Degree Of Imbalance Research Articles

5613374 ASSOCIATION BETWEEN GENOTYPE AND DISEASE COMPLICATIONS IN EGYPTIAN PATIENTS WITH BETA THALASSEMIA

Background: In beta thalassemia, the degree of globin chain imbalance is determined by the nature of the mutation of the β-gene. β° refers to the complete absence of production of β-globin on the affected allele. β+ refers to alleles with some residual production of β-globin. The homozygous state results in severe anemia that necessitates regular blood transfusion. On the other hand, frequent blood transfusion can lead to iron overload resulting in progressive dysfunction of the heart, Liver as well as multiple endocrinopathies. Aim: We aimed to evaluate the impact of genotype on the development of disease complications in patients with β thalassemia. Methods: A Cross sectional study was carried on 73 patients with beta thalassemia. Genotyping was determined by DNA sequencing technique. Routine investigations as well as MRI liver and heart were performed to assess iron overload. Results: We found that β+β+ was the most common genotype in our patients followed by β°β° and β°β+. Mean Liver iron content (LIC) was significantly higher in β°β° compared to β°β+ and β+β+ genotypes and mean cardiac T2* was significantly lower in β°β° compared to β°β+ and β+β+ genotypes. Hepatic complications, hepatitis C, cardiac complications and some endocrinopathies were significantly higher in patients with β°β° genotype compared to other genotypes which explain the role of the underlying genetic defect in thalassemia patients in development of disease complications. Conclusion: Clinical severity of thalassemia and development of such complications can be influenced by the type of β-thalassemia mutation and genotype.

НИКОТИНОВАЯ ЗАВИСИМОСТЬ И ГОРМОНАЛЬНЫЙ ДИСБАЛАНС У БЕРЕМЕННЫХ КУРИЛЬЩИЦ С РАЗНОЙ СТЕПЕНЬЮ ТЯЖЕСТИ ХОБЛ

Introduction. The relationship between the degree of nicotine addiction and the functional state of the thyroid gland, the adrenocorticotropic hormone-cortisol system and hormone imbalance in pregnant women smokers with chronic obstructive pulmonary disease was studied. Aim. The aim of the study was to identify and evaluate the relationship between the degree of nicotine dependence and hormonal imbalance of the thyroid gland, cortisol and progesterone in pregnant smokers with varying degrees of chronic obstructive pulmonary disease severity. Material and methods. The study is based on the results of studying changes in blood hormone indicators in 107 smoking women with chronic obstructive pulmonary disease and 136 non-smoking women in different trimesters of pregnancy observed in the antenatal clinic. The control group consisted of healthy non-smoking patients. The diagnosis of chronic obstructive pulmonary disease was made based on the clinical recommendations of the Russian Respiratory Society (2021). The reliability of the results was evaluated using parametric statistics methods. The data obtained were processed using the Statistica 10 program, Microsoft Excel 2010. Results and discussion. The presented data revealed the presence in smokers of a statistically significant and very close relationship between the degree of nicotine dependence and the indicators of the imbalance of the hormones studied in all trimesters of pregnancy, with maximum values in the third trimester.This is due to the fact that it is in the third trimester of pregnancy that the maximum increase in the indicators of all studied hormones occurs, even if at the initial stage of pregnancy the indicator could be less than the lower limit of the reference interval, as in the case of thyroid-stimulating hormone.Conclusion. Nicotine addiction creates environment for the development of endocrine imbalance of thyroid hormones, adrenocorticotropic hormone, cortisol and progesterone.

Daily allergy burden and heart rate characteristics in adults with allergic rhinitis based on a wearable telemonitoring system.

Allergic rhinitis includes a certain degree of autonomic imbalance. However, no information is available on how daily changes in allergy burden affect autonomic imbalance. We aimed to estimate associations between daily allergy burden (allergy symptoms and mood) and daily heart rate characteristics (resting heart rate and sample entropy, both biomarkers of autonomic balance) of adults with allergic rhinitis, based on real-world measurements with a wearable telemonitoring system. Adults with a tree pollen allergy used a smartphone application to self-report daily allergy symptoms (score 0-44) and mood (score 0-4), and a Mio Alpha 2 wristwatch to collect heart rate characteristics during two pollen seasons of hazel, alder and birch in Belgium. Associations between daily allergy burden and heart rate characteristics were estimated using linear mixed effects distributed lag models with a random intercept for individuals and adjusted for potential confounders. Analyses included 2497 participant-days of 72 participants. A one-point increase in allergy symptom score was associated with an increase in next-day resting heart rate of 0.08 (95% CI: 0.02-0.15) beats per minute. A one-point increase in mood score was associated with an increase in same-day sample entropy of 0.80 (95% CI: 0.34-1.26)×10-2 . No associations were found between allergy symptoms and heart rate sample entropy, nor between mood and resting heart rate. Daily repeated measurements with a wearable telemonitoring system revealed that the daily allergy burden of adults with allergic rhinitis has systemic effects beyond merely the respiratory system.

5613275 USE OF CONSUMER WEARABLES TO PREDICT PAIN IN PATIENTS WITH SICKLE CELL DISEASE

Background: Sickle cell disease (SCD) is associated with increased morbidity and mortality. Episodes of acute and severe pain known as vaso-occlusive crises (VOC) are the most common cause for hospital admission. Mobile health (mHealth) has developed promising, patient-friendly, minimally invasive tools to monitor patients remotely. Our previous work has leveraged data from mHealth apps and wearable devices to evaluate several machine learning (ML) models to accurately predict pain scores in patients with SCD currently admitted for VOC to the SCD Day Hospital. Aims: Evaluate the feasibility of extended monitoring for 30 days post-discharge and to refine the development of ML models to predict pain scores in patients with SCD. Methods: Patients with SCD aged 18 and above who were admitted for a VOC to the SCD Day Hospital or to Duke University Hospital were eligible for this study. All patients were followed for 30 days following discharge. Following informed consent, patients were provided: 1) a mobile app (Nanpar); and 2) an Apple Watch. Patients were instructed to report their pain to nurses as per standard of care monitoring and at least once daily within the Nanpar app. Patients were asked to continuously wear the Apple Watch, removing only to charge. Physiological data collected by the Apple Watch included heart rate, heart rate variability, oxygen saturation and step count. These data were associated with self-reported pain scores to fit five different machine learning classification models. The performance of the models is evaluated by the following metrics: accuracy, F1-score, root mean squared error (RMSE) and area under the ROC curve (AUC). Results: Nineteen patients were included in this study from April through June 2022. The median age at time of inclusion was 30 years (IQR:22-34). The majority of the patients had genotype HbSS (68%) and all were Black or African American. Eleven patients (55%) were enrolled from the SCD Day Hospital. This preliminary dataset consisted of 1480 data points. After micro-averaging due to the imbalanced dataset, the performance of all the models were very similar. The metrics of the best performing model, the random forest model, were: micro-averaged accuracy:0.89, micro-averaged F1-score:0.50, RMSE:1.52, AUC:0.83. There was no correlation between any of the data elements recorded by the Apple Watch. Our random forest model was able to accurately predict higher pain scores not only for patients who were admitted to the hospital, but also for patients after discharge from the hospital. Discussion: Our model was able to predict pain using data from the Apple Watch with high accuracy, but with low F1-score. The consistency in the performance of each model, along with the low F1-scores reflects the high degree of class imbalance and lack of data most probably due to inconsistent pain reporting after discharge. To alleviate this issue of class imbalance, different oversampling approaches and generative models need to be examined. Future efforts will focus on larger numbers of patients and monitoring patients for longer periods of time to provide a larger dataset, in an attempt to further improve the accuracy of pain prediction. Conclusion: The consumer wearable Apple Watch was a feasible method to collect physiologic data and provided accuracy in prediction of higher pain scores. We believe mHealth efforts can provide valuable insights for patient monitoring and pain prediction for patients with SCD.

Hybrid grey wolf and improved particle swarm optimization with adaptive intertial weight-based multi-dimensional learning strategy for load balancing in cloud environments

The popularization of Internet applications and rapid advent of information technologies have invited increased number of developers and companies to focus on the area of Cloud Computing (CC). The most significant issues and challenges in the domain of CC include Load Balancing (LB), scheduling of task executions and managing resource allocation. In particular, LB being the process of distributing computing resources and workloads has attracted maximum attention as it is the predominant issue in CC. In this paper, Hybrid Grey Wolf and Improved Particle Swarm Optimization Algorithm with Adaptive Intertial Weight-based multi-dimensional Learning Strategy (HGWIPSOA)-based LB scheme is proposed for enhancing precision and rapidness in task scheduling and assignment of resources to Virtual Machines (VMs) in cloud environments. In the proposed scheme, initially, Grey Wolf Optimization Algorithm (GWOA) is incorporated into Particle Swarm Optimisation (PSO) for considering the highest fitness particle as alpha wolf search agent, such that the objective of allocating tasks to VMs is attained effectively and efficiently. It then integrates chaos, Adaptive Inertial Weight (AIW) and Dimensional Learning (DL) into PSO specifically to prevent premature convergence and achieve better convergence speed and global search ability depending on the best experience determined by particles for effective LB. The simulation experiments of the proposed HGWIPSOA mechanism confirm better results by offering 21.32 % improved throughput, 19.84 % reduced makespan, 24.98 % minimized degree of imbalance, 18.74 % reduced latency and 27.92 % reduced execution time independent of the number of tasks in the cloud environment on par with benchmarked LB schemes.

Cloud Task Scheduling Using Modified Penguins Search Optimization Algorithm

The cloud computing has emerged as a novel distributed computing system in past few years. It provides computation and resources over the Internet via dynamic provisioning of services. There are quite a few challenges and issues connected with implementation of cloud computing. This paper considers one of its major problems, i.e. task scheduling. The function of an efficient task scheduling algorithm is that it concentrates not only on attaining the requirements of the user but also in enhancing the efficiency of the cloud computing system. Cloud task scheduling is an NP-hard optimization problem, and many meta-heuristic algorithms have been proposed to solve it. This paper proposes a modified Penguins Search Optimization Algorithm (MPeSOA) for efficient cloud task scheduling. The main contribution of our work is to schedule all tasks to available virtual machines so that the makespan is minimized, resource utilization is increased and the degree of imbalance is reduced. The proposed scheduling algorithm was simulated using the CloudSim 4.0 simulator. Experimental results showed that the proposed MPeSOA outperformed three existing meta-heuristics, namely Penguins Search Optimization Algorithm (PeSOA), Genetic Algorithm (GA) and Particle Swarm Optimization (PSO).

LBAA: A novel load balancing mechanism in cloud environments using ant colony optimization and artificial bee colony algorithms

SummaryRecently, cloud computing has been recognized as an effective paradigm for offering an on‐demand platform, software services, and an efficient infrastructure to cloud clients. Due to the exponential growth of cloud tasks and the rapidly increasing number of cloud users, scheduling and balancing these tasks among involved heterogeneous virtual machines becomes an Non‐deterministic Polynomial hard (NP‐hard) optimization problem considering significant constraints, such as high rate of resource usage, low scheduling time, and low implementation cost. Therefore, various meta‐heuristic algorithms have been widely used to tackle the issue. The current paper proposes a novel load balancing mechanism using the ant colony optimization and artificial bee colony algorithms, called LBAA, which aims to balance the load division among systems in data centers. The simulation outcomes confirm that our algorithm outperforms previous works regarding response time, imbalance degree, makespan, and resource utilization up to 25%, 15%, 12%, and 10%, respectively.

Analysis of Spatial-Temporal Evolution Pattern and Its Influencing Factors of Warehouse Supermarkets in Liaoning Province

Based on the data of existing warehouse supermarkets in Liaoning Province, China, spatial autocorrelation analysis, kernel density analysis, composite correlation coefficient analysis and other methods have been adopted to analyze their spatial-temporal evolution pattern to reflect the general law of the development of China’s existing warehouse supermarkets and fill the gap in this research field. The results show that the spatial distribution of warehouse supermarkets in Liaoning Province is extremely uneven, and areas with high nuclear density are distributed along the “Shenyang-Dalian” line belonging to the aggregation distribution. The Lorentz curve shows a downward trend with a large degree of spatial imbalance, that is, the regional concentration of warehouse supermarkets is high. Through global and local autocorrelation analysis, the regions with similar development levels of warehouse supermarkets in Liaoning Province tend to gather together, and the spatial distribution has a strong correlation. The distribution of warehousing supermarkets in Liaoning Province is affected by traffic location conditions, economic conditions, population quantity and population density, the number of urban functional areas, policy conditions and the role of the government, especially by economic conditions.

Genetic diversity and signatures of selection in BoHuai goat revealed by whole-genome sequencing

BackgroundCross breeding is an important way to improve livestock performance. As an important livestock and poultry resource in Henan Province of China, Bohuai goat was formed by crossing Boer goat and Huai goat. After more than 20 years of breeding, BoHuai goats showed many advantages, such as fast growth, good reproductive performance, and high meat yield. In order to better develop and protect Bohuai goats, we sequenced the whole genomes of 30 BoHuai goats and 5 Huai goats to analyze the genetic diversity, population structure and genomic regions under selection of BoHuai goat. Furthermore, we used 126 published genomes of world-wide goat to characterize the genomic variation of BoHuai goat.ResultsThe results showed that the nucleotide diversity of BoHuai goats was lower and the degree of linkage imbalance was higher than that of other breeds. The analysis of population structure showed that BoHuai goats have obvious differences from other goat breeds. In addition, the BoHuai goat is more closely related to the Boer goat than the Huai goat and is highly similar to the Boer goat. Group by selection signal in the BoHuai goat study, we found that one region on chromosome 7 shows a very strong selection signal, which suggests that it could well be the segment region under the intense artificial selection results. Through selective sweeps, we detected some genes related to important traits such as lipid metabolism (LDLR, STAR, ANGPTL8), fertility (STAR), and disease resistance (CD274, DHPS, PDCD1LG2).ConclusionIn this paper, we elucidated the genomic variation, ancestry composition, and selective signals related to important economic traits in BoHuai goats. Our studies on the genome of BoHuai goats will not only help to understand the characteristics of the crossbred but also provide a basis for the improvement of cross-breeding programs.

Imbalanced Turbulence Modified by Large-scale Velocity Shears in the Solar Wind

We have investigated how the degree of imbalance in solar wind turbulence is modified by large-scale velocity shears in the solar wind plasma. The balance between counterpropagating Alfvénic fluctuations, which interact nonlinearly to generate the turbulence, has been quantified by the cross helicity and Elsasser ratio. Velocity shears at a 30 minute timescale were identified, with the shear amplitude defined in terms of the linear Kelvin–Helmholtz (KH) instability threshold. The shears were associated with 74 interplanetary coronal mass ejection (ICME) sheaths observed by the Wind spacecraft at 1 au between 1997 and 2018. Typically weaker shears upstream of the sheaths and downstream in the ICME ejecta were also analyzed. In shears below the KH threshold, imbalance was approximately invariant or weakly rising with shear amplitude. Above the KH threshold, fluctuations tended toward a balanced state with increasing shear amplitude. Magnetic compressibility was also found to increase above the KH threshold. These findings are consistent with velocity shears being local sources of sunward fluctuations that act to reduce net imbalances in the antisunward direction, and suggest that the KH instability plays a role in this process.

An Energy-Aware QoS Load Balance Scheduling Using Hybrid GAACO Algorithm for Cloud

Abstract In recent days, resource allocation is considered to be a complex task in cloud systems. The heuristics models will allocate the resources efficiently in different machines. Then, the fitness function estimation plays a vital role in cloud load balancing, which is mainly used to minimize power consumption. The optimization technique is one of the most suitable options for solving load-balancing problems. This work mainly focuses on analyzing the impacts of using the Genetic Algorithm and Ant Colony Optimization (GAACO) technique for obtaining the optimal solution to efficiently balance the loads across the cloud systems. In addition to that, the GA and ACO are the kinds of object heuristic algorithms being proposed in the work to increase the number of servers that are operated with better energy efficiency. In this work, the main contribution of the GAACO algorithm is to reduce energy consumption, makespan time, response time, and degree of imbalance.

Keep on smiling: Market imbalance, option pricing, and the volatility smile

This article argues that the volatility smile is �real� in the sense that volatility and price change are correlated through the degree of market imbalance.

Exploiting domain knowledge to address class imbalance and a heterogeneous feature space in multi-class classification

Real-world data of multi-class classification tasks often show complex data characteristics that lead to a reduced classification performance. Major analytical challenges are a high degree of multi-class imbalance within data and a heterogeneous feature space, which increases the number and complexity of class patterns. Existing solutions to classification or data pre-processing only address one of these two challenges in isolation. We propose a novel classification approach that explicitly addresses both challenges of multi-class imbalance and heterogeneous feature space together. As main contribution, this approach exploits domain knowledge in terms of a taxonomy to systematically prepare the training data. Based on an experimental evaluation on both real-world data and several synthetically generated data sets, we show that our approach outperforms any other classification technique in terms of accuracy. Furthermore, it entails considerable practical benefits in real-world use cases, e.g., it reduces rework required in the area of product quality control.

Research on China's Interregional Trade in Value-Added from the Perspective of Value Chain

The statistical accounting of international trade is often based on the gross value method of customs data. With the maturity of the global value chain, the international division of labor is becoming more specialized, and the trade of intermediate products is becoming more frequent. The gap between the statistical results of the traditional gross value trade accounting method and the real trade situation is widening. This paper uses the value-added trade statistical model and China's interregional input-output data to calculate the value-added trade volume of China's eight regions in 2002, 2007 and 2010, compares it with traditional statistical methods, and establishes an empirical model with value-added trade as the explanatory variable. The study found that the gross trade statistics method would distort the trade volume and exaggerate the degree of trade imbalance; Trade scale, R&D investment, fixed asset investment, employment scale and other factors will significantly affect the scale of value-added trade.

Multivariate analysis of covariance for heterogeneous and incomplete data.

This article discusses the robustness of the multivariate analysis of covariance (MANCOVA) test for an emergent variable system and proposes a modification of this test to obtain adequate information from heterogeneous normal observations. The proposed approach for testing potential effects in heterogeneous MANCOVA models can be adopted effectively, regardless of the degree of heterogeneity and sample size imbalance. As our method was not designed to handle missing values, we also show how to derive the formulas for pooling the results of multiple-imputation-based analyses into a single final estimate. Results of simulated studies and analysis of real-data show that the proposed combining rules provide adequate coverage and power. Based on the current evidence, the two solutions suggested could be effectively used by researchers for testing hypotheses, provided that the data conform to normality. (PsycInfo Database Record (c) 2024 APA, all rights reserved).

An Energy-Aware Load Balancing Method for IoT-Based Smart Recycling Machines Using an Artificial Chemical Reaction Optimization Algorithm

Recycling is very important for a sustainable and clean environment. Developed and developing countries are both facing the problem of waste management and recycling issues. On the other hand, the Internet of Things (IoT) is a famous and applicable infrastructure used to provide connection between physical devices. It is an important technology that has been researched and implemented in recent years that promises to positively influence several industries, including recycling and trash management. The impact of the IoT on recycling and waste management is examined using standard operating practices in recycling. Recycling facilities, for instance, can use IoT to manage and keep an eye on the recycling situation in various places while allocating the logistics for transportation and distribution processes to minimize recycling costs and lead times. So, companies can use historical patterns to track usage trends in their service regions, assess their accessibility to gather resources, and arrange their activities accordingly. Additionally, energy is a significant aspect of the IoT since several devices will be linked to the internet, and the devices, sensors, nodes, and objects are all energy-restricted. Because the devices are constrained by their nature, the load-balancing protocol is crucial in an IoT ecosystem. Due to the importance of this issue, this study presents an energy-aware load-balancing method for IoT-based smart recycling machines using an artificial chemical reaction optimization algorithm. The experimental results indicated that the proposed solution could achieve excellent performance. According to the obtained results, the imbalance degree (5.44%), energy consumption (11.38%), and delay time (9.05%) were reduced using the proposed method.

Fuzzy Firefly Based Intelligent Algorithm for Load Balancing in Mobile Cloud Computing

This paper presents a novel fuzzy firefly-based intelligent algorithm for load balancing in mobile cloud computing while reducing makespan. The proposed technique implicitly acts intelligently by using inherent traits of fuzzy and firefly. It automatically adjusts its behavior or converges depending on the information gathered during the search process and objective function. It works for 3-tier architecture, including cloudlet and public cloud. As cloudlets have limited resources, fuzzy logic is used for cloudlet selection using capacity and waiting time as input. Fuzzy provides human-like decisions without using any mathematical model. Firefly is a powerful meta-heuristic optimization technique to balance diversification and solution speed. It balances the load on cloud and cloudlet while minimizing makespan and execution time. However, it may trap in local optimum; levy flight can handle it. Hybridization of fuzzy firefly with levy flight is a novel technique that provides reduced makespan, execution time, and Degree of imbalance while balancing the load. Simulation has been carried out on the Cloud Analyst platform with National Aeronautics and Space Administration (NASA) and Clarknet datasets. Results show that the proposed algorithm outperforms Ant Colony Optimization Queue Decision Maker (ACOQDM), Distributed Scheduling Optimization Algorithm (DSOA), and Utility-based Firefly Algorithm (UFA) when compared in terms of makespan, Degree of imbalance, and Figure of Merit.

Cost-Effective Scheduling and Load Balancing Algorithms in Cloud Computing Using Learning Automata

Cloud computing is a distributed computing model in which access is based on demand. A cloud computing environment includes a wide variety of resource suppliers and consumers. Hence, efficient and effective methods for task scheduling and load balancing are required. This paper presents a new approach to task scheduling and load balancing in the cloud computing environment with an emphasis on the cost-efficiency of task execution through resources. The proposed algorithms are based on the fair distribution of jobs between machines, which will prevent the unconventional increase in the price of a machine and the unemployment of other machines. The two parameters Total Cost and Final Cost are designed to achieve the mentioned goal. Applying these two parameters will create a fair basis for job scheduling and load balancing. To implement the proposed approach, learning automata are used as an effective and efficient technique in reinforcement learning. Finally, to show the effectiveness of the proposed algorithms we conducted simulations using CloudSim toolkit and compared proposed algorithms with other existing algorithms like BCO, PES, CJS, PPO and MCT. The proposed algorithms can balance the Final Cost and Total Cost of machines. Also, the proposed algorithms outperform best existing algorithms in terms of efficiency and imbalance degree.

Published Papers Volume 30 - Issue 4 (Award Issue) Awarded Paper Use of insulation resistance imbalance degree for the condition assessment of power distribution cables

Medium-voltage (MV) underground power distribution cables are critical assets of The Hong Kong Electric Co., Ltd. (HK Electric) for ensuring reliable customer power delivery. The inspection and condition assessment of these power distribution cables are conducted regularly to determine their health. During diagnostic tests, all MV cables have to pass a threshold of insulation resistance (IR) before they can be energised. Low IR readings may indicate a deterioration in insulation related to a high leakage current. However, defining a threshold value to assess the intrinsic condition of the power distribution cables for every test is challenging. This is because the reference value is determined by several factors such as cable length, the composition of the cable circuit, e.g., cable type, number and types of cable joints, service years and surface leakage from cable splices and terminations. In order to tackle this problem, a concept of imbalance degree in IR testing is developed to supplement the existing tests on dielectric losses for the purpose of enhancing the condition assessment of three-phase power MV distribution cables when performing IR measurement. The effectiveness of this method has been validated with historical testing records of 11kV distribution power cables at HK Electric. Based on our experience and study results, this improved method could further enhance the diagnosis and maintenance efficacy of HK Electric’s power distribution cables.

Expression of protein phosphatase 4 in different tissues under hypoxia.

Relevant research data shows that there is a certain degree of energy metabolism imbalance in highland residents. Protein phosphatase 4 (PP4) has been found as a new factor in the regulation of sugar and lipid metabolism. Here, we investigate the differential expression of PP4 at a simulated altitude of 4,500 m in the heart, lung, and brain tissues of rats. A hypoxic plateau rat model was established using an animal decompression chamber. A blood routine test was performed by an animal blood cell analyzer on rats cultured for different hypoxia periods at 4,500 m above sea level. Quantitative polymerase chain reaction (qPCR) and western blot were used to detect the changes of protein phosphatase 4 catalytic subunit (PP4C) gene and protein in heart, lung, and brain tissues. The PP4C gene with the highest expression level found in rats slowly entering the high altitude area (20 m-2200 m-7 d-4500 m-3 d) was about twice as high as the low elevation group (20 m above sea level). The simulated high-altitude hypoxia induced an increase of PP4C expression level in all tissues, and the expression in the lung tissue was twice as expressed as heart and brain tissue at high altitude (P < 0.05). These results suggest that the PP4 phosphatase complex is ubiquitously expressed in rat tissues and likely involved in adaptation to or disease associated with high-altitude hypoxia.