Physical Clusters Research Articles

Telematics Collaborative Resource Allocation Algorithm Based on Cloud Sidecar

This paper provides an in-depth study and analysis of a distributed allocation algorithm for collaborative resources for cloud-edge-vehicle-based Telematics. The approach starts from the emerging application of urban environmental monitoring based on vehicular networking, with an integrated design of data sensing detection and transmission, and collaborative monitoring of vehicle swarm intelligence based on urban air quality collection to avoid redundancy of information and communication overload. A hybrid routing method with minimal delay for reliable data transmission is proposed. The power adjustment algorithm divides the channel into 3 states. When the CBR is less than 0.5, the channel is in an idle state, and when the CBR is greater than 0.5 and less than 0.8, the channel is in an active state. The algorithm designs redundancy strategies based on coding mechanisms to improve the reliability of data transmission, combines coding mechanisms with routing design, incorporates routing switching ideas, and performs probability-based routing decisions to minimize the delay. In straight-line road sections, a fuzzy logic prediction-based vehicle adaptive connectivity clustering routing algorithm is proposed to reduce the communication overhead during vehicle collaboration and ensure high network connectivity; at intersections, a probability-based minimum delay routing decision algorithm is proposed to reduce the information transmission delay. Experiments show that the proposed method effectively improves the efficiency of data-aware collection and transmission, and increases the reliability of transmission. With the explosive growth of video services, the problem of intelligent transmission of DASH-based video streams has become another research hotspot in mobile edge networks. Based on the edge container cloud architecture of vehicular networking, the resource constraints of microservices when deployed in the edge cloud platform were analyzed, and a multi-objective optimization model for microservice resource scheduling was established with the comprehensive performance objectives of shortest microservice invocation distance, highest resource utilization of physical machine clusters, and ensuring load balancing as much as possible.

P05-12 Objectively measured physical activity, chronic illness and health service utilisation - a latent class analysis of activity behaviours in older adults

BackgroundPhysical activity contributes to the prevention of chronic illness as well as promotion of physical and mental health, but most adults remain inactive. Chronic illness affects mainly middle aged and older adults, and very little objectively measured data on physical activity behaviours and associated health outcomes of this population is published. The aims of this study are to: 1. Objectively measure physical behaviour outcomes of adults participating in the Move for Life study; 2. Develop distinct activity profiles based on six behaviour variables; 3. Investigate whether health outcomes differ across the activity profiles.MethodsParticipants were Irish adults aged 50 years and older. Using the activPAL, objectively measured data were collected on average daily: light intensity physical activity (hours); moderate to vigorous intensity physical activity (minutes); step count; time in bed (hours); standing time (hours); and waking sedentary time (hours). Data were obtained on chronic illness and health service utilisation. Validated questionnaires were used to collect data on wellbeing, loneliness and social isolation. Hierarchical cluster analysis using squared Euclidian distance was used to cluster behaviours based on similarity, using SPSS version 26. Regression models explored associations between health outcomes and activity profiles, adjusted for age and sex.ResultsData from 485 participants were analysed, and four activity profiles were identified: sedentary (n = 50, 10.3% of total), low active (n = 295, 60.8%), moderate active (111, 22.9%) and higher active (n = 29, 6%). We will present the differences across the activity profiles for chronic illnesses, multi-morbidity, health service utilisation and validated health tools, comparing to data from the Irish Longitudinal Study on Ageing (TILDA) and the English Longitudinal Study on Ageing (ELSA).ConclusionsThe use of physical activity behaviour clusters may identify people with multi-morbidity and higher utilisation of health services. These findings could be factored into the development of future targeted physical activity interventions.

Symptoms persisting after hospitalisation for COVID-19: 12 months interim results of the CO-FLOW study.

IntroductionA large proportion of patients experience a wide range of sequelae after acute COVID-19, especially after severe illness. The long-term health sequelae need to be assessed. Our objective was to longitudinally assess persistence of symptoms and clusters of symptoms up to 12 months after hospitalisation for COVID-19 and to assess determinants of the main persistent symptoms.MethodsIn this multicenter prospective cohort study patients with COVID-19 are followed up for 2 years with measurements at 3, 6, 12 and 24 months after hospital discharge. Here, we present interim results regarding persistent symptoms up to 12 months.ResultsWe included 492 patients; mean±sd age was 60.2±10.7 years, 335 (68.1%) were males, median length of hospital stay was 11 (6.0–27.0) days. At 3 months after discharge 97.0% of the patients had at least one persisting symptom, this declined to 95.5% and 92.0% at 6 and 12 months, respectively (p=0.010). Muscle weakness, exertional dyspnoea, fatigue, and memory and concentration problems were the most prevalent symptoms with rates over 50% during follow-up. Over time, muscle weakness, hair loss and exertional dyspnoea decreased significantly (p<0.001), while other symptoms such as fatigue, concentration and memory problems, anosmia and ageusia persisted. Symptoms from the physical and respiratory cluster declined significantly over time, in contrast to the fatigue and cognitive symptom clusters.ConclusionThe majority of patients experienced COVID-19 sequelae up to 12 months after severe infection. Whereas physical and respiratory symptoms showed slow gradual decline, fatigue and cognitive symptoms did not evidently resolve over time.

The impacts of viral infection and subsequent antimicrobials on the microbiome-resistome of growing pigs

BackgroundAntimicrobials are used in food-producing animals for purposes of preventing, controlling, and/or treating infections. In swine, a major driver of antimicrobial use is porcine reproductive and respiratory syndrome (PRRS), which is caused by a virus that predisposes infected animals to secondary bacterial infections. Numerous antimicrobial protocols are used to treat PRRS, but we have little insight into how these treatment schemes impact antimicrobial resistance (AMR) dynamics within the fecal microbiome of commercial swine. The aim of this study was to determine whether different PRRS-relevant antimicrobial treatment protocols were associated with differences in the fecal microbiome and resistome of growing pigs. To accomplish this, we used a metagenomics approach to characterize and compare the longitudinal wean-to-market resistome and microbiome of pigs challenged with PRRS virus and then exposed to different antimicrobial treatments, and a group of control pigs not challenged with PRRS virus and having minimal antimicrobial exposure. Genomic DNA was extracted from pen-level composite fecal samples from each treatment group and subjected to metagenomic sequencing and microbiome-resistome bioinformatic and statistical analysis. Microbiome-resistome profiles were compared over time and between treatment groups.ResultsFecal microbiome and resistome compositions both changed significantly over time, with a dramatic and stereotypic shift between weaning and 9 days post-weaning (dpw). Antimicrobial resistance gene (ARG) richness and diversity were significantly higher at earlier time points, while microbiome richness and diversity were significantly lower. The post-weaning shift was characterized by transition from a Bacteroides-dominated enterotype to Lactobacillus- and Streptococcus-dominated enterotypes. Both the microbiome and resistome stabilized by 44 dpw, at which point the trajectory of microbiome-resistome maturation began to diverge slightly between the treatment groups, potentially due to physical clustering of the pigs. Challenge with PRRS virus seemed to correspond to the re-appearance of many very rare and low-abundance ARGs within the feces of challenged pigs. Despite very different antimicrobial exposures after challenge with PRRS virus, resistome composition remained largely similar between the treatment groups. Differences in ARG abundance between the groups were mostly driven by temporal changes in abundance that occurred prior to antimicrobial exposures, with the exception of ermG, which increased in the feces of treated pigs, and was significantly more abundant in the feces of these pigs compared to the pigs that did not receive post-PRRS antimicrobials.ConclusionsThe fecal microbiome-resistome of growing pigs exhibited a stereotypic trajectory driven largely by weaning and physiologic aging of the pigs. Events such as viral illness, antimicrobial exposures, and physical grouping of the pigs exerted significant yet relatively minor influence over this trajectory. Therefore, the AMR profile of market-age pigs is the culmination of the life history of the individual pigs and the populations to which they belong. Disease status alone may be a significant driver of AMR in market-age pigs, and understanding the interaction between disease processes and antimicrobial exposures on the swine microbiome-resistome is crucial to developing effective, robust, and reproducible interventions to control AMR.Aquo5tJ4SdeSfrijD3bjSqVideo

Gas distribution from clusters to filaments in IllustrisTNG

Matter distribution in the environment of galaxy clusters, from their cores to their connected cosmic filaments, must in principle be related to the underlying cluster physics and its evolutionary state. We aim to investigate how radial and azimuthal distribution of gas is affected by cluster environments and how it can be related to cluster-mass assembly history. We first analysed the radial physical properties of gas (velocity, temperature, and density) around 415 galaxy cluster environments from IllustrisTNG simulations at z = 0 (TNG300-1). Whereas hot plasma is virialised inside clusters (&lt; R200), the dynamics of a warm, hot, intergalactic medium (WHIM) can be separated in two regimes: accumulating and slowly infalling gas at cluster peripheries (∼R200) and fast infalling motions outside clusters (&gt; 1.5 R200). The azimuthal distribution of dark matter (DM), hot, and warm gas phases is secondly statistically probed by decomposing their 2D distribution in harmonic space. Inside clusters, the azimuthal symmetries of DM and hot gas trace cluster structural properties well. These include their centre offsets, substructure fractions, and elliptical shapes. Beyond cluster-virialised regions, we find that WHIM gas follows the azimuthal distribution of DM, thus tracing cosmic filament patterns. Azimuthal symmetries of hot and warm gas distribution are finally shown to be imprints of cluster mass assembly history, strongly correlated with the formation time, mass accretion rate, and dynamical state of clusters. The azimuthal mode decomposition of 2D gas distribution is a promising probe to assess the 3D physical and dynamical cluster properties up to their connected cosmic filaments.

A significant U-shaped association between physical activity level and posttraumatic stress disorder in U.S. military Veterans: Results from the 2019–2020 National Health and Resilience in Veterans Study

ObjectiveThe present study sought to extend previous research by examining associations between physical activity levels, PTSD, and PTSD symptom clusters in a large, independent, nationally representative sample of U.S. military veterans. MethodsUsing data from the 2019–2020 National Health and Resilience in Veterans Study (NHRVS), we examined relations between a comprehensive self-report measure of physical activity that reflects public health guidelines and a self-report measure of DSM-5-defined PTSD and a 7-factor model of PTSD symptom dimensions. A total of 3,875 participants completed all study measures. Post-stratification weights were applied to all analyses (weighted n = 3,732). ResultsQuadratic logistic regression revealed a significant (p < .05) U-shaped association between physical activity levels (insufficient, moderate, and active) and a positive PTSD screen. A moderate activity level was associated with the lowest likelihood of PTSD (3.8% adjusted probability) while an active level was associated with the highest likelihood of PTSD (7.8% adjusted probability). Multinomial logistic regression suggested that more severe posttraumatic intrusive symptoms, particularly nightmares, and anhedonic symptoms were associated with a significantly decreased probability of a moderate activity level (ps ≤ .05, false discovery rate corrected). ConclusionsFindings suggest that a moderate physical activity level is associated with a lower likelihood of PTSD but that specific PTSD symptoms may be differentially associated with exercise behaviors. Candidate mechanisms and clinical implications of these findings are discussed.

PiRNA-like small RNAs target transposable elements in a Clade IV parasitic nematode

The small RNA (sRNA) pathways identified in the model organism Caenorhabditis elegans are not widely conserved across nematodes. For example, the PIWI pathway and PIWI-interacting RNAs (piRNAs) are involved in regulating and silencing transposable elements (TE) in most animals but have been lost in nematodes outside of the C. elegans group (Clade V), and little is known about how nematodes regulate TEs in the absence of the PIWI pathway. Here, we investigated the role of sRNAs in the Clade IV parasitic nematode Strongyloides ratti by comparing two genetically identical adult stages (the parasitic female and free-living female). We identified putative small-interfering RNAs, microRNAs and tRNA-derived sRNA fragments that are differentially expressed between the two adult stages. Two classes of sRNAs were predicted to regulate TE activity including (i) a parasite-associated class of 21–22 nt long sRNAs with a 5′ uridine (21-22Us) and a 5′ monophosphate, and (ii) 27 nt long sRNAs with a 5′ guanine/adenine (27GAs) and a 5′ modification. The 21-22Us show striking resemblance to the 21U PIWI-interacting RNAs found in C. elegans, including an AT rich upstream sequence, overlapping loci and physical clustering in the genome. Overall, we have shown that an alternative class of sRNAs compensate for the loss of piRNAs and regulate TE activity in nematodes outside of Clade V.

Hunting for open clusters inGaiaEDR3: 628 new open clusters found with OCfinder

Context.The improvements in the precision of the published data inGaiaEDR3 with respect toGaiaDR2, particularly for parallaxes and proper motions, offer the opportunity to increase the number of known open clusters in the Milky Way by detecting farther and fainter objects that have thus far gone unnoticed.Aims.Our aim is to continue to complete the open cluster census in the Milky Way with the detection of new stellar groups in the Galactic disc. We useGaiaEDR3 up to magnitudeG= 18 mag, increasing the magnitude limit and therefore the search volume explored in one unit with respect to our previous studies.Methods.We used theOCfindermethod to search for new open clusters inGaiaEDR3 using a big data environment. As a first step,OCfinderidentified stellar statistical overdensities in five-dimensional astrometric space (position, parallax, and proper motions) using theDBSCANclustering algorithm. Then, these overdensities were classified into random statistical overdensities or real physical open clusters using a deep artificial neural network trained on well-characterisedG,GBP–GRPcolour-magnitude diagrams.Results.We report the discovery of 628 new open clusters within the Galactic disc, with most of them being located beyond 1 kpc from the Sun. From the estimation of ages, distances, and line-of-sight extinctions of these open clusters, we see that young clusters align following the Galactic spiral arms while older ones are dispersed in the Galactic disc. Furthermore, we find that most open clusters are located at low Galactic altitudes with the exception of a few groups older than 1 Gyr.Conclusions.We show the success of theOCfindermethod leading to the discovery of a total of 1274 open clusters (joining the discoveries here with the previous ones based onGaiaDR2), which represents almost 50% of the known population. Our ability to perform big data searches on a large volume of the Galactic disc, together with the higher precision inGaiaEDR3, enable us to keep completing the census with the discovery of new open clusters.

Mediating Effects of Malnutrition on the Relationship between Depressive Symptoms Clusters and Muscle Function Rather than Muscle Mass in Older Hemodialysis Patients.

The purpose of this study was to investigate the association and mediation pathways among muscle mass, muscle function (muscle strength and physical performance), and malnutrition with depressive symptoms clusters in the older hemodialysis patients. A multi-center cross-sectional study. A total of 499 patients aged ≥ 60 on hemodialysis from seven facilities in Shanghai of China from 2020 to 2021. Muscle mass was assessed by skeletal muscle index(SMI). Muscle strength was measured by handgrip strength, and physical performance was measured via gait speed and Timed Up and Go Test (TUGT). Nutritional status was assessed by Malnutrition Inflammation Score (MIS). Depressive symptoms were evaluated by the Patient Health Questionnaire-9 (PHQ-9). Logistic regression and mediation analyses fully adjusted for all potential confounding factors. Among 499 participants (312 men, mean age 69.2±6.6 years), 108 (21.6%) had depressive symptoms. The muscle strength, physical performance and malnutrition were associated with depressive symptoms. Furthermore, malnutrition significantly mediated the association of muscle function with total, cognitive-affective symptoms. The association of the muscle function with somatic symptoms were mediated by the nutritional status. The mediated proportions of malnutrition in the relationship between physical performance and depressive symptoms clusters were stronger in somatic symptoms than in cognitive-affective symptoms. Our findings suggest that muscle function rather than muscle mass may contribute substantially to the development of depressive symptoms clusters in the hemodialysis via malnutrition. The malnutrition mediated stronger in the association of muscle function with somatic symptoms. These findings may help guide clinicians to better diagnose and manage depression in the context of concomitant muscle function and malnutrition.

Colloidal dynamics of emulsion droplets in mouth

The interaction of emulsions with the tongue is key to the sensory appeal of food and can potentially be exploited for oral/buccal pharmaceutical delivery. Whilst there is good understanding of the different mucoadhesive forces governing emulsion interaction with the tongue, their relative importance is not well understood. In addition, the physical location of emulsions within the saliva papillae on the tongue is not understood at all. A combination of ex vivo salivary film, and in vivo oral coating experiments were used to determine the importance of different mucoadhesive forces. Mucoadhesion of cationic emulsions was largely driven by electrostatic complexation. SDS-PAGE of the in vivo saliva coating highlighted that mucins were largely responsible for cationic emulsion mucoadhesion. Anionic emulsions were bound via hydrophobic/steric interactions to small salivary proteins typically located away from the mucin anchor points. The physical location and clustering of emulsions relative to the salivary film/papillae was probed via the invention of a fluorescent oral microscope. Cationic emulsions were densely clustered close to the papillae whilst anionic emulsions were suspended in the salivary film above the papillae. Interestingly, non-ionic emulsions were also trapped within the salivary film above the papillae as individual droplets. These findings highlight that whilst electrostatic complexation with saliva is a powerful mucoadhesive force, hydrophobic and steric interactions also act to induce oral retention of emulsions. The differences in physical location and clustering of emulsions within the salivary film hint at the 3D locations of the different salivary proteins driving each mucoadhesive interaction. This novel understanding of emulsion saliva/papillae interactions has potential to aid efficacy of buccal pharmaceutical delivery and the reduction of astringency in plant-based foods.

Positively selected genes in the hoary bat (Lasiurus cinereus) lineage: prominence of thymus expression, immune and metabolic function, and regions of ancient synteny.

BackgroundBats of the genus Lasiurus occur throughout the Americas and have diversified into at least 20 species among three subgenera. The hoary bat (Lasiurus cinereus) is highly migratory and ranges farther across North America than any other wild mammal. Despite the ecological importance of this species as a major insect predator, and the particular susceptibility of lasiurine bats to wind turbine strikes, our understanding of hoary bat ecology, physiology, and behavior remains poor.MethodsTo better understand adaptive evolution in this lineage, we used whole-genome sequencing to identify protein-coding sequence and explore signatures of positive selection. Gene models were predicted with Maker and compared to seven well-annotated and phylogenetically representative species. Evolutionary rate analysis was performed with PAML.ResultsOf 9,447 single-copy orthologous groups that met evaluation criteria, 150 genes had a significant excess of nonsynonymous substitutions along the L. cinereus branch (P < 0.001 after manual review of alignments). Selected genes as a group had biased expression, most strongly in thymus tissue. We identified 23 selected genes with reported immune functions as well as a divergent paralog of Steep1 within suborder Yangochiroptera. Seventeen genes had roles in lipid and glucose metabolic pathways, partially overlapping with 15 mitochondrion-associated genes; these adaptations may reflect the metabolic challenges of hibernation, long-distance migration, and seasonal variation in prey abundance. The genomic distribution of positively selected genes differed significantly from background expectation by discrete Kolmogorov–Smirnov test (P < 0.001). Remarkably, the top three physical clusters all coincided with islands of conserved synteny predating Mammalia, the largest of which shares synteny with the human cat-eye critical region (CECR) on 22q11. This observation coupled with the expansion of a novel Tbx1-like gene family may indicate evolutionary innovation during pharyngeal arch development: both the CECR and Tbx1 cause dosage-dependent congenital abnormalities in thymus, heart, and head, and craniodysmorphy is associated with human orthologs of other positively selected genes as well.

Abstract P128: Prevalence Of Pre-frailty And Frailty In The Physical Activity Daily (PAD) Trial

Introduction: Frailty is a multidimensional, geriatric syndrome characterized by decreased physiologic reserve and resistance to stressors. Frailty is associated with increased mortality among cardiac patients; however, data on patients with peripheral arterial disease (PAD) are limited. Purpose: The purpose of this observational study was to identify the prevalence of unique physical frailty clusters in a sedentary, PAD cohort in the Physical Activity Daily (PAD) Trial. Methods: This study utilized baseline information from the Physical Activity Daily (PAD) Trial (2014 – 2021). Sedentary adults with documented PAD, defined as ankle-brachial index [ABI] &lt; 0.9, within the University of Michigan and University of Alabama at Birmingham health systems were enrolled. We clustered three PAD-relevant frailty phenotypes using the Fried’s frailty index —1) self-reported exhaustion (SRE), 2) weakness (WN), and 3) slow walking speed (SWS)—and observed the prevalence of pre-frailty (1-2 characteristics) and frailty (3 characteristics) in the PAD cohort. Results: A total of 106 participants (age, 66.9 [8.4] years; female 37 [34.9%]; White 89 [84.8%]) were assessed for unique frailty clusters. Of the 106, 34.9% were characterized as robust/non-frail, 61.3% were pre-frail and 3.7% were frail. Overall, the most common phenotype component among this PAD cohort was WN (44.3%). Among pre-frail and frail participants, WN (30.4%) was the single-most occurring phenotype followed by SRE (23.2%) and SWS (5.8%). Additionally, WN + SWS (18.9%) was the most prevalent pre-frailty cluster followed by SRE + WN (13.0%) and SRE + SWS (2.9%). Conclusions: In this cohort of participants with PAD, pre-frailty was the most common accounting for 61.3%. Given the high number of adults who transition from pre-frailty to frailty, efforts to identify the clustering of phenotypes in PAD patients who are pre-frail is warranted. The implementation of rehabilitative strategies within this population is of importance to prevent the development of pre-frailty into frailty.

Weak Intermolecular Interactions and Molecular Cluster in Ionic Liquids

Presently, we are working on weak intermolecular interaction (aliphatic H-bonding and ?-? stacking interaction) in imidazolium and piperidinium-based ionic liquids. The weak interactions play a crucial role in the physical properties of ILs. Further, the significance of weak interactions on cluster formation and extended intermolecular interaction in these ILs have been investigated in our laboratory. The vibrational spectroscopic techniques (Raman and FTIR) have been employed to understand the effect of H-bonding interaction on physical property and molecular cluster formation of ILs. Further, DFT calculations help for better understanding the intermolecular interactions at the molecular level.

User-friendly fault detection method for building chilled water flowmeters: Field data validation

In building HVAC systems, a chilled water flowmeter is an important sensor. Its reading can be used to measure the real-time cooling load, which is a critical variable for the automated control of building HVAC systems, especially central chiller plants. To maintain accurate reading data, fault detection and diagnosis (FDD) of flowmeters are necessary. The available FD/FDD methods for chilled water flowmeters have several common disadvantages: (1) high requirements on sensor integrity: multiple sensors are usually involved in the construction of energy balance models; (2) complex methodology: the fault of any monitored sensor could trigger a detection hit, and a diagnosis procedure must be used to isolate the faulty sensor; and (3) the more sensors that are involved, the more difficult it is to collect fault-free historical data for the establishment of a fault-free benchmark. To address these problems, a user-friendly fault detection (FD) method for building chilled water flowmeters is proposed in this study. The proposed method requires three types of variables to function: the pump frequency, the pump power, and the measured chilled water flow rate on the header pipe. The real-time pump frequency and electrical power data that are collected by VFDs/power meters are used to estimate the true value of the chilled water flow rate with random forest regressor. An auxiliary alarm rule is proposed to tackle the tradeoff issue between detector sensitivity and false alarm rate. Field data of a real HVAC system are used in a case study to evaluate the performance of the proposed method in comparison with three existing FD methods. The results of the validation case study suggest that compared to existing FD methods, which are based on the physical pump model, ensemble learning and clustering techniques, the proposed method could realize higher hit rates and higher alarm rates when confronting different faults (bias, noise and drift) at various levels. Besides, due to the proposed alarm rule, false alarms caused by occasional data errors are completely evaded. Sensitivity analysis to the proposed method indicates that the random forest with default hyperparameters is good enough for the FD task. In brief, the good features of the proposed method (i.e., the simple workflow, low sensor requirements, accessible robust input variable data, handy estimation model, auxiliary alarm rule with tight detection threshold) render the proposed method feasible and user-friendly for engineering practice.

Patterns of Adverse Childhood Experiences, Social Problem-Solving, and Mental Health Among Latina Immigrants.

Researchers have established the long-term negative impact of adverse childhood experiences (ACEs) on mental health. Evidence also shows that different types of ACEs often co-occur and that ACEs profiles have differential impact on mental health. However, this prior research has often omitted first-generation Latino immigrants-a growing segment of the population, with potentially higher risk for ACEs, decreased access to mental health services, and increased risk for remaining in poor mental health. In this study, we conducted a cluster analysis using a sample of 336 Latina immigrant to examine: (1) patterns of ACEs, and (2) the mediating role of social problem-solving in the association between ACEs and mental health (depression, anxiety, and post-traumatic stress disorder symptoms [PTSD]) and life satisfaction. We identified 5 clusters: (a) Global ACEs (n = 52, 15.5%), (b) Community Violence and Physical Abuse (n = 80, 23.8%), (c) Physical and Emotional Abuse (n = 72, 21.4%), (d) Household Dysfunction with Physical and Emotional Abuse (n = 56, 16.7%), and (e) Low ACEs (n = 76, 22.6%). The clusters differed by social problem-solving, chronic life burden, mental health, and life satisfaction. Compared to the Low Abuse cluster, the Community Violence and Physical Abuse, and Global ACEs clusters were significantly more likely to have higher depression, anxiety, and PTSD symptoms. Social problem-solving was independently associated with all mental health variables and life satisfaction, and mediated the association between ACEs and depression and anxiety for those in the Community Violence and Physical Abuse cluster. Our study sheds light on how ACEs are experienced by Latina immigrants. Social problem-solving also emerged as a significant determinant of mental health and life satisfaction, and may be a point of intervention for improving mental health in this population.

Fractured Landscape, Flowing Culture &amp; Fabricated Identities: Exploring and Evoking the Transnational Middle Class in Amitav Ghosh's Fiction

The transnational middle class provides us with notions about culture-areas that overlap several nation-states, or of multicultural nations. Cultural diversity, historical memory, and societal organizations are never inscribed on a neutral grid in transnational space. Amitav Ghosh invokes a crucial inquiry of transnationalism: how do we locate the hybrid middle class transnational culture? Does the transnational encounter unsettle the idea that cultures and countries are analogous, leading to a new middle-class culture? In my paper I am going to explore how transnationalism complicates the relationship between space and culture. How the question of interpreting social change and cultural transformation as occurring within interconnected areas is raised by the fractured landscape of independent nations and sovereign cultures. We can see that identity of place emerges from a specific dialogic involvement in a framework of hierarchically organized spaces with its cultured fabrication as a community in the middleclass notions of locality or community, which refer to both a demarcated physical space and clusters of interaction. Ghosh's transnational middle class has been mobile and identities less fixed than the static approaches of classical anthropology. The rapidly growing and intensifying movement of the middle class, combined with a rejection of cultural products and practices, has created a profound sense of loss of territorial roots, erosion of place cultural distinctiveness, and anthropological theory ferment. In the world of Diaspora, transnational cultural flows and mass movement of populations, old-fashioned attempts to map the globe as a set of culture regions or homelands are bewildered by a dazzling zrray of postcolonial simulacra, doublings and redoubling.

Research and Application of Network Anomaly Traffic Detection System

This paper puts forward a network security computing platform which based on the open source big data technology. This network anomaly traffic detection system includes a comprehensive and effective traffic anomaly detection algorithm. The algorithm combines the exponentially weighted moving average algorithm (EWMA) and the anomaly flow interval mapping algorithm two methods. The test experiments contain the physical cluster building parameters, the performance of the system and the DDoS attack verification. It is verified by the Tianjin Education metropolitan area network traffic and the NSFOCUS against denial attack event log service system (NSFOCUS ANTI-DDoS system). In Particular, the network anomaly traffic detection system can accurately identify the occurrence period of anomaly traffic such as the flow direction and the event priority. What’ more, it has strong fault tolerance which can achieve error recovery in a short time. All the test experiment results can verify that the system can effectively detect anomaly traffic and real-time monitor the dynamic network.

Effect of physical cluster formation on the behavior of correlation functions for a fluid system

Physical cluster formation in a fluid system causes a characteristic dependence of the pair correlation function on the distance r between a specific pair of particles at the liquid–vapor critical point. Its dependence deviates from that expressed by the product of a negative power r−1 and a specific function provided by a Taylor series with respect to positive powers of r. The effects of the physical cluster formation can be estimated by representing the pair correlation function as a sum of two correlation functions; these two correlation functions can be provided as exact solutions for two differential equations that result from a system of two integral equations equivalent to the Ornstein–Zernike equation.

Model and data driven transient thermal system modelings for contained data centers

The growing cloud computing infrastructures increase the demand for energy-intensive machines, such as high-performance servers and the associated cooling system. That brings challenges to the energy management of the concerned modern buildings such as the data centers. Transient Thermal System Modeling (TTSM) is an essential solution to illustrate the instant thermal variation inside the buildings based on heat transformation prediction. TTSM can help plan, organize and optimize corresponding settings to improve energy efficiency. The adoption of row-based and rack-based structures as a cooling solution has recently emerged, especially boosted by its high efficiency. In contrast, few studies have concerned the related TTSM. In this study, we propose two frameworks exclusively targeting the TTSM of data centers that adopted row-based or rack-based cooling systems. A model-driven framework (MDF) and a data-driven framework (DDF) are developed, respectively. The MDF is expressed by Ordinary Differential Equations (ODEs), it is built based on the lumped-capacitance method under several pre-declared assumptions as a general TTSM solution to related structures. The DDF is developed as an extension of the Long-Short-Term-Memory (LSTM) method to realize multivariate I/O (MIMO) predictions, which is commonly demanded in Industrial Internet of Things (IIoT) applications. Both frameworks are evaluated experimentally on a physical cluster equipped with a high-level IIoT sensor measurement system. The guide for parameter-tuning of the model is also provided in the experiment part for use case analysing. The validation results show that both frameworks can build reliable TTSM in terms of real-time temperature predictions. Moreover, a comprehensive comparison study is conducted with valuable application suggestions based on experimental discoveries.

Multiparameter Optimization Framework of Cyberphysical Systems: A Case Study on Energy Saving of the Automotive Engine

Multiparameter optimization of complex electromechanical systems in a physical space is a challenging task. CPS (Cyberphysical system) technology can speed up the solution of the problem based on data interaction and collaborative optimization of physical space and cyberspace. This paper proposed a general multiparameter optimization framework by combining physical process simulation and clustering genetic algorithm for the CPS application. The utility of this approach is demonstrated in the instance of automobile engine energy-saving in this paper. A 1.8-L turbocharged GDI (gasoline direct injection) engine model was established and calibrated according to the test data and physical entity. A joint simulation program combining CGA (Clustering Genetic Algorithm) with the GDI engine simulation model was set up for the engine multiparameter optimization and performance prediction in cyberspace; then, the influential mechanism of multiple factors on engine energy-saving optimization was analyzed at 2000 RPM (Revolutions Per Minute) working condition. A multiparameter optimization with clustering genetic algorithm was introduced for multiparameter optimization among physical and digital data. The trade-off between fuel efficiency, dynamic performance, and knock risk was discussed. The results demonstrated the effectiveness of the proposed method and that it can contribute to develop a novel automotive engine control strategy in the future.