Application Placement Research Articles

Prevention of uterine perforation during intracavitary brachytherapy of cervical cancer

PurposeIntracavitary brachytherapy (ICBT) is a part of standard treatment for loco-regionally advanced cervical cancers. ICBT requires a tandem applicator insertion through cervical canal into uterine cavity. Accurate placement through cervical canal, which is distorted by cancer, is crucial to successful treatment. The objective of this study was to investigate actual complication rate of a Smit sleeve insertion performed by experienced gynecologists in a tertiary referral center.Material and methodsClinical data of 328 patients with cervical cancer treated using ICBT, between January 2013 and August 2019, were retrospectively evaluated. Predisposing factors that could have increased the risk of uterine perforation were recorded. Pre-operative ultrasound was carried out for visualization of uterine curvature and selection of an appropriate Smith sleeve length. All applications were performed by a gynecologic oncology fellow or an expert gynecologist.Results317 patients were suitable for analysis. Only one (0.3%) applicator placement resulted in uterine perforation. In two patients, Smit sleeve dislocated after first brachytherapy and reinserted. Adequate applicator placement was achieved, and treatment was completed as planned in 316 cases.ConclusionsA cervical sleeve technic, which reduced the need for multiple insertions and placement of this instrument by an expert gynecologist minimize the risk of complication relative to historical controls.

Towards a Green Approach for Minimizing Carbon Emissions in Fog-Cloud Architecture

Fog computing is developed to complement cloud computing by extending the cloud services (computing, storage, networking, and management) to the edge of the network in order to reduce service latency. Correspondingly, the incremental use of cloud/fog resources and their applications has increased energy consumption and carbon emissions (CO2) of the data centers, which caused significant environmental challenges. Optimizing the placement of the requested resources and applications (e.g., in the form of virtual machines (VMs)) is one of the main solutions, which has a primary effect in reducing the energy consumption of cloud/fog architectures and consequently their CO2 emissions. However, due to the geographic distribution of cloud and fog data centers, there are varying levels of CO2 emissions to consider, which makes optimizing the placement of resources and applications in distributed cloud/fog more challenging than in centralized clouds in terms of carbon efficiency. In this paper, we propose a multi-level approach using a mixed-integer linear programming (MILP) model to minimize the CO2 emissions of data centers by optimizing the resources usage and the placement of VMs in fog-cloud environments. This approach calculates the CO2 emissions of the British Telecom (BT) network based on the carbon intensity data from the National Grid ESO, considering several scenarios of traffic demand during different times of the day and year. The results show that the optimal location to host applications highly relies on the carbon intensity and traffic demands. The results also show there is a trade-off between CO2 emission reduced by shortening network journey, and CO2 emission increased by hosting more applications into the fog nodes. In addition, the results demonstrate that the proposed green fog-cloud architecture outperforms the central cloud and the distributed clouds in terms of reducing the total CO2 emission by up to 91% and 71%, respectively. Finally, we develop a heuristic algorithm to mimic and validate the presented work, and it shows comparable results to the MILP model.

Application and Storage-Aware Data Placement and Job Scheduling for Hadoop Clusters

As one of the most popular frameworks for large-scale analytics processing, Hadoop is facing two challenges: both applications and storage devices become heterogeneous. However, existing data placement and job scheduling schemes pay little attention to such heterogeneity of either application I/O requirements or I/O device capability, thus can greatly degrade system efficiencies. In this paper, we propose ASPS, an Application and Storage-aware data Placement and job Scheduling approach for Hadoop clusters. The idea is to place application data and schedule application tasks considering both application I/O requirements and storage device characteristics. Specifically, ASPS first introduces novel metrics to quantify I/O requirements of applications. Then, based on the quantification, ASPS places data of different applications to the preferred storage devices. Finally, ASPS tries to launch jobs with high I/O requirements on the nodes with the same type of faster devices to improve system efficiency. We have implemented ASPS in Hadoop framework. Experimental results show that ASPS can reduce the completion time of a single application by up to 36% and the average completion time of six concurrent applications by 27%, compared to existing data placement policies and job scheduling approaches.

Flexible computation offloading in a fuzzy-based mobile edge orchestrator for IoT applications

In the Internet of Things (IoT) era, the capacity-limited Internet and uncontrollable service delays for various new applications, such as video streaming analysis and augmented reality, are challenges. Cloud computing systems, also known as a solution that offloads energy-consuming computation of IoT applications to a cloud server, cannot meet the delay-sensitive and context-aware service requirements. To address this issue, an edge computing system provides timely and context-aware services by bringing the computations and storage closer to the user. The dynamic flow of requests that can be efficiently processed is a significant challenge for edge and cloud computing systems. To improve the performance of IoT systems, the mobile edge orchestrator (MEO), which is an application placement controller, was designed by integrating end mobile devices with edge and cloud computing systems. In this paper, we propose a flexible computation offloading method in a fuzzy-based MEO for IoT applications in order to improve the efficiency in computational resource management. Considering the network, computation resources, and task requirements, a fuzzy-based MEO allows edge workload orchestration actions to decide whether to offload a mobile user to local edge, neighboring edge, or cloud servers. Additionally, increasing packet sizes will affect the failed-task ratio when the number of mobile devices increases. To reduce failed tasks because of transmission collisions and to improve service times for time-critical tasks, we define a new input crisp value, and a new output decision for a fuzzy-based MEO. Using the EdgeCloudSim simulator, we evaluate our proposal with four benchmark algorithms in augmented reality, healthcare, compute-intensive, and infotainment applications. Simulation results show that our proposal provides better results in terms of WLAN delay, service times, the number of failed tasks, and VM utilization.

Efficient Algorithms for Multi-Component Application Placement in Mobile Edge Computing

In this article, we address the Multi-Component Application Placement Problem ( <inline-formula><tex-math notation="LaTeX">${\sf MCAPP}$</tex-math></inline-formula> ) in Mobile Edge Computing (MEC) systems. We formulate this problem as a Mixed Integer Non-Linear Program (MINLP) with the objective of minimizing the total cost of running the applications. In our formulation, we take into account two important and challenging characteristics of MEC systems, the mobility of users and the network capabilities. We analyze the complexity of <inline-formula><tex-math notation="LaTeX">${\sf MCAPP}$</tex-math></inline-formula> and prove that it is <inline-formula><tex-math notation="LaTeX">$NP$</tex-math></inline-formula> -hard, that is, finding the optimal solution in reasonable amount of time is infeasible. We design two algorithms, one based on matching and local search and one based on a greedy approach, and evaluate their performance by conducting an extensive experimental analysis driven by two types of user mobility models, real-life mobility traces and random-walk. The results show that the proposed algorithms obtain near-optimal solutions and require small execution times for reasonably large problem instances.

Simulation-based graduate medical education in MR-guided brachytherapy for cervical cancer

PurposeBrachytherapy is critical for the curative treatment of locally advanced cervical cancer. Although brachytherapy use is declining in the United States (U.S.), novel interstitial or intracavitary applicators and advances in image guidance for applicator placement and treatment planning have allowed for tumor dose escalation while reducing normal tissue toxicity. Recent survey data have suggested insufficient brachytherapy training for radiation oncology trainees in the United States. This study aimed to address these gaps by developing and piloting a simulation-based education (SBE) workshop for MR-guided cervical cancer brachytherapy. Methods and MaterialsAn SBE workshop was developed for graduate medical education (GME) trainees focusing on MR-guided brachytherapy for cervical cancer. Four hands-on stations, simulating aspects of the procedure, were led by a team of gynecological brachytherapy experts. The learners were radiation oncology residents and fellows in a U.S. GME training program. The primary outcome was feasibility, assessed by completion of the workshop within the time constraints of the curriculum. Learners completed preworkshop and postworkshop surveys to provide information on efficacy. ResultsThe workshop was successfully completed in a 1-h block of GME didactic time. Ten trainees completed all four stations, and all completed preworkshop and postworkshop surveys, which showed improvements in knowledge and technical proficiency. Feedback was positive, and trainees requested additional learning opportunities. ConclusionsThis study showed that GME-focused SBE in MR-guided cervical cancer brachytherapy was feasible. SBE provided a nonclinical environment in which to practice aspects of MR-guided brachytherapy. Ongoing work includes collaboration with other U.S. institutions. Future studies should focus on international adaptation.

Comparative Dosimetric Analysis of Combined Intracavitary and Interstitial Needles Placement vs. Intracavitary Alone for Cervical Cancer

To compare the target volume coverage and normal tissue avoidance for high-dose rate (HDR) brachytherapy boost for cervical cancer between intracavitary (IC) tandem and ovoid (T&O) with additional free-hand interstitial (IS) needles (IC-IS) vs. IC brachytherapy alone under ultrasound guidance. We reviewed IC T&O cases with free-hand IS needles for cervical cancer treatment from Nov 2018 to Oct 2019. Ultrasound guidance was used for applicator placement. Treatment planning was CT-based; high-risk clinical target volumes (HR-CTVs) and organs at risk (OARs) were delineated before each fraction. Minimum acceptable target coverage was 90% of the prescription dose delivered to the HR-CTV (D90). If these objectives were unmet with the standard IC applicator, IS needles were added at the discretion of the physician. For this analysis, each IC-IS case was re-planned (r) with IC alone and again with IC-IS, matching the dose-limiting normal tissue structure for that treatment. HR-CTV with D90 along with OARs D2cc means and 95% confidence intervals were evaluated between rIC-IS and rIC plans using a paired t-tests, p = 0.05. Acute side effects were evaluated using CTCAE v5.0 criteria. We identified 6 patients with 20 IC-IS treatments after whole pelvis radiation. Between two to six IS needles were placed for each treatment. All fractions were planned for 7 Gy. Mean D90 per fraction to the HR-CTV for iIC-IS, rIC, and rIC-IS needles was 7.6 Gy (6.3–9.4) vs. 7.1 Gy (4.9–9.3) vs. 7.8 Gy (6.3–9.8) (p = 0.0000008), respectively, with a relative percent-dose increase of 10.6% from rIC alone to rIC-IS. Regarding OARs, mean D2cc per fraction for iIC-IS, rIC alone, and rIC-IS were: Bladder - 5.3 Gy (3.7–6.4) vs. 5.5 (4.1–6.2) vs. 5.5 (3.93 – 6.19) (p = 0.78); Rectum - 3.1 Gy (2.2–4.2) vs. 3.2 (2.1–4.1) vs. 3.3 Gy (2.2–4.1) (p = 0.14); Sigmoid - 3.4 Gy (2.3–4.2) vs. 3.4 (2.5–4.2) vs. 3.4 (2.5– 4.2) (p = 0.49). Dosimetric results demonstrated in the table. All patients tolerated the procedure well without any CTCAE v5.0 Grade 3 to 5 adverse events. For appropriately selected patients, adding IS needless to IC HDR brachytherapy boost can improve dose coverage of HR-CTV for cervical cancer while not increasing dose to OARs. Long term follow-up is warranted for evaluating differences in clinical outcomes and associated toxicity.Tabled 1Abstract 2856; Table; Comparison of initial (i) IC + IS cases vs. re-planned (r) IC alone vs. rIC-IS (Gy)Patient Number (# fractions)iIC-IS D90 HRCTVrIC alone D90 HRCTVrIC-IS D90 HRCTViIC-IS D2cc BladderrIC alone D2cc BladderrIC-IS D2cc bladderiIC-IS D2cc RectumrIC alone D2cc RectumrIC-IS D2cc rectum1 (3)7.67.67.94.64.74.82.42.62.72 (4)7.77.98.34.54.94.72.82.72.83 (3)6.867.15.76.16.14.13.944 (4)8.7895.75.95.82.83.13.15 (3)75.46.96.25.55.83.73.93.96 (3)7.27.17.35.75.75.73.33.23.3Average (CI)7.6 (6.3–9.4)7.1 (4.9–9.3)7.8 (6.3–9.8)5.3 (3.7–6.4)5.5 (4.1–6.2)5.5 (3.9–6.2)3.1 (2.2–4.2)3.2 (2.1–4.1)3.3 (2.2-4.1)p - value0.0000080.780.14 Open table in a new tab

Intraoperative Ultrasound Guided Intracavitary Brachytherapy: Improving Toxicity and Precision of Tandem Applicator Placement in Cervical Cancer

Intraoperative Ultrasound Guided Intracavitary Brachytherapy: Improving Toxicity and Precision of Tandem Applicator Placement in Cervical Cancer

Multi-material 3D Printing in Brachytherapy– Prototyping Teaching Tools

The utilization of brachytherapy for cervical cancer has been declining, resulting in worsening survival. To improve resident confidence in performing of intracavitary and interstitial HDR brachytherapy procedures, an educational multi-material modular 3D printed gynecologic pelvic phantom training kit simulating both normal and pathological conditions has been developed. The implementation of this phantom in resident education is intended to allow for a more thorough and comprehensive training in tandem and ovoid and needle insertions. CT and MR scans from 50 patients with locally advanced cervical cancer were analyzed. Dimensions of the uterine bodies and canals were measured from the T2 weighted MRI images. These measurements were used to construct four model uteri based off of averages and standard deviations. The vaginal canal was constructed based on standard gynecological speculum dimensions. Individual STL files were imported into Autodesk Meshmixer and manipulated to include features such as hollowed out cavities and canals to allow for applicator placement. Modular components of the phantom were designed and integrated into patient anatomy using CAD 3DShapr. Five HRCTVs were extracted from patient scans to be incorporated into the uterine body. Elasticity properties of tissues were used to calculate appropriate flexible and rigid shore values (0-100) to assign to each anatomical structure for multi-material printing. Average dimensions and SD from the 50-patient study are presented in Table 1 for the anatomical structures considered. The finalized prototype kit consists of three anteverted uteri based on the smallest, the average, and the largest dimensions from our patient set. The fourth uterus is retroverted and uses average dimensions. Clip-on HRCTV attachments expanding outside of the uterine body are included to mimic different pathology. Color staining with different shore values within the uterus was done to represent internal HRCTVs. All uterus bodies and the vaginal canal were printed using Agilus (shore 30A) in clear as a means of evaluation for tandem/ovoid and needle placement. The rectum and HRCTVs were printed using a blend of polymers that allow for variable stiffness (Vero and Agilus, shore 70A and 40A respectively). The bladder was printed using pure Agilus (shore 30A). A multi-material modular 3D printed pelvic phantom training kit simulating normal and cervix pathological conditions has been developed for practicing tandem and ovoid insertions. This tool may be used to help address a gap in decreased resident confidence in performing brachytherapy procedures.Tabled 1Abstract 2983; TableCanal Length [cm]Canal Width [cm]Body Length [cm]Body Width (top) [cm]Body Width (midpoint) [cm]Canal Angle [deg]U15.810.345.812.023.1519.1U26.230.427.872.744.4527.6U37.630.3439.933.455.7436.1U4*6.230.427.872.744.4527.6∗Retroverted Open table in a new tab

Do Air Gaps With Image-Guided Vaginal Cuff Brachytherapy Impact Failure Rates in High Intermediate Risk FIGO Stage I Endometrial Cancer Patients?

In patients with intermediate to high intermediate risk (HIR) FIGO stage 1 endometrial cancer, adjuvant vaginal cuff brachytherapy (VCBT) reduces the risk of vaginal cuff failure (VCF). Image-guided brachytherapy (IGBT) utilizing computed tomography (CT) scans helps confirm applicator placement but may show air gaps at the applicator surface. While multiple studies have quantified air gaps and their dosimetric impact, the objective of this study is to assess their impact on the rate of VCF after IGBT. We performed a retrospective review of patients treated with adjuvant image-guided VCBT alone from 2009-2016 for HIR FIGO stage I endometrial cancer. Patients were defined as HIR if they met the criteria outlined in GOG 99 or PORTEC-2. VCBT was 21 Gy in 3 fractions (85% of patients) or 30 Gy in 5 fractions (15%). Air gaps present at the applicator surface on the first post-insertion CT were contoured. VCFs were captured by review of imaging, pathology, and/or clinical notes. Vaginal cuff failure free survival (VCFFS) was measured from the start of VCBT to VCF. Patients without VCF were censored at last follow up or death. The Kaplan-Meier method was used to estimate actuarial event-time probabilities for VCFFS. Predictors of VCFFS were analyzed using log rank tests between groups and with Cox regression for continuous variables. A total of 234 patients were identified. Median age was 66 years (Interquartile range [IQR] 60-72 years). Most patients (80%) had FIGO grade 2 or 3 carcinoma; 49% were stage FIGO IB, and 54% had LVSI. A total of 85% were HIR by GOG 99 criteria and 68% by PORTEC-2 criteria. 25% had non-endometrioid histology. Air gaps were present on the first post-insertion CT scan in 82% of patients. They were present laterally in 74% of patients, anteriorly in 35%, at the apex in 30%, and posteriorly in 18%. One hundred eleven patients (47%) had air gaps in multiple locations. The median number of air gaps was 2 (IQR 1-3). The median depth of the largest air gap was 2.7 mm (IQR 2.1-3.4 mm), and the median cumulative volume of air gaps was less than 0.1 cm3 (range < 0.1 – 0.7 cm3). At a median follow-up of 56 months (IQR 41 – 69), 12 patients (5%) experienced VCF, of which 4 had an isolated VCF and 8 had synchronous pelvic and/or distant failure. Five-year any VCFFS and isolated VCFFS were 96% (95% CI 93-98%) and 98% (95% CI 96% - 100%), respectively. Mean air gap volume was no different between patients with or without VCF (0.03 vs 0.06 cm3, p = 0.35). On univariate analysis, no factors, including the presence, number, maximum depth, or cumulative volume of air gaps, were predictive for VCFFS. In this population of HIR FIGO stage I endometrial cancer patients treated with adjuvant image-guided VCBT, VCFFS remained high (96% at 5 years) despite the majority having small volume air gaps present on CT scan, suggesting that small air gaps < 0.7 cm3 do not increase the risk for VCF.

BADEP: Bandwidth and delay efficient application placement in fog‐based IoT systems

AbstractNowadays, fog computing is considered as a new computing method that provides the resources required to provide various services and applications to Internet of Things (IoT) devices near them. Nonetheless, in recent years, the increasing growth of IoT devices with heavily dependent on resources has made it impossible for fog computing to provide these resources efficiently in many cases, which will lead to delay in the delivery of various services. Thus, the placement of application/service in fog computing‐based environments will turn into a challenging issue. This was the motivation for this paper to present a method for placement of various IoT applications in fog computing‐based environments to reduce the delay in these systems. In the proposed method, it is tried to reduce the resulting delay by reducing the network usage in the fog computing layer. In doing so, the proposed method tried to place the applications so that the interdependent modules in these applications, with a high data interaction rate, are placed on a node as much as possible to reduce the need for data transfer. IFogSim simulator was used to evaluate the proposed method, where the results of the simulation show that the proposed method improved the delay by 9% and the network usage by 21% compared to the existing methods.

Method for Effective Use of Cloudlet Network Resources

The article addresses the issue of balanced placement of mobile software applications of mobile users in cloudlets deployed near base stations of Wireless Metropolitan Area Networks (WMAN), taking into account their technical capabilities. It is noted that the proposed model is more efficient in meeting the demand for computing and memory resources of mobile devices, eliminating network delays and using a reliable communication channel. At the same time, a minimum of cloudlet-based communication channels with a mobile user was suggested, reducing the network load and reliability of the communication channel when using multimedia software on mobile devices. The article reviews the balanced distribution of the tasks in the cloudlet network. If a user offloads the task to the nearest cloud and resolves it there, then the delays and energy consumption will be less. When the cloudlet is far from the mobile device, as the number of communication channels increases the delays are observed. Moreover, the article discusses the issue of selecting the cloudlets that meet some of the user requirements. Using the possible values that determine the importance of cloudlets (vacant resources in cloudlets, closeness of cloudlets to the user, high reliability, etc.), the conditions, according to which the user's application is offloaded to the certain cloudlet, are studied and a method is proposed.

Radiation protection in radiological imaging: a survey of imaging modalities used in Japanese institutions for verifying applicator placements in high-dose-rate brachytherapy.

Institutional imaging protocols for the verification of brachytherapy applicator placements were investigated in a survey study of domestic radiotherapy institutions. The survey form designed by a free on-line survey system was distributed via the mailing-list system of the Japanese Society for Radiation Oncology. Survey data of 75 institutions between August 2019 and October 2019 were collected. The imaging modalities used were dependent on resources available to the institutions. The displacement of a brachytherapy applicator results in significant dosimetric impact. It is essential to verify applicator placements using imaging modalities before treatment. Various imaging modalities used in institutions included a computed tomography (CT) scanner, an angiography X-ray system, a multi-purpose X-ray system and a radiotherapy simulator. The median total exposure time in overall treatment sessions was n}{}le75 s for gynecological and prostate cancers. Some institutions used fluoroscopy to monitor the brachytherapy source movement. Institutional countermeasures for reducing unwanted imaging dose included minimizing the image area, changing the imaging orientation, reducing the imaging frequency and optimizing the imaging conditions. It is worth noting that half of the institutions did not confirm imaging dose regularly. This study reported on the usage of imaging modalities for brachytherapy in Japan. More caution should be applied with interstitial brachytherapy with many catheters that can lead to potentially substantial increments in imaging doses for monitoring the actual brachytherapy source using fluoroscopy. It is necessary to share imaging techniques, standardize imaging protocols and quality assurance/quality control among institutions, and imaging dose guidelines for optimization of imaging doses delivered in radiotherapy should be developed.

QoE-Based Multi-Criteria Decision Making for Resource Provisioning in Fog Computing Using AHP Technique

Application placement in the fog environment is becoming one of the major challenges because of its distributed, hierarchical, and heterogeneous nature. Also, user expectations and various features of IoT devices further increase the complexity of the problem for the placement of applications in the fog computing environment. Therefore, to improve the QoE of various end-users for the use of various system services, proper placement of applications in the fog computing environment plays an important role. In this paper, the authors have proposed a service placement methodology for the fog computing environment. For a better selection of application services, AHP technique has been used which provides results in the form of ranks. The performance evaluation of the proposed technique has been done by using a customized testbed that considers the parameters like CPU cycle, storage, maximum latency, processing speed, and network bandwidth. Experimental results obtained for the proposed methodology improved the efficiency of the fog network.

Throughput-Aware Partitioning and Placement of Applications in Fog Computing

Fog computing promises to extend cloud computing to match emerging demands for low latency, location-awareness and dynamic computation. It thus brings data processing close to the edge of the network by leveraging on devices with different computational characteristics. However, the heterogeneity, the geographical distribution, and the data-intensive profiles of IoT deployments render the placement of fog applications a fundamental problem to guarantee target performance figures. This is a core challenge for fog computing providers to offer fog infrastructure as a service, while satisfying the requirements of this new class of microservices-based applications. In this article we root our analysis on the throughput requirements of the applications while exploiting offloading towards different regions. The resulting resource allocation problem is developed for a fog-native application architecture based on containerised microservice modules. An algorithmic solution is designed to optimise the placement of applications modules either in cloud or in fog. Finally, the overall solution consists of two cascaded algorithms. The first one performs a throughput-oriented partitioning of fog application modules. The second one rules the orchestration of applications over a region-based infrastructure. Extensive numerical experiments validate the performance of the overall scheme and confirm that it outperforms state-of-the-art solutions adapted to our context.

Deep learning segmentation of general interventional tools in two-dimensional ultrasound images.

Many interventional procedures require the precise placement of needles or therapy applicators (tools) to correctly achieve planned targets for optimal diagnosis or treatment of cancer, typically leveraging the temporal resolution of ultrasound (US) to provide real-time feedback. Identifying tools in two-dimensional (2D) images can often be time-consuming with the precise position difficult to distinguish. We have developed and implemented a deep learning method to segment tools in 2D US images in near real-time for multiple anatomical sites, despite the widely varying appearances across interventional applications. A U-Net architecture with a Dice similarity coefficient (DSC) loss function was used to perform segmentation on input images resized to 256×256pixels. The U-Net was modified by adding 50% dropouts and the use of transpose convolutions in the decoder section of the network. The proposed approach was trained with 917 images and manual segmentations from prostate/gynecologic brachytherapy, liver ablation, and kidney biopsy/ablation procedures, as well as phantom experiments. Real-time data augmentation was applied to improve generalizability and doubled the dataset for each epoch. Postprocessing to identify the tool tip and trajectory was performed using two different approaches, comparing the largest island with a linear fit to random sample consensus (RANSAC) fitting. Comparing predictions from 315 unseen test images to manual segmentations, the overall median [first quartile, third quartile] tip error, angular error, and DSC were 3.5 [1.3, 13.5] mm, 0.8 [0.3, 1.7]°, and 73.3 [56.2, 82.3]%, respectively, following RANSAC postprocessing. The predictions with the lowest median tip and angular errors were observed in the gynecologic images (median tip error: 0.3mm; median angular error: 0.4°) with the highest errors in the kidney images (median tip error: 10.1mm; median angular error: 2.9°). The performance on the kidney images was likely due to a reduction in acoustic signal associated with oblique insertions relative to the US probe and the increased number of anatomical interfaces with similar echogenicity. Unprocessed segmentations were performed with a mean time of approximately 50ms per image. We have demonstrated that our proposed approach can accurately segment tools in 2D US images from multiple anatomical locations and a variety of clinical interventional procedures in near real-time, providing the potential to improve image guidance during a broad range of diagnostic and therapeutic cancer interventions.

QoE Aware IoT Application Placement in Fog Computing Using Modified-TOPSIS

Over the years, fog computing has emerged as a paradigm to complement the cloud computing in handling the delay sensitive IoT applications in a better manner. Using fog resources, better performance such as in-time service delivery, reduced network load, optimal energy usage etc. can be achieved. With such performance gain, users availing the IoT services are more satisfied. A well-known metric Quality of Experience (QoE), used to measure the satisfaction of IoT users, can be improved by enhancing the performance of the IoT applications. Fog computing is a geographically distributed paradigm and primary service of fog computing may not include the execution of offloaded tasks/applications from the IoT devices. This makes QoE aware placement of applications in fog computing a greater challenge. Since placement algorithm is itself a computational task and both IoT applications and fog nodes need a mediator fog node to execute the placement algorithm, the placement policy should be light weighted in terms of computational complexity. This work proposes a lightweight QoE aware application placement policy in fog computing using Modified TOPSIS that prioritizes the applications and fog instances based on their expectation and computational capability respectively for the placement. Modified TOPSIS inherits all the features of classical TOPSIS while it removes rank reversal problem of classical TOPSIS. Simulation experiments, for a comparative study, depict that the proposed model not only achieves the desired resource utilization, processing time, and reduced network congestion but reduces the application placement time also significantly compared to the state of art.

Geometrically variable three-dimensional ultrasound for mechanically assisted image-guided therapy of focal liver cancer tumors.

Image-guided focal ablation procedures are first-line therapy options in the treatment of liver cancer tumors that provide advantageous reductions in patient recovery times and complication rates relative to open surgery. However, extensive physician training is required and image guidance variabilities during freehand therapy applicator placement limit the sufficiency of ablation volumes and the overall potential of these procedures. We propose the use of three-dimensional ultrasound (3D US) to provide guidance and localization of therapy applicators, augmenting current ablation therapies without the need for specialized procedure suites. We have developed a novel scanning mechanism for geometrically variable 3D US images, a mechanical tracking system, and a needle applicator insertion workflow using a custom needle applicator guide for targeted image-guided procedures. A three-motor scanner was designed to use any commercially available US probe to generate accurate, consistent, and geometrically variable 3D US images. The designed scanner was mounted on a counterbalanced stabilizing and mechanical tracking system for determining the US probe orientation, which was assessed using optical tracking. Further exploiting the utility of the motorized scanner, an image-guidance workflow was developed that moved the probe to any identified target within an acquired 3D US image. The complete 3D US guidance system was used to perform mock targeted interventional procedures on a phantom by selecting a target in a 3D US image, navigating to the target, and performing needle insertion using a custom 3D-printed needle applicator guide. Registered postinsertion 3D US images and cone-beam computed tomography (CBCT) images were used to evaluate tip targeting errors when using the motors, tracking system, or mixed navigation approaches. Two 3D US image geometries were investigated to assess the accuracy of a small-footprint tilt approach and a large field-of-view hybrid approach for a total of 48 targeted needle insertions. 3D US image quality was evaluated in a healthy volunteer and compared to a commercially available matrix array US probe. A mean positioning error of 1.85±1.33mm was observed when performing compound joint manipulations with the mechanical tracking system. A combined approach for navigation that incorporated the motorized movement and the in-plane tracking system corrections performed the best with a mean tip error of 3.77±2.27mm and 4.27±2.47mm based on 3D US and CBCT images, respectively. No significant differences were observed between hybrid and tilt image acquisition geometries with all mean registration errors ≤1.2mm. 3D US volunteer images resulted in clear reconstruction of clinically relevant anatomy. A mechanically tracked system with geometrically variable 3D US provides a utility that enables enhanced applicator guidance, placement verification, and improved clinical workflow during focal liver tumor ablation procedures. Evaluations of the tracking accuracy, targeting capabilities, and clinical imaging feasibility of the proposed 3D US system, provided evidence for clinical translation. This system could provide a workflow for improving applicator placement and reducing local cancer recurrence during interventional procedures treating liver cancer and has the potential to be expanded to other abdominal interventions and procedures.

MAESP: Mobility aware edge service placement in mobile edge networks

Mobile edge networks can provide ultra-low latency by deploying services at the edge of networks. However, it is impracticable to place services on all the edge servers due to limited deployment cost requirement. It is desirable to make the optimal edge application placement decisions in a minimum response time and deployment cost, which involve two possibly conflicting objectives. An important issue here is that the number of edge application request from mobile users, which is the key factor determining the realization of two objectives, can vary considerably and the number of edge application request usually unknown before deploying edge application in a particular edge site. It has been found recently that human mobility (location and time features) have a strong influence on what kinds of application that mobile users choose to use. Based on the above observation, we investigate the mobility aware edge service placement problem aiming at optimizing service latency and deployment cost. The problem is formulated as a multi-objective optimization problem and can be solved by multi-objective context multi-armed bandit with a dominant objective. The features of user mobility (time, location) are considered as the context information guiding the edge application placement decisions. We develop mobility-aware edge service placement (MAESP) method and analyse performance measures of MAESP using the 2-dimensional (2D) regret. We show that the 2D regret of MAESP are sublinear in the number of rounds. Based on a real-world dataset, we carry out extensive simulations to evaluate the performance of MAESP. The results show that MAESP outperforms the benchmark algorithms.

Effective Placement Methods of Vermicompost Application in Urban Tree Species: Implications for Sustainable Urban Afforestation

Knowledge on growth and nutrient uptake characteristics of urban trees and effective strategies to grow trees can help accomplish the goal of urban afforestation initiatives in a sustainable way. Thus, the study investigated the effects of different vermicompost (VC) application placements on the growth and nutrient uptake of three contrasting tree species (fast-growing Betula platyphylla and Larix kaempferi and slow-growing Chamaecyparis obtusa) to provide implications for growing tree stocks for sustainable urban afforestation programs. Five placement methods were used in the greenhouse trial: no fertilization (CON), surface placement (VCs), subsurface placement at 6-cm depth (VCc), bottom placement (35-cm depth (VCb)), and mixed with soil (VCm). We measured the growth parameters such as height, root collar diameter (RCD), and biomass and analyzed foliar nutrient concentrations in response to different placement treatments of VC. Relative height growth was the highest at VCc (132% (B. platyphylla), 114% (L. kaempferi)) and VCs ((57%) C. obtusa). Significant improvement in aboveground and belowground biomass growth of all species at VCs and VCc compared to the other treatments was also observed. Generally, VC treatments significantly increased N concentration compared to CON in all species. In conclusion, fertilizing the fast- and slow-growing urban tree species using VCs and/or VCc is relevant to growing high quality planting stocks for sustainable urban afforestation purposes.