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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.

This paper presents a Function-as-a-Service (FaaS) approach for deploying managed cloud functions onto heterogeneous cloud infrastructures. Current FaaS systems, such as AWS Lambda, allow domain-specific functionality, such as AI, HPC and image processing, to be deployed in the cloud while abstracting users from infrastructure and platform concerns. Existing approaches, however, use a single type of resource configuration to execute all function requests. In this paper, we present a novel FaaS approach that allows cloud functions to be effectively executed across heterogeneous compute resources, including hardware accelerators such as GPUs and FPGAs. We implement heterogeneous scheduling to tailor resource selection to each request, taking into account performance and cost concerns. In this way, our approach makes use of different processor types and quantities (e.g. 2 CPU cores), uniquely suited to handle different types of workload, potentially providing improved performance at a reduced cost. We validate our approach in three application domains: machine learning, bio-informatics, and physics, and target a hardware platform with a combined computational capacity of 24 FPGAs and 12 CPU cores. Compared to traditional FaaS, our approach achieves a cost improvement for non-uniform traffic of up to 8.9 times, while maintaining performance objectives.

Activated sludge process is the common industrial wastewater treatment scheme. However, intensive aeration requirements made it impossible for wastewater treatment plants (WWTPs) to be energy- positive. The current study designed energy positive industrial wastewater treatment trains by using innovative technologies such as micro-sieving, up-flow anaerobic sludge blanket (UASB), and partial nitrification/Anammox (PN/A). Excel-based models were developed to compare the innovative treatment systems and the activated sludge system based on unit energy, cost and environmental indicators. Results showed that the innovative systems could save unit energy and cost by 67–85% and 59–85%, respectively. Innovative technologies increase the average unit energy production for meat processing, tannery, and textile wastewater treatment up to 1.71, 1.63, and 1.58 kWh/kg BODremoved. Furthermore, the high energy recovery reduces the overall global warming potential of these systems. Electrical self-sufficiency in industrial wastewater treatment could be obtained by using innovative technologies, which present great potential for future designs of industrial wastewater treatment.

Importance Protocol driven ED observation units (EDOU) have been shown to improve outcomes for patients and payers, however their impact on an entire health system is unknown. Two thirds of US hospitals do not have such units. Objective To determine the impact of a protocol-driven EDOU on health system length of stay, cost, and resource utilization. Methods A retrospective, observational, cross-sectional study of observation patients managed over 25 consecutive months in a four-hospital academic health system. Patients were identified using the “admit to observation” order and limited to adult, emergent / urgent, non-obstetric patients. Data was retrieved from a cost accounting database. The primary study exposure was the setting for observation care which was broken into three discrete groups: EDOUs (n = 3), hospital medicine observation units (HMSOU, n = 2), and a non-observation unit (NOU) bed located anywhere in the hospital. Outcomes included observation-to-inpatient admission rate, length of stay (LoS), total direct cost, and inpatient bed days saved. Unadjusted outcomes were compared, and outcomes were adjusted using multiple study variables. LoS and cost were compared using quantile regressions. Inpatient admit rate was compared using logistic regressions. Results The sample consisted of 48,145 patients who were 57.4% female, 48% Black, 46% White, median age of 58, with some variation in most common diagnoses and payer groups. The median unadjusted outcomes favored EDOU over NOU settings for admission rate (13.1% vs 37.1%), LoS [17.9 vs 35.6 h), and cost ($1279 vs $2022). The adjusted outcomes favored EDOU over NOU settings for admission rates [12.3% (95% CI 9.7–15.3) vs 26.4% (CI 21.3–32.3)], LoS differences [11.1 h (CI 10.6–11.5 h)] and cost differences [$127.5 (CI $105.4 - $149.5)]. Adjusted differences were similar and favored EDOU over HMSOU settings. For the health system, the total adjusted annualized savings of the EDOUs was 10,399 bed days and $1,329,443 in total direct cost per year. Conclusion Within an academic medical center, EDOUs were associated with improved resource utilization and reduced cost. This represents a significant opportunity for hospitals to improve efficiency and contain costs.

Energy storage unit (ESU) is playing an increasingly important role in load shifting and uncertainty mitigation. This paper aims to quantify the value of ESU in the unit commitment (UC) with renewable generation. By treating the power and energy capacities of ESU as continuous parameters, the stochastic UC problem is cast as a multi-parametric mixed-integer linear program (mp-MILP), whose optimal value function (OVF) gives the relation between storage capacity and the daily UC cost in an analytical manner. It encompasses abundant sensitivity information, and its surface can be easily visualized. The reduced cost compared with the benchmark case without storage can be regarded as the value of ESU. As a potential application, the OVF is used to formulated an optimal storage sizing problem that maximizes the ratio between the reduced operation cost and the investment cost, ensuring the minimum time of cost recovery. The solution consists of two steps: the first step constructs the OVF parameterized in storage capacity; the second step reformulates the fractional storage sizing program into an MILP, leveraging the expression of the OVF and variable transformations. Case studies conducted on the IEEE 9-bus system and IEEE 118-bus system illustrate how the OVF offers useful reference for power system operation and planning. The proposed optimal sizing model shows advantages in efficient utilization of investment in terms of payback time and rate of return.

Adding redundant converters or switches are two common strategies to achieve fault-tolerant operation of converters. The former one is straightforward but costly, while the latter one is cost-effective but with sacrifice on the reliability as it only works for switches’ failure. Aiming to achieve a good trade-off between cost and reliability, a novel fault-tolerant full-bridge series-resonant converter (FB-SRC) which only needs a redundant half-bridge SRC (HB-SRC) to gain fault tolerance for all fault-prone components is proposed in this article. The proposed converter can keep working normally when short-circuit fault or open-circuit fault happens on switches, diodes, or the output filter capacitor. After fault occurs, the redundant HB-SRC converter will combine the remaining healthy part of original FB-SRC to reconstruct an input-parallel and output-series dual HB-SRC, which can maintain the rated output power with reduced cost. In the article, component's reliability of SRC is first analyzed. Then, operation and performance analysis of the proposed fault-tolerant converter is introduced in detail. As the voltage/current stresses of components are almost the same during the prefault and postfault operation, the converter design is simple. Finally, experimental results of a 500 W prototype are also given to verify the effectiveness.

Рассмотрены вопросы оптимальной организации системы теплообмена установки первичной перегонки нефти ЭЛОУ АВТ. Для этого с целью выявления экономической целесообразности функционирующей системы теплообмена проведен пинч-анализ. В результате пинч-анализа были построены композитные кривые горячих и холодных потоков и большая составная кривая, позволившие определить предельно возможное количество рекуперируемой энергии технологическими потоками, тип энергоносителей по энергетическому уровню и значение оптимальной движущей силы, при которой обеспечивается минимум критерия оптимальности — суммарных приведенных и капитальных затрат. Результаты пинч-анализа показали наличие около 30 % полезной энергии для рекуперации технологическими потоками установки. Анализ методов синтеза оптимальных систем теплообмена показал, что одним из наиболее эффективных методов решения крупномасштабных задач является декомпозиционный подход, который подразумевает разбиение задачи синтеза на несколько подзадач меньшей размерности: задачи линейного программирования для минимизации энергетических затрат, задачи дискретно-непрерывного линейного программирования для минимизации числа теплообменного оборудования, задачи нелинейного программирования для определения минимальных суммарных приведённых затрат. В результате использования алгоритма декомпозиционного синтеза, были найдены оптимальные структуры системы теплообмена, режимы работы входящих в их состав теплообменников и даны экономические оценки найденным структурам. Результаты синтеза оптимальных систем теплообмена показали возможность для снижения суммарных приведенных затрат на 40 % при реорганизации функционирующей системы теплообмена. Анализ результатов синтеза схем с возможностью регенерации тепловой энергии показал возможность дополнительной экономии до 6377 тыс. у.е./г, что стало возможным вследствие использования излишков энергии для подогрева технологических потоков близлежащих установок.

Fast replacement models have been playing an increasing role in high-frequency electronics, including the design of antenna structures. Their role is to improve the computational efficiency of the procedures that normally entail large numbers of expensive full-wave electromagnetic (EM) simulations, e.g., parametric optimization or uncertainty quantification. Recently introduced performance-driven modeling methods, such as the nested kriging framework, alleviate some of the common difficulties pertinent to conventional modeling methods. These include the curse of dimensionality but also the need for rendering models to be valid for broad ranges of antenna parameters and operating conditions, as dictated by the design utility of the surrogates. The keystone of performance-driven methods is an appropriate confinement of the model domain so that the training data are only acquired in the regions containing high-quality designs. Identification of such regions is realized using a set of so-called reference designs preoptimized for selected ensembles of performance requirements. The CPU cost of generating the reference points may be considerable and compromise the savings obtained by operating in a constrained domain. In this article, a technique for automated, reliable and low-cost acquisition of the reference designs is proposed. Our methodology involves inverse sensitivities, iterative correction procedures, and accelerated feature-based gradient search with sparse Jacobian updates. It is validated using three microstrip antenna examples and demonstrated as an efficient tool for lowering the cost of building surrogate models within the nested kriging framework. The intended use of our approach is expedited construction of database designs for constrained modeling frameworks, construction of inverse surrogates, as well as procedures for rapid redesign and dimension scaling of antenna structures.

This chapter reviews power electronics technology for distributed generation integrated into smart grid. It presents an introduction to typical distributed generation systems with the power electronics. The improvement of photovoltaics (PV) conversion efficiency, advancement in manufacturing technology and reduced cost of PV modules, are the main driving forces for the intensive utilization of PV power. The chapter introduces power electronic converters in grid‐connected AC systems and their control technologies. It also presents control strategies of voltage‐source converters, followed by an overview of multilevel converters and modular multilevel converters. The chapter explains converter level control methodologies and system level coordination. It shows that system frequency and voltage restorations, obtained by both centralized and distributed control schemes. DC microgrids have been progressively developed with the increasing penetration of solar PV power and wide deployment of battery system which are naturally in DC.

Solar energy is a strongly intermittent renewable energy source, which is affected by varied meteorological conditions, and thus produces arbitrary power outputs in photovoltaic (PV) power generation. Complex weather variations make it challenging to develop an efficient PV power forecasting method. In this study, a graph modeling method is proposed for short-term PV power prediction. Unlike many conventional machine-learning models, the proposed model is capable of evaluating interconnections among various meteorological input factors. This study details the design and operation of graph modeling, including graph construction, node feature construction, message transfer, and readout. An entire model is established consisting of spectral graph convolution, multiple graphical edges and a hierarchical output manner. The testing results suggest that the proposed multi-graph model outperforms other benchmark models in terms of accuracy under day-ahead forecasting cases. Besides, the single-graph model achieves a reduced cost of training time comparing to deep-learning benchmark models. This work was supported by National Natural Science Foundation of China under Grant 52077062.

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 (

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.

It has been broadly argued that, in the near future, the demand for skilled labor will increase whilst that for routine activities will decrease. In this regard, the need for making greater investments in education to re-skill workers and support continuous learning has been invoked as an essential requirement for preserving people’s employability.Digital technology is deemed increasingly necessary to sustain the educational endeavor, for the possibilities it offers to make more accessible and low-cost educational interventions. It allows for the creation of personalized learning paths and customized digital learning solutions, for courses to be available to a large attendance of learners, and for teaching-learning activities to be offered at significantly reduced cost.In this article, a learning unit structure designed to improve adaptive learning is proposed, and mechanisms for adaptive learning in a smart learning environment are discussed.The implemented teaching-learning solution is also illustrated. This is a preliminary application based on an approach that combines the teacher experience with learning analytics.

Patient monitoring is an important issue in the medical field, requiring planning, effort, and time. One of the challenges that physicians, nurses, paramedics, and emergency medical technicians face is the shortage of specialized medical equipment and staff with the ability to measure, display, store, and transmit the vital signs of patients for remote diagnosis under critical conditions. Therefore, and based on our evaluation for such needs, our main research objective was to develop a new system to enable these professionals to perform such monitoring functions with a higher level of safety, performance, and time effectiveness with reduced cost. An advanced, integrated, effortless, and portable system was developed, which is capable to measure, monitor, display, store, and transmit data related to the Electrocardiography (ECG) signal along with its derivatives, such as the heart rate using a hybrid system that combines hardware and software.

The recent advancement in cloud computing enabled technologies to offer scalable and elastic computing components at a reduced cost to manage the IT infrastructure organizations effectively. It addresses the cost optimization problem, which arises in the context of traditional web-enabled computing models where bottle-neck conditions arise owing to managing high-scale dynamic clients along with the flexible infrastructure to map heterogeneous/complex data traffic with storage elements associated with appropriate server units. The trend of research evolution has given birth to the conceptualization of cloud computing, which offers storage, infrastructure, and platform as services with a virtualized mechanism to deal with uncertain complex and high-scaled data traffic irrespective of application domains. It has got a wide range of applications starting from health-care to service industries. Hence, it has become de-facto for any digital and information technology (IT) oriented application design and deployment aspects. Managing complex data flow in the context of the cloud is a highly challenging task, and the principle of multi-tenancy has come up as a solution approach to enhance resource sharing among cloud clients with isolated virtualization and application instances with services. The prime agenda here is to reduce the cost of computation and cloudlet handling time. The study introduces a unique load-balancing approach, namely, multi-tenant load balance (MTLBP), to cope with the service-oriented architecture of multi-tenancy in the cloud environment. It also ensures that the end client user can perform their task execution efficiently on the multi-tenant environment. The experimental approach adopts a behavioral traffic analysis, where the formulated approach's performance is compared with the traditional baseline approaches. The outcome obtained for the proposed approach found quite promising in terms of resource utilization and task execution in comparison with the other models. It shows that the formulated MTLBP approach outperforms the baseline approaches with approximately 26.83% when small value cloudlet processing time is concerned and 33.625% when medium volume cloudlet processing time is concerned and approximately 62% when large volume cloudlet handling time is concerned. It is also observed that for small volume cloudlets, MTLBP attains approximately 12.55% performance improvement on average when resource utilization cost is concerned. For medium and large volume cloudlets, it attains approximately the performance improvement of 9.85% and 3.14%, respectively.

Redundant cells within a battery energy storage system (BESS) are an important aspect to be considered in a BESS planning, design, and operation in order to achieve high reliability. The conventional topology does not have the ability to take advantage of its idle cells, at least one-third of its cells are constantly idling in a typical three-phase BESS. This article presents a new topology for a three-phase grid-scale BESS, where it offers three ways to deal with its idle cells. First, the idle cells were eliminated from the BESS plant while still achieving the desired output power with reduced cost. Second, the idle cells were utilized where no redundant cell is required. Finally, they were utilized to meet the output power required during peak demand. Consequently, the overall construction cost of BESS plant, losses, size, and control complexity are reduced while the reliability is improved. The simulation results indicate that at least 140 out of 333 modules (3996 cells) are in an idle state during the BESS operation, with the three-phase output voltage achieved in both topologies. Using 333 modules, the three-phase output power has increased by 58.8% in the proposed topology (306 kW) compared with the conventional topology (180 kW).

This article proposes a differential power processing (DPP) configuration applied to a series photovoltaic (PV) string. The proposed configuration operates each PV element at its local maximum power point (MPP) while processing only a small portion of its total generated power through the modular-integrated converters. The current processed through each converter is the difference between the local PV element MPP current and the local PV string current. This leads to higher energy capture at an increased conversion efficiency while overcoming the difficulties associated with unmatched MPPs that result from partial shading, temperature gradients, manufacturing defects, and nonuniform cells aging. A state-space model of the proposed system is driven, and a comparative analysis is carried out with respect to the conventional DPP architectures. Additionally, modular and compact design is proposed for a large number of PV panels in a series PV string. The authors designed and built a hardware prototype for three PV panels connected in series to validate the proposed configuration effectiveness and experimentally demonstrate its robustness.

Safe and efficient training using flight simulation training devices (FSTD) is one of the fundamental components of training in the commercial, military, and general aviation. When compared with the live training, the most significant benefits of ground trainers include improved safety and the reduced cost of a pilot training process. Flight simulation is a multidisciplinary subject that relies on several research disciplines which have a tendency to be investigated separately and in parallel with each other. This paper presents a comprehensive overview of the research within the FSTD domain with a motivation to highlight contributions from separate research topics from a general aspect, which is necessary as FSTD is a complex man–machine system. Application areas of FSTD usage are addressed, and the terminology used in the literature is discussed. Identification, classification, and overview of major research fields in the FSTD domain are presented. Specific characteristics of FSTD for fighter aircraft are discussed separately.

A stretcher is a lightweight and portable apparatus used to facilitate the movement of patients who require medical care. While its design and functions have been known to man for over 600 years, there is still much room for improvement with regard to comfortability, mobility and ergonomics aspects. This study aims to synthesise design features from patents, journals and products for the conceptualisation of multifunctional stretchers. Based on the conceptual synthesis of this study, if a stretcher is redesigned to be foldable, then less storage space would be required, thus allowing the paramedic or medical person to store more stretchers and attend to multiple patients at a time. In comparison to a regular stretcher, a foldable stretcher would not only be smaller and more portable but would also help medical personnel reach the patient in a timelier manner. A reduction in response time for emergency medical services might increase the survival rate among patients who are in critical conditions. In the aspect of multifunctionality, the medical person may also be able to respond to different types of medical transportation needs. Multifunctional stretchers can increase the versatility of emergency medical service teams in adapting to different situations at a reduced cost.

A modular multilevel converter (MMC)-based multiport power electronic transformer (PET) topology with shared medium-frequency transformer (MFT) is proposed in this brief, where the proposed PET is suitable for the hybrid AC/DC distribution network because of the existence of four essential voltage ports, including medium-voltage AC and DC ports, low-voltage AC and DC ports. Moreover, a multi-frequency modulation and the design of voltage decoupling circuit are introduced. One MFT is shared by three-phase MMC arms, which contributes to the compact structure and reduced cost of PET. The operating principle, applied control strategy and parameter design of proposed PET are introduced in detail. The experimental results are provided to verify the feasibility of PET.

In today’s competitive business environment, the cost of a product is one of the most important considerations for its sale. Businesses are heavily involved in research strategies to minimize the cost of elements that can impact on the final price of the product. Logistics is one such factor. Numerous products arrive from diverse locations to consumers in today’s digital era of online businesses. Clearly, the logistics sector faces several dilemmas from order attributes to environmental changes in this regard. This has specially been noted during the ongoing Covid-19 pandemic where the demands on online businesses have increased several fold. Consequently, the methodology to optimise delivery cost and its impact on environmental focus by reducing CO2 emissions has gained relevance. The resultant strategy of Shipment Consolidation that has evolved is an approach that combines one or more transport orders in the same vehicle for delivery. Shipment Consolidation has been categorized in three order scheduling approaches: Time based consolidation, Quantity based consolidation, and a Hybrid (Time-Quantity) based consolidation. In this paper, a new Hybrid Consolidation approach is presented. Using the Hybrid approach, it has been shown that order delivery can be facilitated by taking into account not only the order pick up time, but also the total order quantity. These results have shown that if a time window is available in respect of the order delivery time, then the order can be delayed from pickup to consolidate it with other orders for cost optimization. This hybrid approach is based on four consolidation principles, two of which work on fixed departure and two, on demand departure. Three of these rules have been implemented and tested here with an application case study. Statistical analysis of the results is illustrated with different planning evaluation indicators. The Result analyses indicate that consolidation of orders is increased with each implemented rule hence motivating us towards the implementation of the fourth rule. Testing with bigger data sets is required. © 2021 Tech Science Press. All rights reserved.

Parameter adjustment through numerical optimization has become a commonplace of contemporary microwave engineering. Although circuit theory methods are ubiquitous in the development of microwave components, the initial designs obtained with such tools have to be further tuned to improve the system performance. This is particularly pertinent to miniaturized structures, where the cross-coupling effects cannot be adequately accounted for using equivalent networks. For the sake of reliability, design closure is normally performed using full-wave electromagnetic (EM) simulation models, which entails considerable computational expenses, often impractically excessive. Available mitigation techniques include acceleration of the conventional (e.g., gradient-based) routines using adjoint sensitivities or sparse sensitivity updates, surrogate-assisted and machine learning algorithms, the latter often combined with nature-inspired procedures. Another alternative is the employment of variable-fidelity simulations (e.g., space mapping, co-kriging), which is most often limited to two levels of accuracy (coarse/fine). This work discusses an EM model management approach coupled with trust-region gradient-based routine, which exploits problem-specific knowledge for continuous (multi-level) modification of the discretization density of the microwave structure at hand in the course of the optimization run. The optimization process is launched at the lowest discretization level, thereby allowing for low-cost exploitation of the knowledge about the device under study. Subsequently, based on the convergence indicators, the model fidelity is gradually increased to ensure reliability. The simulation fidelity selection is governed by the algorithm convergence indicators. Computational speedup (i.e., reduction in the number of EM simulations required by the optimization process to converge) is achieved by maintaining low resolution in the initial stages of the optimization run, whereas design quality is secured by eventually switching to the high-fidelity model when close to concluding the process. Numerical verification is carried out using two microstrip circuits, a dual-band power divider and a dual-band branch-line coupler, with the average savings of almost sixty percent when compared to single-fidelity optimization.

Solar PV panels are playing a pivotal role in green energy generation, since their diffusion in global renewable energy market is greatly increasing every day owing to their reduced cost. Their efficiency is less and wavering around 15% only. Further, an inherent problem with their performance is the deterioration of their efficiency with the change in surface temperature. This issue could be resolved suitably through the proper thermal administration of the PV panel during working hours. Predominantly, this current work is intended to address the aforementioned issue with the assist of a Nano Phase Change Material (NPCM) which was primed by properly disbanding 0.5% volume of TiO2 nanoparticles within paraffin base. Two solar PV panels with similar configuration were experimented in this work in such a way that one without any NPCM and another one with NPCM amalgamated behind the panel. The results revealed that the NPCM effectively administered and reduced the daily average surface temperature of the panel by 13 °C and further, the daily efficiency of the panel was enhanced by 2.1%.

The LoRa radio technology is one of the most prominent choices in the Internet of Things Low-Power Wide Area Networks (LPWANs) industry due to its versatile and robust technical characteristics along with its ability to achieve long communication ranges combined with low energy consumption and reduced cost. One of the main issues in LoRa networks is how many end-devices can be reporting efficiently while meeting the requirements set by the application they support. This is known as the capacity metric and it is affected by many network parameters and various factors. A literature overview is presented in this work, studying works on LoRa-based networks, outlining their behavior and categorizing them based on their technological breakthroughs. Throughout this survey, a number of performance determinants that stand out are highlighted. These factors span five main categories that encompass physical layer characteristics, deployment and hardware features, end device transmission settings, LoRa MAC protocols, and application requirements. Discussion follows the presentation of each of the factors pinpointing the relevant research, and describing the impact of each one of them on the achieved network efficiency focusing especially on the capacity metric. Open issues and research directions are also highlighted for each of the five identified categories.

With the increased number and reduced cost of smart devices, Internet of Things (IoT) applications such as smart home (SHome) are increasingly popular. Owing to the characteristics of IoT environments such as resource constrained devices, existing authentication solutions may not be suitable to secure these environments. As a result, a number of authentication solutions specifically designed for IoT environments have been proposed. This paper provides a critical analysis of existing authentication solutions. The major contributions of the paper are as follows. First, it presents a generic model derived from an SHome use-case scenario. Secondly, based on the model, it performs a threat analysis to identify possible means of attacks. The analysis leads to the specification of a set of desirable security requirements for the design of authentication solutions for SHome. Thirdly, based on the requirements, existing authentication solutions are analysed and some ideas for achieving effective and efficient authentication in IoT environments are proposed.

Recent years have witnessed growing interest in reduced cost radar systems operating with low power. Multiple-input multiple-output (MIMO) radar technology is known to achieve high performance sensing by probing with multiple orthogonal waveforms. However, implementing a low cost low power MIMO radar is challenging. One of the reasons for this difficulty stems from the increased cost and power consumption required by analog-to-digital convertors (ADCs) in acquiring the multiple waveforms at the radar receiver. In this work we study reduced cost MIMO radar receivers restricted to operate with low resolution ADCs. We design bit-limited MIMO radar (BiLiMO) receivers which are capable of accurately recovering their targets while operating under strict resolution constraints. This is achieved by applying an additional analog filter to the acquired waveforms, and designing the overall hybrid analog-digital system to facilitate target identification using task-based quantization methods. In particular, we exploit the fact that the target parameters can be recovered from a compressed representation of the received waveforms. We thus tune the acquisition system to recover this representation with minimal distortion, from which the targets can be extracted in digital, and characterize the achievable error in identifying the targets. Our numerical results demonstrate that the proposed BiLiMO receiver operating with a bit budget of one bit per sample achieves target recovery performance which approaches that of costly MIMO radars operating with unlimited resolution ADCs, while substantially outperforming MIMO receivers operating only in the digital domain under the same bit limitations., Comment: 14 pages, 11 figures

The modular multilevel converter is considered the state-of-the-art power electronic solution for high-power high-voltage applications that require a DC stage and that operate with fixed frequency at their AC terminals. However, in high-power AC-AC variable-frequency applications, it has been proved that this converter topology presents an unnecessarily large number of components and an intolerably high submodule-capacitor voltage ripple at low frequencies. The modular multilevel matrix converter was proposed as a promising alternative for high-power medium-voltage electrical-machine drives since it presented improved performance at low frequencies and a reduced number of components in comparison to the modular multilevel converter resulting in reduced costs and improved efficiency. Even though, the modular multilevel matrix converter is one of the converter solutions that stands out the most for high-power machine drives, it presents some operational drawbacks such the unstable behavior under unbalanced grid conditions and the extremely complex internal control and modulation. Recently, a new converter topology named modular multilevel series converter was proposed presenting attractive operational characteristics such as the high performance at low frequencies, high performance under unbalanced grid conditions, and straightforward control and modulation, which are important advantages compared to the modular multilevel converter and the modular multilevel matrix converter. Nonetheless, as demonstrated in this paper, the modular multilevel series converter presents large component count and poor efficiency. Thus, in this paper, a new converter solution is proposed, which is an enhanced version of the modular multilevel series converter that preserves the previously mentioned advantageous operational characteristics and that presents an improved efficiency and a considerably reduced number of components in comparison to the other three converter topologies, resulting in reduced cost, size and weight.

A green biopolymer, lignosulfonate acid (LSA), was first used as an additive in the Nafion membrane for fuel cell applications. The Nafion/LSA composite membrane displayed enhanced thermal stability and other satisfactory properties due to the stable aromatic groups and multiple active sites of LSA. More importantly, the cost-effectiveness and simple fabrication of such novel composite PEMs make their use in PEMFCs very attractive and economical.

Electric variable speed drives (VSDs) based on two VSDs connected to a multiphase machine are an attractive solution to replace high-power mechanic and hydraulic systems in many sectors of industry and transportation because they present high performance with reduced cost, volume and weight. Among the causes which affect the reliability of dual VSDs, the common-mode current flowing through the machine bearing is an important issue. This paper faces the mitigation of the common-mode current by reducing the common-mode voltage (CMV) generated by the operation of a dual VSD. The CMV reduction is carried out without introducing any extra device and/or passive filtering method. This CMV reduction is performed by applying a specific phase-displacement between the modulation strategies of each single inverter drive. The proposed technique has been evaluated in a down scaled experimental setup in order to test its effectiveness. H2020 Spartan 821381 Ministerio de Ciencia e Innovación (España) TEC2016-78430-R Oficina de Ciencia y Tecnología de Ningbo 2018B10082 Consejería de Economía, Conocimiento, Empresas y Universidad / Secretaría General de Universidades, Investigación y Tecnología PY18-1340