Enigmatic thymus: Variations in anatomical localisation of thymic tissue as an easily misdiagnosed congenital anomaly in surgical practice

We point out the issue of differential diagnosis regarding the finding of ectopically localised thymic tissue(a thymic cyst)in the neck.Thymic tissue can be found anywhere along its developmental tract of descent,from the angle of the mandible to the upper mediastinum.Disruption of the thymic descent can result in ectopically/abnormally localised islets of accessory thymic tissue,which may undergo cystic changes,as described in a case report by Sun et al.This anatomical variation of the thymus may be clinically misinterpreted as a neoplasm or other congenital anomalies as a branchial cyst,lymphatic malformation or cystic hygroma.The present editorial focuses on the challenge of establishing a diagnosis of ectopically localised tissue of thymus often presented as a lateral cervical mass,especially in the case of cystic variation/degeneration of this thymic tissue.We summarise hypotheses on the origin of such congenital cervical thymic cysts from the point of view of evolutionary history and embryology.We also discuss lesser-known facts about the anatomy,histopathology and developmental biology of the thymus as one of the most enigmatic organs in the human body.

Adaptive Network Sustainability and Defense Based on Artificial Bees Colony Optimization Algorithm for Nature Inspired Cyber Security

Cyber Defense is becoming a major issue for every organization to keep business continuity intact.The presented paper explores the effectiveness of a meta-heuristic optimization algorithm-Artificial Bees Colony Algorithm(ABC)as an Nature Inspired Cyber Security mechanism to achieve adaptive defense.It experiments on the Denial-Of-Service attack scenarios which involves limiting the traffic flow for each node.Businesses today have adapted their service distribution models to include the use of the Internet,allowing them to effectively manage and interact with their customer data.This shift has created an increased reliance on online services to store vast amounts of confidential customer data,meaning any disruption or outage of these services could be disastrous for the business,leaving them without the knowledge to serve their customers.Adversaries can exploit such an event to gain unauthorized access to the confidential data of the customers.The proposed algorithm utilizes an Adaptive Defense approach to continuously select nodes that could present characteristics of a probable malicious entity.For any changes in network parameters,the cluster of nodes is selected in the prepared solution set as a probable malicious node and the traffic rate with the ratio of packet delivery is managed with respect to the properties of normal nodes to deliver a disaster recovery plan for potential businesses.

Terrorism Attack Classification Using Machine Learning: The Effectiveness of Using Textual Features Extracted from GTD Dataset

One of the biggest dangers to society today is terrorism, where attacks have become one of the most significantrisks to international peace and national security. Big data, information analysis, and artificial intelligence (AI) havebecome the basis for making strategic decisions in many sensitive areas, such as fraud detection, risk management,medical diagnosis, and counter-terrorism. However, there is still a need to assess how terrorist attacks are related,initiated, and detected. For this purpose, we propose a novel framework for classifying and predicting terroristattacks. The proposed framework posits that neglected text attributes included in the Global Terrorism Database(GTD) can influence the accuracy of the model’s classification of terrorist attacks, where each part of the datacan provide vital information to enrich the ability of classifier learning. Each data point in a multiclass taxonomyhas one or more tags attached to it, referred as “related tags.” We applied machine learning classifiers to classifyterrorist attack incidents obtained from the GTD. A transformer-based technique called DistilBERT extracts andlearns contextual features from text attributes to acquiremore information from text data. The extracted contextualfeatures are combined with the “key features” of the dataset and used to perform the final classification. Thestudy explored different experimental setups with various classifiers to evaluate the model’s performance. Theexperimental results show that the proposed framework outperforms the latest techniques for classifying terroristattacks with an accuracy of 98.7% using a combined feature set and extreme gradient boosting classifier.

Dark Matter and Dark Energy from Lattice Model of Universe

The article considers a conceptual universe model as a periodic lattice (network) with nodes defined by the wave function in a background-independent Hamiltonian based on their relations and interactions. This model gives rise to energy bands, similar to those in semiconductor solid-state models. In this context, valence band holes are described as dark matter particles with a heavy effective mass. The conducting band, with a spontaneously symmetry-breaking energy profile, contains particles with several times lighter effective mass, which can represent luminous matter. Some possible analogies with solid-state physics, such as the comparison between dark and luminous matter, are discussed. Additionally, tiny dark energy, as intrinsic lattice Casimir energy, is calculated for a lattice with a large number of lattice nodes.

Current and further trajectories in designing functional materials for solid oxide electrochemical cells:A review of other reviews

Complex oxides are an important class of materials with enormous potential for electrochemical appli-cations.Depending on their composition and structure,such complex oxides can exhibit either a single conductivity(oxygen-ionic or protonic,or n-type,or p-type electronic)or a combination thereof gener-ating distinct dual-conducting or even triple-conducting materials.These properties enable their use as diverse functional materials for solid oxide fuel cells,solid oxide electrolysis cells,permeable membranes,and gas sensors.The literature review shows that the field of solid oxide materials and related electro-chemical cells has a significant level of research engagement,with over 8,000 publications published since 2020.The manual analysis of such a large volume of material is challenging.However,by examining the review articles,it is possible to identify key patterns,recent achievements,prospects,and remaining obstacles.To perform such an analysis,the present article provides,for the first time,a comprehensive summary of previous review publications that have been published since 2020,with a special focus on solid oxide materials and electrochemical systems.Thus,this study provides an important reference for researchers specializing in the fields of solid state ionics,high-temperature electrochemistry,and energyconversiontechnologies.

Machine Learning Empowered Security and Privacy Architecture for IoT Networks with the Integration of Blockchain

The Internet of Things(IoT)is growing rapidly and impacting almost every aspect of our lives,fromwearables and healthcare to security,traffic management,and fleet management systems.This has generated massive volumes of data and security,and data privacy risks are increasing with the advancement of technology and network connections.Traditional access control solutions are inadequate for establishing access control in IoT systems to provide data protection owing to their vulnerability to single-point OF failure.Additionally,conventional privacy preservation methods have high latency costs and overhead for resource-constrained devices.Previous machine learning approaches were also unable to detect denial-of-service(DoS)attacks.This study introduced a novel decentralized and secure framework for blockchain integration.To avoid single-point OF failure,an accredited access control scheme is incorporated,combining blockchain with local peers to record each transaction and verify the signature to access.Blockchain-based attribute-based cryptography is implemented to protect data storage privacy by generating threshold parameters,managing keys,and revoking users on the blockchain.An innovative contract-based DOS attack mitigation method is also incorporated to effectively validate devices with intelligent contracts as trusted or untrusted,preventing the server from becoming overwhelmed.The proposed framework effectively controls access,safeguards data privacy,and reduces the risk of cyberattacks.The results depict that the suggested framework outperforms the results in terms of accuracy,precision,sensitivity,recall,and F-measure at 96.9%,98.43%,98.8%,98.43%,and 98.4%,respectively.

Trade-Offs and Synergie Effects of Regulating Ecosystem Services along an Climatic Gradient in Slovakia

This study aims to assess the synergies and trade-offs of regulating ecosystem services. Ecosystem services are non-linearly interconnected and changes in one service can positively or negatively affect another. We evaluated ecosystem services based on biophysical indicators using an expert scoring system that determines the corresponding soil functions and is part of the existing databases available in Slovakia. This methodological combination enabled us to provide unique mapping and assessment of ecosystem services within Slovakia. Correlation analysis between individual regulating ecosystem services and climate regions, slope, texture, and altitude confirm the statistically significant influence of climate and slope in all agricultural land, arable land, and grassland ecosystems. Statistically significant synergistic effects were established between the regulation of the water regime and the regulation of soil erosion within each climate region, apart from the very warm climate region. Only in a very warm climate region was potential of regulating ecosystem services mutually beneficial for soil erosion control and soil cleaning potential (immobilization of inorganic pollutants).

焊接烟尘及其对呼吸系统颗粒沉积影响的研究进展

世界卫生组织国际癌症研究机构已将焊接烟尘归类为一类致癌物,为了进一步了解焊接烟尘的发尘、传播及对人体危害的规律,综述了焊接烟尘的发尘与传播特性及对呼吸系统影响的研究进展,总结了焊接工艺与焊材涂层成分对烟尘发尘特性的影响,归纳了扩散距离对烟尘分布的影响,分析了尺寸和浓度对烟尘粒子沉积情况的影响.结果表明:熔化极气体保护焊发尘率和发尘尺寸均较大,非熔化极气体保护焊则相反,焊材表面涂镀微纳米涂层可降低发尘率.随着扩散距离增加,烟尘浓度降低,微纳米颗粒占比增加;烟尘粒径小于2μm时,颗粒可直接沉积于肺泡,当粒径达到纳米级别时,可进入血液循环系统,对人生理和心理健康造成影响.

电接触领域导电润滑添加剂及其摩擦学行为的研究现状

导电润滑剂对减少电接触过程的摩擦磨损,提高效率,延长服役寿命,促进电力电子、航空航天、轨道交通领域的绿色化、智能化和高效化发展具有重要作用。但现阶段导电润滑剂存在耐热性、分散稳定性、抗氧化性和抗腐蚀性能不足等问题,难以满足电磁场、温度场、摩擦热应力场及多场耦合复杂工况的苛刻要求。国内外学者对各种新型导电润滑添加剂进行了研究,包括石墨烯、碳纳米管、导电聚合物以及离子液体等,发现上述材料作为油基添加剂可在电接触副界面发生摩擦物理吸附和化学反应,形成摩擦反应膜,起到减小接触电阻、提高界面载流效率、减少接触副磨损、提高表面抗腐蚀性等作用。本文通过综述金属基、碳基、离子液体基以及复合型导电润滑添加剂的摩擦学行为的研究现状,分析电接触副间接触摩擦反应膜的尺寸、形态、分布以及力学性能对配副摩擦学行为的影响,总结了添加剂尺寸、成分、浓度、分散性对导电润滑剂减摩抗磨性能的影响规律,归纳了添加剂的润滑机理,并展望了导电润滑添加剂在电接触领域的发展趋势。

CD-FL:Cataract Images Based Disease Detection Using Federated Learning

A cataract is one of the most significant eye problems worldwide that does not immediately impair vision and progressively worsens over time.Automatic cataract prediction based on various imaging technologies has been addressed recently,such as smartphone apps used for remote health monitoring and eye treatment.In recent years,advances in diagnosis,prediction,and clinical decision support using Artificial Intelligence(AI)in medicine and ophthalmology have been exponential.Due to privacy concerns,a lack of data makes applying artificial intelligence models in the medical field challenging.To address this issue,a federated learning framework named CDFL based on a VGG16 deep neural network model is proposed in this research.The study collects data from the Ocular Disease Intelligent Recognition(ODIR)database containing 5,000 patient records.The significant features are extracted and normalized using the min-max normalization technique.In the federated learning-based technique,the VGG16 model is trained on the dataset individually after receiving model updates from two clients.Before transferring the attributes to the global model,the suggested method trains the local model.The global model subsequently improves the technique after integrating the new parameters.Every client analyses the results in three rounds to decrease the over-fitting problem.The experimental result shows the effectiveness of the federated learning-based technique on a Deep Neural Network(DNN),reaching a 95.28%accuracy while also providing privacy to the patient’s data.The experiment demonstrated that the suggested federated learning model outperforms other traditional methods,achieving client 1 accuracy of 95.0%and client 2 accuracy of 96.0%.

Use of a rat model to characterize 35 arterial pulse wave parameters in a comparative study of isoflurane and Zoletil/ xylazine anesthesia and the effect of Acanthopanax senticosus extract

Background : Information obtained from arterial pulse waveforms (APW) can be usefulfor characterizing the cardiovascular system. To achieve this, it is necessary to know thedetailed characteristics of APWs in different states of an organism, which would allowAPW parameters (APW- Ps) to be assigned to particular (patho)physiological conditions.Therefore, our work aimed to characterize 35 APW- Ps in rats under the influence ofisoflurane (ISO) and Zoletil/xylazine (ZO/XY) anesthesia and to study the effect of rootextract from Acanthopanax senticosus (ASRE) in these anesthetic conditions.Methods : The right jugular vein of anesthetized rats was cannulated for the administrationof ASRE and the left carotid artery for the detection of APWs from which 35APW- Ps were evaluated.Results : We obtained data on 35 APW- Ps, which significantly depended on the anesthesia,and thus, they characterized the cardiovascular system under these two conditions.ASRE transiently modulated all 35 APW- Ps, including a transient decrease insystolic and diastolic blood pressure (BP) and heart rate or increases in pulse BP, d P /d t max , and systolic and diastolic areas. Whereas the transient effects of ASRE weresimilar, the extract had prolonged disturbing effects on the cardiovascular system inrats under ZO/XY but not under ISO anesthesia. This negative effect can result fromthe disturbance caused by ZO/XY anesthesia on the cardiovascular system.Conclusions : We characterized 35 APW- Ps of rats under ISO and ZO/XY anesthesiaand found that ASRE contains compounds that can modulate the properties of thecardiovascular system, which significantly depended on the status of the cardiovascularsystem. This should be considered when using ASRE as a nutritional supplementby individuals with cardiovascular problems.

Chest Radiographs Based Pneumothorax Detection Using Federated Learning

Pneumothorax is a thoracic condition that occurs when a person’s lungs collapse,causing air to enter the pleural cavity,the area close to the lungs and chest wall.The most persistent disease,as well as one that necessitates particular patient care and the privacy of their health records.The radiologists find it challenging to diagnose pneumothorax due to the variations in images.Deep learning-based techniques are commonly employed to solve image categorization and segmentation problems.However,it is challenging to employ it in the medical field due to privacy issues and a lack of data.To address this issue,a federated learning framework based on an Xception neural network model is proposed in this research.The pneumothorax medical image dataset is obtained from the Kaggle repository.Data preprocessing is performed on the used dataset to convert unstructured data into structured information to improve the model’s performance.Min-max normalization technique is used to normalize the data,and the features are extracted from chest Xray images.Then dataset converts into two windows to make two clients for local model training.Xception neural network model is trained on the dataset individually and aggregates model updates from two clients on the server side.To decrease the over-fitting effect,every client analyses the results three times.Client 1 performed better in round 2 with a 79.0%accuracy,and client 2 performed better in round 2 with a 77.0%accuracy.The experimental result shows the effectiveness of the federated learning-based technique on a deep neural network,reaching a 79.28%accuracy while also providing privacy to the patient’s data.

Aspect Level Songs Rating Based Upon Reviews in English

With the advancements in internet facilities,people are more inclined towards the use of online services.The service providers shelve their items for e-users.These users post their feedbacks,reviews,ratings,etc.after the use of the item.The enormous increase in these reviews has raised the need for an automated system to analyze these reviews to rate these items.Sentiment Analysis(SA)is a technique that performs such decision analysis.This research targets the ranking and rating through sentiment analysis of these reviews,on different aspects.As a case study,Songs are opted to design and test the decision model.Different aspects of songs namely music,lyrics,song,voice and video are picked.For the reason,reviews of 20 songs are scraped from YouTube,pre-processed and formed a dataset.Different machine learning algorithms—Naïve Bayes(NB),Gradient Boost Tree,Logistic Regression LR,K-Nearest Neighbors(KNN)and Artificial Neural Network(ANN)are applied.ANN performed the best with 74.99%accuracy.Results are validated using K-Fold.

Ensemble Learning for Fetal Health Classification

Cardiotocography(CTG)represents the fetus’s health inside the womb during labor.However,assessment of its readings can be a highly subjective process depending on the expertise of the obstetrician.Digital signals from fetal monitors acquire parameters(i.e.,fetal heart rate,contractions,acceleration).Objective:This paper aims to classify the CTG readings containing imbalanced healthy,suspected,and pathological fetus readings.Method:We perform two sets of experiments.Firstly,we employ five classifiers:Random Forest(RF),Adaptive Boosting(AdaBoost),Categorical Boosting(CatBoost),Extreme Gradient Boosting(XGBoost),and Light Gradient Boosting Machine(LGBM)without over-sampling to classify CTG readings into three categories:healthy,suspected,and pathological.Secondly,we employ an ensemble of the above-described classifiers with the oversamplingmethod.We use a random over-sampling technique to balance CTG records to train the ensemble models.We use 3602 CTG readings to train the ensemble classifiers and 1201 records to evaluate them.The outcomes of these classifiers are then fed into the soft voting classifier to obtain the most accurate results.Results:Each classifier evaluates accuracy,Precision,Recall,F1-scores,and Area Under the Receiver Operating Curve(AUROC)values.Results reveal that the XGBoost,LGBM,and CatBoost classifiers yielded 99%accuracy.Conclusion:Using ensemble classifiers over a balanced CTG dataset improves the detection accuracy compared to the previous studies and our first experiment.A soft voting classifier then eliminates the weakness of one individual classifier to yield superior performance of the overall model.

An Ensemble Machine Learning Technique for Stroke Prognosis

Stroke is a life-threatening disease usually due to blockage of blood or insufficient blood flow to the brain.It has a tremendous impact on every aspect of life since it is the leading global factor of disability and morbidity.Strokes can range from minor to severe(extensive).Thus,early stroke assessment and treatment can enhance survival rates.Manual prediction is extremely time and resource intensive.Automated prediction methods such as Modern Information and Communication Technologies(ICTs),particularly those inMachine Learning(ML)area,are crucial for the early diagnosis and prognosis of stroke.Therefore,this research proposed an ensemble voting model based on three Machine Learning(ML)algorithms:Random Forest(RF),Extreme Gradient Boosting(XGBoost),and Light Gradient Boosting Machine(LGBM).We apply data preprocessing to manage the outliers and useless instances in the dataset.Furthermore,to address the problem of imbalanced data,we enhance the minority class’s representation using the Synthetic Minority Over-Sampling Technique(SMOTE),allowing it to engage in the learning process actively.Results reveal that the suggested model outperforms existing studies and other classifiers with 0.96%accuracy,0.97%precision,0.97%recall,and 0.96%F1-score.The experiment demonstrates that the proposed ensemble voting model outperforms state-of-the-art and other traditional approaches.

A Fused Machine Learning Approach for Intrusion Detection System

The rapid growth in data generation and increased use of computer network devices has amplified the infrastructures of internet.The interconnectivity of networks has brought various complexities in maintaining network availability,consistency,and discretion.Machine learning based intrusion detection systems have become essential to monitor network traffic for malicious and illicit activities.An intrusion detection system controls the flow of network traffic with the help of computer systems.Various deep learning algorithms in intrusion detection systems have played a prominent role in identifying and analyzing intrusions in network traffic.For this purpose,when the network traffic encounters known or unknown intrusions in the network,a machine-learning framework is needed to identify and/or verify network intrusion.The Intrusion detection scheme empowered with a fused machine learning technique(IDS-FMLT)is proposed to detect intrusion in a heterogeneous network that consists of different source networks and to protect the network from malicious attacks.The proposed IDS-FMLT system model obtained 95.18%validation accuracy and a 4.82%miss rate in intrusion detection.

N-Body Problem Extension Hypothesis

A system of mutually interacting point-mass objects is considered.All objects act on each other with mutual forces.A consistent relativity of motion based on the elimination of the observer is introduced.The center of inertia of the entire system is a common reference point.Central and mutual quantities are defined and the relationship between them is derived.A simple method of numerical approximation of the evolution of the central motion is presented.The scale invariance of the classical n-body problem is used to challenge the physical correctness of the problem formulation.Subsequently,a hypothesis is expressed about the extension of Coulomb’s law as well as Newton’s law of gravity.The consequences of the hypothesis are illustrated by the simulation of the helium-2 atom and the simulation of the motion of the stars S2,S4716 in the vicinity of the black hole in the core of our galaxy.

Automated File Labeling for Heterogeneous Files Organization Using Machine Learning

File labeling techniques have a long history in analyzing the anthological trends in computational linguistics.The situation becomes worse in the case of files downloaded into systems from the Internet.Currently,most users either have to change file names manually or leave a meaningless name of the files,which increases the time to search required files and results in redundancy and duplications of user files.Currently,no significant work is done on automated file labeling during the organization of heterogeneous user files.A few attempts have been made in topic modeling.However,one major drawback of current topic modeling approaches is better results.They rely on specific language types and domain similarity of the data.In this research,machine learning approaches have been employed to analyze and extract the information from heterogeneous corpus.A different file labeling technique has also been used to get the meaningful and`cohesive topic of the files.The results show that the proposed methodology can generate relevant and context-sensitive names for heterogeneous data files and provide additional insight into automated file labeling in operating systems.

Investigating adverse events in long-term care facilities:a systematized review

Objective:This systematized review aimed to synthesize the results of empirical studies focused on the types and factors of adverse events(AEs)that contribute to them in long-term care(LTC)settings.Methods:The search was conducted in Pro Quest,Scopus,and Pub Med in January 2021 and resulted in 1057 records.The content analysis method was used in the data analysis.Results:In all,35 studies were identified as relevant for the review.The analysis revealed 133 different types of AEs and 60 factors that contributed to them.Conclusions:In LTC,various AEs occur,most of which are preventable,while many factors that influence their occurrence could be significantly modifiable.Through an effective analysis of AEs in LTC,it is possible to minimize their occurrence and,at the same time,minimize their negative impact on all par ties concerned.