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Anatomical structures of the liver could be reconstructed three dimensionally from preoperative cross-sectional imaging for over 20 years. This three-dimensional (3D) representation not only optimizes the preoperative planning options but also the communication of specific operation-relevant structures can be improved using 3D models. In addition to a plastic and interactive visualization of 3D organ models, the disruptive technology of virtual reality (VR) can also provide a possibility for structured training and further education regarding surgical anatomy of the liver and operation planning. This makes examinations in virtual reality also seem tangible. The latest prototypes even enable patient-specific simulation of intraoperative sonography. This review article provides an overview of the current status of available technologies for training and further education using VR exemplified by liver surgery. [ABSTRACT FROM AUTHOR]

The potential of nuclear power in tackling the global energy crisis hinges on the development of effective decommissioning strategies. While the use of human workforce is still common practice, robotics has the potential to revolutionise decommissioning tasks. However, the restricted access, crammed environments, and harsh environmental conditions of nuclear power plants are not suited for conventional robots. In this scenario, the slender design and overall length of long-reach manipulators represent a promising solution. Yet, the same features that make these manipulators a suitable candidate, also introduce significant engineering challenges in design and control. The key challenges and hazards of nuclear decommissioning are summarized using the decommissioning of the Fukushima Daiichi nuclear power plant as a representative use case, in which a manipulator over 20 m long with 18 degrees of freedom is planned to be used for fuel debris retrieval. Critical research gaps on the state-of-the-art of long-reach manipulators for nuclear decommissioning are presented to summarise the focused key research areas ongoing and foster further research engagement within the community. [ABSTRACT FROM AUTHOR]

The rising demand for clean energy production due to climate change emphasizes the importance of optimizing water resources, particularly in countries with significant hydropower potential. Existing models for the Operational Planning of Hydropower Systems (HPSOP) typically focus on the natural flows of rivers, often overlooking the potential of water transfers between rivers and basins. To address this gap, this article employs an improved mathematical model of hydropower production, considering the adjustment of the water transfer in the operation schedule as an additional optimization variable. A customized meta-heuristic, named the Evolutionary Socio-Bio Inspired Technique (ESBIT), has been tailored to integrate water transfer mechanisms into the operational planning model. The proposed model was validated through a case study at the Henry Borden Complex in São Paulo, Brazil, using real power plant parameters and inflow data from the Brazilian system. The results obtained from the test case, both with and without water transfer, demonstrate that the proposed methodology effectively captures the operational characteristics of a system that allows water transfers between rivers or basins to optimize the available water resources and system costs. [ABSTRACT FROM AUTHOR]

Copyright of Systems Engineering & Electronics is the property of Journal of Systems Engineering & Electronics Editorial Department and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Arthroskopie is the property of Springer Nature and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

The complexity of modern war is increasing, and the rapid operational mission planning is of great importance to improve the efficiency of command and control. This paper presents a joint operational Task Matrix (TM) model, which is a theoretical method for mission planning. A belief network model is put forward to describe the relationship among the elements in TM model. A naive bayesian learning method for belief network is designed. A mechanism of imagination is put forward to speed up the learning process. A search algorithm named Deep Minimum Threat Generation Tree(DMTGT) is proposed, which can efficiently calculate task priority by balancing search error and search speed. Finally, the validity of above models and algorithms is verified by simulation experiments.

Abstract Energy sources, like hydro and wind power, present uncertain nature, characteristic that affects the electric power systems operation planning. Such factors require using methodologies capable to handle these uncertainties in interconnected hydro-thermal-wind power systems operation. This work proposes a computational model for the day-ahead unit commitment problem for interconnected hydro-thermal-wind power systems, considering the uncertainties related to wind power, water inflow, and energy demand, employing the Robust Optimization (RO). The optimization model consists in a three-level mixed integer linear programming problem, which is resolved using a two-stage decomposition approach, solved using the column-and-constraint generation algorithm. The model is validated using the 30 nodes IEEE and the 33 nodes Brazilian test system. The model proposed proved to have an operating cost proportional to the level of uncertainty and the methodology employed provides a preventive view of what may occur during the hydro-thermal-wind systems operation, allowing the central system operator to take certain actions in order to ensure reliable operating conditions. In addition, the model allowed the identification of important aspects related to computational costs, such as the verification that constraints that couple the problem in time and space significantly impact the simulation times of the problem.

This study delves into the vital missions of the armed forces, encompassing the defense of territorial integrity, sovereignty, and support for civil institutions. Commanders grapple with crucial decisions, where accountability underscores the imperative for reliable field intelligence. Harnessing artificial intelligence, specifically, the YOLO version five detection algorithm, ensures a paradigm of efficiency and precision. The presentation of trained models, accompanied by pertinent hyperparameters and dataset specifics derived from public military insignia videos and photos, reveals a nuanced evaluation. Results scrutinized through precision, recall, map@0.5, mAP@0.95, and F1 score metrics, illuminate the supremacy of the model employing Stochastic Gradient Descent at 640 × 640 resolution: 0.966, 0.957, 0.979, 0.830, and 0.961. Conversely, the suboptimal performance of the model using the Adam optimizer registers metrics of 0.818, 0.762, 0.785, 0.430, and 0.789. These outcomes underscore the model's potential for military object detection across diverse terrains, with future prospects considering the implementation on unmanned arial vehicles to amplify and deploy the model effectively. [ABSTRACT FROM AUTHOR]

ABSTRACT The operation planning of the National Interconnected System (NIS) is based on optimization models that use synthetic inflow scenarios to represent the periodic behavior observed in historical data. Currently, the PAR(p)-A model (Periodic Autoregressive with Annual Component) is officially employed in computational models by the responsible organizations for short and medium-term operation planning. This paper has the aim of presenting an experiment using an alternative model that takes into consideration information regarding climatic variables, which can influence the hydrological regime of river basins and therefore the entire energy planning. The evaluated model employs the ONI index as a measure of the El Niño-Southern Oscillation (ENSO) phenomenon, in addition to a Markovian switching process. The results of the experiment demonstrate that the methodology is able to capture the influence of this phenomenon on inflows and generate scenarios closer to observed flow values.

Copyright of Command Control & Simulation / Zhihui Kongzhi yu Fangzhen is the property of Command Control & Simulation Editorial Office and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

This paper considers a multi-objective model in which operation planning and tool assignment are simultaneously in a Flexible Manufacturing System (FMS). In this regard, the main characteristics of FMS have been analyzed. Then, a comprehensive model, including major system parameters and cost components, has been designed and presented. The proposed model contains cost factors, including machining cost, earliness or tardiness penalties, tool and part movement or switch costs, and idle time costs of tools and machines. Then, a multi-objective model for the problem has been proposed, in which the relative importance of each cost through weighting these costs based on the decision-making goals and the sum of the mentioned costs have been considered simultaneously. Based on the complex nature of the problem, standard solution techniques have yet to be employed. Therefore, to reduce computational times, the Simulated Annealing (SA) algorithm has been used for about 30 minutes (10,000 movements). The total production costs have been decreased from 7,000 to 4333 units using the SA algorithm. Based on the results, a 38% reduction in total production costs has been achieved. Computational results revealed that the proposed method is quite efficient in the multi-objective optimization of FMS within a short computational time. [ABSTRACT FROM AUTHOR]

At operational level, fossil fuel phase-out and high shares of non-dispatchable renewable energy resources (RES) will challenge the system operator’s (SO) ability to balance generation, and the demand at any time. The variability of RES output ranges from one hour to a season, and critical events such as low supply and high demand might occur more frequently and for more extended periods. When evaluating the role of Energy Storage Systems (ESSs) in this context, the need for a long time scope to capture the different RES variabilities must be reconciled with the need for modeling the hourly chronology. This paper presents a medium-term operation planning model, addressing both the energy dispatch and the balancing services. This study shows that representing the combined chronological variability of demand and RES production is essential to properly assess the roles of different kinds of ESSs in the future 2030 electricity mix. Otherwise, it would not be possible to appropriately capture the frequency, depth, and length of events for which ESSs are activated. The analysis also highlights the importance of considering balancing services, given the significant contribution of batteries to the reserve market. Finally, the results show that batteries and Pumped Storage Hydro (PSH) have different roles in the Spanish electricity system with a high renewable penetration. While PSH is mainly used to provide energy during critical periods, batteries mostly provide balancing services.

The core of graphical programming for sheet-metal bending is operation planning based on parameter identification, which can directly determine the bending quality and efficiency. Given this, the proposed graphical programming system and method can predict the inner arc radius of bending oriented to programming demands and optimize multi-step bending operations. At first, a neural network model based on multi-layer perceptron (MLP) was established, considering the number of neurons and hidden layers. In the bending process a fitness function was defined, and the discrete particle swarm optimization (DPSO) was improved using the adaptive inertia weight and the genetic algorithm (GA). Finally, the operation planning of 10-step and 20-step bending was simulated, and bending tests were conducted on sheet-metal workpieces for verification. The results show that the algorithm's maximum positive error and minimum negative error in predicting the arc radius are 0.12 mm and − 0.16 mm, respectively. When there are 10 bending steps, the particle swarm optimization-genetic algorithm (PSO-GA) converges after only ten evolutions; when the bending steps increase to 20, the fitness value finally stabilizes at 1.31. The optimal operations of the experimental six-step bending part are 6 → 5 → 4 → 3 → 1 → 2. This result indicates that the workpiece is turned over three times and turned around once in the sheet-metal bending process, which is consistent with the simulation, thus verifying the bending efficiency and accuracy. [ABSTRACT FROM AUTHOR]

The selection and employment of proper methods in water distribution causes an increase in water productivity and the level of satisfaction of water users. It is faced with more difficulties in aged irrigation projects due to temporal changes such as changes in the crop patterns, development of the command area and destruction of canals and hydraulic structures. The plan of operation methods have some hydraulic and social complexities and therefore is usually simplified or implemented experimentally. This research investigates the best options for water distribution to the paddy fields in a subunit of Sefidroud irrigation scheme based on field survey, recording real data and hydraulic simulation with the employment of a SOBEK hydrodynamic model. Different operation scenarios were defined and then simulated in the current physical state of the scheme through replacing the exhausted intake structures with sluice gates. Finally, improved operation scenarios during the irrigation season were suggested based on the distribution indices. The results show that in spite of the current situation, water loss could reach the minimum by employing modification scenarios and indices of adequacy and equity of water distribution improve. HIGHLIGHTS Using Hydraulic-Numerical models to assess the operation and rehabilitation phase of an irrigation scheme.; Proposing and planning for the best operation scenarios based on the hydraulic modeling and practicability.; Investigation of re-designing some hydraulic structures and their impacts in an aged irrigation scheme using modelling.; With precise planning while less water is consumed, water distribution indices improve.;

Urban rail transit often has high sensitivity against random disruptions. Hence regularity of the rail services should be maintained to prevent adverse impacts. This study focused on an objected-oriented discrete-event simulation platform to generate an efficient operation plan for such networks in the cases of degraded modes. The main goal is to generate an operation plan by taking into account the headway regularity index for a two-direction loop urban rail line under temporary line blockage disruptions. Accordingly, an optimized injection-withdrawal schedule proposed using a simulation-optimization approach enriched with Iterated Local Search heuristic method. The proposed method has the capability of quantifying the stochastic system resiliency. Real experiments derived from the 2nd line of the Karaj underground railway. Results revealed an improvement of 5.6% for the regularity index and 4.7% for the inbound and outbound routes, respectively. [ABSTRACT FROM AUTHOR]

Abstract The optimization of microgrids present challenges such as managing distributed energy resources (DERs) and the high reliance on intermittent generation such as PV and wind turbines, which present an aleatory behavior. The most popular techniques to deal with the uncertainties are stochastic optimization, which comes with a high computational burden, and adaptive robust optimization (ARO), which is often criticized for the conservativeness of its solutions. In response to these drawbacks, this work proposes a mixed-integer linear programming (MILP) model using a data-driven robust optimization approach (DDRO) solved by a two-stage decomposition using the column-and-constraint generation algorithm (C&CG). The DDRO model uses historic data to create the bounds of its uncertainty set, eliminating the conservativeness created by the arbitrary definition of the uncertainty set that is seen in ARO while maintaining a low computational burden. The DDRO model applied was not previously utilized in MGs, only in bulk power systems. A benchmark MG system was simulated for a 24-hour period without uncertainties, with uncertainties using ARO (15% uncertainty budget) and with uncertainties and DDRO. The operational costs without uncertainty were $124,600,60, while the ARO approach rose those costs by 32.5% ($ 165,137.18). Finally, the DDRO approach managed to keep the costs in $ 126,934.54, a mere 1.8% increase from the base case without uncertainty. All simulations were performed in less than 1 minute. The results confirm a) the advantages of bounding the uncertainty set with historical data instead of an arbitrary definition of bounds and b) the fast-converging times of DDRO.

Augmented reality (AR) technology has been used and is of great benefit to the formulation of operation planning, simulation of operation scheme, improvement of operation outcome and reduction of risk in joint surgery, and also creates favorable conditions for the understanding of common diseases in joint surgery and training of practical operation skills of young orthopedic surgeons.

Background: Virtual reality (VR) with head-mounted displays (HMD) may improve medical training and patient care by improving display and integration of different types of information. The aim of this study was to evaluate among different healthcare professions the potential of an interactive and immersive VR environment for liver surgery that integrates all relevant patient data from different sources needed for planning and training of procedures. Methods: 3D-models of the liver, other abdominal organs, vessels, and tumors of a sample patient with multiple hepatic masses were created. 3D-models, clinical patient data, and other imaging data were visualized in a dedicated VR environment with an HMD (IMHOTEP). Users could interact with the data using head movements and a computer mouse. Structures of interest could be selected and viewed individually or grouped. IMHOTEP was evaluated in the context of preoperative planning and training of liver surgery and for the potential of broader surgical application. A standardized questionnaire was voluntarily answered by four groups (students, nurses, resident and attending surgeons). Results: In the evaluation by 158 participants (57 medical students, 35 resident surgeons, 13 attending surgeons and 53 nurses), 89.9% found the VR system agreeable to work with. Participants generally agreed that complex cases in particular could be assessed better (94.3%) and faster (84.8%) with VR than with traditional 2D display methods. The highest potential was seen in student training (87.3%), resident training (84.6%), and clinical routine use (80.3%). Least potential was seen in nursing training (54.8%). Conclusions: The present study demonstrates that using VR with HMD to integrate all available patient data for the preoperative planning of hepatic resections is a viable concept. VR with HMD promises great potential to improve medical training and operation planning and thereby to achieve improvement in patient care. [ABSTRACT FROM AUTHOR]

Background: Hepatectomy, living donor liver transplantations and other major hepatic interventions rely on precise calculation of the total, remnant and graft liver volume. However, liver volume might differ between the pre- and intraoperative situation. To model liver volume changes and develop and validate such pre- and intraoperative assistance systems, exact information about the influence of lung ventilation and intraoperative surgical state on liver volume is essential. Methods: This study assessed the effects of respiratory phase, pneumoperitoneum for laparoscopy, and laparotomy on liver volume in a live porcine model. Nine CT scans were conducted per pig (N = 10), each for all possible combinations of the three operative (native, pneumoperitoneum and laparotomy) and respiratory states (expiration, middle inspiration and deep inspiration). Manual segmentations of the liver were generated and converted to a mesh model, and the corresponding liver volumes were calculated. Results: With pneumoperitoneum the liver volume decreased on average by 13.2% (112.7 ml ± 63.8 ml, p < 0.0001) and after laparotomy by 7.3% (62.0 ml ± 65.7 ml, p = 0.0001) compared to native state. From expiration to middle inspiration the liver volume increased on average by 4.1% (31.1 ml ± 55.8 ml, p = 0.166) and from expiration to deep inspiration by 7.2% (54.7 ml ± 51.8 ml, p = 0.007). Conclusions: Considerable changes in liver volume change were caused by pneumoperitoneum, laparotomy and respiration. These findings provide knowledge for the refinement of available preoperative simulation and operation planning and help to adjust preoperative imaging parameters to best suit the intraoperative situation. [ABSTRACT FROM AUTHOR]

Background: The irregular anatomical shape and complex structures of irregular bones make it more difficult to repair and reconstruct bone defects in irregular bones than in the long bones of the extremities. Three-dimensional (3D) printing technology can help to overcome the technical limitations of irregular bone repair by generating simulations that enable structural integration of the lesion area and bone structure of the donor site in all directions and at multiple angles. Thus, personalized and accurate treatment plans for restoring anatomical structure, muscle attachment points, and maximal function can be made. The present study aimed to investigate the ability of 3D printing technology to assist in the repair and reconstruction of scapular aneurysmal ABC defects.Methods: The study included seven patients with ABCs of the scapula. Based on computed tomography (CT) data for the patient, the scapula (including the defect) and pelvis were reconstructed using Mimics Medical software. The reconstructed scapula model was printed using a 3D printer. Before the operation, the model was used to design the surgical approach and simulate the operation process, to determine the length and radius of the plate and the number and direction of screws, and to determine the bone mass of the ilium and develop reasonable strategies for segmentation and distribution. The operation time, amount of bleeding, length and radius of the plate, and direction and number of screws were recorded.Results: The average duration of follow-up was 25.6 months, and none of the seven patients experienced recurrence during the follow-up period. The surgical approach, the length and radius of internal fixation, and the number and direction of screws were consistent with the designed operation plan. Patients gradually recovered the anatomical structure of the scapula and function of the shoulder joint.Conclusions: In the treatment of bone defects caused by irregular bone tumors, 3D printing technology combined with surgery has the advantages of less trauma, short operation time, less bleeding and reducing the difficulty of operation, which can reduce the waste of bone graft, and more complete reconstruction of the anatomical structure of the defective bone. [ABSTRACT FROM AUTHOR]

More and more factories and commercial buildings are installing combined heat and power (CHP) systems that include various energy storage devices. To reduce the energy cost of CHPs, optimal operation plans to satisfy time‐varying energy demands with minimum energy cost are required. Conventional operation planning methods using optimized calculation have an issue with long computing time. To address this, we adapted a convolutional neural network (CNN) to learn the time‐series features of input–output pairs of optimized calculations. The architecture of the proposed CNN has the following key features: (i) the application of one‐dimensional convolutional filters to extract the time‐series features of input data and (ii) the elimination of pooling layers that compress the input data. We also designed rule‐based flows to remove constraint violations from the generated operation plans. Our evaluations showed that the proposed method could generate operation plans with the average error of 3.3%, which is much lower than the 8.0% average error when using the conventional CNN with pooling calculation. © 2021 Institute of Electrical Engineers of Japan. Published by Wiley Periodicals LLC. [ABSTRACT FROM AUTHOR]

Case-based reasoning (CBR) is an effective reasoning technology. The core idea of CBR is using past experience and expert knowledge to solve new problems. Operation planning of helicopter mainly relies on the experiences of the decision-maker, so CBR is an effective reasoning technology which can be used in the operation planning. Two key issues, evaluation method and case adaptation, are widely focused in CBR method. However, the existing CBR is not suitable for non-numeric optimization problem such as operation planning, because it belongs to small sample size problem. To solve this problem, the comprehensive evaluation based multi-CBR (CEB-mCBR) method is proposed in this paper, which can be used for operation planning of helicopter. According to the comprehensive evaluation theory, similar historical case set of the target case is established, and the historical operation plans are ranked. On this basis, the optimum initial plan for the target case is generated by the case screening CBR model. Then the solution element screening CBR model is further put forward to solve the issue of case adaptation. The CEB-mCBR method comprises multi-CBR (case screening CBR and solution element screening CBR) and combination evaluation method. It can integrate expert experience and human thinking better, especially when solution space is limited. Finally, through a case study analysis, the multi-CBR model can generate a feasible solution for the target case, and the solution elements can be adjusted according to the situation in the actual operation, which can improve the efficiency of operation planning while keeping the safety, economy and operating efficiency.

The increased penetration of wind power introduces more operational changes of critical corridors and the traditional time-consuming transient stability constrained total transfer capability (TTC) operational planning is unable to meet the real-time monitoring need. This paper develops a more computationally efficient approach to address that challenge via the analytical deep learning-based surrogate model. The key idea is to resort to deep learning for developing a computationally cheap surrogate model to replace the original time-consuming differential-algebraic constraints related to TTC. However, the deep learning-based surrogate model introduces implicit rules that are difficult to handle in the optimization process. To this end, we derive the Jacobian and Hessian matrices of the implicit surrogate models and finally transfer them into an analytical formulation that can be easily solved by the interior point method. Surrogate modeling and problem reformulation allow us to achieve significantly improved computational efficiency and the yielded solutions can be used for operational planning. Numerical results carried out on the modified IEEE 39-bus and 68-bus systems demonstrate the effectiveness of the proposed method in dealing with complicated TTC constraints while balancing the computational efficiency and accuracy. [ABSTRACT FROM AUTHOR]

Copyright of Bylye Gody is the property of Cherkas Global University Press and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Recently, the demand of complex shape parts has increased mainly in the aircraft and medical industries. In these parts machining, the displacement and vibration of workpiece that strongly affect the machining efficiency are induced as a result of the heavy change of the unmachined workpiece shape and stiffness while rough machining. On the other hand, it is difficult to automatically determine the manufacturing information by using a commercial CAM system because there is a large number of combinations. Thus, in order to improve the machining efficiency of complex shape parts, the authors have proposed a determination method of workpiece transition shapes during a rough machining operation based on topology optimization. However, tool paths are not generated automatically to create the obtained workpiece transition shapes in the previous study. Therefore, in this study, a tool path generation method is proposed considering both static stiffness of workpiece and machining efficiency. The proposed method supposes 5-face milling and in the machining the tool orientation is determined to minimize the change of tool orientations by using the obtained parameters in topology optimization. The tool paths are continuously generated based on not only the design variables of topology optimization but also geometric feature of machining area, target shape, final support. A case study assuming rough machining of complex shape parts is conducted to confirm the effectiveness of the proposed method.

Copyright of Bylye Gody is the property of Cherkas Global University Press and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

A generation company (GENCO) which has a conventional power plant (CPP) intends to add an energy storage system (ESS) beside the CPP to increase its flexibility and profitability. For this purpose, a new model is proposed for coordinated operation planning of the CPP and ESS in energy and spinning reserve markets in the presence of a bilateral contract. The proposed model is based on the stochastic price based unit commitment (PBUC) and price based storage commitment (PBSC). The uncertainties of the energy and spinning reserve prices and delivery requests in the spinning reserve market are modeled via scenarios based upon historical data. The proposed model maximizes the profitability of the ESS beside the CPP and encourages the GENCO to invest the ESS. ESS technology options to use beside the CPP are determined by economic assessments. Numerical results show that utilization of ESSs improves the technical operation of CPPs, as well as GENCOs' profitability.

Summary: Solid oxide fuel cell (SOFC) can supply backup power for a remote island microgrid that generates power from tidal energy, photovoltaics (PV), and wind turbines. Since SOFCs need high temperature operation, though, the possible output adjustments are relatively slow. Therefore, SOFC with renewable energy requires the electricity compensation by a storage battery. This paper reports the analysis results using MATLAB/Simulink 2016b simulations of a microgrid with tidal energy, photovoltaics, and SOFC generators. The dynamic characteristics of the modeled SOFC are estimated from experimental tests of a product in Japan, and the suitable battery capacity for such a microgrid is determined. Furthermore, proposed system stores surplus electricity using a hydrogen energy carrier, energy supply can be carried out only in renewable energy. The remote tropical islands of Ternate and Pulau‐Tidore served as a case study area for the proposed microgrid. Proposed microgrid for a remote island was modeled using MATLAB/Simulink 2016b. The simulations clarified the suitable combination of tidal power generation, PV, and SOFC capacity assuming a maximum load. The required capacity of a Li‐ion storage battery in the example case of Indonesia's remote Ternate and Pulau‐Tidore Island needs to be 3.2% of the maximum hourly load if the maximum load on the microgrid is 1200 kW. [ABSTRACT FROM AUTHOR]

The combat operation planning of missile force plays an important role in operation command decision of missile force. According to the practical problems in operational actions of missile force, the development circumstance of research on the operation planning of missile force is summed up, including the main contents, the basic methods and the planning models. The evaluation index system for operational action effectiveness of missile force is put forward, and the effectiveness evaluation methods are given. The research results can provide support for the decision-making of missile force combat command, improve the operational effectiveness and overall combat capability of conventional missile force, and provide certain theoretical basis for the further research. [ABSTRACT FROM AUTHOR]

Hydrocarbon supply chain (HCSC) is a complex network that extends from oil and gas fields to demand nodes. Integrating operation and maintenance activities along this complex network is crucial since the hydrocarbon industry is the most influential sector in the world economy, and any disruptions or variations in hydrocarbon product supply will affect the whole world economy. Therefore, effective and thoughtful maintenance extends the life of an asset and enhances its reliability. To prevent huge losses in production and ultimately satisfy customer needs, the maintenance jobs are preferred to be performed during times of low demand. Thus, operation planning and maintenance scheduling decisions are dependent and should be optimized simultaneously. Therefore, the aim of this study is to develop an integrated mathematical model for the operation and maintenance planning of the oil and gas supply chain. The utility of the proposed model has been demonstrated using the Saudi Arabian HCSC. The proposed model effectively produces optimal operation and maintenance schedule decisions. A sensitivity analysis was conducted to study the effect of critical parameters on the obtained results. [ABSTRACT FROM AUTHOR]

There is an increasing demand of high-value-added workpieces that generally have three dimensional and complex shapes such as aircraft parts. In complex parts machining, workpiece shape and stiffness change greatly during a rough machining operation and induce workpiece displacement and vibration that strongly affect machining accuracy and tool life. However, it is difficult to automatically determine machining process parameters such as cutting sequence, tool paths and cutting conditions by using a commercial CAM system because there is a large number of combinations. Therefore, in order to improve the efficiency of complex parts machining, it is necessary to clarify these tacit knowledges and to formulate the implicit machining know-how owned by skillful experts. The authors have been proposed a determination method of complex workpiece shapes during a rough machining operation based on topology optimization. In the method, topology optimization is used to calculate workpiece shapes to ensure the stiffness by dividing a rough machining operation into several steps. However, the operation planning has not been adequately considered to determine machining process parameters. Moreover, it is necessary to repeat the calculation of topology optimization because a rough machining operation is divided into many machining steps. Thus, in this study, a determination method of cutting sequence is proposed by using the design variables obtained in the calculation process of topology optimization of workpiece. In order to estimate the machined shape based on the proposed cutting sequence, cutting experiments are conducted. The results show the effectiveness of the proposed cutting sequence to reduce the deformation of workpiece during a rough machining operation of complex parts machining.

This study mainly aimed to compare operation planning schemes for polymer electrolyte fuel cell cogeneration systems (PEFC–CGSs) to analyze derived operation plans from various perspectives. PEFC–CGSs provide hot water by using waste heat produced in electricity generation. Therefore, they have considerable potential for further energy saving and can be efficient systems by generating and storing heat according to household demand. Operation planning scheme based on the prediction of household electricity and hot water demands is a promising approach to achieving further energy saving. However, operation results imply that improvement in accuracy from the viewpoint of prediction error does not always lead to efficient PEFC–CGS operation. In this study, we implemented several operation schemes and analyzed them by simulation from various perspectives, namely, operation cost breakdown, demand types and robustness required for the operation plan, to attempt further operation cost reduction. [ABSTRACT FROM AUTHOR]

The completion of passenger-dedicated lines (PDLs) has released the transport capacity of many existing railways, making room for railway freight transport. To make full use of the released capacity, this paper puts forward an integer linear programming model based on the linear correlation between the number of trains and the demand increment, and solves the model with IBM ILOG CPLEX Optimization Studio 12.4. The proposed model was successfully applied to prepare a suitable operation plan of two types of freight block trains (FBTs) in a railway network with multiple routes and nodes. To further increase the transport profit, two only adjustable parameters, namely, the capacity of railway section and the number of trains, were subjected to sensitivity analysis. Through the analysis, the author determined which railway sections should expand their capacities and which way stations should increase the number of FBTs. [ABSTRACT FROM AUTHOR]

Pricing and operation planning are two key issues for railway trainer transportation. Most of the researches tried to optimize the pricing or operation planning strategies separately. As in fact, these two factors are related to each other. The isolated research on either of them cannot get the optimal solution. In this paper, a bi-level model is proposed, which integrate the pricing, the operation planning, and competition from road transportation, to maximize the profit of rail operators. The lower level is designed to minimize the customer's general cost, and the upper level is to maximize the revenue of rail operators. A descent algorithm based on the sensitivity analysis method is designed to solve the bi-level model. A real transportation network with 5 freight stations of China Railway Corporation is applied to evaluate the proposed model. The results show that our method can improve the rail operator's profit about 14% compared with the fixed pricing policy under the competition freight transportation market. [ABSTRACT FROM AUTHOR]

A reasonable train operation plan for railway passenger transportation should jointly optimize the service quality and rail operator's revenue. This paper synthesizes revenue management and train operation planning to develop an integrated model to maximize the operator's revenue and minimize passenger's general cost, which is applied to optimize the rail train frequencies, stopping patterns and seats' allocation dynamically. An integer program is designed to describe this problem, which is solved by the CVX toolbox. The Chengdu–Chongqing high-speed rail line is applied to demonstrate the effectiveness of the proposed methodology. The optimal frequencies are 95 trains per day, comparing the actual frequencies of 69 trains per day. The total profit increased by 24%, and the passengers' waiting time reduced greatly. It can be seen that the proposed model is effective for the optimization of the rail planning, which significantly increases the total revenue and reduces the passengers' waiting time. [ABSTRACT FROM AUTHOR]

Abstract The price and service time are two critical aspects that affect the benefits for customers and rail operators. In the rail freight transportation system, the profit maximization object is explored deeply, which is widely adopted by rail operators. This paper bridges the gap between the maximization profit of rail operators and the minimization of customers' waiting queue (backlog). We propose a new optimization model which encompasses pricing, operation planning, carbon emissions and service queuing for an infinite-horizon decision process. The infinite-horizon decision problem is NP-hard. More precisely, a backlog control and pricing optimization algorithm is proposed to solve the NP-hard problem. In particular, it is theoretically proved that the result of the proposed algorithm is equal to the optimal solution. The explicit trade-off between the optimization objective and queue backlog is also proven. Finally, a case study was carried out to testify the efficiency of the proposed model. The result shows that the proposed methodology not only improve the rail freight service quality but also reduce the greenhouse gas emission at the freight transportation sector. Highlights • An integrated optimization model is designed for rail cargo operators, which aims to optimize the pricing policy, the operation planning, the reduction of green gas emissions, and the freight service quality. • The Lyapunov penalty drift is adopted to transform the NP-hard model to a greedy decision model, which is equal to the original model. [ABSTRACT FROM AUTHOR]

Purpose: Knowing the location of the blood vessels supplying the skin and subcutaneous tissue is a requirement during the planning of tissue transfer in reconstructive surgery. Commonly used imaging techniques such as computed tomography angiography and indocyanine green angiography expose the patient to radiation or a contrast agent, respectively. Infrared thermal imaging was evaluated with success as a non-invasive alternative. To support the interpretation of thermograms, a method to automatically detect the perforators was developed and evaluated.Methods: A system consisting of a thermal camera, a PC and custom software was developed. The temperature variations of the skin surface were analysed to extract the perforator locations. A study was conducted to assess the performance of the algorithm by comparing the detection results of the algorithm with manually labelled thermal images by two clinicians of the deep inferior epigastric perforator flap of 20 healthy volunteers.Results: The F measure, precision and recall were used to evaluate the system performance. The median F measure is 0.833, the median precision is 0.80, and the median recall is 0.907.Conclusion: The results of this study showed that it is possible to automatically and reliably detect the skin perforators in thermograms despite their weak temperature signature. Infrared thermal imaging is a non-invasive and contactless approach suitable for intraoperative use. Combined with a computer-assisted tool for the automatic detection of perforator vessels, it is a relevant alternative intraoperative imaging method to the standard indocyanine green angiography. [ABSTRACT FROM AUTHOR]