahwu untuk Kelas 10 SMA AN NUR Malang
Authors: Frima Abdur Rahman , Khasairi Moh.
Published: April 2023

Pembelajaran Nahwu di beberapa lembaga formal kurang mendapatkan perhatian sehingga kele-mahan siswa terhadap tata bahasa menjadi permasalahan paling kompleks dari sekian permasalah-an bahasa Arab. Oleh karena itu, urgensi pembelajaran Nahwu begitu besar. Dengan demikian pe-nelitian ini bertujuan mengembangkan bahan pelajaran berupa materi Nahwu untuk kelas 10 Ida-man SMA AN-NUR dan menguji tingkat kelayakan materi hasil pengembangan dalam pembelajaran Nahwu. Pengembangan bahan pelajaran dilakukan dengan menggunakan model ADDIE (Analysis, Design, Development, Implementation and Evaluation). Uji validitas produk hasil pengem-bangan dilakukan oleh validator ahli materi dan validator ahli media lalu diujicobakan pada sepuluh siswa kelas 10 Idaman yang telah mempelajari materi Nahwu dasar. Pengembangan ini menghasilkan produk berupa buku Nahwu Ibtida’i yang mencakup beberapa materi Nahwu, yaitu: Isim Isyarah, Isim Dhamir, Adawatul Istifham, Adad Tartiby, Mufrad, Mutsanna, Jama’, Dzaraf Zaman dan Makan, serta Sifat Mausuf. Hasil uji validitas menunjukkan bahwa produk hasil pengembangan tersebut layak digunakan: nilai hasil uji kelayakan materi 85 persen, nilai hasil uji kelayakan media 81,3 persen, dan nilai hasil uji keterbacaan oleh siswa 91,25 persen.
Kata kunci: Nahwu; bahan pelajaran; materi pembelajaran
The Development of Nahwu Learning Materials for 10th Grade of Islamic High School AN-NUR Malang
Nahwu learning in some formal institutions receives less attention so that students' weakness in grammar is the most complex problem of the many Arabic language problems, therefore the urgen-cy of Nahwu learning is so great. Thus, this research aims to develop learning materials of Nahwu material for grade 10 of AN-NUR High School Dream and test the feasibility level of material from development in Nahwu learning. The development of learning materials is carried out using the ADDIE (Analysis, Design, Development, Implementation and Evaluation) model. The validity test of the product from the development was carried out by material expert validators and media expert validators and then tested on ten Dream 10th grade students who had studied basic Nahwu ma-terial. Thisidevelopmentiresulted in a product in the form of a Nahwu Ibtida'i book which includes several Nahwu materials namely: Isim Isyarah, Isim Dhamir, Adawatul Istifham, Adad Tartiby, Mufrad, Mutsanna, Jama', Dzaraf Zaman and Makan, and Sifat Mausuf. The results of the validity test show that theiproduct developed is suitable for use: the value of the material feasibility test results is 85 persent, the media feasibility test result score is 81.3 percent, and the student readability test result score is 91.25 percent.
Keywords: Nahwu; study materials; learning materials
Konflik Batin dan Rasa Nasionalisme Tokoh Riani dalam Film 5 cm Karya Rizal Mantovani
Authors: Auliasari Dwita , Khoriroh Nur Laila , Karkono Karkono
Published: April 2023

Film 5 cm yang disutradarai oleh Rizal Mantovani merupakan adaptasi dari novel berjudul sama karya Donny Dhirgantoro. Film 5 cm bergenre drama yang menceritakan lima orang sahabat, yaitu Genta, Zafran, Riani, Ian, dan Arial yang ingin keluar dari zona nyaman untuk mencapai cita-cita. Menariknya, film 5 cm diangkat dari kisah nyata yang dikemas dengan menekankan konflik batin serta rasa nasionalisme pada setiap karakter sebagai motivasi untuk penonton. Tujuan artikel ini, yaitu untuk mengetahui penyebab dan dampak konflik batin tokoh Riani dalam film 5 cm, serta untuk mengetahui rasa nasionalisme tokoh Riani dalam film 5 cm. Metode penelitian yang digunakan ada-lah penelitian kualitatif dengan desain studi dokumen berupa film. Teori yang digunakan untuk menganalisis data adalah pendekatan psikologi sastra. Data yang digunakan berupa dialog tokoh Riani dalam film 5 cm. Sumber data yang digunakan adalah tampilan visual, audio, dan verbal dalam film 5 cm. Hasil yang didapat dari penelitian ini, yaitu tokoh Riani mengalami konflik batin dan di-temukannya rasa nasionalisme. Dalam film 5 cm, terdapat empat adegan yang merupakan konflik batin tokoh Riani. Adegan tersebut meliputi penyebab dan dampak konflik batin yang dialami oleh Riani. Selanjutnya ditemukan dua adegan tentang rasa nasionalisme tokoh Riani pada bagian akhir film 5 cm.
Kata kunci: konflik batin, rasa nasionalisme, psikologi sastra
Riani’s Inner Conflict and Nationalism in Films 5 cm By Rizal Mantovani
The 5 cm movie is directed by Rizal Mantovani is an adaptation of the novel with the same name by Donny Dhirgantoro. 5 cm is a drama genre that tells the story of five friends, namely Genta, Zafran, Riani, Ian, and Arial who want to get out of their comfort zone to achieve their goals. Interestingly, 5 cm is based on a true story which is packaged by emphasizing inner conflict and a sense of na-tionalism in each character as motivation for the audience. The purpose of this article is to find out the causes and effects of Riani’s inner conflict in the 5 cm, and to find out the sense of nationalism of Riani's character in the 5 cm movie. The research method used is qualitative research with a docu-ment study design in the form of a movie. The theory used to analyze the data is a psychology of literature. The data used is dialogue of Riani's character in the 5 cm movie. The data sources used are visual, audio, and verbal displays in a 5 cm movie. The results of the analysis are namely the character Riani experiences inner conflict and nationalistic sentiments. In the 5 cm movie, there are four scenes which are the inner conflicts of Riani's character. The scene includes the causes and effects of Rianis’s inner conflict. Furthermore, there are two scenes about Riani's character's sense of nationalism at the end of the 5 cm movie.
Keyword: inner conflict, nationalism, psychology of literature
Pengaruh Mediasi Orang Tua pada Penggunaan Media Digital Terhadap Perilaku Eksternalisasi Anak Usia Dini
Authors: Ratnaningtyas Lia , Supriyono Supriyono , Ishaq M.
Published: April 2023

Abstract: The study aimed to investigate the effect of parental mediation on children's externalizing behavior related to digital media use. The research used a quantitative method and a correlational descriptive approach, with a sample of 63 parents from Raudhatul Athfal, Lembeyan District, Magetan Regency. The results indicated a high level of parental mediation in the restrictive, active, and observant sub-variables. Children's externalization behavior was found to be low, with a negative effect between parental mediation and externalization behavior. The study recommended that parents increase their knowledge about parenting and the mediation model of parents in using digital media, while educators should develop effective parenting models for early childhood. Future researchers are advised to use qualitative research methods to further investigate parental mediation in the use of digital media and its impact on children's externalizing behavior.

Abstrak: Penelitian ini bertujuan untuk mengetahui pengaruh mediasi orang tua terhadap perilaku eksternalisasi anak di Raudhatul Athfal Kecamatan Lembeyan Kabupaten Magetan. Dalam penelitian ini, 63 responden dipilih dari 634 orang tua menggunakan metode kuantitatif dengan pendekatan deskriptif korelasional. Hasil penelitian menunjukkan bahwa mediasi orang tua secara keseluruhan berada pada kategori tinggi, sedangkan perilaku eksternalisasi anak berada pada kategori rendah. Hasil uji regresi linear sederhana menunjukkan adanya pengaruh negatif antara mediasi orang tua terhadap perilaku eksternalisasi anak. Peneliti menyimpulkan bahwa orang tua yang menerapkan kombinasi dari ketiga jenis mediasi orang tua, memiliki tingkat pendidikan yang cukup tinggi, memiliki komunikasi yang baik, dan persepsi risiko penggunaan internet netral, dapat mengurangi munculnya perilaku eksternalisasi anak. Disarankan untuk meningkatkan pengetahuan mengenai parenting terutama model mediasi orang tua dalam penggunaan media digital bagi orang tua anak usia dini. Pendidik PAUD juga disarankan untuk mengembangkan model parenting yang efektif terkait media digital bagi orang tua anak usia dini. Peneliti selanjutnya disarankan untuk menggunakan metode penelitian kualitatif untuk menggali lebih dalam mengenai mediasi orang tua pada penggunaan media digital dan dampaknya pada perilaku eksternalisasi anak.