bluuebunny/crossref_metadata_2025 huggingface dataset:
Security Privacy Process Involvement in Cloud Security for Data Preservation against Data Malicious Activity
Authors: K Prabu Et al.
Published: December 2023

Cloud data sent from the person is attacked, leading to data hacking. Data classification can be made by malware detection, leading to the data warehouse technique and data storage. The cloud data from a particular internet protocol address cannot be hacked. Only random cloud data is hacked. Even though this leads to some illegal issues. The method of managing the cloud data and maintaining the factor to hack illegal cloud data has been proposed. The method of malware detection and ML-based end-to-end malware detection are used in calculating the time efficiency. The malware detection and defence method has been introduced for managing the data tracking and the system's formation to hack unwanted data. The time efficiency calculation for the data transmitted in the network has been enabled for the cloud data sent and received. The data from each router makes the data store 12% of the unwanted compared to the original messages. The factor for managing the individual aspect to produce the data is 30% of the database. This will contain 20% of the data in formulating the cloud storage system, which makes the data classifications. 4% of redundant data from the database has been enveloped for the data classifications. Meanwhile, the data attack can be evaluated using the malware detector and also manages classification method for evaluation of data and formation of the system to produce data from the appearance of Secure data clouds.
Steganography using AES and Edge Image
Authors: Jannah Raad Taher Et al.
Published: December 2023

Steganography is the art of hiding messages in an image. This is a great way to send a secret message without drawing attention to it. Many technologies have been developed to ensure data security, and cryptography is considered one of the most important sciences used since the dawn of humanity. Using this proposed technique, we take the input text(128-bit) with key(128-bit), encrypt it using AES technology, and then hide out text(128-bit) inside the edges of the image free size. This is done by extracting the edges of the image and combining them with the ciphertext to make them disappear completely. Text data security is a good thing with this proposed new technology. It is also easy to extract text from the edges of the image and re-decode it. The resulting image is the same as the selected image, clear with raised edges, with text disappearing within the edges of the image indistinguishable from the naked eye. Therefore, this proposed method is considered the safest in hiding data inside the image.
Software-Defined Networking-Based Campus Networks Via Deep Reinforcement Learning Algorithms: The Case of University of Technology
Authors: Ali Sadiq Salim Et al.
Published: December 2023

As a consequence of the COVID-19 pandemic, networks need to be adopted to satisfy the new situation. People have been introduced to new modes of working from home, attending teleconferences, and taking part in e-learning. Other technologies, including smart cities, the Internet of Things, and simulation tools, have also seen a rise in demand. In the new situation, the network most affected is the campus network. Fortunately, a powerful and flexible network model called the software-defined network (SDN) is currently being standardized. SDN can significantly improve the performance of campus networks. Consequently, many scholars and experts have focused on enhancing campus networks via SDN technology.

Integrating deep reinforcement learning (DRL) with SDN is pivotal for advancing the quality of service (QoS) of contemporary networks. Their integration enables real-time collaboration, intelligent decision making, and optimized traffic flow and resource allocation.

The system proposed in this research is a DRL algorithm applied to a campus network—the University of Technology—and investigated as a case study. The proposed system employs a two-method approach for optimizing the QoS of a network. First, the system classifies service types and directs TCP traffic by using a deep Q-network (DQN) for intelligent routing; then, UDP traffic is managed using the Dijkstra algorithm for shortest-path selection. This hybrid model leverages the strengths of machine learning and classical algorithms to ensure efficient resource allocation and high-quality data transmission. The system combines the adaptability of DQN with the proven reliability of the Dijkstra algorithm to enhance dynamically the network performance.

The proposed hybrid system, which used DQN for TCP traffic and the Dijkstra algorithm for UDP traffic, was benchmarked against two other algorithms. The first algorithm was an advanced version of the Dijkstra algorithm that was designed specifically for this study. The second algorithm involved a Q-learning (QL)-based approach. The evaluation metrics included throughput and latency. Tests were conducted under various topologies and load conditions.

The research findings revealed a clear advantage of the hybrid system in complex network topologies under heavy-load conditions. The throughput of the proposed system was 30% higher than the advanced Dijkstra and QL algorithms. The latency benefits were pronounced, with a 50% improvement over the competing algorithms.
Trust Based Mechanism for Isolation of Malicious Nodes in Internet of Things
Authors: Zakiya Manzoor Khan Et al.
Published: December 2023

The Internet of Things systems are prone to the attacks as they have ad-hoc and limited resource structure. IoT-based systems are utilized for managing a large volume of information and assist in services related to industrial and medical applications. Due to this, the IoT attains becomes a target for a multitude of attackers and adversaries namely occasional hackers, cybercriminals, hacktivists, government, etc. The major goal of potential attackers is to steal the sensitive information such as credit card numbers, location data, credential of financial account and information related to health, by hacking the Internet of Things devices.  The version number attack is one of malicious activity of IoT which affect network performance to great extend. The version number attack is triggered by the malicious nodes which can flood unlimited hello packets in the network. The hello flood attack raised situation of denial of service in the network. The trust based mechanism is proposed in this research work in which trust value is assigned to each node based on their activities. The node which is least trusted will be marked as malicious and get isolated from the network. The proposed scheme is implemented in NS2 and results are analyzed in terms of throughput, packetloss, energy consumption and delay.
Radio Resource Management Satellite Communication Network MCDM Method
Authors: Devika SV Et al.
Published: December 2023

Worldwide deployment of heterogeneous wireless networks is growing as a result of consumer demand for connectivity at all times and in all places. These customers' interest in multimedia apps like video streaming and VoIP, which demand tight Quality of Service (QoS) support, is growing at the same time. With such limitations, provisioning network resources is a difficult undertaking. In fact, it might be challenging for a network operator to identify trustworthy criteria to choose the optimum network that ensures user happiness while maximising network utilisation, given the availability of numerous access technologies (WiFi, WiMAX, or cellular networks). To solve this problem, each eNB just needs to learn the traffi c conditions or patterns of its owncell in our proposal. Wireless communication systems depend heavily on radio resource management (RRM). To ensure the efficient and successful operation of wireless networks, it involves the allocation and control of radio frequency spectrum, power, and other resources. RRM is significant because it can use scarce radio resources as efficiently as possible, enhancing capacity, lowering interference, and improving service quality. Successful deployment and operation of wireless communication systems like cellular networks, Wi-Fi, and Bluetooth depend on effective RRM approaches. The need for wireless communication is growing, and new technologies and standards are constantly being developed. The methodology of radio resource management (RRM) involves a variety of techniques and algorithms designed to allocate radio resources in a way that maximizes network performance while minimizing interference. Taken as alternate parameter is Laser communication, optical networks, satellite optical communication, vibrations, satellite networks. Taken as is solar radiation power, thermal bending, micro meteorite impact, solar and lunar gravity, earth oblations method. satellite optical communication has reached near 2000 data set compare other data set. The operation of wireless communication networks depends on radio resource management (RRM). Wireless networks would have interference, congestion, and a lacklustre level of service if effective RRM procedures weren't used. RRM is therefore a key component in ensuring that wireless communication systems can provide users with dependable and high-quality services.
Design and Performance Evaluation of Cavity-Backed SIW Antenna for Monopulse Applications
Authors: Pradeep Mane Et al.
Published: December 2023

The design of a cavity-backed substrate integrated waveguide (SIW) dual-plane antenna for monopulse application is presented in this publication. The primary objective is to create a four-element circularly polarized cavity-backed slot antenna array that is compact and highly efficient. Two antenna subarrays are utilized to develop a complete analysis, design, and implementation of an antenna, and each antenna element can be used as an independent antenna for monopulse applications. The antenna element is made up of two square ring slot antenna components, a power divider, and a feed point. To produce sum and difference signals, the feed point of each antenna element can be connected to one of the input ports on a monopulse comparator. The antenna's performance at 10 GHz was assessed regarding radiation pattern, gain, return loss, bandwidth, axial ratio, and efficiency. A high-frequency structure simulator (HFSS) simulates a complete planar and compact SIW structure with a footprint of 61.6 mm x 58.1 mm. With a permittivity of 3.55 and a thickness of 1.524 mm, an antenna is manufactured using a photo-lithographic process on a single-layer Rogers RO4003 substrate. As per experimental findings, the antenna has a return loss of -40.17 dB, an RL bandwidth of 395 MHz, a gain of 13.11 dB, an axial ratio of 2.97 dB, and an efficiency of 89.85% at a frequency of 9.92 GHz. Experimental findings reveal exceptional performance parameters for the designed antenna, which can be used for monopulse applications at 10 GHz.
A Novel Periodontal Disease Grade Classification Methodology using Convolutional Neural Network
Authors: Saptadeepa Kalita Et al.
Published: December 2023

With the advancement of artificial intelligence, the demand of automated assistance in the domain of medical imaging has become important to reduce time and inaccuracy in physical examination. Modern lifestyle choices are resulting in a variety of dental diseases which led to multiple researches challenges which are carried out for pre-emptive detection in order to deal with these diseases within time. Periodontal diseases (PD) is a type of dental disorder which is rapidly increasing and being the major cause of early teeth loss. The machine learning based convolutional neural network (CNN) model is carried out in detecting grade wise classification of the periodontal disease. A dataset containing 350 dental images in Radio Visio Graphy (RVG) format belonging to an age group of 18-75 years is used for both training as well as testing. This method has successfully detected mild periodontitis, moderate periodontitis and severe periodontitis by achieving a satisfactory accuracy of as high as 94% with minimum loss, precision value, recall and F1score of 0.41, 0.93-0.95 and 0.91-0.94 respectively for all the three classes of Periodontitis.
A Novel and Efficient Spatio-Temporal Clustering Algorithm to Discover Patterns in Indian Earthquake Data
Authors: Swati Meshram Et al.
Published: December 2023

The seismic activity in India, including the Himalayas, the North-East region, and the Andaman-Nicobar Islands, is prominently displayed on the seismic map. It is important to analyze the specific characteristics of seismic events on the Indian sub-continent. This research paper introduces a new algorithm for data analysis using a partitioning technique that mines clustering patterns. The algorithm generates clusters of spatial and spatio-temporal data by distributing the data into spatial bins or buckets, identifying neighboring bins, and minimizing distance calculations. Furthermore, the selection of centroids is based on the density of data in the spatial region, utilizing a random selection method. It requires minimal parameter settings, enhancing its efficiency and practicality for analyzing large-scale spatio-temporal datasets. We validate the algorithm's efficacy through experiments conducted on the Indian earthquake spatio-temporal dataset, demonstrating its ability to detect spatio-temporal patterns and identify earthquake-prone regions.The conducted experiments reveal a correlation between the frequency of earthquake events and the number of clusters formed, indicating that regions with a higher occurrence of earthquakes exhibit a greater clustering tendency, signifying their susceptibility to seismic activity. The results imply promising clustering quality, with Silhouette index in the range of 0.88 to 0.93.
Design of SRAM Cell using Modified Lector and Dual Threshold Method Based on FINFET
Authors: Ramesh Gullapally Et al.
Published: December 2023

FinFET (Fin Field Effect Transistor) is a new technology that satisfies the demand for a superior storage system by improving transistor circuit design (SS). CMOS devices experience a wide range of issues due to the gate's diminishing ability to control the channel. Increased total production costs are a few of these disadvantages. But this store needs to dissipate less power, have a quick access time, and a low leakage current. The increased power dissipation and leakage current of traditional CMOS-based SRAM (Static RAM) architectures cause a sharp decline in performance. The nanoscale gadget called FinFET is being introduced for use in SRAM fabrication due to its 3D gate architecture. The adoption of FinFET has helped boost overall performance in terms of efficiency, power, and footprint. And because it is immune to SCEs, FinFET has become the transistor of choice. In this study, we have examined a number of FinFET-based SRAM cells and compared them with CMOS technology. We have also suggested a novel 14T SRAM design that uses the Dual Threshold Method and Modified Lector Approach with FinFET, and it is implemented for the 1bit, 4bit, and 8bit.
Image Recognition Using Text and Audio Translation for the Visually Challenged
Authors: Rishita Khurana Et al.
Published: December 2023

WHO has expressed that out of the general populace on the planet there are 253 million individuals are outwardly impeded around the world. It comes to the standpoint that visually impaired individuals are finding burdensome to curve out their ordinary life. It is vital for take significant measure with the current innovations so they can experience the ongoing scene with next to no troubles. To lift the visually impaired people in the public, this project has been proposed, which can identify images and translates the description of image into text and then produce the audio. This can assist the individual with perusing any text and recognize the image and get the result in vocal structure. Motivated by late work in machine interpretation also, object recognition, a CNN-RNN based attention model is presented in this project. Through the proposed framework, an image is converted into text description first; then, utilizing a basic text-to-speech API, the extracted caption/subtitle is converted into speech which further assists the visually impaired to understand the image or visuals they are looking at. So, the focal part is centered on building the subtitle/text model while the subsequent part, which is changing the text-to-speech, is moderately simple with the text-to-speech API. When the model is fabricated, it is deployed on the local framework utilizing a Flask-based model to produce audio-based caption for any image fed to the model.
Edge AI for Real-Time Video Analytics in Surveillance Systems
Authors: K P N V Satyasree Et al.
Published: December 2023

More and more surveillance systems are being used to increase security and safety for the general public. However, the conventional method of processing all video data in the cloud can be ineffective and slow response times. By performing video analytics at the network's edge, close to where the data is created, Edge AI is a promising new strategy that can address these issues.

The most recent developments in edge AI for real-time video analytics in security systems are discussed in this paper. We discuss the different techniques that are being used, as well as the applications that are being enabled by edge AI. The paper also discusses the challenges and limitations of edge AI, and the future research directions in this area.
GPU Accelerated Simulation of Scene Generation of 3D Photonic Mixer Device Camera
Authors: Lade Sangita Gautam , Pawale Sanjesh , Patil Aniket
Published: December 2023

Simulation of Photonic Mixer Device (PMD) sensors have the capability to create virtual environment to test 3D camera design. This simulation comprises of multiple steps like scene generation using ray tracing, power calculation, raw data generation and raw data processing.  However, each step-in situation process takes longer time to implement and they are simulation process, simulators need to be faster. In this paper, we propose parallel implementation method for scene generation using GPGPUs. The feasibility of the method is confirmed using Amdahl’s law before implementation. The method is implemented and tested on GeForce 820M, GeForce 750Ti and Volta V100.Tthe highest speed up obtained is 219.913 using Volta (GV100) GPU for block size 1024. Thus, parallel method optimizes the scene generation time as compared to serial processing and the implemented results are better than the state of the art in the literature.
Harnessing Data-Driven Insights: Predictive Modeling for Diamond Price Forecasting using Regression and Classification Techniques
Authors: Basha Md Shaik Amzad , Oveis Peerzadah Mohammad
Published: December 2023

In the multi-faceted world of gemology, understanding diamond valuations plays a pivotal role for traders, customers, and researchers alike. This study delves deep into predicting diamond prices in terms of exact monetary values and broader price categories. The purpose was to harness advanced machine learning techniques to achieve precise estimations and categorisations, thereby assisting stakeholders in informed decision-making. The research methodology adopted comprised a rigorous data preprocessing phase, ensuring the data's readiness for model training. A range of sophisticated machine learning models were employed, from traditional linear regression to more advanced   ensemble methods like Random Forest and Gradient Boosting. The dataset was also transformed to facilitate classification into predefined price tiers, exploring the viability of models like Logistic Regression and Support Vector Machines in this context. The conceptual model encompasses a systematic flow, beginning with data acquisition, transitioning through preprocessing, regression, and classification analyses, and culminating in a comparative study of the performance metrics. This structured approach underscores the originality and value of our research, offering a holistic view of diamond price prediction from both regression and classification lenses. Findings from the analysis highlighted the superior performance of the Random Forest regressor in predicting exact prices with an R2 value of approximately 0.975. In contrast, for classification into price tiers, both Logistic Regression and Support Vector Machines emerged as frontrunners with an accuracy exceeding 95%. These results provide invaluable insights for stakeholders in the diamond industry, emphasising the potential of machine learning in refining valuation processes.
Performance Analysis of Deep-Learning and Explainable AI Techniques for Detecting and Predicting Epileptic Seizures
Authors: Patil Ashwini , Patil Megharani
Published: December 2023

Epilepsy is one of the most common neurological diseases globally. Notably, people in low to middle-income nations could not get proper epilepsy treatment due to the cost and availability of medical infrastructure. The risk of sudden unpredicted death in Epilepsy is considerably high. Medical statistics reveal that people with Epilepsy die more prematurely than those without the disease. Early and accurately diagnosing diseases in the medical field is challenging due to the complex disease patterns and the need for time-sensitive medical responses to the patients. Even though numerous machine learning and advanced deep learning techniques have been employed for the seizure stages classification and prediction, understanding the causes behind the decision is difficult, termed a black box problem. Hence, doctors and patients are confronted with the black box decision-making to initiate the appropriate treatment and understand the disease patterns respectively. Owing to the scarcity of epileptic Electroencephalography (EEG) data, training the deep learning model with diversified epilepsy knowledge is still critical. Explainable Artificial intelligence has become a potential solution to provide the explanation and result interpretation of the learning models. By applying the explainable AI, there is a higher possibility of examining the features that influence the decision-making that either the patient recorded from epileptic or non-epileptic EEG signals. This paper reviews the various deep learning and Explainable AI techniques used for detecting and predicting epileptic seizures  using EEG data. It provides a comparative analysis of the different techniques based on their performance.
Location Based Power Reduction Cloud Integrated Social Sensor Network
Authors: Dave Devangini , Parikh Swapnil , Patel Pratik , Qadri Syed Shah Sultan Mohiuddin
Published: December 2023

It is great to hear about the advancements in wireless sensor networks and their applications, as well as the integration of cloud computing to enhance data analysis and storage capabilities. Indeed, these technologies have opened up numerous possibilities across various fields, including infrastructure tracking, environmental monitoring, healthcare, and more. The concept of a social sensor cloud, as you mentioned, brings an interesting dimension to this technology landscape by focusing on knowledge-sharing and connecting like-minded individuals or organizations. This could potentially lead to more collaborative and efficient solutions across a wide range of domains. Energy efficiency is a critical consideration in the design and operation of wireless sensor networks and the cloud infrastructure that supports them. The limited battery life of sensors necessitates careful management of energy consumption to ensure optimal functionality and longevity. Sleep scheduling methods are a common technique used to manage energy consumption in these networks. By coordinating when sensors are active and when they are in a low-power sleep mode, energy consumption can be significantly reduced without compromising the network's overall effectiveness. In the context of the Social Sensor Cloud, managing energy efficiency becomes even more crucial due to the shorter battery life of the sensors involved. This is particularly relevant given the growing concerns about environmental sustainability and the need to reduce energy consumption across technological systems. It's clear that your research paper addresses these challenges head-on, by exploring energy-efficient techniques for the Social Sensor Cloud. Sleep scheduling is just one of the many strategies that researchers and engineers are working on to strike a balance between functionality and energy consumption. Other methods might include optimizing data transfer protocols, developing energy-harvesting mechanisms, and enhancing sensor hardware efficiency. As technology continues to evolve, the integration of wireless sensor networks, cloud computing, and social networks will likely pave the way for innovative solutions and transformative applications. Addressing energy efficiency concerns will undoubtedly play a crucial role in ensuring the long-term viability and positive impact of these technologies.
Image Segmentation based on Energy Fitting Models – A Review
Authors: Sivakumar. K Et al.
Published: December 2023

As a result of changes in imaging technology, segmenting the area of interest (ROI) from medical images is an extremely important yet challenging task. It is still difficult for the global energy-based active contour model (ACM) to properly extract the ROI from medical images, despite the fact that many techniques based on the local region-based active contour model have been proposed to deal with intensity inhomogeneity. This brief study aims to assess the performance of current techniques that have been published in the recent years and have been used to image segmentation. The methods under consideration include the various energy fitting models that have been created to drive the active contour are highlighted in this review study. Each model was examined against a medical image, an MRI brain image, and an image that was not taken by a medical professional. According to the results of the comparison study, it can be determined which technique is better appropriate for image segmentation even when there is intensity inhomogeneity in the images.
Revitalizing Legacy Systems: Extracting Key Features for Software Transplantation
Authors: Gurjot Singh Sodhi Et al.
Published: December 2023

The creation of intelligent software depends on the ability to transfer software without any restrictions. In this article, a crucial stage in software engineering, the feature extraction for effective software transplantation, is discussed. As hardware, operating systems, or other factors change, it is commonly necessary to move software from one environment to another. It is vital to identify and extract the relevant software characteristics, which might be challenging given how complex software is, in order to carry out efficient software transplantation. On the other hand, the procedure to extract these attributes from the software might be time-consuming and need extensive understanding. To address this, we propose a transplantation strategy that prioritizes automation with the help of AWS. Our approach involves an agent running on the application server (on-premises). It performs the task of feature identification, extraction and deployment on AWS Cloud. Currently, our strategy is confined to Java and .NET applications.
Trust based Mechanism for Isolation of Malicious Nodes in Internet of Things
Authors: Zakiya Manzoor Khan Et al.
Published: December 2023

The Internet of Things systems are prone to the attacks as they have ad-hoc and limited resource structure. IoT-based systems are utilized for managing a large volume of information and assist in services related to industrial and medical applications. Due to this, the IoT attains becomes a target for a multitude of attackers and adversaries namely occasional hackers, cybercriminals, hacktivists, government, etc. The major goal of potential attackers is to steal the sensitive information such as credit card numbers, location data, credential of financial account and information related to health, by hacking the Internet of Things devices.  The version number attack is one of malicious activity of IoT which affect network performance to great extend. The version number attack is triggered by the malicious nodes which can flood unlimited hello packets in the network. The hello flood attack raised situation of denial of service in the network. The trust based mechanism is proposed in this research work in which trust value is assigned to each node based on their activities. The node which is least trusted will be marked as malicious and get isolated from the network. The proposed scheme is implemented in NS2 and results are analyzed in terms of throughput, packetloss, energy consumption and delay.
Effect of Aerobic Exercise Versus Vestibular Stimulation on Lipid Profile in Premenstrual Syndrome
Authors: Elbably Eman MI , Awad Mohamed AM , Emara Hala MH , Gabre Amir A
Published: December 2023

Objective:  To determine the effect of aerobic exercise versus vestibular stimulation on lipid profile in premenstrual syndrome. Subjects and Methods: Sixty patients with premenstrual syndrome had regular menstrual cycles were participated in this study. Their ages were ranged between 23-30 year. Their body mass index did not exceed 30 kg/ m2. They were not participating in physical therapy exercise program at the last 3 months. Patients with cardiovascular problems, pulmonary problems and endometriosis are excluded from the study. All patients were divided randomly into two equal groups (A&B). Group A: It consisted of thirty patients with premenstrual syndrome. They were treated by aerobic exercises in the form of treadmill training at moderate intensity of 60- 70% of the maximum heart rate, 3 times per week for 2 months. Group B: It consisted of thirty patients with premenstrual syndrome. They were treated by vestibular stimulation, 3 times per week for 2 months. Outcome measures: Total cholesterol level, HDL, LDL, triglycerides were assessed for all patients before and after treatment in both groups (A&B). Premenstrual syndrome scale was used to confirm the diagnosis of PMS before study and to evaluate the severity of PMS symptoms for all patients in both groups (A&B) before and after treatment. Results:  Results revealed that, between groups; pretreatment, there was no significant difference between both groups A and B in total cholesterol, triglycerides, HDL, LDL and premenstrual syndrome scale. While post treatment, there was significant difference between both groups A and B in total cholesterol, triglycerides, HDL, LDL and premenstrual syndrome scale (with favour of group A; more decrease in total cholesterol, triglycerides, LDL and premenstrual syndrome scale and more increase in HDL). Conclusion: Aerobic exercise was found to be effective more than vestibular stimulation on lipid profile in premenstrual syndrome, in relieving stress and symptoms of PMS.
Ultrasonographic Characters of Uterine Myoma as Predictors for Successful Laparoscopic Myomectomy at Mansoura University Hospital
Authors: Hossam M. ElSonny  , Adel Fathi  , Ahmed F. Gibrel  , Hossam E. Gouda
Published: December 2023

Background: Myomectomy is the surgical procedure of choice for symptomatic myoma in the reproductive age, especially if future fertility is desired. LM is the surgical removal of uterine myoma through small incisions in the abdomen. It is an appropriate, if not preferred, alternative to abdominal myomectomy in well-selected patients since it offers shorter hospitalization, short recovery period and resumption of activities within 1–2 weeks, reduced risk of blood transfusion, and intraoperative adhesions. Aim: The aim of the current study was to determine the diagnostic accuracy of different ultrasonographic characters of uterine myoma in predicting success of laparoscopic myomectomy. Methods: The present study was prospective interventional study that was carried out on 35 cases with Chronic pelvic pain. All patients had radiological evaluation by TVS and TAS. All Laparoscopic myomectomies done under general anaesthesia. Outcomes included determining ultra-sonographic predictors for successful laparoscopic myomectomy as regards site, size, and character of Myoma, presence of capsule and line of separation around myoma. Results: The most common complain among the studied cases was bleeding followed by pain and lastly infertility. Regarding location, the most common site was posterior followed by fundal, then anterior and lastly at the cornu. Operative times, blood loss amount, method of extraction, need laparoscopic suturing, complications during surgery recovery and hospital stay after operation demonstrated significant relation with outcomes. Myoma characters, numbers and type demonstrated insignificant relation with outcomes. Conclusion: The current study concluded that, myoma characters, numbers and type could not be used as predictors for successful LM; outcomes. However, operative times, blood loss amount, complications during surgery recovery and hospital stay after operation were less in successful LM than in LAM; and laparoscopic suturing and morcellation have less time consumption and better results than LAM.
The peculiarities of cell elongation growth of cereal coleoptiles under normal and flooding conditions
Authors: Kirpichnikova Anastasiia A. , Kudoyarova Guzel R. , Yemelyanov Vladislav V. , Shishova Maria F.
Published: December 2023

The review examines modern knowledge on the mechanisms of the early stages of plant cell elongation growth. Coleoptiles are used as a model object representing juvenile organs of cereal seedlings. Elongation growth is considered to be a protective morphophysiological stage of seedling development during hypogeal germination. The molecular mechanisms of elongation growth include: changes in the properties of the cell wall, activation of proton pumps, as well as aquaporins of plasma membrane and tonoplast. Particular attention is paid to the hormonal system of regulation, including auxin and ethylene. Coleoptiles of rice, a semi-aquatic plant tolerant to oxygen deficiency, demonstrate that the mechanisms of elongation growth are changing intensively under submergence, but they completely ensure cell growth. There is also a redistribution of importance and abundance between phytohormones. The data presented in the review indicate the necessity to continue investigations on the mechanisms of elongation growth under normal and stress conditions.
Evaluation of Empirical Ground-Motion Models for the 2022 New Zealand National Seismic Hazard Model Revision
Authors: Lee Robin L. , Bradley Brendon A. , Manea Elena F. , Hutchinson Jesse A. , Bora Sanjay S.
Published: December 2023

This article presents an evaluation of empirical ground-motion models (GMMs) for active shallow crustal, subduction interface, and subduction slab earthquakes using a recently developed New Zealand (NZ) ground-motion database for the 2022 New Zealand National Seismic Hazard Model revision. This study considers both NZ-specific and global models, which require evaluation to inform of their applicability in an NZ context. A quantitative comparison between the models is conducted based on intensity measure residuals and a mixed-effects regression framework. The results are subsequently investigated to assess how the models are performing in terms of overall accuracy and precision, as well as to identify the presence of any biases in the model predictions when applied to NZ data. Many models showed reasonable performance and could be considered appropriate for inclusion within suites of models to properly represent ground-motion predictions and epistemic uncertainty. In general, the recent models that are NZ-specific or developed on large international databases performed the best. This evaluation of models helped inform suitable GMMs for the ground-motion characterization model logic tree. In addition, spatial trends in systematic site-to-site residuals to the west of the Taupō Volcanic Zone demonstrated the need for backarc attenuation modifications for slab earthquakes.
Papel del yodo radiactivo en pacientes con cáncer diferenciado de tiroides como parte del tratamiento primario después de la tiroidectomía
Authors: Pérez Zambrano Arleyson Daniel , Cárdenas Gándara Frank Julio
Published: December 2023

Antecedentes: El cáncer de tiroides (CT) es la neoplasia endocrina más frecuente en el mundo, con una incidencia creciente. Según cifras de la Organización Mundial de la Salud, es la décimo octavo patología cancerosa más común en hombres y mujeres. Este aumento que se presenta en gran parte del mundo se está produciendo notablemente a expensas de la TC de tipo papilar, que representa el 85% de la TC, pero también se ha notado un aumento de tipo folicular.

Objetivo: Discutir el uso de yodo radiactivo para el tratamiento de cáncer de tiroides después de la tiroidectomía inicial dependiendo del riesgo de recurrencia o enfermedad persistente.

Metodología: Con el propósito de profundizar en el conocimiento relacionado con el cáncer diferenciado de tiroides y en la terapia con yodo como uno de los estandartes principales de su manejo se realizó una búsqueda exhaustiva y sistemática de la literatura publicada recientemente sobre todo en las últimas 2 décadas tanto nacional como extranjera. Se consultaron bases de datos de Elsevier y Medline mediante portales de búsqueda como Clinicalkey y Ovid, utilizando los términos de búsqueda Thyroid cáncer, thyroidectomy, endocrinology, Thyroid nodule y sus equivalentes en idioma español. La unidad de análisis de la presente revisión lo constituyó el cáncer diferenciado de tiroides y su manejo postquirúrgico con yodoterapia según su estratificación.

Resultados: Existen en la literatura numerosas evidencias científicas sobre la influencia de la terapia con yodo postquirúrgico en el pronóstico de pacientes con esta patología, lo cual va a depender de la estratificación del riesgo de recidiva.

Conclusión: No se recomienda yodoterapia para pacientes con CDT de bajo riesgo ATA; los pacientes de riesgo intermedio y características agresivas del tumor presentan beneficios en la supervivencia global. Por otro lado, se recomienda de rutina la yodoterapia para pacientes de alto riesgo.
Hernia de Amyand encarcelada en paciente anciano: reporte de caso y revisión de literatura
Authors: Arraut Gamez Rafael Eduardo , Gómez-Barrios Jesús , Valencia-Zapata Laura , Thorne-Vélez Hernando , Molinares-Pérez Darit , Cerra-Ortegón David
Published: December 2023

La hernia de Amyand fue descrita por Claudius Amyand en 1735, y en la actualidad se considera una entidad poco frecuente que ocurre en el 1% de todas las hernias inguinales en adultos. Tal patología consiste en el hallazgo de una rara presentación, en donde el apéndice cecal se encuentra dentro del contenido del saco herniario inguinal. Evidentemente, un concepto clave a la hernia en cuestión es la apendicitis aguda, como la enfermedad abdominal quirúrgica más común de predominio en edad joven con una incidencia de por vida del 7%, presentándose en pacientes mayores de 60 años en un 5-10% de los casos, siendo la patología más frecuente capaz de provocar abdomen agudo. En la presente revisión, reportamos el caso de un masculino en la novena década de la vida que acude a un centro hospitalario en curso de una hernia inguinal, con signos de encarcelamiento y obstrucción intestinal asociada, con hallazgo intraoperatorio de hernia de Amyand tipo 3 en la clasificación Losanoff y Basson, constituyendo un excepcional reto quirúrgico.
Difficulties and Prospects of Primary Liver Cancer Treatment
Authors: Cheng Jin
Published: December 2023

Primary liver cancer occurs in hepatocytes and intrahepatic bile duct cells. As one of the common clinical tumors, it is associated with high degree of malignancy and rapid disease progression. Apart from that, the survival time of the patients with primary liver cancer is generally short, and the cancer is difficult to cure. Primary liver cancer is one of the major diseases that tremendously endanger human life and health. According to statistics, the global incidence of liver cancer ranks fifth among other malignant tumors each year, and 55% of these cases occur in China
Metastatic Choroidal Melanoma and Liver Metastasis: A Case Report
Authors: Sabhita Shabir Shaikh
Published: December 2023

Metastatic melanoma to liver is rare in adults. Melanoma can arise from the skin and uveal tract of eyes and metastasizes to other organs, including liver. A middleaged an who had right eye enucleation 5 years ago due to choroidal melanoma was presented with nonspecific abdominal symptoms for a 6-month duration. He had mild tenderness in the right hypochondriac region. An abdominal ultrasound examination showed two hypoechoic lesions that are suggestive of metastatic deposits. Triphasic computed tomography scan of the abdomen showed heterogeneously enhancing lesion that is indicative of hepatoma. Magnetic resonance imaging revealed T2 iso- to hyperintense lesion, giving differential diagnoses of focal nodular hyperplasia, hepatic adenoma, and fibrolamellar carcinoma. Immunohistochemistry results showed that the tumor cells derived from liver biopsy contain melanin as they are positive for both S-100 and MART-1, indicating that the tumor is melanoma. The histopathological examination should be performed on tumor cells from patients who have an increased risk for metastatic melanoma in the liver due to choroidal melanoma.
An Insight into Coronavirus Disease 2019 in Patients with Pre-Existing Viral Hepatitis
Authors: Mohammad K. Parvez
Published: December 2023

The emergence of a novel coronavirus called the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), which is the third highly pathogenic coronavirus after the SARS-CoV-1 and Middle East respiratory syndrome coronavirus (MERS-CoV), at the end of 2019 has continued to cause massive devastation in the pandemic.
Effects of Sarcopenia on Post-Operative Complications in Patients Undergoing Gastrectomy for Gastric Cancer: An Integrative Review
Authors: Leal Ana Karoline Sousa Soares , Mendes Vitória Ribeiro , Sousa Dallyla Jennifer Morais de , Sousa Larissa Layana Cardoso de , Lima Vanessa Batista de Sousa
Published: December 2023

The objective of this paper is to investigate the influence of sarcopenia on postoperative complications in patients undergoing gastrectomy for gastric cancer. PubMed, Cochrane, Science Direct and SciELO databases were used, with the search strategy: Sarcopenia and Stomach Neoplasms. The integrative review was structured with fifteen articles, published from 2015 to 2019. Data analysis was performed in a descriptive way. Twelve studies identified sarcopenia as a risk factor for postoperative complications in patients undergoing gastrectomy for gastric cancer. The reduction in skeletal muscle mass was associated with decreased intake or bioavailability of nutrients and metabolic changes, such as increased production of pro-inflammatory molecules resulting in weakening of the immune system and poor postsurgical healing. It was concluded that the presence of sarcopenia in these patients is related to a higher occurrence of postoperative complications.
Role of barrier layer in the developing phase of “Category 6” super typhoon Haiyan
Authors: Zhang Yuhong , Chen Junyao , Du Yan
Published: December 2023

With the remarkable intensity of 170 knots, Typhoon Haiyan started as a tropical depression on November 3 and developed to the peak as super tropical cyclone (TC) on November 7 in 2013. This intensity makes Haiyan one of the strongest TCs record ever observed and 35 knots higher than the maximum of the existing highest category. Haiyan originated from the eastern part of the Northwest Pacific Warm Pool and moved westward over warm water with a thick barrier layer (BL). The BL reduced the vertical mixing and entrainment caused by Haiyan and prevented the cold thermocline water into the mixed layer (ML). As a result, sea temperature cooling associated with wind stirring was suppressed. Relative high sea surface temperature (SST) kept fueling Haiyan via latent heat flux release, which favored the rapid development of a “Category 6” super typhoon
Water clarity patterns in South Florida coastal waters and their linkages to synoptic-scale wind forcing
Authors: Pirhalla Douglas E , Sheridan Scott C , Lee Cameron C , Barnes Brian B , Ransibrahmanakul Varis , Hu Chuanmin
Published: December 2023

Temporal variability in water clarity for South Florida’s marine ecosystems was examined through satellite-derived light attenuation (Kd coefficients, in the context of wind- and weather patterns. Reduced water clarity alongFlorida’s coasts is often the result of abrupt wind-resuspension events and other exogenous factors linked to frontalpassage, storms, and precipitation. Kd data between 1998 and 2013 were synthesized to form a normalized Kd index(KDI) and subsequently compared with Self-Organizing Map (SOM)-based wind field categorizations to reveal spatiotemporal patterns and their inter-relationships. Kd climatological maximums occur from October through Decemberalong southern sections of the West Florida Shelf (WFS) and from January through March along the Florida Straits.Spatial clusters of elevated Kd occur along 3 spatial domains: central WFS, southern WFS, and Florida Straits near theFlorida Reef Tract, where intra-seasonal variability is the highest, and clarity patterns are associated with transitionalwind patterns sequenced with cyclonic circulation. Temporal wind transitions from southerly to northerly, typically accompanying frontal passages, most often result in elevated Kd response. Results demonstrate the potential of usingsynoptic climatological analysis and satellite indices for tracking variability in water clarity and other indicators relatedto biological health.
Cerebellar antibodies in post-stroke sera of acute ischemic stroke patients
Authors: Atmaca Murat Mert , Erdağ Ece , Demir Serkan , Yüceer Hande , Atmaca Melek Çolak , Küçükali Cem İsmail , Kürtüncü Murat , Tüzün Erdem
Published: December 2023

Background and purpose – Although serum anti-neuronal antibodies are found in acute ischemic stroke (AIS) patients, it is not completely clear whether they are already present before the cerebrovascular event or emerge thereafter. Methods – Sera of 21 consecutive firstever AIS patients were collected within the first day of AIS (baseline), as well as 1 and 6 months after AIS. Well-characterized and novel anti-neuronal antibodies were investigated by cell-based assays, immunoblotting and indirect immunohistochemistry. Results – None of the AIS sera collected at different time points showed well-characterized antibodies. In 7 patients, 1- and 6-month sera (but not baseline sera) showed IgG mostly reacting with soma and dendrites of cerebellar Purkinje cells. Antibody-positive patients did not differ in terms of clinical and etiological features. Conclusion – Our results provide evidence for the antibody-triggering action of AIS. Although anti-cerebellar antibodies are not associated with the severity of stroke, they may potentially contribute to chronic poststroke complications and disability.