Modern Techniques Research Articles

A Comparative Analysis of IoT based Network Anomaly Detection and Prediction Using Vector Autoregressive Models

This research provides a comparative analysis of the use of Vector Autoregressive models for network anomaly detection and prediction. It starts by giving a brief overview of the models and going over the two versions that are available for network anomaly detection. Ultimately, the study offers an empirical assessment of the two types of models, just considering how well they detect and forecast anomalies overall. The results show that the unmarried-node anomaly detection performance of the model is superior. Simultaneously, the Adaptive Learning version is particularly effective in identifying anomalies among a few nodes. The fundamental reasons for the differences in the two fashions' overall performance are also examined in this research. This work provides a comparative analysis of two widely utilized algorithmic approaches: vector autoregressive models and community anomaly detection and prediction. Each method's effectiveness is assessed using two different network datasets: one based on real-world global measurements of latency and mobility ranges, and the other focused on a fictional community. The study also examines the trade-offs between employing the versus other modern and classic techniques, Markov Chain Monte Carlo, and Artificial Neural Networks for network anomaly detection. Finally, it provides an overview of the advantages and disadvantages of each technique as well as suggestions for improving performance.

Individualized approach for MEN1-associated duodenopancreatic neuroendocrine neoplasms

Multiple endocrine neoplasia type 1 (MEN1)-associated duodenopancreatic neuroendocrine neoplasms (dpNEN) represent the most frequent syndrome-associated cause of death, but the adequate treatment is sometimes considered controversial. Presentation of possible diagnostic and therapeutic options for MEN1-associated dpNENs. In this review article retrospective case studies, expert recommendations, national and international guidelines as well as personal experiences were analyzed and evaluated. Due to early detection programs and the use of the most modern imaging techniques, dpNEN are nowadays diagnosed much earlier. Nonfunctional pNENs currently represent the most frequent dpNENs with about 70%, followed by gastrinomas and insulinomas. Regardless of their functional activity, dpNENs with asize of > 2 cm are generally an indication for surgery. The choice of the optimal treatment strategy, however, in most cases remains the subject of controversial discussions, although nowadays surgery should always be performed in an organ-preserving and minimally invasive way when feasible. Recurrences or new dpNENs are expected in more than 60% of cases, necessitating areoperation in up to 40% of these cases. Duodenopancreatic resections and reoperations can be carried out safely by experienced practitioners and with an acceptable level of risk. The planning of treatment requires careful consideration of the suitable timing, the extent of the operation, the risk of recurrence and potential morbidities. Furthermore, preserving pancreatic function and the quality of life is of utmost importance. In view of the complexity of the disease, MEN1 patients should be treated in specialized centers.

Chemical Composition of Commercial Cannabis.

Cannabis is widely used for medicinal and recreational purposes. As a result, there is increased interest in its chemical components and their physiological effects. However, current information on cannabis chemistry is often outdated or scattered across many books and journals. To address this issue, we used modern metabolomics techniques and modern bioinformatics techniques to compile a comprehensive list of >6000 chemical constituents in commercial cannabis. The metabolomics methods included a combination of high- and low-resolution liquid chromatography-mass spectrometry (MS), gas chromatography-MS, and inductively coupled plasma-MS. The bioinformatics methods included computer-aided text mining and computational genome-scale metabolic inference. This information, along with detailed compound descriptions, physicochemical data, known physiological effects, protein targets, and referential compound spectra, has been made available through a publicly accessible database called the Cannabis Compound Database (https://cannabisdatabase.ca). Such a centralized, open-access resource should prove to be quite useful for the cannabis community.

Are we still chasing molecules that were never there? The role of quantum chemical simulations of NMR parameters in structural reassignment of natural products

Covering: 2019 to 2023. Even with the advent of modern and complementary spectroscopy techniques, comprehensive characterization of natural product continues to represent an onerous and time-consuming task, being far away to become rather “routine”. Mainly due to their highly complex structures and small amount of isolated sample, in milligram or sub-milligram quantities, structural misassignment of natural products are still a recurrence theme in the modern literature. Since the seminal paper from Nicolau and Snider, in 2005, evaluating the various cases of reassignment of natural products, from the present era, in which NMR parameters calculations play such an important role in the structural elucidation of natural products, helping to uncover and ultimately revise the structure of previously reported compounds, a pertinent question arises: are we still chasing molecules that were never there? In this minireview, we intent to discuss the current state of computational NMR parameter calculations, with a particular focus on their application in the structural determination of natural products. Additionally, we have conducted a comprehensive survey of the literature spanning the years 2019–2023, in order to select and discuss recent noteworthy cases of incorrectly assigned structures that were revised through NMR calculations. Therefore, our main goal is to show what can be done through computational simulations of NMR parameters, currently user-friendly and easily implemented by non-expert users with basic skills in computational chemistry, before venturing into complex and time-consuming total synthesis projects. In conclusion, we anticipate a promising future for NMR parameter calculations, fueled by the ongoing development of user-friendly tools and the integration of artificial intelligence. The emergence of these advancements is poised to broaden the applications of NMR simulations, offering a more accessible and reliable means to address the persistent challenge of structural misassignments in natural product chemistry.

An adaptive evolutionary multi-objective clustering based on the data properties of the base partitions

Evolutionary multi-objective clustering (EMOC) is a modern clustering technique in which the general concepts of evolutionary multi-objective optimization are applied to the clustering problem. The design and definition of clustering are difficult problems, and the choice of the objective functions and parameter setting of the algorithms are still challenges. In this paper, we propose the adaptive evolutionary multi-objective clustering approach based on data properties (AEMOC). AEMOC considers a new metric to evaluate the base partitions (candidate solutions used in the initial population) generated by minimum spanning tree clustering. This metric is applied to: (1) determine properties of the data, such as separation and overlap; and (2) define an offline selection of objective functions and parameter settings of the multi-objective algorithm. The information regarding the data properties allows AEMOC to avoid unnecessary data processing in datasets in which the initialization provides optimal solutions and optimization is not required. AEMOC presented promising results considering a diverse set of artificial and real-life datasets, based on two aspects: (1) it succeeded in the determination of the data properties of the base partitions and verifying the potential clustering quality, presenting a correlation of 0.8 with a reference metric, and (2) it provided better clustering results than reference EMOC approaches, achieving a general gain in the clustering performance of 3% in real-life datasets and 7% in artificial datasets.

Commemoration, modernism and self-identity in contemporary graphic memoir

ABSTRACT This essay focuses on two texts, Mary M. Talbot’s and Bryan Talbot’s Dotter of her Father’s Eyes (2012) and Sarah Laing’s Mansfield and Me: A Graphic Memoir (2016). Both texts are in a direct line of influence from modernist authors such as James Joyce and Katherine Mansfield, but also from an earlier appropriation of modernism to support a life narrative within the graphic novel form, Alison Bechdel’s celebrated text Fun Home: A Family Tragicomic (2006). As I will show by responding to the critical background from both modernist studies and comics studies, these more recent graphic memoirs, which I term ‘auto/biographics’, use comparative memory to register historical changes between our generation and the modernist generation in order to fully develop their memory narratives. Talbot’s and Laing’s combination of words and images, memoir and biography, combine difficulties of identity and resemblance in hybrid forms that both seek to commemorate modernism as a historical period and attempt to render the ambiguities of memory through modernist techniques.

Out-of-distribution- and location-aware PointNets for real-time 3D road user detection without a GPU

Abstract3D road user detection is an essential task for autonomous vehicles and mobile robots, and it plays a key role, for instance, in obstacle avoidance and route planning tasks. Existing solutions for detection require expensive GPU units to run in real-time. This paper presents a light algorithm that runs in real-time without a GPU. The algorithm combines a classical point cloud proposal generator approach with a modern deep learning technique to achieve a small computational requirement and comparable accuracy to the state-of-the-art. Typical downsides of this approach, such as many out-of-distribution proposals and loss of location information, are examined, and solutions are proposed. We have evaluated the performance of the method with the KITTI dataset and with our own annotated dataset collected with a compact mobile robot platform equipped with a low-resolution LiDAR (16-channel). Our approach reaches a real-time inference on a standard CPU, unlike other solutions in the literature. Furthermore, we achieve superior speed on a GPU, which indicates that our method has a high degree of parallelism. Our method enables low-cost mobile robots to detect road users in real-time.

High-Resolution Respirometry for Mitochondrial Function in Rodent Brain.

High-resolution mitochondrial respirometry is a modern technique that enables to measure mitochondrial respiration in various cell types. It contains chambers with oxygen sensors that measure oxygen concentration via polarography and calculate its consumption. The chamber contains plastic stoppers with injection ports that allow the injection of samples and different substrates, inhibitors, and uncoupler substances to measure mitochondrial respiration with high efficiency. These substances act on the mitochondrial electron transport chain (ETC) and help toassess the mitochondrial ATP production capacity and oxidative phosphorylation. The respirograph obtained with the help of software represents the oxygen consumption in each stage after adding different reagents.

Active flutter suppression for an aircraft wing structure by utilizing FE analysis for Al7075+Sic alloy

An instable stimulated vibration resulting from the combination of aerodynamic, inertia, and aeroelastic forces is known as "active flutter" in the context of aircraft wing structures. The primary destabilizing element that the airplane structures display and which causes active flutter is limit cycle oscillation. Ultimately, it results in fatiguerelated harm and catastrophic aircraft mishaps. Two modern techniques, namely enhanced aero-elastic analysis and flutter control for wings and panel structures, are established by means of these developing technologies. comparable stiffness values and comparable stresses are assessed, together with theoretical and practical approaches, to enhance the features of aero-elastic analysis of aircraft structures. Additionally, it makes reducing flutter mandatory in order to enhance aero-elastic stability. Using Ansys software, a thorough FE analysis is carried out to improve and analyze the benefits of Al7075+ SiC alloy aircraft wing structure. With the aid of Ansys software, a thorough FE study is carried out to enhance their aeroelastic stability and to offer recommendations for the design of revised wings and panel structures. For the chosen NACA4412 aircraft wing, modal analysis is carried out, and frequencies are assessed. The advantages of AL7075+ SiC alloy material will be examined with equal stress, equivalent strain, and equivalent stiffness.

Modern technology, artificial intelligence, machine learning and internet of things based revolution in sports by employing graph theory matrix approach

<abstract> <p>The sports industry is gaining popularity with time and all the countries are investing a lot of money for fame and entertainment around the world. To ensure the high quality of sports, modern techniques such as machine learning (ML), artificial intelligence (AI) and the Internet of Things (IoT) are playing a very optimistic role. Various IoT-grounded smart sensors are implemented with integration in AI and ML for the safety and high performance of the players. Based on the numerous applications of modern technologies, it is very convenient to capture different body movements of the players and avoid any severe injuries and long-term health issues. AI and IoT-driven smart devices are revolutionizing the analysis of athletes' training and performance, offering precise insights for their improvement. This article delved into the remarkable strides made in scientific sports, highlighting how computer-based elements are reshaping the sports landscape for athletes and spectators alike. These innovations enable real-time health monitoring, prevent accidents, capture diverse postures and analyze sporting outcomes. By extensively reviewing existing literature, key features have been identified and prioritized. Using the graph theory matrix approach (GTMA), this piece compared and ranks available alternatives based on these selected features. Moreover, the parameter matrix and normalized matrix were reported in tabulated form and the ranks for ten paradigms are illustrated graphically for better visualization.</p> </abstract>

Natural Compounds Synthesis: A Perspective of Modern Chemistry

Abstract: Natural products have been created by nature and are derived from organisms such as plants or microorganisms. The natural compounds are isolated from natural sources in very small quantities, making it difficult for the form to achieve the compounds' good yields. The last is relevant for doing biological essays and chemical structure elucidation. Besides, they have diverse chemical structures, which have inspired organic chemists to do their organic synthesis. It is important to construct a strategy based on a well-designed retrosynthesis. The reproducibility of the selected methodology, the yields of the compounds of each step and stereoselectivity or diastereoselectivity of the reactions that are being used are the main key points to guarantee the success of a natural compound synthesis. Modern technologies can be the solution. In this review, we try to compile in the form of a critic perspective the most modern techniques that organic chemists are applying for the synthesis of complex natural compounds.

Teacher-student rapport and gamified learning: Investigating the role of interpersonal variables in classroom integration

Using the educational setting of Jordan, this research aims to investigate the complex relationship between teacher-student rapport and student involvement in gamified learning experiences. As it relates to the effective use of gamified learning methodologies, this research digs into the far-reaching ramifications of cultivating strong interpersonal interactions between instructors and students. This research uses a strict quantitative technique to investigate the complex relationships between 400 children and 40 teachers. This study's results shed light on an interesting and statistically significant phenomenon: a significant positive connection (r = 0.742, p 0.001) between teacher-student rapport and the amount of engagement seen in gamified learning sessions. The finding, in line with Jordan's educational reforms, highlights the critical role of positive rapport in generating dynamic and significant participation within modern instructional techniques. The practical repercussions highlight the need for teachers to work to improve their interpersonal skills. It becomes clear that this is a crucial factor in enabling effective teaching and learning, especially within the context of contemporary pedagogical approaches. The current research helps us better understand the complex dynamics at play in teacher-student relationships, illuminating their far-reaching consequences for the pursuit of educational excellence in the Jordann setting.

An object in the interior design as a concept of integrating decorative and applied art into the design of premises

The subject of the research is the types of decorative and applied art, the connection of decorative and applied art with the interior design of residential and public spaces. Special attention is paid to the fact that in modern interior design, the tendency to transform interior space using decorative and applied art objects is widespread, which determine the overall style of the room, despite the particular academic design style used in the project. The author examines in detail such aspects of the topic as the influence of the author's thing on the exclusivity of the project, giving a typical interior individuality by including an object of decorative and applied art made specifically for this room, or competently selected by a specialist taking into account the specifics and purpose of the room, the transformation of the project from a typical to an individual one. The author analyzed and systematized modern methods and techniques of introducing the author's art into the interior design of premises for various purposes. Based on the results of the study, a general methodology was formed and proposed for integrating things into the interior of the proposed spaces, the concept of transforming rooms using objects and techniques of decorative and applied art. The main conclusion of the research is that the author's art object of decorative and applied art, created in a certain way and integrated into the interior design, allows to form a certain concept of a thing in the interior, a philosophy or even your own unique and recognizable author's style. The novelty of the research lies in determining the current trend of integrating handmade objects into the interiors and spaces of residential and commercial premises for various purposes in order to give uniqueness, which contributes to the development of the design industry and art in general.

Knowledge Distillation Guided Interpretable Brain Subgraph Neural Networks for Brain Disorder Exploration.

The human brain is a highly complex neurological system that has been the subject of continuous exploration by scientists. With the help of modern neuroimaging techniques, there has been significant progress made in brain disorder analysis. There is an increasing interest about utilizing artificial intelligence techniques to improve the efficiency of disorder diagnosis in recent years. However, these methods rely only on neuroimaging data for disorder diagnosis and do not explore the pathogenic mechanism behind the disorder or provide an interpretable result toward the diagnosis decision. Furthermore, the scarcity of medical data limits the performance of existing methods. As the hot application of graph neural networks (GNNs) in molecular graphs and drug discovery due to its strong graph-structured data learning ability, whether GNNs can also play a huge role in the field of brain disorder analysis. Thus, in this work, we innovatively model brain neuroimaging data into graph-structured data and propose knowledge distillation (KD) guided brain subgraph neural networks to extract discriminative subgraphs between patient and healthy brain graphs to explain which brain regions and abnormal functional connectivities cause the disorder. Specifically, we introduce the KD technique to transfer the knowledge of pretrained teacher model to guide brain subgraph neural networks training and alleviate the problem of insufficient training data. And these discriminative subgraphs are conducive to learn better brain graph-level representations for disorder prediction. We conduct abundant experiments on two functional magnetic resonance imaging datasets, i.e., Parkinson's disease (PD) and attention-deficit/hyperactivity disorder (ADHD), and experimental results well demonstrate the superiority of our method over other brain graph analysis methods for disorder prediction accuracy. The interpretable experimental results given by our method are consistent with corresponding medical research, which is encouraging to provide a potential for deeper brain disorder study.

Analysis of the Technique and Morphological Language of Modern Painting Creation Based on Generative Adversarial Networks

Abstract With the development and progress of society, traditional painting creation techniques cannot realize the development needs of modern society for paintings, and it is necessary to constantly innovate and improve the painting creation techniques. Based on the structure of the generative adversarial network, this paper utilizes the one-dimensional midpoint substitution method and dichotomous method to generate the rock outline in painting creation and combines the generative adversarial network to establish the style migration model of modern painting creation techniques and morphological language. Unity was used to construct the validation dataset, and for the style migration of painting creation techniques, we verified it in terms of stroke curvature, FID value, and peak signal-to-noise ratio, and analyzed the evolution of painting creation techniques and morphology language. The results show that the difference in stroke curvature before and after the contour migration of painting creation techniques is 3.27, the peak signal-to-noise ratio reaches 25.43 dB, and the evolution of comprehensive painting in creation techniques is in an upward trend, with an average annual growth rate of 13.07% from 2012 to 2020. Generative adversarial networks can be used in modern painting creation techniques to increase the richness of paintings and establish a spiritual connection between painters and audiences.

Enhancing English Writing Proficiency in TESOL: Integrating Traditional and Technological Approaches for a Multifaceted Learning Experience

This scholarly article delves into the latest advancements within the Teaching English to Speakers of Other Languages (TESOL) arena, specifically concentrating on their impact on teaching English writing. The study critically analyses a variety of pioneering methods, tactics, and tools currently employed in TESOL, with a special emphasis on refining students’ English writing capabilities. It explores the intersection of traditional and modern pedagogical techniques, highlighting how blended approaches can significantly enhance the writing skills of learners. The paper provides in-depth insights into how these innovative strategies not only improve writing proficiency but also foster a deeper understanding of the English language among students. It argues that the effectiveness of these teaching methodologies is pivotal in equipping students with the skills necessary to excel in English writing, thereby contributing to their overall academic success. The research emphasizes the role of tailored instruction, feedback mechanisms, and the integration of digital tools in creating an engaging and effective learning environment.

Modern training methods and technique in the field of occupational safety – a literature review

The growing diversity of the workforce and the changing context of employment in the world present significant challenges for developing and implementing occupational safety and health training. New approaches to content development, format, and implementation need to be developed, as traditional training methods are often not effective with workers or do not account for changes in employment patterns and the way work is organized today. This paper reviews modern training methods from computer games to virtual reality, as found in the specialized literature published in the last 10 years. The article presents the results of a state of art literature review realized with the new version of PRISMA methodology, for systematic reviews, as a part of National Institute for Research and Development for Occupational Safety INCDPM Alexandru Darabont ongoing project "Development of informative content and online training system of occupational safety and health information in the context of intelligent specialization".

Trends in Acupuncture Therapy for Microcirculation and Hemorheology from 1998 to 2023: A Bibliometric and Visualized Study.

The aim of this study is to explore and illustrate the focal points concerning acupuncture's impact on microcirculation and hemorheology over the past 26 years, and to identify future directions in this field. Data in this area were gathered from the Web of Science Core Collection database. Employing CiteSpace, VOSviewer, Scimago Graphica, and Microsoft Excel software, we analyzed authors, institutions, and countries to evaluate scientific collaboration. Moreover, we carried out an analysis of keyword clustering, references, and burst detection to examine the prominent research areas and emerging trends in this domain. The study analyzed 706 documents, 471 institutions, 632 journals, 40 countries, 581 keywords, and 3289 authors related to acupuncture for microcirculation and hemorheology. Data revealed a consistent increase in research output over 26 years. China, with the most publications and citations, significantly contributed to the field, often collaborating with the United States. Elisabet Stener-Victorin and the China Academy of Chinese Medical Sciences were the most productive author and institution, respectively. The journal Evidence-based Complementary and Alternative Medicine held the most influence. Common keywords included "vasoactive substances", "neurotransmitters", "signaling pathways", and "oxidative stress", among others. Research topics focused on female infertility, ischemic stroke, and pain syndromes, with treatment approaches such as electroacupuncture, manual acupuncture, auricular acupuncture, and cupping therapy. Women's infertility, ischemic stroke, and pain syndromes have emerged as hotspots in research. Future directions may include comparative studies of traditional and modern acupuncture techniques to evaluate their respective therapeutic effects. There is potential for in-depth research in these areas and the discovery of new intervention strategies as well as mechanisms.

Dosimetric Comparison of Hippocampal Sparing Whole Brain Radiotherapy by Volumetric Modulated Arc Therapy, Linac-based Intensity-modulated Radiation Therapy and 3-D Conformal Radiation Therapy: A Cross-sectional Study

Introduction: Brain metastases are treated with Whole-Brain Radiotherapy (WBRT) using the 3-D Conformal Radiation Therapy (3DCRT) technique. Attempts have been made to perform dosimetric analysis of WBRT with hippocampal sparing using 3DCRT, Volumetric Modulated Arc Therapy (VMAT), and Linac-based Intensity Modulated Radiation Therapy (linac-based IMRT), anticipating technical challenges during contouring and treatment planning. Aim: To perform a dosimetric analysis and comparison ofhippocampal sparing cranial irradiation by 3DCRT, VMAT, and IMRT treatment plans in brain metastases patients. Materials and Methods: The analytical cross-sectional dosimetric study was conducted from November 2022 to September 2023 at Government Medical College, Kottayam, Kerala, India. Ten patients treated for brain metastases with WBRT using 3DCRT were considered for dosimetric analysis. The Planning Target Volume (PTV) dosimetry and hippocampal dosimetry were studied for all ten patients. The important dosimetric parameters included volume receiving 100% dose, Target Coverage (TC), Homogeneity Index (HI) in PTV dosimetry, the mean hippocampal dose, and maximal hippocampal dose in hippocampal dosimetry. The hippocampus and hippocampal avoidance volume were contoured. Treatment plans for 3DCRT, VMAT, and Linac-based IMRT were generated for each patient’s prescription dose of 30 Gy in 10 fractions. The non-parametric Kruskal-Wallis test was used for data analysis. Results: The mean whole brain Planned Target Volume (PTV) was 1190 cm3 . The mean hippocampal avoidance volume was 30 cm3 , which occupied 2.5% of the whole brain PTV. The average median dose received by the hippocampus was 30.05 Gy, 17.1 Gy, and 17.5 Gy for 3DCRT, Linac-based IMRT, and VMAT, respectively. The mean dose for the hippocampus was 31.03 Gy, 17.7 Gy, and 17.5 Gy for 3DCRT, Linac-based IMRT, and VMAT, respectively (p<0.001). VMAT offered better hippocampal sparing compared to IMRT and 3DCRT. On average, VMAT offered a 2% improvement, and 3DCRT offered a 5% improvement in TC compared to IMRT. The HI of 3DCRT was 0.09, IMRT 0.199, and VMAT 0.150. Conclusion: VMAT and LINAC-based IMRT permit hippocampal-sparing WBRT with adequate target volume coverage and acceptable homogeneity when compared to 3DCRT plans. Thus, the dosimetric study suggests that modern radiotherapy techniques should be advocated for hippocampal-sparing WBRT.

Multiplexed Immunofluorescence and Single-Molecule RNA Fluorescence In Situ Hybridization in Mouse Skeletal Myofibers.

RNA fluorescence in situ hybridization (FISH) is a powerful method to determine the abundance and localization of mRNA molecules in cells. While modern RNA FISH techniques allow quantification at single molecule resolution, most methods are optimized for mammalian cell culture and are not easily applied to in vivo tissue settings. Single-molecule RNA detection in skeletal muscle cells has been particularly challenging due to the thickness and high autofluorescence of adult muscle tissue and a lack of in vitro models for mature muscle cells (myofibers). Here, we present a method for isolation of adult myofibers from mouse skeletal muscle and detection of single mRNA molecules and proteins using multiplexed RNA FISH and immunofluorescence.