Proposed Classification System Research Articles

Enhancing Fault Identification, Classification and Location Accuracy in Transmission Lines

This research paper presents a proposed system for fault identification, classification and location in transmission lines using a Support Vector Machine (SVM)-based technique in conjunction with a Positive Sequence Analyzer. The objective is to develop an accurate and reliable method for identifying, classifying and locating different fault types in transmission lines. The proposed system leverages the capabilities of SVMs in handling high-dimensional feature spaces and the fault signature extraction capabilities of the Positive Sequence Analyzer. Experimental evaluations are conducted to assess the performance and effectiveness of the proposed system, comparing it with existing fault identification and classification methods. The results demonstrate the superior performance and robustness of the SVM-based technique utilizing the Positive Sequence Analyzer, providing a valuable contribution to fault management and system reliability in transmission line networks.

A detailed study of resampling algorithms for cyberattack classification in engineering applications.

The evolution of engineering applications is highly relevant in the context of protecting industrial systems. As industries are increasingly interconnected, the need for robust cybersecurity measures becomes paramount. Engineering informatics not only provides tools for knowledge representation and extraction but also affords a comprehensive spectrum of developing sophisticated cybersecurity solutions. However, safeguarding industrial systems poses a unique challenge due to the inherent heterogeneity of data within these environments. Together with this problem, it's crucial to acknowledge that datasets that simulate real cyberattacks within these diverse environments exhibit a high imbalance, often skewed towards certain types of traffics. This study proposes a system for addressing class imbalance in cybersecurity. To do this, three oversampling (SMOTE, Borderline1-SMOTE, and ADASYN) and five undersampling (random undersampling, cluster centroids, NearMiss, repeated edited nearest neighbor, and Tomek Links) methods are tested. Particularly, these balancing algorithms are used to generate one-vs-rest binary models and to develop a two-stage classification system. By doing so, this study aims to enhance the efficacy of cybersecurity measures ensuring a more comprehensive understanding and defense against the diverse range of threats encountered in industrial environments. Experimental results demonstrates the effectiveness of proposed system for cyberattack detection and classification among nine widely known cyberattacks.

Analysis of the classification of signs of the right to work by civil servants in the conditions of the European integration of Ukraine

This article is devoted to analyzing the classification of signs of the right to work of civil servants. In modern society, the right to work for public servants is a key element in ensuring effective public service. The classification of the signs of this right is important for determining the duties and responsibilities of civil servants. In particular, this may include aspects such as professional qualifications, level of education, work experience, knowledge of legislation, and ethical standards. The specificity of the right to work for a civil servant is the existence of a valid contract that defines the terms of employment and the obligations of the parties. An important aspect is the ability to interact and cooperate with colleagues and senior managers, which contributes to building an effective work team.
 It is expedient to summarize that the classification of signs of the right to work of civil servants is a relevant topic of research, which allows a better understanding of the main aspects and requirements for the activity of this category of employees. The study of this issue can be useful for improving the system of management of civil servants and increasing the efficiency of public administration as a whole.
 The author analyzed the approaches of other scientists regarding the analysis of this or that aspect of the right to work of civil servants in the conditions of the European integration of Ukraine, as well as certain features or features of this legal phenomenon. Criticism of certain positions of scientists is provided, and the author’s own position is substantiated. The norms of current legislation (both constitutional and special) relating to the issue of the right to work of civil servants are given. The need to update and improve the current legislation in the specified area was emphasized.
 The author of the article concludes that his proposed system of classification of the signs of the right to work of civil servants allows for a more thorough investigation of this problem. The scientist has examined in detail the essence and meaning of each of the mentioned general and special features of the right to work of civil servants.

Embedded artificial intelligence system using deep learning and raspberrypi for the detection and classification of melanoma

<div lang="EN-US"><span>Melanoma is a kind of skin cancer that originates in melanocytes responsible for producing melanin, it can be a severe and potentially deadly form of cancer because it can metastasize to other regions of the body if not detected and treated early. To facilitate this process, Recently, various computer-assisted low-cost, reliable, and accurate diagnostic systems have been proposed based on artificial intelligence (AI) algorithms, particularly deep learning techniques. This work proposed an innovative and intelligent system that combines the internet of things (IoT) with a Raspberry Pi connected to a camera and a deep learning model based on the deep convolutional neural network (CNN) algorithm for real-time detection and classification of melanoma cancer lesions. The key stages of our model before serializing to the Raspberry Pi: Firstly, the preprocessing part contains data cleaning, data transformation (normalization), and data augmentation to reduce overfitting when training. Then, the deep CNN algorithm is used to extract the features part. Finally, the classification part with applied Sigmoid Activation Function. The experimental results indicate the efficiency of our proposed classification system as we achieved an accuracy rate of 92%, a precision of 91%, a sensitivity of 91%, and an area under the curve- receiver operating characteristics (AUC-ROC) of 0.9133.</span></div>

Femoral bone defect classifications in revision total hip arthroplasty: a comprehensive review and proposal of a new algorithm of management.

The number of primary total hip arthroplasties (THAs) and revisions is expected to steadily grow in the future. The femoral revision surgery can be technically demanding whether severe bone defects need to be addressed. The femoral revision aims to obtain a proper primary stability of the stem with a more proximal fixation as possible. Several authors previously proposed classification systems to describe the morphology of the bony femoral defect and to drive accordingly the surgeon in the revision procedure. The previous classifications mainly considered cortical and medullary bone at the level of the defect of poor quality by definition. Therefore, the surgical strategies aimed to achieve a distal fixation bypassing the defect or to fill the defect with bone impaction grafting or structured bone grafts up to the replacement of the proximal femur with megaprosthesis. The consensus on a comprehensive and reliable classification system and management algorithm is still lacking. A new classification system should be developed taking into account the bone quality. The rationale of a new classification is that 'functional' residual bone stock could be present at the level of the defect. Therefore, it can be used to achieve a primary (mechanical) and secondary (biological) stability of the implants with a femoral fixation more proximal as possible.

Comparative study of respiratory sounds classification methods based on cepstral analysis and artificial neural networks

In this paper, we investigated and evaluated various machine learning-based approaches for automatically detecting wheezing sounds. We conducted a comprehensive comparison of these proposed systems, assessing their classification performance through metrics such as Sensitivity, Specificity, and Accuracy. The main approach to developing a machine learning-based system for classifying respiratory sounds involved the combination of a technique for extracting features from an unknown input sound with a classification method to determine its belonging class. The characterization techniques used in this study are based on the cepstral analysis, which was extensively employed in the automatic speech recognition field. While MFCC (Mel-Frequency Cepstral Coefficients) feature extraction methods are commonly used in respiratory sounds classification, our study introduces a novelty by employing GFCC (Gammatone-Frequency Cepstral Coefficients) and BFCC (Bark-Frequency Cepstral Coefficients) for this purpose. For the classification task, we employed two types of neural networks: the MLP (Multilayer Perceptron), a feedforward neural network, and a variant of the LSTM (Long Short-Term Memory) recurrent neural network called BiLSTM (Bidirectional LSTM). The proposed classification systems are evaluated using a database consisting of 497 wheezing segments and 915 normal respiratory segments, which are recorded from individuals diagnosticated with asthma and individuals without any respiratory issues, respectively. The highest classification performance was achieved by the BFCC-BiLSTM model, which demonstrated an exceptional accuracy rate of 99.8%.

Classification of cotton water stress using convolutional neural networks and UAV-based RGB imagery

<p>Embracing smart irrigation management techniques empowers growers to irrigate with greater efficiency, thereby promoting sustainable agricultural production. In this context, growers often rely on crop evapotranspiration (ETc) as a key factor in making informed irrigation decisions, underscoring the significance of accurately determining and spatially mapping crop water status. Technological progress, exemplified by the emergence of unmanned aerial vehicles (UAVs), has brought about a revolutionary shift in agricultural monitoring. UAV platforms can capture high-resolution images with centimeter-level spatial accuracy and offer higher temporal coverage compared to satellite imagery. Considering these advancements, this study introduces a robust method for classifying water stress in cotton using a compact UAV platform and convolutional neural networks (CNN). The experiment was conducted at the USDA-ARS Cropping Systems Research Laboratory (CSRL) in Lubbock, Texas, where the cotton field was divided into 12 drip zones. The study included three replications to evaluate four irrigation treatments: “rainfed”, “full irrigation”, “percent deficit of full irrigation”, and “time delay of full irrigation”. The results demonstrated that the CNN model successfully classified the cotton water stress using the UAV-based RGB image, achieving an overall best prediction accuracy of approximately 91%. By segmenting the original cotton images into separate canopy and soil areas using morphological image processing methods, the authors also isolated and analyzed the individual contributions of these components to cotton water stress. Additionally, a random forest classifier revealed the relative importance of different image features in the classification process through feature importance analysis. These findings highlighted the state-of-the-art performance of the proposed system in cotton water stress classification and provided valuable insights into the key image features contributing to accurate classification. The authors concluded that integrating UAV-based RGB imagery and CNN models had great potential for assessing water stress in cotton.<strong></strong></p>

Classification of cotton water stress using convolutional neural networks and UAV-based RGB imagery

<p>Embracing smart irrigation management techniques empowers growers to irrigate with greater efficiency, thereby promoting sustainable agricultural production. In this context, growers often rely on crop evapotranspiration (ETc) as a key factor in making informed irrigation decisions, underscoring the significance of accurately determining and spatially mapping crop water status. Technological progress, exemplified by the emergence of unmanned aerial vehicles (UAVs), has brought about a revolutionary shift in agricultural monitoring. UAV platforms can capture high-resolution images with centimeter-level spatial accuracy and offer higher temporal coverage compared to satellite imagery. Considering these advancements, this study introduces a robust method for classifying water stress in cotton using a compact UAV platform and convolutional neural networks (CNN). The experiment was conducted at the USDA-ARS Cropping Systems Research Laboratory (CSRL) in Lubbock, Texas, where the cotton field was divided into 12 drip zones. The study included three replications to evaluate four irrigation treatments: “rainfed”, “full irrigation”, “percent deficit of full irrigation”, and “time delay of full irrigation”. The results demonstrated that the CNN model successfully classified the cotton water stress using the UAV-based RGB image, achieving an overall best prediction accuracy of approximately 91%. By segmenting the original cotton images into separate canopy and soil areas using morphological image processing methods, the authors also isolated and analyzed the individual contributions of these components to cotton water stress. Additionally, a random forest classifier revealed the relative importance of different image features in the classification process through feature importance analysis. These findings highlighted the state-of-the-art performance of the proposed system in cotton water stress classification and provided valuable insights into the key image features contributing to accurate classification. The authors concluded that integrating UAV-based RGB imagery and CNN models had great potential for assessing water stress in cotton.<strong></strong></p>

Radiographic features of congenital thumb duplication type C2 of Wu et al. classification: new subtypes and surgical strategies.

This study aimed (i) to evaluate the radiographic characteristics of patients with congenital thumb duplication (CTD) type C2 according to the classification of Wu et al., (ii) to describe the various subtypes of type C2 CTD, and (iii) to propose a classification system that allows the identification of different surgical strategies based on the radiographic anatomy of this specific subtype of duplication. We retrospectively reviewed 92 patients (92 thumbs) with type C2 CTD according to the Wu et al. classification in our institution between August 2015 and April 2021. All CTDs were classified according to the interphalangeal joint alignment of the main thumb at the posteroanterior radiograph of the thumb before operation: type I (no deviation), type II (ulnar deviation), and type III (radial deviation). All CTDs (n = 92) could be classified according to the proposed classification system: 76 (82.6%) were type I, 10 (10.9%) were type II, and six were type III (6.5%). According to the Kim system of subtype classification, there were 55 (59.8%) type 1, 24 (26.1%) type 2, and 13 (14.1%) type 3 cases. The suggested classification completes the Wu et al. system and has the potential to guide surgical treatment in children with type C2 CTD. III.

Research Classification using the Malaysian Research and Development Classification System (MRDCS)

Classifying research projects or research publication into research areas is crucial in many statistical analyses including for example in bibliometric analysis. Research publication classification usually takes place at the level of journals, where subject categories based on available databases like the Web of Science or SciVal are some of the popular classification systems. However, journal-level classification systems have limitation especially when classifying multidisciplinary journals. To overcome this limitation, it is suggested that a classification system that can classify research be constructed based on the areas of research proposals and their related publications. Successful grant applications/research projects and their publications were clustered into research areas based on the Malaysian Research and Development Classification System (MRDCS) (6th Edition). A total of 1738 research projects managed by Universiti Malaya’s Research Cluster from year 2015 to 2017 were mapped. The strengths and the limitations of the proposed classification system are also discussed. Results indicate that out of the 20 research categories, Medical and Health Sciences, Social Sciences, Economics, Business and Management, Humanities, and Engineering and Technology emerge as the top five research categories with the highest critical mass value based on the number of projects. Classification mapping also found Engineering and Technology, Medical and Health Sciences, Material Sciences, Social Sciences, and Applied Sciences and Technologies to be the top five research categories that produced the highest number of outputs in terms of publications.

New classification of soils by seismic properties for the building code in Uzbekistan

ABSTRACT The accurate assessment of soil conditions is crucial for ensuring the seismic safety of buildings and structures. The existing classification of soils in the building code of Uzbekistan has several drawbacks that affect the accuracy of the input parameters of soil properties, the determination of the seismic intensity of the ground, and the underestimation of external environmental factors that affect seismic intensity. In response to these shortcomings, this paper presents the results of long-term engineering geological, macroseismic, and geophysical studies conducted to assess the influence of ground conditions on seismic intensity parameters. Based on the findings, a new classification of soils according to seismic properties is proposed. The classification system considers the unique characteristics of each soil type and provides a more accurate representation of their seismic properties. The proposed classification system is expected to provide valuable insights for seismic hazard assessment and earthquake-resistant design of structures.

Comparative analysis of machine learning algorithms on myoelectric signal from intact and transradial amputated limbs

<p>Control strategies of smart hand prosthesis-based myoelectric signals in<br />recent years don't provide the patients with the sensation of biological<br />control of prostheses hand fingers. Therefore, in current work<br />hyperparameters optimization in machine learning algorithm and hand<br />gesture recognition techniques were applied to the myoelectric signal-based<br />on residual muscles contraction of the amputees corresponding to intact<br />forearm limb movement to improve their biological control. In this paper,<br />myoelectric signals are extracted using the MYO armband to recognize ten<br />gestures from ten volunteers (healthy and transradial amputation) on the<br />forearm, thereafter the noise of myoelectric signals using a notch filter (NF)<br />is removed. The proposed classification system involved two machine<br />learning algorithms: (1) the decision tree (DT), tri-layered neural network<br />(TLNN), k-nearest-neighbor (KNN), support vector machine (SVM) and<br />ensemble boosted tree (EBT) classifiers. (2) the optimized machine learning<br />classifiers, i.e., OKNN, OSVM, OEBT with optical diffraction tomography<br />(ODT) and ommatidia detecting algorithm (ODA). The experimental results<br />of classifiers comparison pointed out an algorithm that outperformed with<br />high accuracy is OEBT closely followed by OKNN achieves an accuracy of<br />97.8% and 97.1% for intact forearm limb, while for transradial amputation<br />with an accuracy of 91.9% and 91.4%, respectively.</p>

Guiding principles and proposed classification system for the responsible adoption of artificial intelligence in scientific writing in medicine.

The integration of large language models (LLMs) and artificial intelligence (AI) into scientific writing, especially in medical literature, presents both unprecedented opportunities and inherent challenges. This manuscript evaluates the transformative potential of LLMs for the synthesis of information, linguistic enhancements, and global knowledge dissemination. At the same time, it raises concerns about unintentional plagiarism, the risk of misinformation, data biases, and an over-reliance on AI. To address these, we propose governing principles for AI adoption that ensure integrity, transparency, validity, and accountability. Additionally, guidelines for reporting AI involvement in manuscript development are delineated, and a classification system to specify the level of AI assistance is introduced. This approach uniquely addresses the challenges of AI in scientific writing, emphasizing transparency in authorship, qualification of AI involvement, and ethical considerations. Concerns regarding access equity, potential biases in AI-generated content, authorship dynamics, and accountability are also explored, emphasizing the human author's continued responsibility. Recommendations are made for fostering collaboration between AI developers, researchers, and journal editors and for emphasizing the importance of AI's responsible use in academic writing. Regular evaluations of AI's impact on the quality and biases of medical manuscripts are also advocated. As we navigate the expanding realm of AI in scientific discourse, it is crucial to maintain the human element of creativity, ethics, and oversight, ensuring that the integrity of scientific literature remains uncompromised.

Refining PRRSV-2 genetic classification based on global ORF5 sequences and investigation of their geographic distributions and temporal changes.

Porcine reproductive and respiratory syndrome virus (PRRSV) is an important swine pathogen affecting the global swine industry. The first open reading frame 5 (ORF5)-based genetic lineage classification system describing global PRRSV-2 genetic diversity was introduced over a decade ago. Although refinements have been proposed for the predominant lineage in the USA (lineage 1), PRRSV-2 phylogenetic classification system at international levels has not been thoroughly evaluated and updated since 2010. In this study, based on analysis of 82,237 global ORF5 sequences reported during 1989-2021, we classified PRRSV-2 into 11 genetic lineages (L1‒L11) and 21 sublineages (L1A‒L1F, L1H‒L1J, L5A‒L5B, L8A‒L8E, and L9A‒L9E). The proposed classification system is flexible for growth if additional lineages, sublineages, or more granular classifications are needed. For example, for more fine-scale epidemiological investigation, L1C was further divided into five groups (L1C.1‒L1C.5), with L1C.5 corresponding to the recently emerged L1C variant. Comparison between restriction fragment length polymorphism (RFLP) typing and phylogenetic classification revealed the inaccuracy of using RFLP to determine PRRSV-2 genetic relatedness in most scenarios. Genetic homology of six commercial PRRSV-2 vaccines to each lineage/sublineage and detection frequency of vaccine-like viruses were determined. Global geographic distribution of each lineage/sublineage was presented. Temporal dynamic changes of PRRSV-2 in the USA during 1989-2021 were investigated by analyzing 73,092 ORF5 sequences. In summary, this study refined PRRSV-2 ORF5-based phylogenetic classification and investigated the geographic distribution and temporal changes of PRRSV-2 at the lineage/sublineage levels. The refined classification system and molecular epidemiology data in this study will be invaluable for future characterization of PRRSV-2.IMPORTANCEIn this study, comprehensive analysis of 82,237 global porcine reproductive and respiratory syndrome virus type 2 (PRRSV-2) open reading frame 5 sequences spanning from 1989 to 2021 refined PRRSV-2 genetic classification system, which defines 11 lineages and 21 sublineages and provides flexibility for growth if additional lineages, sublineages, or more granular classifications are needed in the future. Geographic distribution and temporal changes of PRRSV-2 were investigated in detail. This is a thorough study describing the molecular epidemiology of global PRRSV-2. In addition, the reference sequences based on the refined genetic classification system are made available to the public for future epidemiological and diagnostic applications worldwide. The data from this study will facilitate global standardization and application of PRRSV-2 genetic classification.

An operational classification system for the South Atlantic pre-salt rocks

Abstract The giant Aptian reservoirs and associated lacustrine rocks of the South Atlantic pre-salt section present a series of unique characteristics, which make the systems created for marine carbonate rocks quite inadequate for their classification. Based on our experience with the characterization of thousands of samples of the pre-salt reservoirs and associated deposits from the Brazilian basins, and on a wide literature evaluation, we propose an objective and operational system for the classification of the unusual yet extremely important pre-salt lacustrine rocks. The system allows the coherent record of structure, fabric, primary texture and composition, and main diagenetic modifications of the in situ and resedimented rock types. The in situ rocks are directly classified according to the original proportion among calcite spherulites, fascicular shrubs, and mud matrix. The resedimented rocks are classified according to the original volume of > 2 mm particles and the proportion between sand and mud, using calcirudite, calcarenite, and calcilutite as unbiased names. Such an approach can also be applied to the classification of other particulate carbonate rocks, avoiding the conceptual problems and dubious interpretation of depositional environment and reservoir quality of currently used classification systems. The direct, objective, and intuitive proposed classification system shall contribute to the understanding, exploration, and production of the extraordinary South Atlantic pre-salt petroleum province.

A proposed classification system for presigmoid approaches: a scoping review.

The "presigmoid corridor" covers a spectrum of approaches using the petrous temporal bone either as a target in treating intracanalicular lesions or as a route to access the internal auditory canal (IAC), jugular foramen, or brainstem. Complex presigmoid approaches have been continuously developed and refined over the years, leading to great heterogeneity in their definitions and descriptions. Owing to the common use of the presigmoid corridor in lateral skull base surgery, a simple anatomy-based and self-explanatory classification is needed to delineate the operative perspective of the different variants of the presigmoid route. Herein, the authors conducted a scoping review of the literature with the aim of proposing a classification system for presigmoid approaches. The PubMed, EMBASE, Scopus, and Web of Science databases were searched from inception to December 9, 2022, following the PRISMA Extension for Scoping Reviews guidelines to include clinical studies reporting the use of "stand-alone" presigmoid approaches. Findings were summarized based on the anatomical corridor, trajectory, and target lesions to classify the different variants of the presigmoid approach. Ninety-nine clinical studies were included for analysis, and the most common target lesions were vestibular schwannomas (60/99, 60.6%) and petroclival meningiomas (12/99, 12.1%). All approaches had a common entry pathway (i.e., mastoidectomy) but were differentiated into two main categories based on their relationship to the labyrinth: translabyrinthine or anterior corridor (80/99, 80.8%) and retrolabyrinthine or posterior corridor (20/99, 20.2%). The anterior corridor comprised 5 variations based on the extent of bone resection: 1) partial translabyrinthine (5/99, 5.1%), 2) transcrusal (2/99, 2.0%), 3) translabyrinthine proper (61/99, 61.6%), 4) transotic (5/99, 5.1%), and 5) transcochlear (17/99, 17.2%). The posterior corridor consisted of 4 variations based on the target area and trajectory in relation to the IAC: 6) retrolabyrinthine inframeatal (6/99, 6.1%), 7) retrolabyrinthine transmeatal (19/99, 19.2%), 8) retrolabyrinthine suprameatal (1/99, 1.0%), and 9) retrolabyrinthine trans-Trautman's triangle (2/99, 2.0%). Presigmoid approaches are becoming increasingly complex with the expansion of minimally invasive techniques. Descriptions of these approaches using the existing nomenclature can be imprecise or confusing. Therefore, the authors propose a comprehensive classification based on the operative anatomy that unequivocally describes presigmoid approaches simply, precisely, and efficiently.

Delayed Traumatic Intracerebral Hematoma: A Pathophysiological Classification and Literature Review.

Delayed traumatic intracerebral hematoma (DTICH) is a relatively common occurrence after a traumatic brain injury (TBI). Several case series have been performed to study DTICH, many of which offer different definitions of DTICH. Some definitions involve a delayed progression of an existing hemorrhage, and others involve a de novo intracerebral hematoma that was not evident on the initial trauma evaluation. We propose a classification system for DTICH that accounts for the subtleties in the clinical manifestation and pathophysiology of the different types of DTICH, with the ultimate goal of providing strategies to prevent and manage DTICH. Based on the senior author's clinical experience, we generated a classification system for DTICH, and each type of DTICH was illustrated with a case. We defined type 1A (case 1A), the classic presentation of DTICH as predominantly characterized in the literature, as an intracerebral hematoma unseen on initial computed tomography imaging that typically develops five days to one week following blunt or penetrating head trauma. We defined type 1B (case 1B) as a hematoma that forms after at least one week following trauma in areas of the brain initially hemorrhage-free. We defined type 2 (case 2) as a hematoma that develops rapidly following a surgical evacuation of a different hematoma. We defined type 3 (case 3) as a hematoma that develops after a traumatic head injury in areas of non-hemorrhagic contusion, usually frontal or temporal. A literature review was performed using select terms on PubMed to find articles related to DTICH, excluding articles describing DTICH from an underlying vascular injury. After performing the literature review and screening articles by title and/or abstract, a total of 79 articles were found to meet the inclusion and exclusion criteria. We recorded which type of DTICH from our classification system best correlated with the articles in our literature review. Taken together with results from the literature, the proposed classification system is based on the senior author's clinical experience. Overall, DTICH is a relatively common occurrence after head trauma, and our pathophysiologic classification has the potential to help outline future studies to recognize and prevent the development of DTICH.

Different Approaches of Laparoscopic Mesohepatectomy for Centrally Located Hepatocellular Carcinomas.

This study aimed to describe a simple and novel positional classification system for centrally located hepatocellular carcinoma (CL-HCC), based on which different surgical approaches for laparoscopic mesohepatectomy (LMH) were chosen. The data of patients with CL-HCC who underwent LMH between January 2017 and December 2021 were retrospectively analyzed. The positional classification method was used to locate tumors. In addition, different approaches were used during the surgery according to the classification type. All 98 patients underwent LMH, of whom 4 were converted to open surgery. Types I, Ⅱ, and Ⅲ were 24, 37, and 37, respectively. Blood transfusions were performed in only 7 patients, of which the amount was 800 (600, 900) mL. All the patients underwent R0 resection. The complication rate was 9.2% in all cases. No deaths occurred. Our proposed classification system and corresponding surgical approach in LMH is conducive to the successful completion of surgery.

Classification features of return losses of troop personnel: the problem of stress-associated mental disorders in modern military conflicts and emergencies

This study investigates the classification features of return losses of the personnel of the troops, specifically focusing on psychiatric profile losses and stress-associated mental disorders in the context of modern military conflicts and emergencies of various natures. The term return losses refers to individuals who have regained their combat and work capacity after a temporary loss during military conflicts and are deemed physically fit to resume their functional duties. Additionally, temporarily lost combat and working capacity refers to individuals unable to perform their functional duties for a short period, typically ranging from several minutes to 24 hours. This category includes individuals with stress-associated mental disorders. The research finds that at least 20% of victims in modern military conflicts and emergencies exhibit signs of mental maladaptation within the first day of battle or during the elimination of the medical and sanitary consequences. To address the classification of personnel losses in psychiatric and stress-related cases, as well as the inconsistency in existing terminology, a proposed classification system based on the ability to return to service is presented. Furthermore, new principles have been established to determine the return losses of military personnel, facilitating the provision of adequate and timely assistance for individuals affected by stress-associated mental disorders. The article also outlines medical and psychological correction measures at various levels and elements of first aid for combat stress-induced maladaptive phenomena. Additionally, the study recommends future research on developing a methodology to assess the size and structure of contingents consisting of individuals who temporarily lose their combat and working capacity due to modern enemy weapons during combat operations, as well as in diverse emergency situations.

Eccentric Resistance Training: A Methodological Proposal of Eccentric Muscle Exercise Classification Based on Exercise Complexity, Training Objectives, Methods, and Intensity

Eccentric resistance training that focuses on the lengthening phase of muscle actions has gained attention for its potential to enhance muscle strength, power, and performance (among others). This review presents a methodological proposal for classifying eccentric exercises based on complexity, objectives, methods, and intensity. We discuss the rationale and physiological implications of eccentric training, considering its benefits and risks. The proposed classification system considers exercise complexity and categorizing exercises by technical requirements and joint involvement, accommodating various skill levels. Additionally, training objectives are addressed, including (i) Sports Rehabilitation and Return To Sport, (ii) Muscle Development, (iii) Injury Prevention, (iv) Special Populations, and (v) Sporting Performance, proposing exercise selection with desired outcomes. The review also highlights various eccentric training methods, such as tempo, isoinertial, plyometrics, and moderate eccentric load, each with different benefits. The classification system also integrates intensity levels, allowing for progressive overload and individualized adjustments. This methodological proposal provides a framework for organizing eccentric resistance training programs, facilitating exercise selection, program design, and progression. Furthermore, it assists trainers, coaches, and professionals in optimizing eccentric training’s benefits, promoting advancements in research and practical application. In conclusion, this methodological proposal offers a systematic approach for classifying eccentric exercises based on complexity, objectives, methods, and intensity. It enhances exercise selection, program design, and progression in eccentric resistance training according to training objectives and desired outcomes.