Values Of Variables Research Articles

Effect Of Online Tracking System And Delivery Timeliness On Customer Satisfaction (Case Study On J & T Express Sampit)

The title of this study is the effect of Online tracking system and Keteptan delivery time on customer satisfaction (Case Study on J&T Express Sampit). This study was conducted at J & T Express Sampit which is located at Jalan MT. Haryono No. 095, Mentawa Baru Hulu, District. Mentawa Baru Ketapang, East Kotawaringin Regency, Central Kalimantan. This study was conducted on customers who use the services of J & T Express Sampit with accidental sampling technique. And the data were tested using validity test, reliability test, hypothesis test and analysis of research data using descriptive analysis, multiple linear regression analysis, correlation coefficient analysis and determination analysis. Based on the results of the hypothesis test in this study is a T-test for online tracking system variables (X1) IE t count 3.146 > t table 2.00324 and a significant level of 0.001 < 0.05 then H0 rejected and Ha accepted. This means that there is a significant influence of Online tracking systems on customer satisfaction. As for the T-test for variable timeliness of delivery (X2) is t count 4.763 > t table 2.00324 and a significant level of 0.000 < 0.05 then H0 rejected and Ha accepted. This means that there is a significant influence between the timeliness of delivery to customer satisfaction. Then for the F-test, namely F count 25.098 > F table 3.16 and a significant level of 0.000 < 0.05 means that simultaneously the Online tracking system and delivery timeliness have a significant effect on customer satisfaction. R number of 0.690 shows the relationship / correlation between customer satisfaction with the independent variable is strong (significant). Adjust R Square of 0.552 means that 55.20% of customer satisfaction variable (Y) can be explained by Online Tracking System variables (X1) and delivery timeliness (X2). While the remaining 44.8% were influenced by other variables that were not included in this study. Based on the results of multiple linear regression test obtained by the equation Y = 1.233+ 0.407 (X1) + 0.580 (X2) means that the score/value of the constant ( ③ ) shows that if there is no increase in the variables of the online tracking system and the accuracy of delivery time then the value of customer satisfaction is equal to 1.233. And each addition of one score / variable value of the online tracking system provides an increase of 0.407 to customer satisfaction with fixed X2 conditions and each addition of one score/variable value of delivery timeliness provides the same increase of 0.580 to customer satisfaction with fixed X1.

Identification and estimation of causal effects in the presence of confounded principal strata.

Principal stratification has become a popular tool to address a broad class of causal inference questions, particularly in dealing with non-compliance and truncation by death problems. The causal effects within principal strata, which are determined by joint potential values of the intermediate variable, also known as the principal causal effects, are often of interest in these studies. The analysis of principal causal effects from observational studies mostly relies on the ignorability assumption of treatment assignment, which requires practitioners to accurately measure as many covariates as possible so that all potential sources of confounders are captured. However, in practice, collecting all potential confounding factors can be challenging and costly, rendering the ignorability assumption questionable. In this paper, we consider the identification and estimation of causal effects when treatment and principal stratification are confounded by unmeasured confounding. Specifically, we establish the nonparametric identification of principal causal effects using a pair of negative controls to mitigate unmeasured confounding, requiring they have no direct effect on the outcome variable. We also provide an estimation method for principal causal effects. Extensive simulations and a leukemia study are employed for illustration.

Accelerometer approach for anticipatory postural adjustments assessment during step initiation in patients with leprosy

Anticipatory postural adjustments (APAs) involve a complex coordination of sensorimotor information that can be impaired in diseases that affect nerve conduction. Assessing APAs typically requires costly video recording technology, posing a challenge to the study of postural changes. This hurdle is compounded in impoverished communities affected by diseases such as leprosy, which often receive limited government support. Recent years have seen the validation of inertial sensors in wearable devices and smartphones for APA analysis in diverse populations, including adults, the elderly and people with Parkinson’s disease. This progress offers economically efficient alternatives for the study of APA in leprosy. Do patterns of activation of anticipatory postural adjustment differ between leprosy patients and healthy controls? We also investigated the validity and replicability of APAs recorded in leprosy patients using inertial measurements and video capture recordings. Thirty healthy individuals in the control group and 30 individuals with leprosy in the leprosy group performed ten gait initiation trials. To record the APA for gait initiation, the participants stood on a 2 m platform. Each participant was informed that the experimenter would give an signal, after which the participant would initiate a two-step walk on the platform. Inertial recordings (low-cost method) and video capture recordings (gold-standard method) from center of mass displacements were used to extract the APA before gait initiation. The results show that APAs are similar between groups (control and leprosy), but leprosy patients have less consistent APAs. In addition, this study highlights the reproducibility and high correlation between the values of variables obtained from both instruments, the video recording as gold standard method and portable digital inertial sensor as a low-cost alternative method. These promising findings support the use of affordable inertial sensors to track and record APAs in underserved populations that lack easy access to gold standard methods such as video recording. This approach has the potential to improve the therapeutic care and rehabilitation of these patients. Although not currently part of official protocols for leprosy patients, this assessment method could prove particularly valuable in situations where significant sensorimotor impairments are suspected or documented.

Analisis Rasio 3 - Bank Komersial dengan Uji - Rls: Implikasi dan Bukti Kinerja Keuangan Bank Indonesia dan Malaysia

Abstracts This research aims to analyze the interaction between commercial banks in Indonesia and commercial banks in Malaysia on bank financial performance reports seen from the ratio aspect of bank financial reports. The method used in this research is a quantitative descriptive method by knowing the existence of the Dependent variable value on the variable (Independent) or more without making relationships and comparisons with other variables. In terms of bank value using the 3 - RLS implementation route, namely, the bank's Profitability Ratio is calculated by looking for Return on Assets (ROA), Return on Equity (ROE), Net Profit Margin (NPM), and Operating Expenses / Operating Income (BOPO). The Liquidity Ratio is calculated by finding the Quick Ratio (QR), Loan to Deposit Ratio (LDR), and Loan to Assets Ratio (LAR). The Solvency Ratio is calculated by looking for the Capital Adequacy Ratio (CAR) and Debt to Equity Ratio (DER). The results of this research evaluate and see the feasibility of 3 commercial banks in Indonesia and Malaysia against the Central Bank using the Triple - RLS method concept. As for commercial banks, there is policy integration with central bank criteria. Therefore, there needs to be discipline and commitment in improving financial performance in financial reports between Indonesian and Malaysian commercial banks.

The Effect of Applying the Teaching at the Right Level (TaRL) Approach Assisted by LKPD to Improve the Learning Outcomes of Grade 6 Elementary School Students on Electrical Circuits

The research aims to examine the effect of implementing the Teaching at the Right Level (TaRL) approach assisted by LKPD to improve the learning outcomes of grade 6 elementary school students on electrical circuits. The research was carried out at SDN 1 Semangkak on class VI students. The sampling technique uses a purposive area sampling method. The research used a quasi-experimental design with data collection techniques through post-tests in the form of multiple choice questions and descriptions, observation, documentation and interviews. The data obtained was analyzed using statistical tests which included normality tests, data analysis using the Paired Samples t-Test. Based on the Validity Test, the results show r = 0.82 α >(0.05) so that all question items show valid results. Reliability test if the value α > 0.738 the value is reliable, namely 0.894. Then the results of the normality test value of learning outcomes are 0.467 > α (0.05) exceed the significant value, then the variable values are normally distributed, followed by using the Paired Sample t-Test test, the test results -t is the learning result at a sig value of 0.00 < α (0.05), so reject H0. So it can be concluded that there is an influence of implementing the Teaching at the Right Level (TaRL) approach assisted by LKPD to improve the learning outcomes of Grade 6 Elementary School students on electrical circuits.

On the asymptotic properties of extreme values of discrete random variables

The article examines the almost sure asymptotic behaviour of extreme values of discrete random variables. We consider the case of random variables whose tails of the distribution are close to the tails of the geometric distribution or decrease faster.

Spatial clustering of overweight/obesity among women in India: Insights from the latest National Family Health Survey.

Overweight/obesity has become global health concern with increasing prevalence. This study examined district-level disparities and spatial clustering of overweight/obesity among women of reproductive age (WRA) in India using the latest National Family Health Survey-5 (2019-2021) data. Information of 623,656 women aged 15 to 49 from the NFHS-5 (2019-2021) were analysed in this study. The outcome variable was BMI as classified by the world health organisation (WHO). Utilising Global Moran's I, Anselin's Local Moran's I, and spatial regression models spatial clustering and associated factors were analysed. The study found that 24% (95% CI: 23.8-24.3) of WRA in India were overweight/obese in 2019-21. The prevalence was greatest in Punjab (41%) and lowest in Meghalaya (11%). Additionally, the Global Moran's I value for the outcome variable was 0.73, indicating a positive spatial autocorrelation in the overweight/obesity. Districts of Tamil Nadu, Andhra Pradesh, Karnataka, Kerala, Telangana, Punjab, Himachal Pradesh, Jammu & Kashmir, Haryana, and Delhi were hotspots of overweight/obesity. Several factors of overweight/obesity among WRA were identified, including place of residence (β: 0.034, p: 0.011), parity (β: 0.322, p: 0.002), social group (β: -0.031, p: 0.016), religion (β: -0.044, p: <0.001), household wealth status (β: 0.184, p: <0.001), mass-media exposure (β: 0.056, p: 0.031), and diabetes (β: 0.680, p: <0.001). The study emphasizes the importance of targeted interventions and region-specific strategies, while also stressing the need to address associated factors to develop effective public health initiatives aimed at reducing overweight/obesity prevalence among WRA in India.

Machine Learning Predicts Unplanned Care Escalations for Post-Anesthesia Care Unit Patients during the Perioperative Period: A Single-Center Retrospective Study

Background: Despite low mortality for elective procedures in the United States and developed countries, some patients have unexpected care escalations (UCE) following post-anesthesia care unit (PACU) discharge. Studies indicate patient risk factors for UCE, but determining which factors are most important is unclear. Machine learning (ML) can predict clinical events. We hypothesized that ML could predict patient UCE after PACU discharge in surgical patients and identify specific risk factors. Methods: We conducted a single center, retrospective analysis of all patients undergoing non-cardiac surgery (elective and emergent). We collected data from pre-operative visits, intra-operative records, PACU admissions, and the rate of UCE. We trained a ML model with this data and tested the model on an independent data set to determine its efficacy. Finally, we evaluated the individual patient and clinical factors most likely to predict UCE risk. Results: Our study revealed that ML could predict UCE risk which was approximately 5% in both the training and testing groups. We were able to identify patient risk factors such as patient vital signs, emergent procedure, ASA Status, and non-surgical anesthesia time as significant variable. We plotted Shapley values for significant variables for each patient to help determine which of these variables had the greatest effect on UCE risk. Of note, the UCE risk factors identified frequently by ML were in alignment with anesthesiologist clinical practice and the current literature. Conclusions: We used ML to analyze data from a single-center, retrospective cohort of non-cardiac surgical patients, some of whom had an UCE. ML assigned risk prediction for patients to have UCE and determined perioperative factors associated with increased risk. We advocate to use ML to augment anesthesiologist clinical decision-making, help decide proper disposition from the PACU, and ensure the safest possible care of our patients.

A hybrid fractional order LMS algorithm for power system harmonic estimation

This article proposes a novel multi-innovation fractional order least mean square algorithm for estimating harmonic parameters in power systems with unknown amplitudes and phases. The algorithm incorporates fractional order calculus with variable initial values, which, in contrast to the fixed initial value approach, overcomes the non-locality issue, and converges to its true extreme value. In addition, by extending scalar innovation term to a vector form, the algorithm fully utilizes innovation within a fixed window to improve computational accuracy. The convex combination between integer order and novel fractional order ensures that the performance of the algorithm will not change significantly with the fractional order. Finally, the effectiveness is verified by numerical simulations and comparisons.

A quantitative comparison of two different models of minefield transit

Naval mines are used to cause damage and inflict casualties on ships and to deny access to mined areas. Two distinctly different Monte Carlo models of surface ships transiting minefields were implemented and compared. An analytic model represents mines abstractly as mine density within the minefield. It determines the probability of a ship encountering a mine as a ratio of the area of the ship’s track through the minefield, with width equal to the mines’ effective radius and the area of the minefield. A geometric model represents the ship’s track and the mines’ locations explicitly. It determines whether a ship encounters a mine using a calculation of the distance between a ship’s track and the mines’ locations. The two models’ results were quantitatively compared for multiple values of five experimental variables: minefield dimensions, mine density, mine placement method, ships per group, and ship transit procedure. Each model was executed for each combination of experimental variable values and their results were compared to determine whether the models produced comparable results. In addition, the effectiveness of two mine placement methods at disabling ships and two ship transit procedures at avoiding mines were also compared. All comparisons used statistical hypothesis tests.

Machine learning based analysis of entropy production and wall shear stress in blood flow through stenosed artery

The building up of cholesterol and various substances on the inner boundary of arterial walls forms a plaque (stenosis), which results in the narrowing of walls of arteries, and this exerts additional stress on the walls. Shear stress at the wall of arteries may cause the formation of plaque, plaque growth, and expansive arterial remodeling, and it may also result in an increased risk of plaque rupture. The purpose of this research work is to use machine learning approaches to analyze the wall shear stress and entropy generation through irreversible processes of blood flow in a porous stenosed artery within the context of magnetohydrodynamic consideration. As a way of illustrating blood’s non-Newtonian behavior, the Casson fluid model is used. A blood flow model in the context of mild stenosis has been developed, and it has subsequently been computationally addressed using an explicit finite difference method. The velocity, temperature, entropy generation, Bejan number, and wall shear stress are visualized for different values of pertinent variables. The result reflects the increased velocity in the stenosed region. A hybrid machine learning method using artificial neural network and particle swarm optimization is implemented to optimize the entropy generation and wall shear stress. The network is trained using Stochastic gradient–descent training procedure as well as Levenberg–Marquardt training procedure, and the outcomes of both training procedures are compared. Using this machine learning process, the minimum entropy production has been found for specific values of the Hartmann number, dimensionless temperature difference, and pulsatile component of pressure gradient. This machine learning-based optimization approach enhances the ability to forecast quantitative changes in affecting parameters, which will contribute to minimized damage to the arterial wall, improved blood flow efficiency and the delivery of necessary components to tissues.

Slope Stability Analysis of Rockfill Embankments Considering Stress-Dependent Spatial Variability in Friction Angle of Granular Materials

Slope stability is a major safety concern of rockfill embankments. Since rockfills are incohesive materials, only friction angle is considered as a shear strength parameter in the slope stability analysis of rockfill embankments. Recently, it was found that confining pressure can significantly affect the mean value and variance of the friction angle of rockfills. Since the confining pressure spatially varies within a rockfill embankment, the effect of stress-dependent spatial variability in the friction angle of rockfills should be investigated for slope stability evaluation of rockfill embankments. In the framework of the Limit Equilibrium Method (LEM), an approach is proposed for the slope stability analysis of rockfill embankments considering the stress-dependent spatial variability in the friction angle. The safety factors of slope stability are computed with variable values of the friction angle at the bases of slices which are determined by the stress-dependent mean value and variance of the friction angle of rockfills. The slope stability of a homogeneous rockfill embankment is analyzed to illustrate the proposed approach, and a parametric analysis is carried out to explore the effect of variation in the parameters of the variance function of friction angle on slope stability. The illustrative example demonstrates that the stress-dependent spatial variability of friction angle along the slip surface is obvious and is affected by the location of the slip surface and the loading condition. The effects of the stress-dependent spatial variability of the friction angle on the slope stability of high rockfill embankments should be considered.

Deep learning-based 3D quantitative total tumor burden predicts early recurrence of BCLC A and B HCC after resection.

This study aimed to evaluate the potential of deep learning (DL)-assisted automated three-dimensional quantitative tumor burden at MRI to predict postoperative early recurrence (ER) of hepatocellular carcinoma (HCC). This was a single-center retrospective study enrolling patients who underwent resection for BCLC A and B HCC and preoperative contrast-enhanced MRI. Quantitative total tumor volume (cm3) and total tumor burden (TTB, %) were obtained using a DL automated segmentation tool. Radiologists' visual assessment was used to ensure the quality control of automated segmentation. The prognostic value of clinicopathological variables and tumor burden-related parameters for ER was determined by Cox regression analyses. A total of 592 patients were included, with 525 and 67 patients assigned to BCLC A and B, respectively (2-year ER rate: 30.0% vs. 45.3%; hazard ratio (HR) = 1.8; p = 0.007). TTB was the most important predictor of ER (HR = 2.2; p < 0.001). Using 6.84% as the threshold of TTB, two ER risk strata were obtained in overall (p < 0.001), BCLC A (p < 0.001), and BCLC B (p = 0.027) patients, respectively. The BCLC B low-TTB patients had a similar risk for ER to BCLC A patients and thus were reassigned to a BCLC An stage; whilst the BCLC B high-TTB patients remained in a BCLC Bn stage. The 2-year ER rate was 30.5% for BCLC An patients vs. 58.1% for BCLC Bn patients (HR = 2.8; p < 0.001). TTB determined by DL-based automated segmentation at MRI was a predictive biomarker for postoperative ER and facilitated refined subcategorization of patients within BCLC stages A and B. Total tumor burden derived by deep learning-based automated segmentation at MRI may serve as an imaging biomarker for predicting early recurrence, thereby improving subclassification of Barcelona Clinic Liver Cancer A and B hepatocellular carcinoma patients after hepatectomy. Total tumor burden (TTB) is important for Barcelona Clinic Liver Cancer (BCLC) staging, but is heterogenous. TTB derived by deep learning-based automated segmentation was predictive of postoperative early recurrence. Incorporating TTB into the BCLC algorithm resulted in successful subcategorization of BCLC A and B patients.

Preliminary Sizing of High-Altitude Airships Featuring Atmospheric Ionic Thrusters: An Initial Feasibility Assessment

When it comes to computing the values of variables defining the preliminary sizing of an airship, a few standardized approaches are available in the existing literature. However, when including a disruptive technology in the design is required, sizing procedures need to be amended, so as to be able to deal with the features of any additional novel item. This is the case of atmospheric ionic thrusters, a promising propulsive technology based on electric power, where thrusters feature no moving parts and are relatively cheap to manufacture. The present contribution proposes modifications to an existing airship design technique, originally conceived accounting for standard electro-mechanical thrusters, so as to cope with the specific features of new atmospheric ionic thrusters. After introducing this design procedure in detail, its potential is tested by showing results from feasibility studies on an example airship intended for a high-altitude mission. Concurrently, the so-obtained results allow the demonstration of the sizing features corresponding to the adoption of atmospheric ionic thrusters at the current level of technology, comparing them to what is obtained for the same mission when employing a standard electro-mechanical propulsion system.

Aberrant functional connectivity in anterior cingulate gyrus subregions in migraine without aura patients.

The anterior cingulate gyrus (ACG) is an important regulatory region for pain-related information. However, the ACG is composed of subregions with different functions. The mechanisms underlying the brain networks of different subregions of the ACG in patients with migraine without aura (MwoA) are currently unclear. In the current study, resting-state functional magnetic resonance imaging (rsfMRI) and functional connectivity (FC) were used to investigate the functional characteristics of ACG subregions in MwoA patients. The study included 17 healthy volunteers and 28 MwoA patients. The FC calculation was based on rsfMRI data from a 3 T MRI scanner. The brain networks of the ACG subregions were compared using a general linear model to see if there were any differences between the two groups. Spearman correlation analysis was used to examine the correlation between FC values in abnormal brain regions and clinical variables. Compared with healthy subjects, MwoA patients showed decreased FC between left subgenual ACG and left middle cingulate gyrus and right middle temporal gyrus. Meanwhile, MwoA patients also showed increased FC between pregenual ACG and right angular gyrus and increased FC between right pregenual ACG and right superior occipital gyrus. The FC values between pregenual ACG and right superior occipital gyrus were significantly positively correlated with the visual analogue scale. Disturbances of FC between ACG subregions and default model network and visual cortex may play a key role in neuropathological features, perception and affection of MwoA. The current study provides further insights into the complex scenario of MwoA mechanisms.

Relationship of Land Cover Heterogeneity and Insecticide Use with Arthropod Community Structure in Rice Agroecosystems

Rice agroecosystems generally experience two kinds of environmental stresses, i.e., a decrease in the heterogeneity of land vegetation types due to the development of settlements in the border zone and the excessive use of insecticides. Both of these factors are known to affect the structure of the arthropod community in rice agroecosystems. However, studies related to the effects of these two stresses in shaping the structure of arthropod communities have not been widely carried out, especially during the planting period in the rainy season. This study aims toanalyze the relationship between land cover heterogeneity and insecticide use with the taxonomic and functional diversity of arthropods in thealpha, beta, and gamma dimensions. The study was conducted during the rainy season (October–March) on four rice agroecosystem sites in Bandung, West Java. Data was collected 30 and 50 days after planting (DAP). Arthropod samples were identified, and their taxonomic andfunctional diversity was calculated using the Shannon entropy formula (D). In general, land cover heterogeneity was positively correlated withthe value of arthropod taxonomic and functional diversity, especially in the early vegetative phase of rice plants (30 DAP). In con- trast,insecticides (g/m2) were negatively correlated with the value of arthropod diversity. The composition of arthropod species locally was relativelythe same except in the rice agroecosystem with the lowest land cover heterogeneity and the highest use of insecticides. Additionally, thecoefficient value of the insecticide variable has more influence on the value of arthropod diversity than the variable coefficient of land coverheterogeneity.

Noninvasive reconstruction of internal heat source in biological tissue using adaptive simulated annealing algorithm

The heat distribution information of human lesions is of great value for disease analysis, diagnosis, and treatment. It is a typical inverse problem of heat conduction that deriving the distribution of internal heat sources from the temperature distribution on the body surface. This paper transforms such an inverse problem of bio-heat transfer into a direct one, thereby avoiding complex boundary conditions and regularization processes. To noninvasively reconstruct the internal heat source and its corresponding 3D temperature field in biological tissue, the adaptive simulated annealing (ASA) algorithm is used in the simulation module, where the position P(x, y, z) of point heat source in biological tissue and its corresponding temperature T are set as the optimization variables. Under a certain optimized sample, one can obtain the simulated temperature distributing on the surface of the module, then subtract the simulated temperature from the measured temperature of the same surface which was measured using a thermal infrared imager. If the sum of absolute values of the difference is smaller, it indicates that the current sample is closer to the true location and temperature of the heat source. When the values of optimization variables are determined, the corresponding 3D temperature field is also confirmed. The simulation results show the simulated position and temperature of the heat source are very approximate with those of the real experimental module. The method presented in this paper has enormous potential and promising prospects in clinical research and application, such as tumor hyperthermia, disease thermal diagnosis technology, etc.

Combination of 3D printing and knitting: the loop stitch design and its effects on tensile strength and elongation at break

ABSTRACT This study explored the creation of 3D printing knit-based structures. Furthermore, the study investigated the relationship between the mechanical performance of these structures and the changes in loop stitch design and 3D printing material. Effects on the mechanical properties of the 3D printing loop structure were evaluated with respect to loop stitch, loop head, loop leg and loop foot. This study analysed the mean differences between the values of independent variables, namely, loop stitch and print material and the values of two dependent variables (i.e. tensile strength and elongation at break) using a multivariate analysis of variance. The results revealed that the mechanical properties of the loop structure vary significantly depending on the loop stitch shape and printing material. Tensile strength is mainly affected by material, whereas elongation property is significantly affected by loop stitch shape.

Water-energy-food-greenhouse gases nexus management in urban environments: A robust multi-agent decision-support system

Conceptual urban water metabolism (CUWM) models provide a holistic view of the efficiency of urban water systems. These models can be linked with multi-agent reinforcement learning (MARL) models to mimic stakeholders' reactions to various strategies. However, the outcomes derived from CUWM-MARL models come with uncertainty. As a result, this paper introduces a decision support system (DSS) that is aware of these uncertainties and uses this information to select robust management strategies based on the output of CUWM-MARL models. First, future scenarios are generated by exploring all combinations of critical values of deeply uncertain variables and values of uncertain variables sampled from an appropriate multivariate copula distribution. Then, a computationally efficient surrogate model is developed to alleviate the computational load of the MARL sub-model. The surrogate model is run through all future scenarios to calculate the system's key performance indicators (KPIs) for each management strategy. Urban water managers can use these KPIs and social choice methods to find consensus management strategies. The proposed methodology has been implemented in the western part of the Tehran metropolitan area, Iran. This study evaluates 26 management strategies consisting of various projects, including reducing water distribution network leakage, implementing urban demand control projects, and utilizing semi-centralized or decentralized wastewater treatment plants. The strategy chosen by the uncertainty-aware DSS enhances the total utility and fairness indices by 125% and 4%, respectively. Moreover, it effectively improves groundwater quality, reduces energy consumption and greenhouse gas emissions, and enhances water supply reliability, ultimately contributing to farmers' job security.

Future global existence of homogeneous solutions to the Einstein-Boltzmann system with soft potentials

We study the Cauchy problem for the Einstein-Boltzmann system with soft potentials in a cosmological setting. We assume the Bianchi I symmetry to describe a spatially homogeneous, but anisotropic universe and consider a cosmological constant Λ>0 to describe an accelerated expansion of the universe. For the Boltzmann equation we introduce a new weight function and apply the method of Illner and Shinbrot to obtain the future global existence of spatially homogeneous, small solutions. For the Einstein equations we assume that the initial value of the Hubble variable is close to (Λ/3)1/2. We obtain the future global existence and asymptotic behavior of spatially homogeneous solutions to the Einstein-Boltzmann system with soft potentials.