System Malfunction Research Articles

UNSUPERVISED MACHINE LEARNING METHODS FOR REAL-TIME ANOMALY DETECTION IN ENDPOINTS

Among all the contemporary digital ecosystems, endpoint devices, including computers, servers, and IoT gadgets, represent at once the most important and the most vulnerable part of the infrastructure of any organization. Traditional security solutions very often cannot identify subtle and sophisticated threats that reveal their presence only through deviations from normal behavior. This paper presents the application of unsupervised machine learning methods-namely, Isolation Forest, One-Class Support Vector Machines, Gaussian Mixture Models and Local Outlier Factor-for real-time anomaly detection in endpoint devices. The efficacy, for this purpose, is shown in this paper using comprehensive telemetry data such as CPU usage, memory consumption, and network traffic, whether anomalies due to security breaches or system malfunction can be detected using these techniques.

Anomaly Detection in IoT Network Traffic Using Bidirectional 3D Quasi‐Recurrent Neural Network Optimize With Coati Optimization Algorithm

ABSTRACTAnomaly detection in Internet of Things (IOT) network traffic involves identifying abnormal patterns or behaviors, enabling early detection of potential security threats or system malfunctions in the IOT ecosystem. IoT encompasses a variety of networks, addressing not only security challenges within sensor networks, the internet, and mobile communication networks but also specifically focusing on issues related to privacy protection, information management, network authentication, and access control. In this manuscript, Anomaly Detection in IoT Network Traffic using a Bidirectional 3D Quasi‐Recurrent Neural Network with Coati Optimization Algorithm (ADIOT‐B3DQRNN‐COA) is proposed. Initially, the input data are collected from the DS2OS Dataset. Then, the collected data is fed into pre‐processing utilizing an Implicit Unscented Particle Filter (IUPF). The IUPF is used to remove the invalid data. Subsequently, the preprocessed data are sent into the Archimedes optimization algorithm (AOA) to select features. Seven characteristics from the DS2OS dataset are chosen using AOA. The selected features are then fed into a Bidirectional 3D Quasi‐Recurrent Neural Network (Bi‐3DQRNN) to classify anomaly detection in an Internet of Things network into the following categories: data probing, malicious control, malicious operation, scan, spying, incorrect configuration, DOS attack, and normal. To guarantee accurate classification of anomaly detection in IoT networks, Bi‐3DQRNN generally does not express any adaptation of optimization algorithms for figuring out the best parameters. Hence, the Coati Optimization Algorithm (COA) to optimize Bi‐3DQRNN accurately classifies anomaly detection in the IOT network. The proposed ADIOT‐B3DQRNN‐COA approach is implemented in MATLAB. The performance of the proposed method was examined utilizing performance metrics like Accuracy, Computational Time, F‐measure, Precision, Recall, and ROC. The proposed ADIOT‐B3DQRNN‐COA approach contains 32.15%, 28.31%, and 24.18% higher accuracy, 28.69%, 33.54%, and 19.46% higher Precision, and 24.50%, 32.34%, and 24.18% lower Error Rate compared with existing methods, such as Anomaly Detection in IOT network depend on enhanced metaheuristic feature selection optimization and hybrid deep neural network (DNN‐ADIOT‐NTT), Investigation on the stability of DRNN depend on the oversampling method in IDIIOT (DRNN‐PSO‐IDIIOT) and light gradient boosting machine in Anomaly Detection in IOT network: concurrent feature selection with hyperparameter tuning concurrent feature selection with hyperparameter tuning (SMSG‐SCADA‐SNN) respectively.

Early-life IL-4 administration induces long-term changes in microglia in the cerebellum and prefrontal cortex.

Microglia are crucial for brain development and their function can be impacted by postnatal insults, such as early-life allergies. These are characterized by an upregulation of interleukin (IL)-4 levels. Allergies share a strong comorbidity with Autism Spectrum Disorders (ASD) and Attention-Deficit/Hyperactivity Disorder (ADHD). We previously showed that early-life allergic asthma induces hyperactive and impulsive behaviors in mice. This phenotype was reproduced in animals administered with IL-4 in the second postnatal week. Mechanistically, elevated IL-4 levels prevented microglia-mediated engulfment of neurons in the cerebellum, resulting in a surplus of granule cells and consequent dysfunction in cerebellar connectivity. Here, we aimed to further understand the impact of early IL-4 administration in microglia of the cerebellum and the prefrontal cortex (PFC), two brain regions with protracted developmental programs and susceptible to immune system malfunction after birth. While IL-4 administration induced differential short-term effects on microglia in the cerebellum and PFC, both regions presented similar microglial features in adult mice. Although Sholl analysis did not reveal significant alterations in overall microglia morphology at postnatal day (P)10, the density of microglia was decreased in the cerebellum at this age, especially in the granular layer (GL), but remained unaltered in the PFC. Interestingly, the presence of microglia with phagocytic cups, morphological features important for whole-cell engulfment, was decreased in both regions. When assessing the long-term consequences of IL-4 administration, cerebellar and PFC microglia were hypo-ramified and exhibited increased overall density. Importantly, microglia alterations were exclusive to the GL of the cerebellum and the infralimbic region of the PFC. Our results show that postnatal elevated levels of IL-4 impair the percentage of microglia engaged in cell clearing in two brain regions with protracted developmental programs. Interestingly, IL-4-exposed microglia adapt a similar phenotype in the adult cerebellum and PFC. Our data suggest that this early-life increase in IL-4 levels is sufficient to elicit long-lasting alterations in microglia, potentially increasing cell susceptibility to later insults.

Age-stratification reveals age-specific intestinal microbiota signatures in juvenile idiopathic arthritis

ObjectiveJuvenile Idiopathic Arthritis (JIA) comprises diverse chronic inflammatory conditions driven by malfunction of the immune system. The intestinal microbiota is considered a crucial environmental factor correlating with chronic inflammatory diseases, and for JIA certain alterations in the microbiota have already been described.MethodsHere, we have characterized intestinal microbiota samples from 54 JIA patients and 38 pediatric healthy controls by conventional 16S rRNA sequencing and by single-cell analysis for phenotypic features by multi-parameter microbiota flow cytometry (mMFC), which complements the population-based taxonomic profiling with the characterization of individual bacterial cells.ResultsWe found age to be a crucial confounder in microbiota analyses of JIA patients. Age stratification revealed specific microbiota alterations neglected by the general comparison of JIA patients and pediatric controls.ConclusionAge groups presented distinct taxonomic profiles and microbiota phenotypic signatures which transitioned with age, highlighting changes in the microbiota-immune system interaction with age.

Analysis of potential risks during the control of an unmanned underwater vehicle (UUV)

This article explores the analysis of potential risks associated with controlling unmanned underwater vehicles (UUVs), focusing on operational, environmental, and technological challenges. UUVs play a critical role in marine research, defense, and offshore industries but are subject to unique risks due to limited visibility, communication latency, and unpredictable underwater conditions. While UUVs have revolutionized underwater exploration and navigation, their reliance on advanced technologies introduces unique challenges. The study identifies the primary risk domains of UUV operations, including operational risks, human-machine interaction vulnerabilities, and environmental unpredictability. Using comprehensive scenario analysis and data from practical implementations, the article evaluates key risks such as navigation errors, system malfunctions, and communication disruptions. It further examines how artificial intelligence (AI) can be leveraged to mitigate these risks through real-time trajectory optimization, adaptive learning, and enhanced situational awareness. This research incorporates scenario-based analyses for UUVs to evaluate the most critical risk factors affecting mission safety. Furthermore, it explores how modern technologies can mitigate these risks by enhancing trajectory optimization and monitoring of UUVs during underwater navigation. By providing a holistic understanding of UUV risks, the findings underscore the need for robust control systems and operator training protocols. The research contributes actionable insights for improving UUV safety, efficiency, and reliability, laying the groundwork for further advancements in maritime robotics

ASSESSMENT OF THE PROBABILITY OF OCCURRENCE OF EMERGENCY SITUATIONS DURING OPERATION OF THE PYROLYSIS PLANT

The article discusses the issues of reliability and safety of operation of a pyrolysis plant for the disposal of used tires, which, in the context of an increasing volume of waste and the need for their effective disposal, is an urgent topic. The problem statement is related to the identification of factors affecting the reliability of the installation and the development of methods to prevent emergencies.The purpose of the study is to analyz the reliability of the pyrolysis plant and develop recommendations for its improvement. To achieve this goal, the causes and probabilities of failures of system elements are investigated, an analysis using the failure tree method is carried out. This method made it possible to identify the weaknesses of the system and assess the probability of their failures, which is critical to prevent emergencies.An assessment of the reliability of the installation showed that the greatest contribution to the probability of accidents is made by a malfunction of the gas separation system. This element is the most vulnerable due to its complexity and importance in the pyrolysis process, as it is responsible for the safe and effective separation of pyrolysis products. Any malfunction of the gas separation system can lead to the accumulation of dangerous gases, increasing the risk of explosions and fires. The identified emergencies require special attention to the development of effective measures to minimize them.The article proposes methods to improve the reliability of the system, the main of which is the redundancy of the most vulnerable elements. This will significantly reduce the probability of failures and increase the overall reliability of the installation. In addition, it is recommended to carry out regular inspections and maintenance of all elements of the system. These measures will help to identify and eliminate potential malfunctions in a timely manner, which will significantly reduce the likelihood of failures that can lead to emergencies.Thus, ensuring the reliability and safety of a pyrolysis plant for the disposal of used tires requires an integrated approach. Enabling redundancy of vulnerable elements and regular maintenance will minimize the likelihood of failures that can lead to emergencies and guarantee stable and safe operation of the system. These measures will not only increase the efficiency of disposal, but also ensure reliable and long-term operation of the installation.This article focuses on the importance of identifying vulnerable elements of the system and conducting regular maintenance to improve the reliability and safety of operation of the pyrolysis plant.

Assessment of potential electronic warfare capabilities to suppress FPV-type UAV radio control and data transmission receivers

The modern stage of world aviation development is characterized by the creation of complexes with unmanned aerial vehicles (UAVs) of various functional purposes, their massive use and rapid improvement with continuous expansion of their nomenclature and methods of application. The rapid development of unmanned aerial vehicle (UAV) systems is due to their potential merits and advantages, primarily in terms of efficiency and cost, both in comparison with manned aircraft systems and other types of weapons and military equipment. Combat application of UAVs in the course of local wars and armed conflicts and its development trends show that the leading and determining role in the effective use of modern and advanced UAVs is played by radio-electronic systems that ensure the solution of the following tasks: communication, navigation, reconnaissance, guidance and control of onboard weapons. A malfunction of onboard radio-electronic systems significantly reduces the effectiveness of their use. Given this, a significant contribution to improving the effectiveness of the fight against UAVs can be achieved by utilizing the capabilities of electronic warfare (EW). The main peculiarities of technical development of UAVs are constant improvement of their tactical and technical characteristics and interference resistance to the impact of electronic warfare (EW), developed according to simplified schematic solutions. The aim of work to evaluate the main methods of electronic countermeasures of FPV-type UAV control and data transmission radio receivers. The main methods of countermeasures against FPV-type UAVs for protection of personnel and vulnerable elements of critical infrastructure of industrial facilities were evaluated. The article contains theoretical calculations and recommendations on the composition and requirements for modern UAV countermeasures.

Understanding prescribing errors for system optimisation: the technology-related error mechanism classification.

Technology-related prescribing errors curtail the positive impacts of computerised provider order entry (CPOE) on medication safety. Understanding how technology-related errors (TREs) occur can inform CPOE optimisation. Previously, we developed a classification of the underlying mechanisms of TREs using prescribing error data from two adult hospitals. Our objective was to update the classification using paediatric prescribing error data and to assess the reliability with which reviewers could independently apply the classification. Using data on 1696 prescribing errors identified by chart review in 2016 and 2017 at a tertiary paediatric hospital, we identified errors that were technology-related. These errors were investigated to classify their underlying mechanisms using our previously developed classification, and new categories were added based on the data. A two-step process was used to identify and classify TREs involving a review of the error in the CPOE and simulating the error in the CPOE testing environment. The technology-related error mechanism (TREM) classification comprises six mechanism categories, one contributing factor and 19 subcategories. The categories are as follows: (1) incorrect system configuration or system malfunction, (2) opening or using the wrong patient record, (3) selection errors, (4) construction errors, (5) editing errors, (6) errors that occur when using workflows that differ from a paper-based system (7) contributing factor: use of hybrid systems. TREs remain a critical issue for CPOE. The updated TREM classification provides a systematic means of assessing and monitoring TREs to inform and prioritise system improvements and has now been updated for the paediatric setting.

Robotic uro-oncology applications of SSI Mantra™ robot

ObjectiveTo report our preliminary clinical experience and outcomes of uro-oncology procedures conducted utilizing the SSI Mantra™ robotic surgical platform. MethodsData of consecutive patients who underwent various robot assisted uro-oncology procedures using the SSI Mantra™ platform at our institution between July 2022 and September 2023 were recorded. The specific surgical configuration employed with the SSI Mantra™ for these procedures were duly noted. We assessed the feasibility of these procedures with this novel robotic platform and report the outcomes. ResultsA total of 156 patients were operated with the SSI Mantra™ system. The spectrum of procedures performed included robot-assisted laparoscopic radical prostatectomy with bilateral extended pelvic lymph node dissection (n=77), robot-assisted radical cystectomy with bilateral extended pelvic lymph node dissection with extra-corporeal urinary diversion (n=39), robot-assisted radical nephrectomy (n=32), robot-assisted partial nephrectomy (n=6), robot-assisted radical nephro-ureterectomy with bladder cuff excision (n=1) and bilateral robot-assisted video endoscopic inguinal lymph node dissection (n=1). One robot-assisted radical prostatectomy had to be converted to open in view of system malfunction. However, no system related intra-operative complications or injuries were encountered. ConclusionThe SSI Mantra™ system demonstrates significant promise as an innovative robotic platform. In this single center experience, we have demonstrated the feasibility of a diverse array of surgical procedures using this platform. Further research, involving a larger cohort of patients, is imperative to refine the operative techniques and comprehensively understand the perioperative outcomes of the SSI Mantra™ system, particularly in comparison to other robotic surgical platforms.

Cholecystectomy with the Hugo™ robotic-assisted surgery system: the first general surgery clinical study in Korea.

The Hugo™ Robotic-Assisted Surgery (RAS) System is an emergent device in the robotic surgery field. This study aims to describe the first general surgery-focused clinical study in Korea using the novel Hugo™ RAS System. This study was a prospective, single-center, single-arm, confirmatory clinical study conducted at Seoul National University Hospital where 20 cholecystectomies were performed. To evaluate the safety and performance of the Hugo™ RAS System the incidence of conversion to laparoscopy or open surgery, major complication (Clavien-Dindo Grade ≥ III) rate, overall complication rate, readmission rate, and reoperation rate were evaluated. All parameters were assessed within 30 days post-procedure. Any device deficiencies encountered during our initial experience and device data such as setup, console, and operative times were also reported. We confirmed that our trial achieved the primary objective with a success rate of at least 95%. This was accomplished with no conversions to other types of surgery due to serious system malfunction and with only one major complication within 24 h post-procedure. The 20 consecutively enrolled patients had a median age and BMI of 58 years old and 23.9 kg/m2, respectively. The major complication rate was 10% (2/20 patients), the overall complication rate was 15% (3/20 patients), the readmission rate was 15% (3/20 patients), and the reoperation rate was 0% (0/20 patients). None of the complications were definitively device related. The median setup, console, and operative times were 16, 17, and 55 min, respectively. The device deficiency rate was 15% (3/20 patients), but all device deficiencies were minor, occurred before the first incision, and did not present a risk to the patient. Based on our initial experience with the Hugo™ RAS System, cholecystectomy is feasible and safe. This trial is registered with ClinicalTrials.gov (NCT05715827).

Quantitative detection of refrigerant charge faults in multi-unit air conditioning systems based on machine learning algorithms

Refrigerant charging discrepancies constitute the predominant malfunctions in air conditioning systems. Achieving the optimal charging level is crucial for system performance, underscoring the importance of precise refrigerant level prediction. This study introduces an algorithm designed for the quantitative detection of refrigerant charging errors by integrating the Markov Transition Field (MTF), Convolutional Neural Networks (CNN), and Multi-head Self-Attention (MSA) mechanisms. A high-precision enthalpy difference chamber was employed to establish a Variable Refrigerant Flow (VRF) refrigerant charging test bench. This setup facilitated the analysis of system parameter sensitivity to charging faults and aided in the creation of a training dataset for the algorithm. Comparative analysis was conducted against Support Vector Machines (SVM), Random Forests (RF), CNN with Self-Attention (AT), and MTF-CNN-MSA. The findings reveal that our method adeptly captures temporal dependencies and dynamic shifts in time series as visual representations, offering novel insights for discerning fault patterns within such data. Notably, the maximum pressure variations at high-pressure and low-pressure points were 0.25 MPa and 0.07 MPa, respectively, with temperature shifts of 12 °C and 3.5 °C at the high and low-temperature points. The high-pressure and high-temperature points are particularly sensitive to changes in refrigerant charging, and parameters from these sections were utilized to construct the dataset. The CNN-MSA algorithm demonstrates consistent performance across various fault types, effectively delineating fault characteristics. The accuracies achieved by SVM, RF, CNN-AT, and MTF-CNN-MSA were 84.38 %, 73.75 %, 88.13 %, and 93.75 %, respectively. In comparison, the CNN-MSA algorithm was able to more accurately detect refrigerant charge faults at different levels.

LncRNA NRIR serves as a biomarker for systemic lupus erythematosus and participates in the disease progression.

Systemic lupus erythematosus (SLE) is a complex autoimmune disorder characterized by a malfunction of the body's immune defense system. The objective of the present investigation was to examine the expression and diagnostic significance of NRIR in SLE and to prove whether it is involved in the progression of SLE. The study involved 110 participants, including 55 healthy individuals and 55 SLE patients. The expression levels of NRIR, miR-31-5p, and ICAM-1 were measured using qRT-PCR. The ROC curve was performed to assess the diagnostic significance of NRIR in SLE patients. Pearson correlation analysis was utilized to explore the relationship between NRIR and other indicators. Cytokines including IL-4, IL-6, and IL-21, along with IgG levels, were assessed using ELISA. The interaction between NRIR and miR-31-5p was validated using a dual-luciferase reporter assay. Upregulated expression of NRIR was observed in individuals with SLE, serving a diagnostic function for SLE. Additionally, abnormal expression of NRIR impacted the viability of CD4+ T cells within SLE patients. NRIR could negatively modulate the expression of miR-31-5p. LncRNA NRIR may be a potential biomarker for SLE and is likely involved in the progression of SLE.

High-precision optical modeling method for galvanometer-driven dual-camera systems.

To build a galvanometer-driven dual-camera sensing system, it is important to accurately correlate the wide-view image coordinates with the pan-tilt mirror angles for adjusting the incident light path of the zoom-in camera. Existing optical modeling methods assume sufficient target distance and simplify dual-camera optical centers as coincident. However, this simplification is not valid in many practical cases and might cause severe system malfunctions, such as complete loss tracking of important targets. To address this problem, we propose a novel approach, to the best of our knowledge, to facilitate high-precision optical modeling and calibration of galvanometer-driven dual-camera systems. The proposed method takes into consideration the dual-camera optical center misalignment issue and builds a model for accurate estimation and rectification of target localization errors under various optical configurations. Qualitative and quantitative experimental results demonstrate the superiority of our method, improving the performance of galvanometer-driven dual-camera systems for high-precision optical sensing applications.

ChronoBak: Automated cost-effective incremental backup software for large-scale data

Background: Data backup is an important component of data protection and management. It is especially imperative to back up large-scale data to guard against losses due to hardware failure, unauthorized access, software/system malfunction, and unintended deletion. However, businesses and organizations suffer from the financial investment required for data backup and recovery systems. Health-care systems suffer from data breaches and hacking incidents, which increase annually. Therefore, a cost-effective and highly secure backup approach is necessary. Methods: Seven different backup scripts were written in bash within Linux, and Crontab was used as the core chronological scheduler to execute the backup scripts on specific dates and times. These bash scripts, together with Crontab as their scheduler, were used to implement automated incremental backups on a Unix/Linux-based system. Results: ChronoBak is a collection of bash scripts that are compiled and executed through Crontab, which is a standard Unix/Linux function used to schedule hourly, daily, weekly, and monthly maintenance routines using Linux scripts. This strategy enabled us to implement cost-effective incremental backups within any time range. Conclusion: We developed ChronoBak software, which executes an automated incremental backup that is scheduled efficiently and systematically using standard and hidden directories, thereby providing an additional layer of security against cyber threats and system malfunctions.

Evaluating the blood Serum Hormones, Cholesterol Levels in Seminiferous Plasma and Certain Semen Parameters in Males with Asthenozoospermia

Infertility is a medical condition that afflicts many men around the world and has many and complex causes. It is a malfunction in the male reproductive system. Its causes can be genetic, hormonal or a defect of semen components. This study was included two groups, the infertile (Asthinozoospermia) and the fertile or the control group (Normozoospermia), where each group consisted of 33 men average age (31.57 ± 1.11, 30.93 ± 1.04) respectively. Seminal fluid analysis (SFA) were conducted for them according to the recommendations of the World Health Organization (WHO), and the concentration level of cholesterol in seminal plasma was estimated using ELISA (Enzyme Linked Immuno-Sorbent Assay) and the concentration levels of blood serum hormones (FSH, LH, Prolactin, Testosterone) were estimated using the Chemiluminescence Immunoassay System (CL-900i) device. The study showed great convergence in the average age, BMI, semen volume and semen pH at asthenozoospermia group and Normazoospermia group, without a statistical significant difference (P>0.05). But there was a statistical significant difference (P<0.01) between Asthenozoospermia group and Normozoospermia group at sperm count, Progressive motility %, No-progressive motility % and immotile%. There was also, a significant difference between the two groups in the seminal plasma cholesterol concentration, (P<0.01). While there was no significant difference(P<0.05) between the two groups in the level of serum FSH, LH, Prolactin and Testosterone concentrations.

Numerical investigation and rapid prediction of the erosion rate of gate valve in gas-solid flow

Gate valves are essential control components in pneumatic conveying systems and often experience erosion on their sealing structures due to impacts from solid particles. The erosion leads to a degradation of the sealing effectiveness and, in severe cases, system malfunction. To address this issue, a numerical model based on computational fluid dynamics (CFD) is developed for the simplified two-dimensional gate valve. The two-way Euler-Lagrange method is utilized to investigate the characteristics of gas-solid two-phase flow and erosion rate. The simulation procedure is verified by comparing the simulation results with experimental data. Results show that the sealing surface separates the free shear layer above the cavity and a low-velocity zone is created on the sealing surface. The particles moving at the downstream bottom are the main particles that impact the sealing surface. The erosion results show that the maximum erosion rate occurs near the top of the sealing surface. And the low-velocity zone reduces the erosion rate within that region. Particle diameter, gas velocity, and valve opening have an important effect on erosion on the sealing surface. The erosion rate increases as the valve opening decreases. The value of the maximum erosion rate increases with particle diameter and gas velocity, and its position changes. Finally, based on the established numerical model, a prediction model for the sealing surface erosion rate is constructed using the surrogate model approach.

Trust in Automation (TiA): Simulation Model, and Empirical Findings in Supervisory Control of Maritime Autonomous Surface Ships (MASS)

Over the past three decades, Trust in Automation (TiA) has been the subject of extensive research. However, a large portion of the research takes a “static” approach to modeling trust and views trust as a linear unidirectional phenomenon. This view fails to recognize that trust is a dynamic construct that changes over time as an outcome of prolonged interaction with automation. The present study aims to address this gap and explore the nonlinear dynamic nature of trust by developing a simulation model of Trust in Automation (TiA) that can demonstrate trust evolution, deterioration, and recovery within the context of supervisory control of Maritime Autonomous Surface Ships (MASS). Employing System Dynamics (SD) approach, the model captures trust’s non-linear and reciprocal nature through dynamic feedback loops, producing behavioral patterns consistent with empirical observations of trust. The simulation results showcase the crucial role of initial trust conditions and the alignment of expectations with system performance in fostering trust and effective automation use. The study also explores the timing of system malfunctions, revealing that early faults have a greater negative impact on trust compared to later faults of the same magnitude. We tested a segment of the proposed model in an experimental study involving 30 human participants to investigate the effects of automation malfunctions on operators’ trust and behavioral responses during the supervisory control of MASS. Results not only validated the proposed model but demonstrated a significant decline in perceived reliability and trust in automation as well as the monitoring strategy after the automation malfunction.

Uniportal Laser-Assisted Video-Assisted Thoracoscopy (U-LA-VATS) for Lung Metastasectomy: Technical Description, Peri-Operative Results and Pertinent Literature Review.

Pulmonary metastasectomy (PM) is a well-established treatment that is able to contribute to the cure of oligometastatic cancer. Surgery should adopt the most lung-sparing approach possible to preserve pulmonary function (and, consequently, the quality of life) and to spare the lung for potential additional lung resections. In this framework, laser technology has been introduced in recent decades, but only few experiences combining laser technology with VATS approaches have been reported till now. The main focus of this manuscript is to report our institutional experience in performing lung-sparing laser-assisted PM by uniportal VATS (uniportal laser-assisted VATS: U-LA-VATS). The surgical technique and peri-operative results from our series of patients were herein presented and compared with the pertinent literature. Methods: Between March 2021 and November 2023, among 98 patients who underwent PM, a total of 24 patients (18 men (75%); 6 women (25%); mean age 61.4 years; age range 13-83 years) were treated with laser-assisted PM at our institution. Patients who underwent anatomical resection were excluded for the purpose of the analysis. The U-LA-VATS procedure adopted a modified laser-assisted lung resection technique for performing PM via VATS. Dedicated instruments are used, characterized by a long shape and a curved shape, with distal and proximal articulations. A surgical laser system (Thulium + Diodo OUTPUT 30-10 W, Quanta System S.p.a., Solbiate Olona, Italy) was used, and a 550-μm sterile optical fibre conducted through a specific thoracoscopic handpiece was introduced in the lowest part of the incision. Peri-operative results were analysed in all cohort and compared according to the surgical technique. Moreover, these results were compared with those reported in the literature. Comprehensive research of the literature was conducted on PubMed from 2000 to 2024. A review was performed and reported in line with the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) statement. Results: In 12 cases (50%), thulium laser-assisted resection was performed using uniportal video-assisted thoracic surgery (VATS), and in the other cases (12, 50%), a (mini)thoracotomy access was adopted. In the thoracotomy group, the mean duration of surgery was 95 ± 57.7 min; meanwhile, it was 73.5 ± 35.5 in the uniportal VATS group. At the univariate analysis, this difference resulted to be statistically significant (p value 0.025). We did not observe intra-operative complications or remarkable malfunction of the laser system. We also did not report major complications after surgery; also the air-leak rate was 8.3% and 0% after thoracotomic and VATS procedures, respectively. Surgical margins were free from disease in all cases. Major and minor post-op complication rates were similar in both groups. The mean hospitalization after surgery was 2.9 ± 0.3 days for the uniportal VATS group and 3.7 ± 0.9 days for the thoracotomy group, this difference being statistically significant at the univariate analysis (p value = 0.015). Conclusions: U-LA-VATS is a safe and effective procedure, able to combine a parenchymal sparing exeresis with a mini-invasive approach. This procedure is associated with a shorter hospital stay compared with PM performed by a thoracotomic approach. Compared with the selected works for the review, our series is the only one describing the use of laser resection combined with a uniportal VATS approach.

A smooth switching strategy for steady-state operation control and fault transient control of doubly fed induction generator

Abstract As the proportion of wind power generation systems in power systems continues to rise, their dynamic response during system malfunctions has gained significant importance. As the grid short-circuit ratio continues to decrease, traditional fault ride-through algorithms for wind power generators may no longer be applicable. This is evidenced by voltage oscillations under significant disturbances and overvoltage issues during low-voltage ride-through, which are further aggravated during asymmetric fault conditions. Therefore, this paper focuses on the low-voltage ride-through issues of doubly fed induction wind power generators in weak power grids. It investigates the steady-state operation and transient control schemes and proposes a smooth transition strategy for steady-state and fault transient operations of doubly fed wind power generators. This approach guarantees the synchronization of active and reactive power, mitigates steady-state reactive power imbalance, and prevents voltage fluctuations triggered by recurring low-voltage ride-through occurrences. Furthermore, during fault recovery, a ramped active power restoration approach is employed to mitigate the coupling of active and reactive power introduced by the phase-locked loop, enabling rapid and stable restoration of active and reactive power outputs. As a result, superior dynamic performance is achieved during both symmetric and asymmetric fault processes. Finally, the superiority of the proposed smooth transition strategy under different fault scenarios is validated through simulations with the RTLAB platform.

Establishing a user demand hierarchy model driven by a mental model for complex operating systems

In complex human-computer interactions, issues such as task failures, system malfunctions, and frequent accidents caused by user errors are common. Therefore, it is necessary to study complex system interactions to enhance overall efficiency. This study focuses on the task interface of a digital twin system for computer numerical control (CNC) machine tools and examines the relationship between the user mental model and interface design elements. Key mental information is obtained through questionnaires and interviews, forming the basis for establishing a user mental model. High-frequency information words are extracted, experimental samples are designed, and importance rating surveys are conducted. Quantitative analysis methods, including factor analysis and weight calculation, are utilized to analyze the needs of target users. Consequently, a user demand hierarchy model is constructed. This approach aims to effectively reduce user errors in the human-computer interaction process within complex systems and enhance cognitive efficiency.