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Rationale: The objective was to assess the cost-effectiveness of staging positron emission tomography/computed tomography (PET/CT) in early-stage follicular lymphoma from the Canadian health care system perspective. Methods: The study population was FL patients staged as early-stage using conventional CT imaging and planned for curative-intent radiation therapy (RT). A decision analytic model simulated the management after adding a staging PET/CT vs. using staging CT alone. In the no-PET/CT strategy, all patients proceeded to curative-intent RT as planned. In the PET/CT strategy, PET/CT information could result in an increased RT volume, switching to a non-curative approach, or no change in RT treatment as planned. Subsequent disease course was described using a state-transition cohort model over a 30-year time horizon. Diagnostic characteristics, probabilities, utilities and costs were derived from the literature. Baseline analysis was performed using quality-adjusted life years (QALYs), costs (2019 Canadian dollars, CAD$) and the incremental cost-effectiveness ratio. Deterministic sensitivity analyses were conducted, evaluating net monetary benefit at a willingness-to-pay threshold of $100,000/QALY. Probabilistic sensitivity analysis using 10,000 simulations was performed. Costs and QALYs were discounted at a rate of 1.5%. Results: In the reference case scenario, staging PET/CT was the dominant strategy, resulting in an average lifetime cost saving of $3,165 and a gain of 0.32 QALYs. In deterministic sensitivity analyses, the PET/CT strategy remained the preferred strategy for all scenarios supported by available data. In probabilistic sensitivity analysis, the PET/CT strategy was strongly dominant in 77% of simulations (i.e., reduced cost and increased QALYs), and was cost-effective in 89% of simulations (i.e., either cost-saving or with an incremental cost-effectiveness ratio below $100,000/QALY). Conclusion: Our analysis shows that the use of PET/CT to stage early-stage FL patients reduces cost and improves QALYs. Patients with early-stage FL should undergo PET/CT prior to curative-intent RT.

Due to the steep rise in global population, urbanization, and industrialization, most of the cities in the world today are witnessing increased carbon footprints and reduced per capita space. In such a scenario, vehicle sharing and carpooling systems, specifically with electric vehicles (EV), can significantly help due to the reduced cost of ownership, maintenance, and parking space. In this work, we study the challenging problem of optimal sizing and efficient routing for an Electric Vehicle-on-Demand (EVoD) system. Users demand EVs at the pooling stations at different time instances with individual deadlines to reach the destinations. The objective is to fulfill all the demands respecting the deadlines with minimum investment, which essentially translates to minimizing the total number of EVs. We define the problem formally using Mixed Integer Linear Programming (MILP) formulation and propose a set of intelligent and efficient heuristic algorithms to solve it efficiently. The proposed algorithms' performances are tested and validated in a simulated environment on a reasonable size city network with many EV demands. The results obtained show that the proposed heuristic algorithms are competent by reducing 200-360 EVs per day on a network of 282 charging ports, indicating their scalability to be implemented in real-world scenarios.

Compact and lightweight sensors with a high-frequency resolution are required for the passive observation of atmospheric water and oxygen emission lines at a reduced cost and power consumption. A molecular assessment high-resolution observation spectrometer (MAHOS) is developed as a compact, low power, digital fast Fourier transform spectrometer to be installed on a microsatellite. MAHOS has a compact design with dimensions of 0.154 × 0.125 × 0.040 m3 and mass of 0.7 kg. It uses only a few materials including a field programmable gate array (FPGA) module with a lightweight aluminum alloy box. The highly stable spectrometer exhibits a sampling speed of 2.6624 GS/s and 16,384 frequency channels. The stability of the spectrometer is longer than 1200 s within the 1 GHz bandwidth. Thermal dissipation is achieved through a heat conductive gel filled in the gap between the most heat generating component, the FPGA and the aluminum alloy case. Results of a finite element analysis indicate that the design stiff and stable enough to survive in the launch environment. Thermal analysis indicates the durability of the system during operation. Even in space where heat dissipation is not possible, self-heating temperatures are not a problem for the FPGA. In the future, the performance of the spectrometer will be verified by conducting environmental tests.

Network Function Virtualization is a key technology that enables network operators to provide diverse communication services flexibly over a common infrastructure, resulting in a significantly reduced cost. This paper addresses the problem of optimal network function virtualization for providing multicast services in wireless mesh networks with minimal total cost. This problem is modeled in two different ways: Link-based Model (LBM) and Path-based Model (PBM). In both models, we formulate the problem as an integer linear program to find the best hosts for virtual network functions and to steer traffic across them by considering wireless interference and resource budgets. Furthermore, we propose a heuristic solution based on the decomposition of the problem into two smaller sub-problems that can be solved sequentially in two phases. In the first phase, a multicast tree for forwarding traffic is constructed, while in the second phase, the required network functions are instantiated in appropriately chosen nodes. Simulation results are presented to compare the performance and complexity of the exact solutions of link-based and path-based approaches and the proposed heuristic approach. These results demonstrate the effectiveness of the proposed path-based model and the heuristic algorithm.

The ability of an algorithm to extract power quality parameters on a low-end digital controller with constrained resources and computational capacity can promote comprehensive adoption of power quality control and monitoring infrastructure because of reduced cost and thus improve operational ability in a microgrid or distribution grid and support more in-depth integration of renewable energy. Since conventional harmonic extraction for single-phase grid mostly operate on Fourier transform, lock-in amplifier and their derivative algorithms, they demand large memory and computational power which are available with high-end processors and FPGAs. In this paper, a computationally efficient harmonic extraction algorithm is proposed, which can be implemented on commercially available microcontrollers with ease, without compromising accuracy. A detailed theoretical analysis of the proposed method, along with various simulation and the experimental results are presented. Workarounds for minor implementation issues on digital controllers are discussed as well.

The circulating current and the large energy storage requirement are the two major limitations of the modular multilevel converter (MMC). The circulating current in MMC is majorly, in addition to the dc current, second harmonic in nature. If not controlled properly, it results in reduced efficiency and/or increased energy storage requirement (ESR). To enhance the efficiency, the second and the higher-order harmonic components in the circulating current of MMC are required to be suppressed. On the other hand, controlling the circulating current to have predefined magnitude and phase-angle can reduce the ESR. There are many control methods available in literature to suppress and also, to control the second harmonic circulating current (SHCC). These methods are mainly based on sensing the arm current and calculating the SHCC and then suppressing or controlling it based on the set objectives. In this paper, two control schemes are proposed to suppress the SHCC and to reduce the ESR. The proposed schemes are based on utilizing the instantaneous voltage signals of the sub-module capacitors in MMC. Hence, the sensing and communicating the sensed signal of arm currents can be avoided using the proposed schemes, which increases the reliability and leads to reduced cost and complexity. The efficacy of the proposed control schemes is verified using the simulation studies and also by using the scaled-down lab prototype of a 3-phase 7-level MMC.

Binder jet additive manufacturing is a viable method that can be used with a variety of materials, such as metals and ceramics, and offer higher speed and reduced cost for high-volume production compared to powder bed fusion printing. However, the true potential of binder jet additive manufacturing has yet to be realized due to a significant knowledge gap in the material-process-property relationships. This study presents a robust approach to identifying ideal process parameters for binder jetting of Co-Cr-Mo alloy to enable accurate and reproducible manufacturing of defect-free products with high green density. An experimental investigation of the effect of process parameters on output quality was performed using the design of experiments, followed by the analysis of variance. Further characterization between process parameters and process outputs was revealed using machine learning techniques. The relationship between process parameters and density, dimensions and surface quality of green parts was established using artificial neural networks; qualitative features of green parts were classified by employing the weighted k-nearest neighbors algorithm. Furthermore, a genetic algorithm-based multi-objective optimization was employed to determine optimum process parameters, which predicted the part quality with greater than 90% accuracy. Collectively, the proposed optimization method is capable of identifying favorable process conditions for binder jetting of defect-free Co-Cr-Mo parts, with high dimensional accuracy, surface quality, and green density, and can be adapted to determine unique process settings for different material classes.

Power quality problems such as voltage sag, voltage swell, voltage transients, voltage interruptions, and harmonics are becoming significant issues throughout the world. It has been seen that power quality problems resulted in the loss of more than 22 billion dollars in one year in industries of US and more than 500 consumers received a loss in the shape of damage or failure of equipments in 1991. A commonly occurring power quality problem in the electrical power distribution network is voltage sag which makes the sensitive loads of the network at risk. Static Synchronous Compensators, Distribution Series Capacitors and Power Factor Corrector, Static VAR Compensator, Uninterruptable Power Supply, and Solid-state Transfer Switches were used for stabilization but we are facing problems such as difficult maintenance, high cost of services and require large space. So valuable solution to mitigate the problem of voltage sag is the DVR. It is a series DVR. DVR is the series connected device which added the voltage to the load when needed. The most important component of a DVR is the Voltage Source Inverter; through this, we can provide the needful controllable voltage for compensation but it required energy storage device which results in high cost. The Matrix Converter has better performance as compared to other inverters due to direct conversion from AC to AC voltage and there is no need of energy storage unit which results in reduced cost and operational complexity. The control circuit is very essential for DVR; it retains a constant voltage and its magnitude at the load point. DVR is non-linear device due to presence of converter circuit, so for satisfactory performance of DVR, non-linear controller is essential. In this review paper, different topologies of DVR are discussed and performances of a DVR is analyzed under open loop system and also control strategy of PID controller through MATLAB simulation software.

Two key challenges in meshed multi-terminal dc (MTdc) grids are line power flow management and fault current interruption, which can be solved by current flow controllers (CFCs) and hybrid dc circuit breakers (HCBs), respectively. However, the power losses and the implementation costs of CFCs and HCBs are relatively high. In order to overcome these drawbacks, a combined HCB (CHCB) is proposed in this article by fully utilizing the core ideas of the interline dual H-bridge CFC and the HCB. The proposed device can substitute multiple HCBs at a dc bus with greatly reduced cost. In addition to the fault current interruption capability, the current flows in the adjacent lines can be controlled by the embedded multi-line CFC in the CHCB expanded from the existing interline dual H-bridge CFC. The system-level behavior of the proposed CHCB is similar to the separate solution of the typical HCB and the multi-line CFC. Extensive simulations and experiments verify the effectiveness of the proposed device. Besides, a comparison of the proposed CHCB and the separate solution is presented.

Cast iron and steel weldments are the most common machine tool base elements. However, both construction methods have associated disadvantages for domestic machine tool manufacturers. This paper documents the investigation of an alternative method for machine tool base production using concrete to fill an additively manufactured polymer mold, where the motion components are attached to the concrete base after the initial concrete curing. Modal testing results for a three-axis, vertical spindle prototype indicate high damping and stiffness can be achieved using the concrete base construction. Advantages are reduced cost and lead time compared to traditional methods.

Transformer-less multilevel inverters (MLIs) based on switched capacitors are gaining importance due to the voltage boosting ability from a single-input DC source and inherent capacitor voltage balancing capability. Most of the grid-connected transformer-less inverters are five-level and are commonly grounded so that their leakage current is zero. However, some major challenges like higher total standing voltage (TSV), more losses due to capacitor voltage ripples, higher charging current, and peak VA rating of the switches are comparatively higher. The motivation of the proposed work is to reduce the voltage ripple across the capacitors, minimum TSV, higher efficiency close to 98% at 1 kW, and lower cost. By using a modified sinusoidal pulse width modulation (SPWM) technique for the proposed single-phase 17-level inverter, a transformer-less grid-interfacing can be realized as the leakage current is (≈22 mA) well within the acceptable value (

Machine-learning interatomic potentials like Gaussian Approximation Potentials (GAPs) constitute a powerful class of surrogate models to computationally involved first-principles calculations. At similar predictive quality but significantly reduced cost, they could leverage otherwise barely tractable extensive sampling as in global surface structure determination (SSD). This efficiency is jeopardized though, if an a priori unknown structural and chemical search space as in SSD requires an excessive number of first-principles data for the GAP training.To this end, we present a general and data-efficient iterative training protocol that blends the creation of new training data with the actual surface exploration process. Demonstrating this protocol with the SSD of low-index facets of rutile IrO2 and RuO2 , the involved simulated annealing on the basis of the refining GAP identifies a number of unknown terminations even in the restricted sub-space of (1×1) surface unit-cells. Especially in an O-poor environment, some of these, then metal-rich terminations, are thermodynamically most stable and are reminiscent of complexions as discussed for complex ceramic materials.

Wide bandgap (WBG) devices are becoming increasingly popular due to their excellent material properties. WBG devices are commercially available in discrete and module packages. Many studies have investigated the design, structure and benefits of module packages. However, a comprehensive and in-depth overview of the discrete package is lacking. Discrete package has the advantages of flexibility, scalability and reduced cost; however, challenges of severe switching oscillations and limited current capacity are associated with it. This review encompasses the switching oscillations and limited current capacity issues of discrete devices. Switching oscillations are categorized in terms of voltage. The underlying oscillation mechanisms are explored in detail. For the current imbalance, the types, root causes and adverse effects in parallel-connected discrete devices application are reviewed. Besides, the most recent techniques to extract stray parameters are also explored. Finally, state-of-the-art methods to mitigate the switching oscillations and the current imbalance are summarized and evaluated. The performance improvement strategies discussed in this paper can assist researchers to better use the discrete package and can stimulate them to come up with new solutions.

Both software-defined networking and big data have gained approval and preferences from both industry and academia. These two important realms have conventionally been addressed independently in wireless cellular networks. The discussion taken into consideration in this study was to analyze the wireless cellular technologies with the contrast of efficient and enhanced spectral densities at a reduced cost. To accomplish the goal of this study, Welch's method has been used as the core subject. With the aid of previous research and classical techniques, this study has identified that the spectral densities can be enhanced at reduced costs with the help of the power spectral estimation methods. The Welch method gives the result on power spectrum estimation. By reducing the effect of noise, the Welch method is used to calculate the power spectral density of a signal. When data length is increased, Welch's method is considered the best as a conclusion to this paper because excellent results are yielded by it in the area of power spectral density estimation.

Detailed planning, sequencing, and scheduling for the installation of multiple pipe systems are greatly needed to efficiently complete a piping project with both reduced cost and duration. ...

We introduce a generic, facile, and environment-friendly strategy to fabricate a flexible, low-cost, highly sensitive nanocomposite by incorporating electrospinning and hydrothermal process with self-powered sensor device that contributes towards a better and safer life. Our research work based on the novel composite α-Bi2Mo3O12/CoSO4 operating at room temperature shows fascinating ability to improve the long-term sensitivity and stability of gas sensors and a reduced cost of operation. This novel composite sensor offers a unique combination of features to overcome the major challenges, especially in gas sensor applications, and can guarantee rapid progress spanning over academic and industrial domains. The composite-based α-Bi2Mo3O12/CoSO4 sensor showed an improvement in high sensitivity and response speed due to the porous active site of the materials to the target gas SO2F2 at RT(room temperature). The porous nature of the CoSO4 nanoparticles loaded α-Bi2Mo3O12 nanofibers improved their surface area from 33.3 m2/g to 173 m2/g, which was confirmed by Brunauer-Emmett-Teller resulting in high haze especially in S3 sample. This can dramatically boost up sensor response up to ∼2970% at room temperature with a low detection range of 100 ppm SO2F2. Particularly, composite-based sensors α-Bi2Mo3O12/CoSO4 exhibits high sensitivity, excellent stability, mild response/recovery times (τres=193 s and τres=319 s) and good durability/repeatability without any power consumption. This results in a potential and versatile mechanical sensor that will pave the way for online monitoring to address environmental and industrial challenges. The resulting composite material can be applied to a polymide substrate to obtain a flexible and smart sensor that could provide a technical roadmap for the next wave of sulfur hexafluoride (SF6) innovations and breakthroughs.

A new kind of virtual machine instance named burstable instance is recently introduced in leading cloud providers, which can help cloud user handle fluctuating demands with reduced cost. There is an impending need to incorporate burst instances in common scheduling. Due to the time varying provisioning of burstable resources, the stochastic demand oriented scheduling can be a better choice where the demands are represented using distribution functions instead of mean values. However, little has been done to the scheduling of the burstable resources with stochastic demands. To address the problem, we present the first stochastic revenue model for the burstable resources, and propose a bounded delta-solution based differential evolution algorithm, which exploits inherent information of the problem to accelerate the searching of optimal solution. Experiments using simulated and real-world data indicate that the algorithm outperforms the existing representative algorithms and can increase the revenue significantly.

Summary The miscible gas injection has been broadly used in many oil fields as an enhanced oil recovery (EOR) method. Minimum Miscibility Pressure (MMP) is a critical parameter both for modeling and field operations. The slim-tube method is recognized as the most reliable for MMP measurement. However, conducting the experiment takes a long time (4–6 weeks), and the sample volume requirement is substantial. Therefore, the slim-tube experiment is very costly and sometimes is not possible due to a lack of sample volume. Many other methods have been proposed, like rising bubbles and vanishing interfacial tension in various versions. Due to the limitations of these methods, there is still no experimental method that can replace the traditional slim-tube for MMP measurements. Micro and nano-fluidics devices have attracted increasing attention in the oil industry. Reduced cost and sample volume requirements, fast turnover, and visualization are clear advantages for lean operation. In this study, we designed a new slim-tube method to determine MMP on a micro-scale fluidics chip. The design is significantly different from previous efforts on fluidics chips with an open flowing tube. In the new design, we introduced porous media fillings similar as in the slim-tube method. The objective is to produce a true multi-contact process in the gas displacement. We tested the new fluidics device using three reservoir fluids, and both hydrocarbon gases and CO2 as injection gases. For pressure lower than MMP, we observed noticeable reservoir oil remained after injection gas passed. For pressure higher than MMP, the miscible displacement front was developed. Behind the miscible displacement front, the oil saturation came down to neglectable. We used a visual sensor to detect the oil saturation after gas flooding for each pressure. MMP was detected at the intersection of miscible pressures and immiscible pressures in a similar way as in the slim-tube test after multiple measurements. All three microchip MMP tests have almost identical results as the slim-tube tests. The new fluidics method is a miniaturization of the slim-tube method on the microchip. The study shows excellent results for the three selected reservoir oils combined with hydrocarbon gas and CO2 as injection gases. The new method has imminent business potential due to its reliability, visualization, low cost, low sample requirement, and fast turnaround. The MMP test threshold will be much lower than before, which will significantly benefit many gas-based EOR projects.

This paper analyzes the stochastic process generated by unit failures, and the corresponding cash flows, for products with cumulative warranties where the seller offers warranty on the total useful life of a batch of units rather than the lifetimes of individual units. Relative to individual warranties, cumulative warranties reduce administrative cost to seller as well as buyer, and are expected to lead to lower selling price due to reduced cost of warranty to the seller. For products with phase type lifetime distributions, matrix-analytic methodology is used to develop algorithms to compute economic measures such as cost of warranty. Results indicate that savings in cost of warranty due to a cumulative warranty relative to individual warranties, increase with increasing product lifetime variability, and that the marginal savings in cost of warranty decrease with increasing batch size. Economic measures are proposed to establish the amounts of discounts that the seller may be willing to offer, or the buyer expects, in order to change from individual warranties to a cumulative warranty.

Sobel edge detection is widely used in computer vision and image processing but its processing time becomes a serious problem in real-time environments, especially when an image is very large. Instead of utilizing a hardware-accelerated approach, we propose a purely software-based method which is simpler and cheaper. Our algorithm reduces the number of arithmetic operations and data loads, so that processing speed is increased and energy consumption reduced. The processing time is further reduced by the use of AVX intrinsics and OpenMP directives which distribute the workload among the AVX engines in a multi-core architecture. Our algorithm reduces the number of arithmetic operations by 22.73% compared to that of the state-of-the-art Sobel (SOAS) algorithm, while the number of data loads are reduced by 43.75% compared to SOAS. Performance and energy consumption comparisons between our algorithm and SOAS, as well as with the Sobel functions offered by the OpenCV and IPP libraries are investigated, and the results demonstrate that a multi-core version of our algorithm, implemented by AVX intrinsics, is on average 3.20, 9.34, and 13.99 times faster than IPP, SOAS, and OpenCV respectively. Also, it consumes an average of 2.91, 8.43, and 11.21 times less energy than IPP, SOAS, and OpenCV. Our algorithm, utilizing software modifications alone, benefits from both shorter development time and reduced cost compared to hardware approaches relying on an FPGA, ASIC, or GPU, making it more suitable for resource-constrained environments.

Multi-objective optimisation of concrete floors for economic and environmental performance is critical in the present context since the building construction sector is responsible for a rising share of the global economy and greenhouse gas emissions. This study explores how the designs with optimum cost and embodied carbon are governed by the selection of a concrete floor system and column spacing. Discrete concrete floor designs were generated parametrically varying the column spacing for eight different construction forms available in practice. Pareto optimal solutions for cost and cradle-to-gate embodied carbon were identified for a range of column spacings. The trends of the sensitivity of the optimum solutions were also investigated due to inherent uncertainty and potential variations in the cost and embodied carbon of different constituents. Column spacings can be increased up to 2 m than the optimum, compromising cost or embodied carbon only up to 10% due to their nonlinear relationship, depending on the slab type. Post-tensioning can reduce embodied carbon of flat slabs for spans longer than 7 m but do not reach Pareto optimality due to available cheaper floor solutions with similar levels of embodied carbon. Flat slabs can be suggested as Pareto optimal for spans within 6 m to 8 m when construction time and storey height is considered, due to reduced cost. However, parallelly Pareto optimal two-way slabs on beams have up to only 8% higher cost but up to 37% lower embodied carbon than flat slabs. While the floor designs are optimum for spans within 5 to 7 m for most of the slab types, two-way slabs on beams and/or hollow-core slabs are the optimum choice for a wide range of spans depending on relevant cost and carbon coefficients., Churchill Jafar Studentship from Cambridge Commonwealth, European and International Trust