bluuebunny/crossref_metadata_2025 huggingface dataset:
Scalable Solutions for Wind and Solar Distributed Generation
Authors: Li Jianzhong , Yang Feiping , Chen Yu , Ao Li , Xiao Wei
Published: February 2024

The main application of the informative data in the sector which are related to the energy are defines and explains as one of the crucial elements of Energy Internet. The advancement of the grid system are very vital as well as promising and faces many issues that are connected with the implementation of the renewable energy including solar and wind energy. The capacity of collecting of the data is the main elements of make are easy in taking decisions. The advancement of the technologies and its improvement has many benefits and advantages which was shown by the data analytic of the renewable source of energy in the various power stations. This is the framework which shows the development and growth of the potential establishment of the analyzation of the data and information in the smart grid and the utilities of power by the renewable resources. The seven domains and approaches are used for the purposed of predicting the stability, flexibility and safety from the advancement of the grid system. The secondary qualitative methods are used to define and explain the importance of the grid system in relation with the renewable source of energy that is wing and solar energy.
Physical Education Teaching Quality Evaluation Method Using Mobile Edge Computing in the Online and Offline Environment
Authors: Zhang Huimin
Published: February 2024

The development of different technology has impacted the different stages of human life. Additionally, with the implication of computing technology sports and physical education can be improved. The use of mobile edge technology aids in gathering precise data related to physical education. Therefore, a specific improvement is possible. This following analysis has looked into the factor of mobile edge computing that aids in the evolution of physical education teaching. Moreover, the study has focused on developing an appropriate path for the integration of MEC into PE education. By utilising Mobile Edge Computing (MEC) technologies in both online and off-line learning contexts, this study offers a thorough way for assessing the quality of sports teaching. The suggested approach integrates multiple characteristics and performance measures to evaluate the efficacy of physical education instruction and participation, taking into consideration the changing educational landscape.
Stability Study of Grid-Connected Power System for Wind Farms Considering Power Control
Authors: Lv Liangnian , Fang Zhong , Yang Zhiqian , Aisikaer  , Wu Lei , Yang Yanchen
Published: February 2024

Wind energy has emerged as a pivotal practice in the contemporary energy landscape, generated through grid-connected power sources aligning with the vernacular principles of systemic approaches. This study explores the surge in initiation rates, offering insights into various factors impacting sustainable electricity production. Intriguingly, this research delves into the intricacies of managing the variability and uncertainty inherent in energy demand, catalysing the integration of grid-based solutions that enhance sustainability. It probes the dynamic nature of power supply paradigms, revealing a journey of continuous enhancement by applying cutting-edge resource methodologies. Amidst the backdrop of global shifts in electricity dynamics, this study uncovers the profound implications of energy depletion and wasteful consumption practices, spotlighting a burgeoning movement towards optimising grid electricity resources on a macro scale. The intricacies and nuances of power supply challenges are comprehensively dissected, offering valuable insights. Furthermore, the study explores the pivotal role played by information technology innovators in consolidating the predictability of wind energy, augmenting its viability. It also aligns with forward-looking reviews, underscoring the actionable strategies taken. The culmination of these efforts not only enhances predictability but also unlocks a spectrum of reflective and adaptive resources in wind energy utilisation.
Optimization Study of Grid Access for Wind Power System Considering Energy Storage
Authors: Le Xubo , Chen Qiuhua , Zhu Minjie , Gao Yucheng , Zhang Bingye , Aisikaer
Published: February 2024

This study gives an optimized study with details discussion of the access of wind power grid systems and the energy storage that is high in demand in recent days. The wind power system is a renewable energy resource that can help to meet the need or crisis of energy related to the fuel resources that are being increased in recent times. This is also helpful where energy access related to the power supply is difficult. This may also help the places where the shortage of energy and power supply is faced. There are some issues that are faced at different times that are related to the maintenance and handling of the power grid of the wind turbines. The technical process of handling the machines and the one-time cost of investment on it at the first time is also difficult. There are some benefits of the wind power grid with high energy storage capacity that may help to fulfill the demand for energy that is the main issue of the total system of power supply nowadays. The issues should be mitigated with the help of expert and at the coastal area where there is plenty of continuous flow of wind may be helpful with supply of power supply with wind. These advanced systems hold the potential to mitigate the pervasive energy demand issues plaguing contemporary power supply systems. By expertly addressing these challenges and strategically locating wind power grids, especially in coastal areas with consistent wind flow, the dependable supply of electrical energy can be significantly enhanced, thereby offering an effective solution to the prevailing energy supply challenges of our time.
Evaluation of Monitoring Technologies and Methods for Micro Plastics in Water as Novel Pollutants
Authors: Hu Ke , Li Dongdong , Cui Xiaolei , Hu Donghua , Chen Junliang , Zhuan Shaopeng , Chang Hao , Zhang Yaping , An Tingting , Zhang Juqin
Published: February 2024

Micro plastics have recently emerged as a major biohazard that has a considerable impact on the environment. Moreover, of the detrimental capabilities of micro plastics, a hope of controlling efforts of micro plastics has been in the headlines. microplastics have gained notoriety due to their adverse effects on the environment and wildlife. Controlling these minuscule yet harmful particles requires effective monitoring, detection, and management strategies. This analysis delves into the diverse techniques and technologies available for tracking and mitigating microplastic pollution. Therefore, the following analysis has aimed at analysing the monitoring technologies and methods for micro plastics. Additionally, the monitoring methods are observed along with the advantages and disadvantages. For the development of the analysis, a secondary qualitative method was used in the process. Additionally, the graphical representation of the efforts for controlling the novel pollutant is analysed along with relevant problems. Hence, a coherent discussion is presented in the following analysis. This research contributes to the broader understanding of microplastic pollution and its monitoring while underlining the need for enhanced control measures. It provides a valuable resource for policymakers, environmentalists, and researchers working toward a cleaner, more sustainable environment. As microplastics continue to infiltrate ecosystems worldwide, comprehensive monitoring and control efforts are of paramount importance
Text Summarization for Online and Blended Learning
Authors: Kirmani Mahira , Kaur Gagandeep
Published: February 2024

Online learning text summarization is vital for managing the constant influx of online information. It involves condensing lengthy online content into concise summaries while retaining the original meaning and information. While several online summarization tools are available, they often fall short in preserving the underlying semantics of the text. In this paper, we introduce an innovative approach to online text summarization that strongly emphasizes capturing and preserving the semantics of the text. Our automatic summarizer leverages distributional semantic models to extract and incorporate semantics, producing high-quality online summaries. To evaluate the effectiveness of our online summarization system, we conducted experiments on a diverse range of online content. We employed ROUGE metrics, a popular evaluation method for text summarization, to assess our system's performance. Additionally, we compared our results with those of four state-of-the-art online summarizers. The outcome of our study demonstrates that our online summarization approach, which integrates semantics as a fundamental feature, outperforms other reference summarizers. This conclusion underscores the significance of leveraging semantics in the context of online learning text summarization. Furthermore, our system's ability to reduce redundancies in online content makes it a valuable tool for managing information overload in the digital age.
Optimization of Radio Energy Transmission System Efficiency Based on Genetic Algorithm
Authors: Du Ruijuan
Published: February 2024

In order to better understand the efficiency optimization problem of radio energy transmission systems, the author proposes a genetic algorithm based research on efficiency optimization of radio energy transmission systems. The author first addresses the issue of improving the efficiency of magnetic coupled radio energy transmission. On the basis of ensuring a certain transmission distance and voltage gain, the system is mathematically modeled using coupling circuit theory, and mathematical expressions such as transmission efficiency, transmission distance, and voltage gain are obtained as the objective functions of the algorithm. Secondly, the impact of metal obstacles on the transmission system was analyzed. Design a radio energy transmission compensation circuit, and through simulation, obtain three transmission system parameter schemes that meet the objective function and constraint conditions. Finally, the multi-objective genetic algorithm is used to optimize the system parameter design and obtain the optimal combination of transmission system parameters, with coupling coefficient k=0.1818 and mutual inductance coefficient M=23.165 × 10^(-5) H. Using multi-objective genetic algorithm, the algorithm has a fast convergence function in terms of the number of iterations, a non dominated function solution, and Pareto graphs have verified that the numerical value (3) in the text is the optimal combination design for the transmission system.
Research on Network Security Situation Awareness Technology Based on Security Intelligent Monitoring Technology
Authors: Yang Bingyu
Published: February 2024

This paper uses data mining technology to dynamically monitor tobacco Industrial Enterprise' information systems. This paper builds an Internet security situation awareness system under a big data environment. The weight clustering method is used to classify users' network behavior. The spacing of weights is optimized to ensure the maximum difference in classification. Then, NAWL-ILSTM technology establishes a security situational awareness model for the Internet environment. In this project, the extended and short-memory Nadam optimal algorithm (NAWL) is used to realize data deep learning. Finally, the tobacco industry network security situation assessment method is designed to complete the dynamic monitoring of tobacco industry network security based on data mining. Simulation results show that the proposed method can effectively improve the safety evaluation performance of the system and reduce evaluation errors.
Linear Anti-interference Algorithm for Digital Signal Transmission in Fiber Optic Communication Networks based on Link Analysis
Authors: Wu Jing , Jin Cheng , Wang Ziwu
Published: February 2024

In order to achieve accurate transmission of protection signals in fiber optic communication networks, it is necessary to perform channel balancing configuration of fiber optic communication networks and adaptive forwarding control processing of relay protection signals, the author proposes an accurate transmission method for relay protection signals in fiber optic communication networks based on time-varying multipath fading suppression and adaptive beamforming. The system analyzes the sources of wireless long-distance pain signal interference signals, introduces anti-interference technologies such as two-dimensional joint processing (STAP), provides anti-interference algorithms and related gain analysis, and conducts signal processing gain simulation using MATLAB. Based on the analysis of comprehensive simulation results, at a given symbol length, the signal bandwidth increases, and the processing gain infinitely approaches the given theoretical limit value, rather than increasing nonlinearly. The reason is that the channel is affected by noise, and the channel estimation value and signal conjugate multiplication produce a noise quadratic term. At this point, the estimated value of the coherent region channel is reduced by the influence of noise, and the signal-to-noise ratio loss caused by the noise quadratic term is reduced, so the processing gain increases. During the process of infinite increase in signal bandwidth, the input signal-to-noise power ratio of the receiver tends to decrease towards an infinite value, limited by the size of the coherent region. The channel estimation value increases under the influence of noise, and the noise quadratic term is the main factor affecting the output noise power. When the symbol length is greater than the coherent time, the smaller the maximum Doppler frequency shift and the larger the coherent detection area, the greater the processing gain.
Application of Improved PSO and BP Hybrid Optimization Algorithm in Electrical Automation Intelligent Control
Authors: Li Lijing , Wang Xiaojian , Yang Mei
Published: February 2024

A fuzzy RBF-PID control strategy based on particle swarm optimization (PSO) algorithm is proposed to solve the problem of large inertia lag in temperature control system of industrial production refuse furnace. In this control system, an improved particle swarm optimization algorithm combined with inertia weight and genetic transformation was used to optimize the initial values of membership functions of fuzzy RBF (radial basis function). Then, BP (error backpropagation) algorithm is used for fine tuning, and fuzzy reasoning and RBF learning ability are combined to adjust the PID control parameters online to achieve the optimal PID control effect. The simulation results show that the algorithm has fast tracking, small overshoot, and is not easily trapped in local minima. At the same time, its robustness and anti-interference performance are better than traditional PID control.
Automatic Control of Low Voltage Load in Power Systems Based on Deep Learning
Authors: Sun Yaohui , Zhang Hongyu , Li Haolin , Wang Shu , Li Chunhai
Published: February 2024

Due to the interference of false data, there is a large error in the mining results of low voltage loads in the power system. In response to this problem, the author proposes a design of an intelligent mining system for low voltage loads in the power system based on deep learning. Using ARM+DSP dual CPU structure, initializing the adapter agent, and using dual arm spiral antennas, designing a low-voltage load monitor to detect partial discharge signals in the 500-1500 MHz frequency band and suppress noise interference; By transmitting monitoring information to the intelligent switch through CAN bus or 485 bus, remote monitoring can be achieved; Based on the contact points and current characteristics of the circuit breaker, a current transformer has been designed to reduce the range of induced voltage variation; Construct a continuous set of functions MMD in the space, adjust the original network structure, establish a deep learning mining model, initial network parameters, eliminate false data in the network, optimize the network using target domain data, and combine mining engines to achieve intelligent data mining. According to the experimental results, the maximum difference between the load of phase A of the data processing system based on numerical simulation and the actual data is 1000 kVA at a time of 6 seconds; When the load of phase B is 4 seconds, the maximum difference between it and the actual data is 2000 kVA; When the load of phase C is 8 seconds, the maximum difference between it and the actual data is 2000 kVA. It has been proven that the mining error of the system is 0, and it has a precise mining effect.
Optimum Batch Scheduling Model for Quality Aware Delay Sensitive Data Transmission over Fog Enabled IOT Network
Authors: Potu Narayana , Jatoth Chandrashekar , Parvataneni Premchand
Published: February 2024

The emerging fog networks in the internet of things (IoT) applications provide flexibility and agility for service providers. The combination of fog nodes and edge nodes enable them to deliver a given network service. However, the selection of suitable edge and fog nodes and their scheduling still remain a research challenge. Finding a globally optimal scheduling of oversized data transmission over IoT applications for industrial requirements is crucial. Optimal batch scheduling has been regarded as a viable way to achieve optimal scheduling in other contemporary network models. This manuscript has projected an Optimum Batch Scheduling Model (OBSM) for Quality aware Delay Sensitive Data Transmission over Fog Enabled IoT Networks. A novel clustering technique has been proposed in this manuscript to group the transmission nodes (fog or edge nodes) and data packets, which further pairs each group of data with one of the corresponding node group to achieve delay sensitivity and other quality factors such as energy efficiency. The data scheduling between data and node group is drawn from the previous contribution -"Quality aware Energy Efficient Scheduling Model (QEESM) for Fog Enabled IoT Network". The simulation results have shown that, in terms of average make span rate, average round trip time, and energy consumption, the batch scheduling model OBSM performs noticeably better than the contemporary scheduling models. The OBSM scheduling model's average make-span rate, roundtrip time, as well as energy consumption per make span are 23.3 7.03, 17.8 5.2, and 11.33 6.9 joules, respectively, which conclusively demonstrate that the OBSM model outperforms the existing models. A novel batch scheduling algorithm has been proposed using a unique unsupervised learning approach that suggested to cluster the transmission requests and transmission channels in to multiple clusters.
Trajectory Interception Classification for Prediction of Collision Scope between Moving Objects
Authors: Babu B. Uma Mahesh , Babu K. Giri , B. T. Krishna
Published: February 2024

In the fields of autonomous navigation and vehicle safety, accurately predicting potential collision field points between moving objects is a significant challenge. A novel computing technique to enhance trajectory interception analysis is presented in this paper. Our objective is to develop a field model that can accurately forecast collision zones, improving road transportation safety and the use of autonomous cars. Our main contribution is a binary classification model called PCSMO (Prediction of Collision Scope between Moving Objects), which is based on zero-shot learning. Gann angles, which are typically 45 degrees, are used to analyze the trajectories of moving objects. This method is inspired by GANN (Gann Angle Numeric Nomenclature). Compared to earlier techniques, this model more accurately identifies potential collision collision interception zones. The technique computes Gann angles for trajectory analysis and extracts GPS coordinates of moving objects from video data using OpenCV. It offers a more sophisticated comprehension of object movement patterns and points of interception. To assess the precision, recall, F1-score, and prediction accuracy of our model, we employ 10-fold cross-validation. Comparing the PCSMO model to existing models, these metrics demonstrate how well the PCSMO model predicts potential collision zones. Our approach, we discovered, enhances trajectory analysis—a critical component of safer autonomous navigation systems. With potential applications in autonomous vehicle and UAV safety, the PCSMO model improves field interception classification.
A Distributed System Fault Diagnosis System based on Machine Learning
Authors: Wang Yixiao
Published: February 2024

Now days distributed system becomes the mainstream system of information storage and processing. Compared with traditional systems, distributed systems are larger and more complex.However, the average probability of failure is higher and the difficulty, complexity of operation and maintenance are greatly increased. Therefore, it is necessary to use efficient methods to diagnose the system. Our aim is to use the trained model to diagnose the fault data of the distributed system, so we can obtain as high diagnostic accuracy as possible, and create a web side for users to use. The technique we proposed uses the integrated learning approach of Stacking to model the superposition of the raw data. To realize this, we trained with a dataset of 10,000 pieces of data and assessed accuracy every once in a while. Our best training results are about 80.69% accurate and can be used on the web side. By training data sets and analyzing distributed system faults with Stacking technology, a model with a test accuracy of 80.69% was obtained. Through this model and the web platform we built, the fault of distributed system can be diagnosed, and the diagnosis results are better than other models.
Security and Privacy of 6G Wireless Communication using Fog Computing and Multi-Access Edge Computing
Authors: Xu Ting , Wang Ning , Pang Qian , Zhao Xiqing
Published: February 2024

The challenges surrounding the confidentiality of data transmission in the context of the upcoming sixth-generation (6G) wireless networks are proposed in this research. The study explores the potential role of blockchain systems in enhancing data security. It examines the integration of machine learning (ML) techniques to address the growing complexities of handling massive data volumes within the 6G environment. This research involves a comprehensive survey of existing strategies for maintaining data confidentiality in automotive communication systems. It further investigates an analysis of confidentiality approaches inspired by the 6G network architecture. The study examines the potential security implications of the Internet of Everything (IoE). It evaluates current research issues related to safeguarding data confidentiality within the framework of 6G communication among vehicles. The exploration involves reviewing ML techniques and their applicability in resolving the data processing challenges inherent in the 6G wireless network environment. The proposed work reveals the increasing complexity and variability of the 6G wireless network environment, leading to potential challenges in protecting private and confidential data during communication. It highlights the promising role of blockchain systems in addressing data security concerns within the 6G network context. Additionally, the study underscores the transformative potential of integrating ML techniques to handle the massive data volumes generated within the 6G ecosystem. The research highlights the importance of these technologies in mitigating data security risks and ensuring the confidentiality of information exchanged within the 6G communication framework.
Sustainable Development in Medical Applications Using Neural Network Architecture
Authors: Jiang Shuyi
Published: February 2024

The purpose of this research is to propose a methodology utilizing machine learning techniques to support medical organizations in effectively managing risks. Specifically, the study aims to connect social media data to identify and assess potential threats, ultimately enabling healthcare management to make informed decisions for their organizations and clients. The research employs machine learning algorithms to analyze user-generated content on social media platforms, generating comprehensive visual representations of various risk categories and their magnitudes. Additionally, the study utilizes data simplification techniques, including categorization, to streamline data processing and enhance overall efficiency. A computational framework is also developed, incorporating closed-form connections for threat assessment and evaluation. The study further empirically analyses the Consumer Value Stores (CVS) established for medical care in the United States. The findings reveal that prevalent threats within the lower quartile of client messages about CVS services include operational, financial, and technological risks. The severity of these risks is distributed among high risk (21.8%), moderate risk (78%), and minimal risk (0.2%). The research also presents several metrics to demonstrate the robustness of the proposed framework, confirming its effectiveness in effectively identifying and addressing potential threats. This research provides insights that can help healthcare management make informed decisions and foster a safer and more secure environment for their organizations and the people they serve.
Group Intelligent City Mobile Communication Network's Control Strategy based on Cellular Internet of Things
Authors: Wei Jiazheng
Published: February 2024

Mobile communication network optimization heavily depends on power control technology, which impacts the effectiveness of the network. This paper aims to enhance control over nonlinear mobile communication networks and achieve superior performance by applying the particle swarm optimization (PSO) algorithm in the control domain. Addressing limitations in the basic PSO algorithm, improvements are made and applied to urban mobile communication networks. The methodology involves modifying the PSO algorithm to address identified issues and applying the enhanced algorithm to communication network scenarios. Simulation results indicate that with an initial particle count of 10 and 100 iterations, the optimized values for and are 0.691 and 0.486, respectively, resulting in an objective function value of 55.514. This achievement validates the successful implementation of the optimization process for mobile communication network control. The findings reveal that the proposed grad particle swarm optimization (Grad-PSO) algorithm exhibits mobile network optimization by robust search capability and rapid convergence.
Iterative Ensemble Learning over High Dimensional Data for Sentiment Analysis
Authors: Saileela P V R N S S V , Rao N. Naga Malleswara
Published: February 2024

For sentiment analysis in particular, the problem of processing and analyzing high-dimensional data becomes more prominent in recent past. This is where the IEL-HDDSA model, which aims to increase accuracy and performance in complex, high-dimensional data streams sentiment analysis comes into play. Iterative approach in ensemble learning; a contribution to the field. It integrates preprocessing techniques such as tokenization, stop word removal, lemmatization and the collection of sentiment-related features. Then the training corpus is divided by label, and features with high mutual information are selected. Highly replicated points of data for model training can also be identified at this point. First a Naive Bayes model is trained, then later it's placed in an ensemble as part of bagging. Its major advantage over earlier methods is that IEL-HDDSA can iteratively train on selected subsets of data until the performance in sentiment analysis for high-dimensional objects reaches an optimum level. A 10-fold cross validation method was used to rigorously evaluate the performance of this model, which showed consistently high levels of operation with almost no variation across different measures. IEL-HDDSA's precision ranged from 0.9359 to 0.9492, and its specificity was between 0. Its accuracy differed from 0.93 to around 0.95, and its F1-measure fluctuated between the values of about 0.94 and above; so here too balance was well maintained in a manner that satisfied both precision and recall requirements equally. The false alarming rate fell from 0.056 to 0.1, a fairly low ratio of incorrect positive classifications; Moreover, MCC quantities ranged from 0.8668 to 0. These results testify to the IEL-HDDSA model's stable effectiveness and high reproducibility in sentiment analysis applications, especially for massive data flows.
IoT based Dance Movement Recognition Model based on Deep Learning Framework
Authors: Ji Zhen , Tian Yaonong
Published: February 2024

Deep Learning is becoming an emerging field in the Internet of Things (IoT) due to its ability to provide a comprehensive approach to automatic feature extraction and predictive modeling for analysis and decision-making. This paper introduces an IoT-based Dance Movement Recognition Model based on a Deep Learning Framework. The framework consists of a convolutional neural network (CNN) with a data-centric architecture to identify dance movements from the acquired data gathered by an IoT device. The IoT device collects 3D motion data captured by three accelerometers. Feature extraction is then done with the CNN architecture, resulting in a flattened matrix representing the movement. Subsequently, a Multi-Layer Perception (MLP) is used to classify the movements. The proposed system is experimentally evaluated on a standardized dataset of 16 dance steps with three-speed levels. The results show that our model outperforms state-of-the-art approaches in accuracy, evaluation time, and classification accuracy. The proposed model reached 90.74% accuracy, 87.12% precision, 83.78% recall and 84.39% F1-Score. The proposed model can serve as a basis for a reliable and intuitive system that can be used to monitor patient’s dance movements with accuracy.
Target Image Processing Based on Super-resolution Reconstruction and Deep Machine Learning Algorithm
Authors: Lin Yang , Zhang Ping , Zhang He , Song Guoping
Published: February 2024

In dictionary-based single-frame image reconstruction algorithms, dictionaries rely on the design of artificial shallow features and are limited in their ability to represent image features. Therefore, this paper proposes a high-accuracy reconstruction method based on deep learning feature dictionary. This algorithm first uses a deep network to learn high-resolution and low-resolution training example images with deep features; Then co-train the feature dictionary under the super dense framework of the sparse dictionary; Finally, a single low-resolution image can be input and a super-resolution reconstruction can be performed using a dictionary. From the theoretical analysis, the introduction of deep network to extract the deep-level features of the image and its use in dictionary training is more beneficial to complement the high-frequency information in the low-resolution image. Experiments show that the proposed method achieves the best results in terms of both the peak signal-to-noise ratio and the gradient energy function of the reconstructed images. This shows that compared with traditional interpolation methods and some deep learning methods, the proposed method can recover image details to a high degree while preserving the original image damage information. This proves that the subjective visual and objective evaluation indicators of the algorithm presented in this article are higher than those of the comparative algorithm.
Enhanced Feature Optimization for Multiclasss Intrusion Detection in IOT Fog Computing Environments
Authors: S. Sonawane Sudarshan
Published: February 2024

To overcome the shortcomings of traditional security measures in IoT fog computing, The Multiclass Intrusion Detection (MCID) model is put forward. The model's goal is to improve intrusion detection by identifying and classifying different attack types. The behavioral, temporal and anomaly features are fused through SVM-BFE for obtaining the best possible selection of high worth features. Finally we use a Random Forest algorithm to robustly classify them. There is also its adaptability to the ever-changing security demands of fog computing. MCID's ability to improve the cloud security of fog computing is shown by a 4-fold cross validation, which returns performances including precision rates up to 99.43%, recall about 95% and F-measures at as much as 97.17%. Moreover there are specificity rate totals coming in over this whole range that hit close or right.
Is Dupilumab a chance for patients with moderate to severe atopic dermatitis? Literature review
Authors: Iwańczuk Paweł
Published: February 2024

Introduction: Atopic dermatitis is an inflammatory disease which can significantly affect quality of life of patients. Especially severe forms of this disease are challenging to treat and for a long time forms of treatment have been limited. Because of that- it is crucial to find new ways of treatment which can be registered and impove moderate and severe dermatitis.

Aim of study: Fundament purpose of this literature review is focusing on effectiveness, safety, side effects or different potential medical applications of Dupilumab. The main question, which is formulated in the title- is Dupilumab a chance for patients with moderate to severe atopic dermatitis?

State of knowledge: Nowadays Dupilumab is well known drug and a lot of trials and analysis focus on efficacy, safety in treatment of moderate and severe dermatitis. The development of Dupilumab and different potential, clinical applications is also a chance for other groups of patients- for example patients with asthma, chronic rhinosinusitis with nasal polyps, eosinophilic esophagitis, prurigo nodularis.

Conclusion: Dupilumab is definitely a relevant and essential element of treatment therapy for patients with moderate to severe atopic dermatitis. There were many analysis and researches which confirmed effectiveness, safety level and described Dupilumab impact on many different parameteres and scales focused on symptoms and life quality of patients during treatment with Dupilumab.
Stress and primary headaches – Mindfulness-Based Stress Reduction (MBSR) as a novel avenue for treatment
Authors: Szymonik Julia , Szopa Sebastian
Published: February 2024

Introduction: In today's fast-paced life, an increasing number of people are experiencing chronic stress, which negatively impacts our mental and physical health, leading to troublesome conditions such as primary headaches. Faced with this challenge, more individuals are turning their attention to mindfulness techniques as an effective tool for stress management. In light of this, mindfulness, focusing on the awareness of the present moment, emerges as a promising method for preventing and alleviating stress-induced headaches and the resulting aftermath. 

Purpose: Offer a comprehensive overview of the current understanding of the efficacy of mindfulness training, particularly Mindfulness-Based Stress Reduction (MBSR), in the prevention and relief of primary headaches and their associated consequences. 

Description of the state of knowledge: MBSR demonstrates the potential for effectively coping with stress-induced headaches and their subsequent effects, including disability related to headaches. This category of pain includes conditions like migraine and tension-type headaches. In the clinical trials conducted thus far participants in the MBSR program show noticeable improvement in headache-related disability, self-efficacy, quality of life, catastrophic pain, and depression scores. 

Summary: Studies suggest that integrating mindfulness training could present a promising strategy for preventing and reducing the severity of primary headaches and their consequences, by mitigating stress, a crucial factor influencing the onset of these headaches. To validate these promising findings, further randomized, large-scale, placebo-controlled studies are required.
Diagnostic and therapeutic process of neurofibromatosis type 1 and type 2
Authors: Leśniewski Michał , Welian-Polus Iwona , Oleksak Izabela , Maliszewska Karolina
Published: February 2024

Neurofibromatosis is one of the most common genetic diseases. It is inherited in an autosomal dominant manner. It is divided into two genetically distinct subtypes, characterised by multiple skin lesions and tumours of the peripheral and central nervous system.
Neurofibromatosis   type   1,   or   Recklinghausen's   disease,   is   the    most   common phakomatosis. The disease is genetically determined by a mutation of the neurofibromin- 1 gene on chromosome 17.
Neurofibromatosis type 2 accounts for 3% of all cases. The disease is genetically determined - caused by a mutation of the neurofibromin-2 gene on chromosome 22.
The diagnostic and therapeutic process of neurofibromatosis is a major challenge for clinicians.  Given the  complexity  of the problem,  we have reviewed the literature  on the diagnostic and therapeutic possibilities of the disease.
Insuring aesthetic excellence: navigating the evolving landscape of aesthetic insurance in the UK
Authors: Hooker Eddie
Published: February 2024

Eddie Hooker, Founder and CEO of Hamilton Fraser, discusses the crucial role of insurance in aesthetic practices, including his recommendations in view of the new licensing scheme
The mistakes over VAT that land businesses in trouble
Authors: Rawson Emma
Published: February 2024

Making a VAT-related mistake can be costly — even those who consider themselves au fait with VAT can make mistakes which prove expensive to resolve
Research Roundup
Authors: McGloin Claudia
Published: February 2024

In this regular feature, aesthetic nurse Claudia McGloin presents a brief synopsis of a range of recently published articles on medical aesthetics. Research roundup aims to provide an overview, rather than a detailed summary and critique, of the papers selected. Should you wish to look at any of the papers in more detail, a full reference is provided at the end of each study summary.
Mechanical Behavior Analysis of High Strength Concrete Beams in Architectural Design: Simplified Calculation Method of Flexural and Shear Bearing Capacity
Authors: Peng Yu , Zhou Shuang
Published: February 2024

This study advances the use of high-strength concrete beams in structural engineering by analyzing their flexural behavior. Utilizing a combination of theoretical and empirical methods, the research develops equations for calculating the cracking moment and ultimate load capacity of these beams. Key findings include a shear-bearing capacity calculation model, validated by experimental data, with discrepancies in cracking moment and ultimate load-bearing capacity formulas being only 6.16% and 1.53% respectively. These results offer significant insights for the design and analysis of high-strength concrete beams in architectural engineering, demonstrating high accuracy and stability.
A Novel Treatment Method for Ankylosing Spondylitis Combined With Sacroiliac Joint Bone Marrow Edema
Authors:   Center of hip Surgery, Henan Luoyang Orthopedic-Traumatological Hospital, Orthopedic Hospital of Henan Province, Luoyang, China., Zhang Leilei , Zhu Xuanye ,   Henan University of Traditional Chinese Medicine, zhengzhou, China., Ling Haonan ,   Center of hip Surgery, Henan Luoyang Orthopedic-Traumatological Hospital, Orthopedic Hospital of Henan Province, Luoyang, China., Zhang Wanyi ,   Center of hip Surgery, Henan Luoyang Orthopedic-Traumatological Hospital, Orthopedic Hospital of Henan Province, Luoyang, China., Zhang Ying ,   Center of hip Surgery, Henan Luoyang Orthopedic-Traumatological Hospital, Orthopedic Hospital of Henan Province, Luoyang, China., Liu Youwen ,   Center of hip Surgery, Henan Luoyang Orthopedic-Traumatological Hospital, Orthopedic Hospital of Henan Province, Luoyang, China., Chen Xiantao ,   Center of hip Surgery, Henan Luoyang Orthopedic-Traumatological Hospital, Orthopedic Hospital of Henan Province, Luoyang, China.
Published: February 2024

Objective: To investigate whether high-energy extracorporeal shock wave therapy (ESWT) combined with conventional oral medicine as a potential novel therapeutic approach for the treatment of ankylosing spondylitis (AS)combined with sacroiliac joint bone marrow edema. Materials & Methods: 40 patients were divided into two groups and were treated with or without ESWT in combination with conventional oral medicine. A visual analog scale (VAS) score of spinal pain, as well as indicators of spinal mobility, Bath Ankylosing Spondylitis Disease Activity Index (BASDAI) and Bath Ankylosing Spondylitis Functional Index (BASFI) scores, inflammatory index (C-reactive protein, blood cell sedimentation rate), and other indicators were compared between the two groups. The Spondyloarthritis Research Consortium of Canada (SPARCC) scoring system was used to evaluate pain and structural damage in the sacroiliac joint. Results: (1) After one month of treatment (T1), VAS, BASDAI, BASFI, and SPARCC scores were lower in both groups than at the start of treatment (T0) (P < 0.05), with greater decreases observed in the treatment group (P < 0.05). (2) Also, at T1, indicators of spinal mobility for the two groups were improved (P < 0.05). (3) ESR and C-reactive protein levels for the two groups decreased significantly at T1 versus T0 (P < 0.05). Conclusion: ESWT combined with oral medication can significantly relieve pain and improve clinical functional symptoms for patients with AS. It can also reduce sacroiliac joint bone marrow edema and control the inflammatory reaction in the sacroiliac joint, which represents a novel, effective, reliable, and safe clinical treatment therapeutic method. Keywords: Ankylosing spondylitis, Sacroiliac joint, extracorporeal shock wave therapy, oral medicine.
Clinical Study on Appropriate Energy of Extracorporeal Shock Wave for Rotator Cuff Non-calcific Tendinopathy Treatment
Authors:   Graduate Union of Changchun University of Chinese Medicine, Changchun China., Xi Jin , Jie Li ,   Department of Sports medicine and joint surgery Jilin Provincial People’s Hospital, Changchun, China., Jin Li ,   Department of Sports medicine and joint surgery Jilin Provincial People’s Hospital, Changchun, China., Hao Luo ,   Department of Sports medicine and joint surgery Jilin Provincial People’s Hospital, Changchun, China., Liheng Zhang ,   Department of Sports medicine and joint surgery Jilin Provincial People’s Hospital, Changchun, China.
Published: February 2024

Objective: This study aims to investigate the short-term clinical efficacy of extracorporeal shock waves with different energy levels on rotator cuff non-calcific tendinopathy. Materials and Methods: A total of 139 patients with rotator cuff rotator non-calcific tendinopathy were randomly divided into eight groups based on the different energy levels of the Dornier Aries smart focus shock wave therapy device: Level 5, 2000 shocks (0.062 mJ/mm2), Level 6, 2000 shocks (0.084 mJ/mm2), Level 7, 2000 shocks (0.096 mJ/mm2), Level 8, 2000 shocks (0.117 mJ/mm2), Level 5, 3000 shocks (0.062 mJ/mm2), Level 6, 3000 shocks (0.084 mJ/mm2), Level 7, 3000 shocks (0.096 mJ/mm2), and Level 8, 3000 shocks (0.117 mJ/mm2). Each group received shock wave treatment corresponding to the respective energy level and shock count. The visual analogue scale (VAS) and Constant-Murley score (CMS) were compared before and 1, 2, and 4 weeks after treatment to determine the short-term efficacy. Results: The VAS scores of all groups significantly decreased at 1, 2, and 4 weeks after treatment compared to before treatment. The VAS score of the Level 7, 2000 shocks (0.096 mJ/mm2) group was significantly lower than the other groups (P < 0.05). The CMS scores of all groups significantly increased at 1, 2, and 4 weeks after treatment compared to before treatment. The CMS score of the Level 7, 2000 shocks (0.096 mJ/mm2) group was significantly higher than the other groups (P < 0.05). There was significant statistical difference in the effective rate among the eight groups (P > 0.05). No serious adverse reactions were observed in any group before or after the treatment. Conclusion: Extracorporeal shock wave therapy for rotator cuff rotator non-calcific tendinopathy can alleviate shoulder joint pain, improve shoulder joint function, and enhance patients quality of life with good efficacy. The optimal therapeutic effect was observed at an energy level of 0.096 mJ/mm2 and 2000 shocks. Keywords: Rotator cuff injury, Rotator cuff non-calcific tendinopathy, Extracorporeal shock wave therapy
Extracorporeal Shock Wave in Combination with Arthroscopy for Calcified Supraspinatus Tendonitis
Authors:   [1] Department of Sports Medicine and Joint Surgery, Jilin Province People’s Hospital, , Changchun, China, [2] Graduate Union of Changchun University of Chinese Medicine, Changchun, China., Li Jin , Li Jie ,   Department of Sports Medicine and Joint Surgery, Jilin Province People’s Hospital, , Changchun, China,, Jin Xi ,   Graduate Union of Changchun University of Chinese Medicine, Changchun, China., Liu Sheng ,   Graduate Union of Changchun University of Chinese Medicine, Changchun, China., Zhao Shaohong ,   Graduate Union of Changchun University of Chinese Medicine, Changchun, China., Zhang Liheng ,   Department of Sports Medicine and Joint Surgery, Jilin Province People’s Hospital, , Changchun, China,
Published: February 2024

Objective: Exploring the therapeutic effect of extracorporeal shockwave combined with Arthroscopic Surgery on calcified supraspinatus tendinitis. Materials and Methods: Sixty patients with calcific supraspinatus tendinitis who received treatment in our hospital from June 2022 to June 2023 were randomly divided into two groups. All patients had disease lasting more than 6 months. The control group received extracorporeal shockwave therapy (ESWT), while the observation group, after undergoing arthroscopic debridement of calcific deposits in the joint, began receiving the same ESWT as the control group after 2 weeks. The differences in Visual Analog Scale (VAS) score, University of California at Los Angeles (UCLA) score, and Constant–Murley score between the two groups before and after treatment were recorded and compared. Results: Before treatment, there was no significant difference in VAS score, UCLA score, and Constant–Murley Scale (CMS) score between the two groups of patients (P > 0.05); compared with before treatment, both groups of patients showed a significant decrease in VAS scores after 1 and 2 months of treatment (P < 0.05). After 1 and 2 months of treatment, the VAS scores of the observation group were significantly lower than the ones of the control group (P < 0.05). Compared with before treatment, the UCLA score and CMS score of both groups of patients significantly increased after 1 and 2 months of treatment (P < 0.05). After 1 and 2 months of treatment, the UCLA score and CMS score of the observation group were significantly higher than those of the control group (P < 0.05). Conclusion: The combination of extracorporeal shockwave and arthroscopy has a significant therapeutic effect on calcified supraspinatus tendinitis, helping to improve shoulder joint function and effectively alleviate pain in patients. Keywords: Extracorporeal shockwave, Arthroscopy, Calcifying supraspinatus tendinitis, Shoulder joint function, Pain.