Disturbance Factors Research Articles

STEWART PLATFORM DYNAMICS MODEL IDENTIFICATION

Context. At the present stage, with the current demands for the accuracy of motion control processes for a moving object on a specified or programmable trajectory, it is necessary to synthesize the optimal structure and parameters of the stabilization system (controller) of the object, taking into account both real controlled and uncontrolled stochastic disturbing factors. Also, in the process of synthesizing the optimal controller structure, it is necessary to assess and consider multidimensional dynamic models, including those of the object itself, its basic components, controlled and uncontrolled disturbing factors that affect the object in its actual motion.
 Objective. The aim of the research, the results of which are presented in this article, is to obtain and assess the accuracy of the Stewart platform dynamic model using a justified algorithm for the multidimensional moving object dynamics identification.
 Method. The article employs a frequency-domain identification method for multidimensional stochastic stabilization systems of moving objects with arbitrary dynamics. The proposed algorithm for multidimensional moving object dynamics model identification is constructed using operations of polynomial and fractional-rational matrices addition, multiplication, Wiener factorization, Wiener separation, and determination of dispersion integrals.
 Results. As a result of the conducted research, the problem of identifying the dynamic model of a multidimensional moving object is formalized, illustrated by the example of a test stand based on the Stewart platform. The outcomes encompass the identification of the dynamic model of the Stewart platform, its transfer function, and the transfer function of the shaping filter. The verification of the identification results confirms the sufficient accuracy of the obtained models.
 Conclusions. The justified identification algorithm allows determining the order and parameters of the linearized system of ordinary differential equations for a multidimensional object and the matrix of spectral densities of disturbances acting on it under operating conditions approximating the real functioning mode of the object prototype. The analysis of the identification results of the dynamic models of the Stewart platform indicates that the primary influence on the displacement of the center of mass of the moving platform is the variation in control inputs. However, neglecting the impact of disturbances reduces the accuracy of platform positioning. Therefore, for the synthesis of the control system, methods should be applied that enable determining the structure and parameters of a multidimensional controller, considering such influences.

Sex-dependent resilience to extreme drought events: implications for climate change adaptation of a South American endangered tree species

BackgroundRecent changes in climatic trends are resulting in an increased frequency and intensity of extreme events, with unknown effect on ecosystem dynamics in the near future. Extreme drought episodes are recognized as disturbance factors capable of modifying forest dynamics and tree growth. Within this context, dioecious tree species may be impacted by climatic extremes, affecting male/female proportions and, consequently, reproductive processes and species persistence. Therefore, there is an urgent need for species-specific assessments of growth tolerance to extreme dry spells in dioecious tree species, to establish effective conservation strategies for these particular natural resources. Araucaria araucana (araucaria), an endangered dioecious Patagonian tree species, has recently undergone decay and mortality episodes in response to increasing dry climatic conditions. While sex-dependent tolerance to extreme drought episodes has been assessed in the species’ humid distribution range, there is still a lack of information on the gender-based resilience of trees growing in the drier environments of the species’ distribution.MethodsWe reconstructed, through dendrochronological methods, the sex-dependent response of 105 araucaria individuals (55 female and 50 male trees) to five regional extreme dry spells employing a set of different indices. Resistance, recovery period, and average growth reduction of standardized tree-ring growth were examined, analysing the effect of biotic (sex, pre-drought stem tree growth) and abiotic (local climatic conditions before, during, and after extreme climatic episodes) factors on tree resilience.ResultsSex influences only the species resistance to climatic disturbance, with male individuals showing lower tolerance to extreme drought events. Pre-drought radial growth rates and local meteorological conditions preceding, during, and following extreme dry spells strongly modulated araucaria radial growth resilience regardless of tree sex, influencing the species resistance, recovery period, and average growth reduction.ConclusionsWe provide novel and crucial information for the species conservation and management in the current climate change scenario, and contribute to the debate regarding the role of tree sex as a factor influencing woody species growth under particularly adverse climatic conditions. In the face of climate change, an increase in extreme drought events is expected in the easternmost araucaria xeric end distribution area, which will likely decrease the species resilience.

Prenatal insomnia disorder may predict concurrent and postpartum psychopathology: A longitudinal study.

While insomnia symptoms may be a risk factor for mental disturbances, few studies evaluated "Insomnia Disorder" and its relationship with perinatal psychopathology. Pregnant women were recruited during their last routine assessment before being hospitalized for delivery during the 3rd trimester at the Gynaecological Unit of the University Hospital of Ferrara and Udine, Italy, from January 2022 to January 2023. Our assessment included baseline evaluation (T0), and evaluations at 1 month (T1) and 3 months (T2) in the postpartum period, with specific questionnaires for insomnia disorder, such as Sleep Condition Indicator, mood and anxiety symptoms and psychosocial functioning, such as Edinburgh Postnatal Depression Scale, Mood Disorder Questionnaire, State-Trait Anxiety Inventory, Work and Social Adjustment Scale. At T0, 181 pregnant women were included. Insomnia disorder affected 22.3% at T0, 23.5% at T1 and 16.2% at T2. Women with insomnia disorder at baseline were significantly more affected by concurrent anxiety and depressive symptoms, had higher bipolar diathesis and poorer psychosocial functioning in the perinatal period. Prenatal insomnia disorder predicted anxiety (T0: odds ratio 4.44, p << 0.001; T1: odds ratio 4.009, p = 0.042) and depressive symptoms (T0: odds ratio 2.66, p = 0.015; T1: odds ratio 11.20, p = 0.001; T2: odds ratio 12.50 p = 0.049) in both the prenatal and postnatal period. It also predicted poor psychosocial function during the prenatal (odds ratio 3.55, p = 0.003) and postpartum periods (T1: odds ratio 2.33, p = 0.004). Insomnia disorder is emerging as an important prenatal factor that may contribute to concurrent and postpartum psychopathology.

UAV Track Planning Based on Improved Marine Predator Algorithm

Aiming at the optimization problem in the field of automatic control of unmanned aerial vehicles (UAVs) for track planning, this paper proposes an optimization method of UAV track planning based on improved Marine predator algorithm, adopts uniform distribution space and pseudo-reverse learning strategy to improve population initialization, and uses T distribution as disturbance factor to update position. The objective function of the improved Marine predator algorithm considering the cost of track length, terrain and flight height is established, and the simulation test is carried out on MATLAB software. The simulation results show that an optimal path to avoid obstacles and threat areas can be obtained with particle swarm optimization and snake optimization, indicating that this method has stronger robustness and feasibility in UAV path planning.

Gene‒environment interaction effect of hypothalamic‒pituitary‒adrenal axis gene polymorphisms and job stress on the risk of sleep disturbances.

Studies have shown that chronic exposure to job stress may increase the risk of sleep disturbances and that hypothalamic‒pituitary‒adrenal (HPA) axis gene polymorphisms may play an important role in the psychopathologic mechanisms of sleep disturbances. However, the interactions among job stress, gene polymorphisms and sleep disturbances have not been examined from the perspective of the HPA axis. This study aimed to know whether job stress is a risk factor for sleep disturbances and to further explore the effect of the HPA axis gene × job stress interaction on sleep disturbances among railway workers. In this cross-sectional study, 671 participants (363 males and 308 females) from the China Railway Fuzhou Branch were included. Sleep disturbances were evaluated with the Pittsburgh Sleep Quality Index (PSQI), and job stress was measured with the Effort-Reward Imbalance scale (ERI). Generalized multivariate dimensionality reduction (GMDR) models were used to assess gene‒environment interactions. We found a significant positive correlation between job stress and sleep disturbances (P < 0.01). The FKBP5 rs1360780-T and rs4713916-A alleles and the CRHR1 rs110402-G allele were associated with increased sleep disturbance risk, with adjusted ORs (95% CIs) of 1.75 [1.38-2.22], 1.68 [1.30-2.18] and 1.43 [1.09-1.87], respectively. However, the FKBP5 rs9470080-T allele was a protective factor against sleep disturbances, with an OR (95% CI) of 0.65 [0.51-0.83]. GMDR analysis indicated that under job stress, individuals with the FKBP5 rs1368780-CT, rs4713916-GG, and rs9470080-CT genotypes and the CRHR1 rs110402-AA genotype had the greatest risk of sleep disturbances. Individuals carrying risk alleles who experience job stress may be at increased risk of sleep disturbances. These findings may provide new insights into stress-related sleep disturbances in occupational populations.

The causal relationship between sleep disturbances and the risk of frailty: a two-sample Mendelian randomization study

ObjectiveAdequate sleep is closely related to people's health. However, with increasing age, the quality of sleep worsens. At the same time, among elderly individuals, frailty is also a disturbing factor, which makes elderly individuals more vulnerable to negative factors. To explore the relationship between the two, we conducted this study.MethodsIn this paper, independent genetic variations related to insomnia, sleep duration and daytime sleepiness were selected as IVs, and related genetic tools were used to search published genome-wide association studies for a two-sample Mendelian randomization (TSMR) analysis. The inverse-variance weighted (IVW) method was used as the main Mendelian randomization analysis method. Cochran's Q test was used to test heterogeneity, MR‒Egger was used to test horizontal pleiotropy, and the MR-PRESSO test was used to remove outliers.ResultsAccording to our research, insomnia (OR = 1.10, 95% CI 1.03–1.17, P = 2.59e−97), long sleep duration (OR = 0.66, 95% CI 0.37–1.17, P = 0.02), short sleep duration (OR = 1.30, 95% CI 1.22–1.38, P = 2.23e−17) and daytime sleepiness (OR = 1.49, 95% CI 1.25–1.77, P = 0.96e−4) had a bidirectional causal relationship with frailty.ConclusionsOur research showed that there is a causal relationship between sleep disturbances and frailty. This result was obtained by a TSMR analysis, which involves the use of genetic variation as an IV to determine causal relationships between exposure and outcome. Future TSMR studies should include a larger sample for analysis.

Abstract P231: Are Resiliency and Religiosity/Spirituality Associated With Better Sleep Outcomes in Midlife Latinas?

Background: The disproportionate cardiovascular disease (CVD) burden among Latinas is not completely explained. An important health behavior in CVD prevention is sleep. In the United States (U.S.), Latinas experience unique sociocultural stressors that may impact sleep, particularly in midlife. Yet, little is known about protective factors such as resiliency and religiosity/spirituality among midlife Latinas. Aims: To assess the relation of resiliency and religiosity/spirituality with sleep disturbances in midlife Latinas. Methods: We conducted a cross-sectional analysis using baseline data from 49 participants in a pilot study to reduce CVD risk in midlife Latinas. Participants were Latinas aged 40-60 years who were perimenopausal or early postmenopausal, free of CVD, with no menopause hormone therapy use in the prior three months. Women were recruited from community settings. The frequency of sleep disturbances (trouble falling asleep, waking early, or nocturnal awakenings) over the past two weeks was assessed, and a sum score was created (0-16). The 6-item Brief Resilience Scale was used to assess the perceived ability to “bounce back” or recover from hardship. Religiosity/spirituality was determined by self-report (how important is religion/spirituality to you). Multivariable linear regression models were performed at p&lt;0.15, adjusting for sociodemographic. Results: Participants were on average aged 47.1±4.5 years, 47% had less than a high school education, and 98% were born outside of the continental U.S. Sixty-nine percent of women experienced trouble falling asleep. The mean sleep score was 6.9±4.6. In adjusted models, we found an inverse relationship between resiliency and sleep (ß[SE]= -0.33[0.13],p=0.01). Religiosity/spirituality was not related to sleep disturbances (ß[SE]= 1.15[1.60],p=0.48). Conclusions: In this analysis of midlife Latinas, higher resiliency was associated with poorer sleep. Findings suggest that the ability to “bounce back” after hardship and religiosity/spirituality may not buffer the negative impact of stress on sleep. Further research is necessary with objective sleep measures to identify protective factors for sleep disturbances that may subsequently improve CVD risk in midlife Latinas.

Implications of the scale of detection for inferring co-occurrence patterns from paired camera traps and acoustic recorders.

Multifunctional landscapes that support economic activities and conservation of biological diversity (e.g., cattle ranches with native forest) are becoming increasingly important because small remnants of native forest may comprise the only habitat left for some wildlife species. Understanding the co-occurrence between wildlife and disturbance factors, such as poaching activity and domesticated ungulates, is key to successful management of multifunctional landscapes. Tools to measure co-occurrence between wildlife and disturbance factors include camera traps and autonomous acoustic recording units. We paired 52 camera-trap stations with acoustic recorders to investigate the association between 2 measures of disturbance (poaching and cattle) and wild ungulates present in multifunctional landscapes of the Colombian Orinoquía. We used joint species distribution models to investigate species-habitat associations and species-disturbance correlations. One model was fitted using camera-trap data to detect wild ungulates and disturbance factors, and a second model was fitted after replacing camera-trap detections of disturbance factors with their corresponding acoustic detections. The direction, significance, and precision of the effect of covariates depended on the sampling method used for disturbance factors. Acoustic monitoring typically resulted in more precise estimates of the effects of covariates and of species-disturbance correlations. Association patterns between wildlife and disturbance factors were found only when disturbance was detected by acoustic recorders. Camera traps allowed us to detect nonvocalizing species, whereas audio recording devices increased detection of disturbance factors leading to more precise estimates of co-occurrence patterns. The collared peccary (Pecari tajacu), lowland tapir (Tapirus terrestris), and white-tailed deer (Odocoileus virginianus) co-occurred with disturbance factors and are conservation priorities due to the greater risk of poaching or disease transmission from cattle.

A network analysis of ICD-11 Complex PTSD, emotional processing, and dissociative experiences in the context of psychological trauma at different developmental stages.

Traumatic experiences are a significant risk factor for psychological disturbances, including disorders such as complex posttraumatic stress disorder, emotion-processing problems, and trauma-related dissociative experiences. The present investigation examined the coexistence of these symptoms using a network analysis model. This study included a sample of 406 people referred to comprehensive health centers in Tehran from September to December 2023 with psychopathological syndromes. Variables were assessed using The International Trauma Questionnaire, International Measurement of Exposure to Traumatic Event checklist, Baker Emotional Processing Questionnaire, and Dissociative Experiences. A regularized partial correlation network and Glasso algorithm, in combination with Extended Bayesian information criteria, were applied to estimate the network structure. Signs of unprocessed emotions and disturbance in self-organization symptoms were the most important symptoms in the symptom network, forming strong connections with other nodes. Thereby, these two symptoms can be regarded as the most important clinical manifestations in the symptom network following traumatic experiences. Three distinct symptom communities were identified: the community of traumatic experiences (childhood, adolescence, adulthood), the community of dissociative experiences (amnesia, depersonalization/derealization, and absorption), and the community of emotional processing (suppression, unpleasant emotional experience, Signs of unprocessed emotions, avoidance, and emotional control, posttraumatic stress disorder symptoms and disturbance in self-organization symptoms). The strongest edges observed were between childhood trauma-adolescence trauma (0.473) in the community of traumatic experiences, between amnesia and depersonalization/derealization (0.644) in the community of dissociative experiences, and between disturbance in self-organization symptoms and unprocessed emotions (0.324) in the community of emotional processing, indicating the recurrent occurrence of these symptoms. In this study, disturbance in self-organization symptoms was identified as the central psychopathologic symptom in individuals experiencing traumas at different developmental stages. It seems that adolescent trauma and not childhood trauma plays a more decisive role in the symptoms that a person manifests after traumatic experiences. Also, posttraumatic stress disorder symptoms and disturbance in self-organization symptoms were recognized in the cluster of emotional processing symptoms and can have substantial roles in prioritizing therapeutic measures.

Fatigue, depression, and sleep disorders are more prevalent in patients with metabolic-associated fatty liver diseases.

To determine the prevalence and risk factors for depression, sleep disturbances, and exhaustion in MAFLD patients. Two hundred twenty-four consecutive patients with MAFLD attending the outpatient clinic from April to October 2023; were subjected to clinical evaluation, laboratory testing including non-invasive laboratory markers, fibroscan (measuring steatosis and fibrosis), and different quantitative and qualitative fatigue scores. A control group including 342 patients without MAFLD was taken. The prevalence of fatigue, depression, and sleeping disorders in the MAFLD group was 67.8%, 75%, 62.5% vs 21%, 16.4%, and 19.5% in the control group respectively ( P = <0.001, P = <0.001 and P = <0.001). MAFLD with fatigue was significantly associated with the presence and severity of steatosis and fibrosis by fibroscan ( P = <0.0001). By univariate and multivariate analysis: age, BMI, waist circumference, T2DM, hypertension, steatosis, fibrosis, and Fib-4 were considered risk factors for fatigue in the MAFLD group. The age, high social level, diabetes, hypertension, steatosis, fibrosis, and fib-4 were considered, by univariate and multivariate analysis, independent risk factors for depression in the MAFLD group. age, BMI, waist circumference, diabetes, hypertension, steatosis, fibrosis, and fib-4 were independent risk factors for sleep disorders in MAFLD. Fatigue, sleeping disorders, and depression are more prevalent in MAFLD patients than in the general population. The lower health utility scores in patients with MAFLD are associated with more advanced stages of the disease.

Prediction of Utility Tunnel Performance in a Soft Foundation during an Operation Period Based on Deep Learning

The underground utility tunnel in a soft foundation is generally affected by the serious disturbance of the vehicle load during the operation period. Therefore, in this study, for the typical utility tunnel engineering in Suqian City of Jiangsu Province, China, field tests were conducted to monitor the performance of the utility tunnel structure in a soft foundation affected by the ground traffic loads during the operation period. Based on the test results, the datasets whose number is 15,376, composed of the five main disturbance factors (four vehicle operating load parameters and one operating time parameter), and the corresponding two main structure responses (displacement and stress) have been constructed. Based on the obtained datasets, using the proposed new deep learning model called WO-DBN, in which the seven hyperparameters of a deep belief network (DBN) are determined by the whale optimization algorithm (WOA), the safety responses of the utility tunnel structure have been predicted. The results show that for the prediction results, the average absolute error for the displacement is 0.1604, and for the stress, it is 12.3726, which are not significant and can meet the requirement of the real engineering. Therefore, the deep learning model can accurately predict the performance of the utility tunnel structure under a vehicle load and other disturbances, and the model has good applicability.

Optimizing Coronary Computed Tomography Angiography Using a Novel Deep Learning-Based Algorithm

Coronary computed tomography angiography (CCTA) is an essential part of the diagnosis of chronic coronary syndrome (CCS) in patients with low-to-intermediate pre-test probability. The minimum technical requirement is 64-row multidetector CT (64-MDCT), which is still frequently used, although it is prone to motion artifacts because of its limited temporal resolution and z-coverage. In this study, we evaluate the potential of a deep-learning-based motion correction algorithm (MCA) to eliminate these motion artifacts. 124 64-MDCT-acquired CCTA examinations with at least minor motion artifacts were included. Images were reconstructed using a conventional reconstruction algorithm (CA) and a MCA. Image quality (IQ), according to a 5-point Likert score, was evaluated per-segment, per-artery, and per-patient and was correlated with potentially disturbing factors (heart rate (HR), intra-cycle HR changes, BMI, age, and sex). Comparison was done by Wilcoxon-Signed-Rank test, and correlation by Spearman’s Rho. Per-patient, insufficient IQ decreased by 5.26%, and sufficient IQ increased by 9.66% with MCA. Per-artery, insufficient IQ of the right coronary artery (RCA) decreased by 18.18%, and sufficient IQ increased by 27.27%. Per-segment, insufficient IQ in segments 1 and 2 decreased by 11.51% and 24.78%, respectively, and sufficient IQ increased by 10.62% and 18.58%, respectively. Total artifacts per-artery decreased in the RCA from 3.11 ± 1.65 to 2.26 ± 1.52. HR dependence of RCA IQ decreased to intermediate correlation in images with MCA reconstruction. The applied MCA improves the IQ of 64-MDCT-acquired images and reduces the influence of HR on IQ, increasing 64-MDCT validity in the diagnosis of CCS.

The dynamical behavior analysis of the fractional perturbed Gerdjikov–Ivanov equation

In this article, the fractional perturbed Gerdjikov–Ivanov equation is investigated. Firstly, the fractional perturbed Gerdjikov–Ivanov equation is transformed into an ordinary differential equation through traveling wave transformation. Secondly, using the trial method of rank homogeneous equation polynomials and the principle of homogeneous equilibrium, a two-dimensional planar dynamic system is presented and its bifurcation behavior is studied. Then, its two-dimensional phase portraits are drawn by using Maple software. Finally, disturbance factors are introduced into the planar dynamical system to study its chaotic behavior, and some two-dimensional phase portraits, three-dimensional phase portraits, Poincaré sections, and sensitivity analysis graphs of the perturbed system are plotted by using Maple software. The novelty lies in studying the dynamic behavior of the objective equation without the need for solving.

Assessment of factors causing landslides using the Analytical Hierarchy Process (AHP) method

Landslides are Indonesia's second most common disaster in the last ten years. Landslides cause losses, reaching hundreds of billions and threatening human safety. For this reason, it is necessary to take action to reduce the negative impact of landslides. Physical and non-physical mitigation can occur before, during, and after a disaster. The most essential thing in disaster mitigation is knowing the major factors that cause landslides. This research aims to analyze the major and minor factors that cause landslides using the Analytical Hierarchy Process (AHP) and the Likert scale. Respondents in this research are experts in disasters, especially landslides—respondents from universities in Indonesia, practitioners, and people involved in disaster mitigation. Validity and reliability tests were carried out on each question used in the questionnaire. The research results show that the landslide disaster was caused by a disruption in balance that occurred due to internal and external disturbance factors. The major external factors that cause slope failures are the cutting of hills and changes in land use to built-up areas. In contrast, the major internal factors that trigger landslides are heavy rainfall and steep topographic conditions. The results of this research in the form of weight and scoring values for each factor causing landslides can be used as a reference for mapping landslide areas to identify areas with the potential for landslides.

Recognizing Predictive Substructures With Subgraph Information Bottleneck.

The emergence of Graph Convolutional Network (GCN) has greatly boosted the progress of graph learning. However, two disturbing factors, noise and redundancy in graph data, and lack of interpretation for prediction results, impede further development of GCN. One solution is to recognize a predictive yet compressed subgraph to get rid of the noise and redundancy and obtain the interpretable part of the graph. This setting of subgraph is similar to the information bottleneck (IB) principle, which is less studied on graph-structured data and GCN. Inspired by the IB principle, we propose a novel subgraph information bottleneck (SIB) framework to recognize such subgraphs, named IB-subgraph. However, the intractability of mutual information and the discrete nature of graph data makes the objective of SIB notoriously hard to optimize. To this end, we introduce a bilevel optimization scheme coupled with a mutual information estimator for irregular graphs. Moreover, we propose a continuous relaxation for subgraph selection with a connectivity loss for stabilization. We further theoretically prove the error bound of our estimation scheme for mutual information and the noise-invariant nature of IB-subgraph. Extensive experiments on graph learning and large-scale point cloud tasks demonstrate the superior property of IB-subgraph.

Enhancing multi-cloud service deployment with [formula omitted]: A loss-aware coordinator in sky computing

Internet of Things (IoT) services deployed across different Cloud Service Providers (CSPs) require seamless migration across heterogeneous clouds. Sky Computing (SC) facilitates the requirement by implementing a coordinator with communication APIs among clouds. However, security concerns of CSPs prevent the coordinator from accessing detailed cloud hardware information, thereby hindering the assessment of Serving Ability (SA) and exacerbating delays in service delivery. To ensure delay experience, we designed a loss-aware coordinator SkyCap for the multi-CSP collaboration in SC. First, to assess the SA of clouds with available information, we design a loss-aware SA assessment mechanism AdaSA that collects delay experience and Packet Loss Ratio (PLR) information for SA assessment. Since the uncontrollable PLR can increase service delay, we incorporate PLR as a disturbing factor in SA assessment to ensure a more precise evaluation. Second, to maximize the admission profit of the collaboration, we design two approximation algorithms within SkyCap to serve requests. Since the coordinator may deploy services in batches, we design a delay-aware offline algorithm AdaOff to serve requests simultaneously, while ensuring the computing capacity of clouds and the delay requirement of requests. Meanwhile, for asynchronously arriving requests, we design an ability-aware online algorithm AdaOn to serve requests in real-time, while guaranteeing the computing capacity of clouds, SA of clouds, and delay requirement of requests. Finally, extensive theoretical analysis and experimental validation confirm the performance of the coordinator SkyCap. The SA assessment mechanism AdaSA outperforms existing solutions in SA assessment by ensuring at most 1% deviation, especially in high-PLR scenarios. Meanwhile, the algorithm AdaOff exhibits a remarkable 20% improvement over the state-of-the-art in admission profit while ensuring stringent service delay. Concurrently, the algorithm AdaOn surpasses existing solutions, achieving a nearly 10% enhancement in both admission profit and service delay. In summary, the coordinator SkyCap stands out in theoretical groundwork and practical implementation, offering advancements in the SA assessment, admission profit, and service delay.

Automatic Fault Detection Method of Rotating Machinery Bearing Based on Complexity Analysis

In order to improve the working condition stability of rotating machinery bearings, an automatic fault detection method of rotating machinery bearings based on complexity analysis is proposed. The parameters of rotating machinery bearings are detected by using discrete sensor detection method, and the fault characteristics of rotating machinery bearings are extracted by fault type parameter extraction method. The complexity of rotating machinery bearings is detected by wavelet transform and fuzzy logic analysis. Considering the load mutation, noise disturbance, control strategy and other factors of rotating machinery bearings, the fault diagnosis and feature analysis model of rotating machinery bearings is established. The clustering and feature detection of rotating machinery bearings’ fault types are realized by the method of discrete complexity fusion clustering. Based on adaptive feedback compensation and parameter estimation, the output diagnostic error is taken as the fault detection index parameter, and the feature estimation in normal state and fault state is realized by harmonic level detection, so as to realize automatic fault detection of rotating machinery bearings. The simulation results show that this method has high accuracy and small deviation in fault detection of rotating machinery bearings, which improves the ability of automatic fault diagnosis.

Effects of dredging wastewater input history and aquaculture type on greenhouse gas fluxes from mangrove sediments along the shorelines of the Jiulong River Estuary, China

Dredging wastewater (DW) from aquaculture ponds is a major disturbance factor in mangrove management, and its effects on the greenhouse gas (GHG) fluxes from mangrove sediment remain controversial. In this study, we investigated GHG (N2O, CH4, and CO2) fluxes from mangrove sediment at typical aquaculture pond–mangrove sites that were stimulated by DW discharged for different input histories and from different farm types. The GHG fluxes exhibited differing cumulative effects with increasing periods of DW input. The N2O and CH4 fluxes from mangrove sediment that received DW inputs for 17 y increased by ∼10 and ∼1.5 times, respectively, whereas the CO2 flux from mangrove sediment that received DW inputs for 11 y increased by ∼1 time. The effect of DW from shrimp ponds on the N2O flux was significantly larger than those of DW from fish/crab ponds and razor clam ponds. Moreover, the total global warming potentials (GWPs) at the field sites with DW inputs increased by 29–129% of which the CO2 flux was the main contributor to the GWP (85–96%). N2O as a proportion of CO2-equivalent flux increased from 2% to 12%, indicating that N2O was an important contributor to the increase in GWP. Overall, DW increased the GHG fluxes from mangrove sediments, indicating that the contribution of mangroves to climate warming was enhanced under DW input. It also implies that the carbon sequestration potential of mangrove sediments may be threatened to some extent. Therefore, future assessments of the carbon sequestration capacity of mangroves at regional or global scales should consider this phenomenon.

Responses of multimetric indices to disturbance are affected by index construction features

Multimetric indices (MMIs) are used worldwide to assess the ecological conditions of aquatic and terrestrial ecosystems. Different criteria and approaches are used to construct MMIs, resulting in widely different indices. Therefore, scientists, managers, and policymakers sometimes question whether such MMIs are useful for biomonitoring and bioassessment programs. Crucial design issues for biomonitoring programs include MMI responsiveness, the bioindicator group used, survey design, field sampling methods, level of taxonomic resolution, metric selection and scoring, and reference condition identification. We performed a meta-analysis on MMI development and applications worldwide to analyze the response of MMIs to different disturbance factors and to determine the degree to which MMI construction features influence their responsiveness to anthropogenic disturbances. We used the Web of Science database to find articles that applied an MMI and related MMI values to an environmental stressor, and we extracted data from 157 articles. We performed random-effects modeling to estimate the overall effect of MMI responses to disturbance and used subgroup analysis to analyze the extent to which the effect sizes varied as a function of different MMI construction features. We found that reference condition criteria had the major effect on MMI responses to disturbance. The environmental disturbance type, the number of metrics, and the ecosystem type to which MMIs were applied contributed more weakly to the effect size variance. The general response of MMIs to disturbance was little affected by the bioindicator group, taxonomic resolution, metric selection criteria, or scoring method. These findings have important implications for designing biomonitoring programs, including developing and improving cost-effective biological indices, because they could enhance MMI development and application protocols.

УЛОГА ДИМЕНЗИЈА КВАЛИТЕТА ПОРОДИЧНИХ ИНТЕРАКЦИЈА И ШКОЛСКОГ ЖИВОТА У ПРЕДИКЦИЈИ ШКОЛСКЕ ДИСЦИПЛИНЕ

The problem of school discipline has a long history, however, it still represents an important disturbing factor in the course of teaching. As the teaching staff faces difficulties in eliminating unwanted behavior of students and maintaining discipline, it is essential to have a good knowledge of the etiology of school discipline in order to effectively work on its prevention. The subject of this research is the examination of the predictive power of dimensions of the quality of family interactions and school life in predicting school discipline. The sample consisted of 200 respondents, fourth-grade students of high schools from the territory of the city of Leskovac. School discipline is operationalized by the School Indiscipline Scalе, dimensions of the quality of family interactions with the Scale of the quality of family interactions, while the Quality of School Life Questionnaire was used to examine the dimensions of the quality of school life. The expected correlations of certain dimensions of the quality of school life and family interactions with the dimensions of school indiscipline were determined. Through the procedure of hierarchical regression analysis, it was determined that the dimensions of the quality of family interactions are not single statistically significant predictors of the dimensions of school indiscipline