Abnormal Conditions Research Articles

An advanced machine vision-based method for abnormal detection of transverse vibrations in ship propulsion shafting

The working environment of ship propulsion shafting is harsh and the force condition is complex, which often produces all-directional vibration. Its working condition will directly affect the navigation performance of the ship. To overcome the limitations of complicated installation route, tedious maintenance process and high cost of traditional contact vibration sensors, an approach of transverse vibration identification model based on machine vision was proposed to realize multi-point vibration displacement sensing and anomaly analysis of shafting. The displacement information of the video signal is extracted by the displacement sensing strip labeling method, and the abnormal state of the ship propulsion shafting is analyzed by the dynamic kernel principal component analysis (DKPCA) algorithm. The experimental results show that the approach can accurately detect the continuous vibration displacement of shafting in the range of 180 r/min, and can work normally under two abnormal conditions: sudden external excitation and continuous uneven external excitation. In addition, this approach can quickly and accurately monitor the motion state of shafting, and realize the perception and recognition of abnormal vibration state of shafting. The research and application of this approach in ship shafting vibration monitoring is of great significance to the development of unmanned and intelligent ships.

Enhancing Lung Ultrasound Diagnostics: A Clinical Study on an Artificial Intelligence Tool for the Detection and Quantification of A-Lines and B-Lines

Background/Objective: A-lines and B-lines are key ultrasound markers that differentiate normal from abnormal lung conditions. A-lines are horizontal lines usually seen in normal aerated lungs, while B-lines are linear vertical artifacts associated with lung abnormalities such as pulmonary edema, infection, and COVID-19, where a higher number of B-lines indicates more severe pathology. This paper aimed to evaluate the effectiveness of a newly released lung ultrasound AI tool (ExoLungAI) in the detection of A-lines and quantification/detection of B-lines to help clinicians in assessing pulmonary conditions. Methods: The algorithm is evaluated on 692 lung ultrasound scans collected from 48 patients (65% males, aged: 55 ± 12.9) following their admission to an Intensive Care Unit (ICU) for COVID-19 symptoms, including respiratory failure, pneumonia, and other complications. Results: ExoLungAI achieved a sensitivity of 91% and specificity of 81% for A-line detection. For B-line detection, it attained a sensitivity of 84% and specificity of 86%. In quantifying B-lines, the algorithm achieved a weighted kappa score of 0.77 (95% CI 0.74 to 0.80) and an ICC of 0.87 (95% CI 0.85 to 0.89), showing substantial agreement between the ground truth and predicted B-line counts. Conclusions: ExoLungAI demonstrates a reliable performance in A-line detection and B-line detection/quantification. This automated tool has greater objectivity, consistency, and efficiency compared to manual methods. Many healthcare professionals including intensivists, radiologists, sonographers, medical trainers, and nurse practitioners can benefit from such a tool, as it assists the diagnostic capabilities of lung ultrasound and delivers rapid responses.

Ray-Resorption Syndrome in European Seabass, Dicentrarchus labrax (Linnaeus, 1758).

Fin abnormalities are common in reared fish. They mainly consist of partial to complete lack of rays and severe abnormalities of fin-supporting skeletal elements, which develop during the larval stage, up to the completion of fin skeleton ontogeny. This study reports a new abnormal condition, ray-resorption syndrome (RSS), which developed after the completion of fin ontogeny in initially normal European seabass larvae, leading to extensive loss of fin rays. At 49 days post-fertilisation (dpf) (15 mm standard length, SL) all fish presented normal fins. However, nineteen days later (21 mm SL), significant fin damage, characterised by ray loss or fractures, was observed in all studied populations. The dorsal fin was most affected (55%-84%), followed by the pelvic (27%-53%) and anal fins (7%-17%). Microscopically, multiple non-mineralised areas resembling resorption lacunae were evident along all fin lepidotrichia. By 98 dpf (40 mm SL), the fin-ray loss had advanced, reaching its higher frequency in the dorsal (74%-83%) and caudal fins (71%-94%). Gene expression analysis revealed a significant upregulation of bglap (osteoblast maturation marker), acp5a (osteoclast maturation marker) and mmp13a (extracellular-matrix remodelling marker) in RSS specimens. The results are discussed in respect of the possible causative factors of RSS.

Correlation-based polarity-check algorithm for instrument transformers

A polarity identification is very important for operation of transformers, measurement and protection equipment, where it is useful in analyzing of transformer connections and operation as well as testing of protective systems. Moreover, it’s essential in assessment of power systems performance during both normal and abnormal operation. Ensuring the correct polarity of the primary and secondary windings in voltage and current transformers is of paramount importance for various measurement and protection schemes in power networks. This paper proposes a digital polarity detector and tester using correlation coefficients and nine polarity indices calculated for instrument transformer signals. In order to test the performance of the proposed polarity tester algorithm, MATLAB code is imported to the LABVIEW model, and the numerical data obtained from the synchronous generator terminals via instrument transformers are interfaced with the computer through the Data Acquisition Card (DAC). The experimental system consists of a motor-generator set supplying a three-phase inductive load with instrument transformers connected to measure each phase voltage and current. The obtained results for various operating conditions and different types of abnormal conditions prove that the suggested algorithm is accurate, reliable and applicable to smart grids and substation automation systems. It can be considered as an integrated system incorporated with digital fault recorders, relays and meters.

Robust load feature extraction based secondary VMD novel short-term load demand forecasting framework

Load forecasting, as a crucial component of the electricity market, plays a significant role in ensuring the secure operation and rational planning of the power grid. However, as the power system becomes increasingly intricate, the demands on load forecasting techniques have escalated. Consequently, to mitigate the errors in short-term load forecasting (STLF) caused by uncertainty factors and to accommodate daily forecasting under abnormal electricity load conditions, this paper proposes a hybrid load forecasting model that combines an improved Secondary Variational Mode Decomposition (SVMD) algorithm with the Informer model. Employing electricity load data from the Panama context, the data is divided into four distinct experimental cases. The outcomes manifest that in contrast to the baseline model, the proposed approach engenders a minimal reduction of 15.08%, 12.95%, and 13.21% in Mean Absolute Percentage Error (MAPE), Mean Absolute Error (MAE), and Root Mean Square Error (RMSE), respectively. Furthermore, supplementary experimental results demonstrate that the model exhibits strong robustness.

Portable Gait Analysis of Patients With Rotating Hinge Knee Megaprosthesis Compared With Total Knee Arthroplasty.

The gait analysis of patients after surgery for tumors around the knee joint relies on the use of a three-dimensional motion capture system. However, obtaining long-term, free-standing, real-world gait data with three-dimensional gait analysis is challenging. In this study, we utilized a portable gait analyzer to collect gait data from patients who underwent rotating hinge knee megaprosthesis (RHK) and total knee arthroplasty (TKA), this study aims to compare via gait analysis patients who underwent megaprosthesis with patients with TKA. A retrospective study was conducted on eight patients with knee bone tumors (RHK group) and ten patients with knee osteoarthritis who underwent standard TKA (TKA group) from January 2018 to January 2022. Gait analysis, was conducted using the Intelligent Device for Energy Expenditure and Activity (IDEEA), and the results were compared with those of a healthy control group. The lower limb alignment of the RHK and TKA groups was evaluated, and the KSS scores of the two groups were collected and compared. Energy consumption during a 20-m walk was measured and compared among the RHK, TKA, and healthy control groups using one-way ANOVA. Paired t-tests were used to compare the operated and nonoperated limbs within groups. All patients exhibited slower walking speeds and cadence than the healthy control participants (p < 0.01), While no significant differences were found between the RHK and TKA groups. The single support time (521.15 ± 94.56 ms) of the RHK-operated limb was significantly shorter than that of the nonoperated limb (576.53 ± 77.40 ms, p = 0.004). The pulling acceleration of the RHK group (0.71 ± 0.27 G) was lower than that of the TKA group (1.04 ± 0.31 G, p = 0.029). The push-off angle in the RHK group (24.91° ± 10.91°) was significantly greater than that in the TKA group (10.64° ± 5.41°, p = 0.007). The RHK group showed significant differences between the operated and nonoperated limbs in terms of swing power, ground impact, footfall, and push-off. The RHK (0.03 ± 0.01 kcal/min/kg) and TKA (0.029 ± 0.01 kcal/min/kg) groups had significantly greater energy expenditures than did the healthy control group (0.02 ± 0.00 kcal/min/kg, p < 0.05). The comparison of HKA angles and KSS scores between the TKA and RHK groups showed statistically significant differences. A portable gait analyzer appears to be suitable for evaluating the effects of RHK. RHK patients demonstrate more pronounced gait abnormalities than TKA patients, reflected in greater energy expenditure, implying reduced walking efficiency. This suggests the need for increased energy expenditure in RHK patients to compensate for abnormal knee joint conditions during walking and maintain body balance.

Self-Assembly of Microstructured Protein Coatings with Programmable Functionality for Fluorescent Biosensors.

Proteins, as genetically programmable functional macromolecules, hold immense potential as biocompatible self-assembling building blocks, owing to their versatility in building coating materials and programming their functionality genetically. In this study, we demonstrate a modular self-assembly of protein coatings that are genetically programmable for a biosensor application. We designed and produced recombinant fusion protein building blocks to form microstructured coatings on diverse substrates, such as glass or polymers, through thermally triggered liquid-liquid phase separation and an orthogonal high-affinity coiled-coil interaction. We incorporated fluorescence proteins into coatings and controlled the protein density to enable fluorescence imaging and quantification in a low-resource setting. Then, we created a coating for a calcium biosensor using a genetically engineered calcium indicator protein. This protein coating served as the foundation for our smartphone-based fluorescent biosensor, which successfully measured free calcium concentrations in the millimolar range at which extracellular calcium homeostasis is maintained. Using this fluorescent biosensor, we were able to detect abnormal physiological conditions, such as mild or moderate hypercalcemia. We envision that this modular and genetically programmable functional protein coating platform could be extended to the development of highly accessible, low-cost fluorescent biosensors for a variety of targets.

Long‐Term Estimation of SoH Using Cascaded LSTM‐RNN for Lithium Batteries Subjected to Aging and Accelerated Degradation

ABSTRACTAccurate estimation of state of health (SoH) of the battery over long‐term is a critical challenge for the battery management systems in electric vehicles. This is due to the challenges in accurately modeling the accelerated aging and degradation phenomena caused by diverse operating conditions of the battery. This paper presents a cascaded recurrent neural networks (RNN) with long short‐term memory (LSTM) to estimate the internal resistance and SoH, taking account of various abnormal operating conditions of the battery. A datasheet‐based degradation model of the battery is developed using fade equations. The training and validation data set for LSTM‐RNN are generated by subjecting the battery model to various factors that cause accelerated degradation, such as fast charging, varying operating temperatures, overutilization, and cell imbalance. The cascaded LSTM‐RNN is trained to estimate SoH only once after the completion of every charge–discharge cycle. The training error index parameters of the proposed SoH estimator are well within 1%, demonstrating the reliability and robustness of the estimator to diverse operating conditions of the battery.

Bone marrow magnetic resonance imaging (MRI): morphological and functional features from reconversion to infiltration.

Bone marrow is a dynamic organ with variable composition in relation to age or pathophysiological changes. Magnetic resonance imaging (MRI) is the technique of choice to assess the different components of the bone marrow based on the different information provided by the different characteristics of the MRI sequences. This article provides an overview of the MRI appearances of normal and abnormal bone marrow. We review the MRI features of normal developmental red marrow- to yellow-conversion, reconversion and physiologic conditions. We review the key imaging techniques used in assessing bone marrow pathology in MRI, including T1-weighted, T2-weighted, Dixon chemical shift imaging and diffusion-weighted imaging, as well as dynamic contrast-enhanced (DCE) MRI. It is discussed the bone marrow characteristics in the different morphological and functional MRI sequences from the normal or abnormal conditions such as; infiltration (metastases), proliferation [multiple myeloma (MM)], vascular edema/necrosis and postreatment changes. We show the different MRI features to differentiate physiological processes from pathological processes in order to provide effective diagnoses, as well as to evaluate the optimal therapeutic monitoring assessment. Insights from recent advancements in imaging technology and emerging MRI techniques are also discussed, providing a comprehensive overview of bone marrow MRI and its clinical implications. This review provides a useful tool for radiologist to decide normal or abnormal findings from the analysis of bone marrow MRI; in order to manage and take decisions that will depend on the imaging findings. The optimal analysis of bone marrow MRI requires knowledge of the physiology of the bone marrow to interpret properly the pathology and avoid diagnostic errors.

Research on comprehensive quality consistency evaluation strategy for TCM granules: A case study with sugar-free Yangwei Granules produced by fluid-bed granulation

ObjectiveTo develop a quality consistency evaluation strategy for traditional Chinese medicine (TCM) granules using sugar free Yangwei Granules as a model drug, and demonstrate the effectiveness of the developed method. MethodsThe strategy integrates several methods including, HPLC fingerprint and physical fingerprint methods analyze the similarity in chemical and physical properties of the TCM granule samples. Near-infrared (NIR) spectroscopy with principal components cluster analysis method is used to monitor normal operating conditions (NOC) samples accurately and to identify different types of abnormal operating conditions (AOC) samples, particularly those that deviate from the normal range. ResultsThe combined use of HPLC fingerprint and physical fingerprint provides insights into the chemical and physical properties of the samples. NIR spectroscopy, combined with principal components cluster analysis, achieves high accuracy in monitoring NOC samples and identifying AOC samples without misjudgment. The approach proves useful as a complementary method in cases where HPLC fingerprint and physical fingerprint alone lack sufficient resolution. ConclusionThis study establishes the feasibility and utility of the integrated approach for assessing the quality consistency of TCM granules. The strategy shows a high degree of generalization and holds significant importance for enhancing the quality control processes of TCM granules.

Overview and Advancement of Power System Topology Addressing Pre- and Post-Event Strategies under Abnormal Operating Conditions

Overview and Advancement of Power System Topology Addressing Pre- and Post-Event Strategies under Abnormal Operating Conditions

Experimental performance verification of an intelligent detection and assessment scheme for disturbances and imbalances of three-phase synchronous machine output using coherence estimators

In this article, power quality disturbances, such as voltage and current unbalances, series and shunt faults, are monitored in three-phase synchronous machines. Because the imbalances affect machine efficiency and service life, it is essential to figure out whether the three-phase voltages/currents are balanced or unbalanced. An integrated detection algorithm is proposed to detect the unbalanced voltages/currents and various abnormal conditions precisely and quickly, which is based on the coherence estimator. Most existing detection methods require more than one cycle time to determine the contingency conditions, while the proposed technique detects these situations in a fast and discrete protection system. The suggested scheme acquires instantaneous measurements of the three-phase voltages and currents taken at the synchronous machine terminals in order to calculate fifteen coherence coefficients that are utilized to detect and assess any unbalance or trouble accurately and swiftly. A new proposal for imbalance and disturbance indicators based on the coherence estimators is developed in this study. Multiple test cases have been conducted to validate the proposed algorithm capabilities using a real power system, which includes a motor-generator set, a three-phase load and measurement transformers. The experimental results have been revealed that the relay reliability and accuracy percentages have been found to be 98.28% and 98.52%, respectively. The quantitative findings of the imbalance and disturbance assessment are recorded.

Arrhythmia Classification with ECG Signal using Extreme Gradient Boosting (XGBoost) Algorithm

Heart disease is one of the most dangerous illnesses because it has the potential to take people's lives. One of the causes of heart disease is arrhythmia, an abnormal condition of the heartbeat. To diagnose arrhythmia, analysis of electrocardiographic (ECG) signals can be performed. However, this analysis is very difficult to do conventionally and has the potential for errors, so there is a need for automatic ECG classification to detect arrhythmia. This study aims to fill the research gap by creating an ECG classification model to detect arrhythmia using the XGBoost algorithm. The results are quite good for each class, with accuracies for class N at 98.87%, class SVEB at 99.37%, class VEB at 99.4%, class F at 99.75%, and class Q at 99.99%. However, compared to existing methods in previous research, these results are still considered not better than those models.

A Survey of Artificial Intelligence Applications in Nuclear Power Plants

Nuclear power plants (NPPs) rely on critical, complex systems that require continuous monitoring to ensure safe operation under both normal and abnormal conditions. Despite the potential of artificial intelligence (AI) to enhance predictive capabilities in these systems, limited research has been conducted on the application of AI algorithms within NPPs. This presents a knowledge gap in the integration of AI for improving safety, reliability, and decision making in NPP. In this study, we explore the use of AI methods, including machine learning and real-time data analytics, applied to NPP components to address the nonlinearity and dynamic behavior inherent in reactor operations. Through the implementation of AI and Internet of Things (IoT) devices, we propose a system that enables early warning and real-time data transmission to regulatory authorities and decision-makers, ensuring better coordination during incidents. Lessons from past nuclear accidents, such as Chernobyl, emphasize the importance of timely information dissemination to mitigate risks. However, this integration also presents challenges, including cybersecurity risks and the need for updated regulations to address AI use in safety-critical environments. The results of this study highlight the urgent need for further research on the application of AI in NPPs, with a particular focus on addressing these challenges to ensure safe implementation.

Long-term efficacy and safety of ablation for atrial fibrillation in patients with hypertrophic cardiomyopathy

Abstract Background Hypertrophic cardiomyopathy (HCM) is often accompanied by atrial fibrillation (AF) but data on safety and long-term outcomes after catheter ablation for AF in HCM are scarce. Purpose To investigate safety, long-term efficacy and clinical outcomes following AF ablation in patients with HCM and in matched controls. Methods Patients with HCM (n=76) undergoing a first catheter ablation for AF or atrial flutter between 1999 and 2022 at a University Hospital in Sweden were included (age 64±12 years, 43% female). Diagnosis of HCM was based on the European Society of Cardiology definition (left ventricular wall thickness ≥15 mm or ≥13 mm not solely explained by abnormal loading conditions, and combined with family history, genetic components and/or electrocardiogram abnormalities). Patients with HCM were matched with controls (n=152) based on age, gender, ablation type and intervention-year. Patients were followed for a mean of 5 years and study endpoints included heart failure hospitalization (HFH), stroke and mortality. Cox regression analyses were performed for the association between HCM and the endpoints. Results Among the overall population, 121 (53%) underwent pulmonary vein isolation (PVI), 62 (27%) isthmus ablation and 45 (20%) His-Bundle ablation. Patients with HCM had a shorter mean duration of AF before ablation (48 months vs. 66 months; p=0.047) and more severe symptoms as indicated by a higher EHRA class (EHRA class IV: 30% vs. 10%; p&amp;lt;0.01). Procedural success rate of PVI and isthmus ablation was lower in patients with versus without HCM (88% vs. 97%; p=0.02). Procedure related complications (vascular complications, tamponade) were overall similar in both groups (9% in HCM vs. 5% in controls, p=0.17) except for higher rates of pulmonary oedema in patients with HCM (7% vs 1%, p=0.03). There was a nearly two-fold increase in risk of AF recurrence after PVI or isthmus ablation in patients with HCM compared to controls (OR 1.95; 95% CI 1.06-3.58; p=0.03). During follow-up, patients with HCM had an increased risk of HFH (HR 2.55, 95% CI 1.21-5.36, p=0.01) but not of stroke or mortality (Figure 1). Conclusion AF ablation was overall safe in patients with HCM but less effective. Patients with HCM had an increased risk of the individual endpoint of HFH after AF ablation but not stroke or mortality during long-term follow-up.

A systematic review and meta-analysis to determine the biological sex ratio in monogenic genetic and non-genetic dilated cardiomyopathy

Abstract Background Dilated cardiomyopathy (DCM) is characterised by left ventricular or biventricular systolic dysfunction and dilatation, in the absence of abnormal loading conditions or coronary artery disease (1). Up to 30% of cases have an identified monogenic cause, usually with autosomal dominant (AD) inheritance (2). The disease appears to be twice as common in males, suggesting either a sex specific penetrance or a potential difference in expressivity. This sex ratio has not been systematically evaluated for genetic forms of DCM. Purpose To assess the sex ratio in patients with all-cause DCM compared to genetic (monogenic) and gene-elusive subtypes, defined by the presence or absence of a pathogenic variant in DCM-associated genes. Methods A literature search was conducted to identify cohorts of DCM patients with identifiable sex ratios. Exclusion criteria were patients with syndromic, X-linked, mitochondrial, or autosomal recessive (AR) DCM. Studies with a small cohort size (n&amp;lt;100), a paediatric population, unidentifiable sex ratio, or ischaemic and peripartum DCM were excluded. 98 studies with a total of 36,662 participants were included in this study. A random-effects meta-analysis generated a pooled proportion of female participants with a 95% confidence interval (95% CI) for an overall DCM cohort as well as for the subtypes – all genetic DCM, individual gene level for 10 key DCM genes (including titin, TTN, and lamin, LMNA), and gene elusive DCM. Results The overall DCM cohort had a 0.30 female proportion (95% CI: 0.28-0.32; Figure 1). This corresponds to a male:female (M:F) ratio of 2.38:1. This ratio did not significantly change in patients with an identified causative DCM variant (0.31 [95% CI 0.26-0.36]; M:F ratio of 2.22:1; p=0.563). There was also no significant difference when compared to patients who had been genetically tested with no identified variant (0.30 [95% CI 0.24-0.37]; M:F ratio of 2.29:1; p=0.814). Furthermore, the ratio within participants with AD gene variants was not significantly different for the specific genes of TTN (0.28 [95% CI 0.22-0.36]; M:F ratio of 2.51:1) and LMNA (0.35 [95% CI 0.27-0.44]; M:F ratio of 1.84:1). Overall, the sex ratio amongst patients with these AD gene variants was similar to the all-cause group (0.34 [95% CI 0.28-0.40]; M:F ratio of 1.98:1; p=0.18); Figure 2. Conclusions This meta-analysis suggests that DCM is twice as prevalent in males than females, even in pooled monogenic genetic subtypes with an equally prevalent genetic risk factor, indicating a difference in penetrance between the sexes. Further studies are required to determine the drivers of this sex specific penetrance.Forest plots: overall DCM cohortForest plots: gene-specific DCM cohort

A mathematical model describing the tip-stalk regulation in angiogenesis

Angiogenesis is the process of new blood vessel growth from existing vessels, involving extensive cell signaling. Under normal conditions, new vessels are robust and organized, with a balance among angiogenesis factors. In abnormal conditions, such as tumor development, vessels are stunted and tangled due to an imbalance of these factors. Pathological angiogenesis stimulates rapid vessel growth to feed the oxygen and nutriente starved tumor. Inhibiting angiogenesis can cause side effects like hypertension, thrombosis, and fatigue. To better understand this process, significant effort has gone into studying signaling pathways, contributing to drug development for diseases like cancer. This study presents a mathematical model describing angiogenesis on a microscopic scale, comparing its results with experimental data on vascular network topology. The model, implemented in MatLab®, uses ordinary differential equations to represent cell behavior. Results show that altering VEGF (Vascular Endothelial Growth Factor) disrupts system balance, impacting angiogenesis and possibly explaining differences in network topology seen experimentally.

Development will (try to) find its way: a qualitative study of Chilean adolescent mental health during and after lockdown

BackgroundThe COVID-19 pandemic has had a well-evidenced impact on adolescents, who are especially sensitive to pandemic disruptions given the critical role of socialization in their development. In Chile too, evidence shows increases in mental health complaints among adolescents over the lockdown period. Our study aimed at exploring the experiences of Chilean adolescents regarding their mental health during the lockdown and school closure (March 2020-December 2021), and during the return to on-site education (2022) as informed by adolescents and school staff, with a focus on family, school, and social sources of risk and support for adolescents’ wellbeing during these periods.MethodsUsing a qualitative approach, we conducted semi-structured interviews with 19 adolescents and 16 staff members from schools in an urban area of Chile.ResultsThrough thematic analysis, we generated five themes: [1] Adolescents in a mental health crisis, comprising a range of distressing experiences and mental health problems. This crisis was fueled by alterations in the functioning of adolescents’ systems: [2] Broken support systems (peers and school); [3] The school agenda must go on, reflecting schools’ strict compliance with the educational curriculum; and [4] Blurred boundaries between home and school life and within the family. Finally [5], Development will (try to) find its way describes how most participants experienced a bouncing back to wellbeing in the course of the school year upon return, and how some developmental milestones took place despite the abnormal conditions, providing evidence for resilience amid pandemic adversity.ConclusionsThe findings give insight into how the exchanges between the adolescent and the social systems they are embedded in were interfered. The results help us understand the challenges for mental health during and after the pandemic, and highlight adolescents’ capacity to thrive as normality was restored. The results also underscore the importance of upholding stability across adolescents’ systems and routines, in order to mitigate impacts on wellbeing amid abnormal circumstances. The findings are relevant for development-informed initiatives in policy design in the aftermath of the pandemic and in future crisis management responses.

Stress-Induced Evolution of the Nucleolus: The Role of Ribosomal Intergenic Spacer (rIGS) Transcripts.

It became clear more than 20 years ago that the nucleolus not only performs the most important biological function of assembling ribonucleic particles but is also a key controller of many cellular processes, participating in cellular adaptation to stress. The nucleolus's multifunctionality is due to the peculiarities of its biogenesis. The nucleolus is a multilayered biomolecular condensate formed by liquid-liquid phase separation (LLPS). In this review, we focus on changes occurring in the nucleolus during cellular stress, molecular features of the nucleolar response to abnormal and stressful conditions, and the role of long non-coding RNAs transcribed from the intergenic spacer region of ribosomal DNA (IGS rDNA).

Design of monitoring system for material vibration screening equipment

In order to effectively reduce the failure rate of vibrating screens, a vibration monitoring system design was proposed based on the structure and working principle. The system should be able to monitor the operating state of the vibrating screen in real time, including vibration amplitude, frequency, axial displacement, etc., and be able to detect abnormal conditions in time and give warning prompts. The key hardware of the monitoring system was designed, including acceleration sensor, power module, signal filtering circuit, etc. The sensitivity of the acceleration sensor was measured using comparative method and accurately completed parameter calibration. The eccentric block was used to simulate different deviation of excitation force for system vibration signal test and calibration. The results show that under normal operating conditions, the amplitude of each frequency of the vibrating screen is less than 0.04 g. Under the condition of excitation force deviation, the amplitude of 16 Hz is obviously lower than that of other frequencies, which means that the vibrating screen is currently experiencing lateral oscillation.