Applications and verification of a computational energy dynamics model for mine climate simulations

A complete thermodynamic model is described for temperature and heat flow distribution simulation for ventilation networks in underground mines.The method is called the Computational Energy Dynamics(CED)model of the heat,mass,and energy transport.The Thermal and Humidity(TH)transport elements of the full model are described for advection,convection,and accumulation,encompassing heat capacity,radiation,latent heat for evaporation,and condensation in the airways,as well as variable heat conduction and accumulation in the rock strata.The thermal flywheel effect for time-dependent temperature field applications is included in the model solution.A CED model validation exercise is described,directly evaluating the iterated,minimized energy balance errors for the mechanical and thermal energy components for each network branch after a converged solution is determined.A simulation example relevant to mine safety and health is shown with the CED-TH model,demonstrating its capabilities in efficiency and accuracy in comparison with measurement results.

COVID-19-related cardiomyopathy:Can dual-energy computed tomography be a diagnostic tool?

BACKGROUND No study on dual energy computed tomography(DECT)has been found in the literature to evaluate possibly fatal cardiac/myocardial problems in corona virus disease 2019(COVID-19)patients.Myocardial perfusion deficits can be found in COVID-19 patients even without any significant coronary artery occlusion,and these deficits can be shown via DECT with a perfect interrater agreement.AIM To assess lung perfusion alterations in COVID-19 patients.To our knowledge,no study using DECT has been performed to evaluate possibly fatal cardiac/myocardial problems in COVID-19 patients.The purpose of this study is to evaluate the role of DECT in the detection of COVID-19-related cardiac diseases.METHODS Two blinded independent examiners evaluated CT images using the 17-segment model according to the American Heart Association’s classification of the segmentation of the left ventricular myocardium.Additionally,intraluminal diseases and abnormalities in the main coronary arteries and branches were investigated.Following segment-by-segment analysis,perfusion deficiencies identified on the iodine map pictures on DECT were identified.RESULTS The study enrolled a total of 87 patients.Forty-two of these individuals were classified as COVID-19 positive,and 45 were classified as controls.Perfusion deficits were identified in 66.6%(n=30)of the cases.All control patients had a normal iodine distribution map.Perfusion deficits were found on DECT iodine map images with subepicardial(n=12,40%),intramyocardial(n=8,26.6%),or transmural(n=10,33.3%)anatomical locations within the left ventricular wall.There was no subendocardial involvement in any of the patients.CONCLUSION Myocardial perfusion deficits can be found in COVID-19 patients even without any significant coronary artery occlusion.These deficits can be shown via DECT with a perfect interrater agreement.Additionally,the presence of perfusion deficit is positively correlated with D-dimer levels.

Complementary comments on diagnosis,severity and prognosis prediction of acute pancreatitis

The radiological differential diagnosis of acute pancreatitis includes diffuse pancreatic lymphoma,diffuse autoimmune pancreatitis and groove located mass lesions that may mimic groove pancreatitis.Dual energy computed tomography and diffusion weighted magnetic resonance imaging are useful in the early diagnosis of acute pancreatitis,and dual energy computed tomography is also useful in severity assessment and prognosis prediction.Walled off necrosis is an important complication in terms of prognosis,and it is important to know its radiological findings and distinguish it from pseudocyst.

A novel virtual machine deployment algorithm with energy efficiency in cloud computing

In order to improve the energy efficiency of large-scale data centers, a virtual machine(VM) deployment algorithm called three-threshold energy saving algorithm(TESA), which is based on the linear relation between the energy consumption and(processor) resource utilization, is proposed. In TESA, according to load, hosts in data centers are divided into four classes, that is,host with light load, host with proper load, host with middle load and host with heavy load. By defining TESA, VMs on lightly loaded host or VMs on heavily loaded host are migrated to another host with proper load; VMs on properly loaded host or VMs on middling loaded host are kept constant. Then, based on the TESA, five kinds of VM selection policies(minimization of migrations policy based on TESA(MIMT), maximization of migrations policy based on TESA(MAMT), highest potential growth policy based on TESA(HPGT), lowest potential growth policy based on TESA(LPGT) and random choice policy based on TESA(RCT)) are presented, and MIMT is chosen as the representative policy through experimental comparison. Finally, five research directions are put forward on future energy management. The results of simulation indicate that, as compared with single threshold(ST) algorithm and minimization of migrations(MM) algorithm, MIMT significantly improves the energy efficiency in data centers.

Energy Optimization Oriented Three-Way Clustering Algorithm for Cloud Tasks

Cloud computing has developed as an important information technology paradigm which can provide on-demand services. Meanwhile,its energy consumption problem has attracted a grow-ing attention both from academic and industrial communities. In this paper,from the perspective of cloud tasks,the relationship between cloud tasks and cloud platform energy consumption is established and analyzed on the basis of the multidimensional attributes of cloud tasks. Furthermore,a three-way clustering algorithm of cloud tasks is proposed for saving energy. In the algorithm,f irst,t he cloud tasks are classified into three categories according to the content properties of the cloud tasks and resources respectively. Next,cloud tasks and cloud resources are clustered according to their computation characteristics（ e. g. computation-intensive,data-intensive）. Subsequently,greedy scheduling is performed. The simulation results showthat the proposed algorithm can significantly reduce the energy cost and improve resources utilization,compared with the general greedy scheduling algorithm.

Energy-Efficient Framework for Virtual Machine Consolidation in Cloud Data Centers

With the advent of the era of cloud computing, the high energy consumption of cloud computing data centers has become a prominent problem, and how to reduce the energy consumption of cloud computing data center and improve the efficiency of data center has become the research focus of researchers all the world. In a cloud environment, virtual machine consolidation(VMC) is an effective strategy that can improve the energy efficiency. However, at the same time, in the process of virtual machine consolidation, we need to deal with the tradeoff between energy consumption and excellent service performance to meet service level agreement(SLA). In this paper, we propose a new virtual machine consolidation framework for achieving better energy efficiency-Improved Underloaded Decision(IUD) algorithm and Minimum Average Utilization Difference(MAUD) algorithm. Finally, based on real workload data on Planet Lab, experiments have been done with the cloud simulation platform Cloud Sim. The experimental result shows that the proposed algorithm can reduce the energy consumption and SLA violation of data centers compared with existing algorithms, improving the energy efficiency of data centers.

Mobile-agent-based energy-efficient scheduling with dynamic channel acquisition in mobile cloud computing

Mobile cloud computing（MCC） combines mobile Internet and cloud computing to improve the performance of mobile applications. However, MCC faces the problem of energy efficiency because of randomly varying channels. A scheduling algorithm is proposed by introducing the Lyapunov optimization, which can dynamically choose users to transmit data based on queue backlog and channel statistics. The Lyapunov analysis shows that the proposed scheduling algorithm can make a tradeoff between queue backlog and energy consumption in the channel-aware mobile cloud computing system. The simulation results verify the effectiveness of the proposed algorithm.

Utility of cardiac bioenzymes in predicting cardiovascular outcomes in SARS-CoV-2

The relationship between coronavirus disease-19(COVID-19)and cardiovascular diseases has been an important issue.Therefore,cardiac biomarkers and cardiac imaging have an important place in the diagnostic phase.It is important to know the relationship of biomarkers in COVID-19 so that we can understand the diagnosis of the disease,the predicted course and results after diagnosis.

Energy spectrum computed tomography multi-parameter imaging in preoperative assessment of vascular and neuroinvasive status in gastric cancer

BACKGROUND Vascular and nerve infiltration are important indicators for the progression and prognosis of gastric cancer(GC),but traditional imaging methods have some limitations in preoperative evaluation.In recent years,energy spectrum computed tomography(CT)multiparameter imaging technology has been gradually applied in clinical practice because of its advantages in tissue contrast and lesion detail display.AIM To explore and analyze the value of multiparameter energy spectrum CT imaging in the preoperative assessment of vascular invasion(LVI)and nerve invasion(PNI)in GC patients.METHODS Data from 62 patients with GC confirmed by pathology and accompanied by energy spectrum CT scanning at our hospital between September 2022 and September 2023,including 46 males and 16 females aged 36-71(57.5±9.1)years,were retrospectively collected.The patients were divided into a positive group(42 patients)and a negative group(20 patients)according to the presence of LVI/PNI.The CT values(CT40 keV,CT70 keV),iodine concentration(IC),and normalized IC(NIC)of lesions in the upper energy spectrum CT images of the arterial phase,venous phase,and delayed phase 40 and 70 keV were measured,and the slopes of the energy spectrum curves[K(40-70)]from 40 to 70 keV were calculated.Arterial Core Tip:To investigate the application value of multiparameter energy spectrum computed tomography(CT)imaging in the preoperative assessment of vascular and nerve infiltration in patients with gastric cancer(GC).The imaging data of GC patients were retrospectively analyzed to evaluate the accuracy and sensitivity of CT for identifying and quantifying vascular and nerve infiltration and for comparison with postoperative pathological results.The purpose of this study was to verify the clinical feasibility and potential advantages of multiparameter energy spectrum CT imaging in guiding preoperative diagnosis and treatment decision-making and to provide a new imaging basis for improving the diagnostic accuracy and prognosis of GC patients.

Advances in the imaging of gastroenteropancreatic neuroendocrine neoplasms

Gastroenteropancreatic neuroendocrine neoplasms comprise a heterogeneous group of tumors that differ in their pathogenesis,hormonal syndromes produced,biological behavior and consequently,in their requirement for and/or response to specific chemotherapeutic agents and molecular targeted therapies.Various imaging techniques are available for functional and morphological evaluation of these neoplasms and the selection of investigations performed in each patient should be customized to the clinical question.Also,with the increased availability of cross sectional imaging,these neoplasms are increasingly being detected incidentally in routine radiology practice.This article is a review of the various imaging modalities currently used in the evaluation of neuroendocrine neoplasms,along with a discussion of the role of advanced imaging techniques and a glimpse into the newer imaging horizons,mostly in the research stage.

A new approach to quantifying vehicle induced turbulence for complex traffic scenarios

Traffic-related pollutants adversely affect air quality, especially in regions near major roadways. The vehicleinduced turbulence(VIT) is a significant factor that controls the initial dilution, dispersion, and ultimately the chemical and physical fate of pollutants by altering the conditions in the microenvironment. This study used a computational fluid dynamics(CFD) software FLUENT to model the vehicle-induced turbulence(VIT) generated on roadways, with a focus on impact of vehicle-vehicle interactions, traffic density and vehicle composition on turbulent kinetic energy(TKE). We show, for the first time, that the overall TKE from multiple vehicles traveling in series can be estimated by superimposing the TKE of each vehicle, without considering the distance between them while the distance is greater than one vehicle length. This finding is particularly significant since it enables a new approach to VIT simulations where the overall TKE is calculated as a function of number of vehicles. We found that the interactions between vehicles traveling next to each other in adjacent lanes are insignificant,regardless the directions of the traffic flow. Consequently, simulations of different traffic scenarios can be substantially simplified by treating two-way traffic as one-way traffic, with less than 5% difference in the overall volume-averaged TKE. We also developed equations that allow the estimation of the overall volume-averaged TKE as a function of the number and the type of vehicles.