Trade-off Curve Research Articles

Carbon molecular sieve gas separation membranes derived from in-situ crosslinked polyimide containing bromine groups

The network polyimide (PI) precursor is a promising candidate to fabricate the advanced carbon molecular sieve (CMS) membrane since its robust crosslinked structure could prevent pore structure collapse during CMS formation. Exploring the relationship between network PI precursor structure and CMS membrane is highly significant. Herein, a novel brominated network PI precursor was fabricated via in-situ cross-linking technique firstly, and then the thermally induced bromination/debromination process was applied to tune the microporosity and gas transport properties of network PI precursor and its derived CMS membranes. After the bromination/debromination and carbonization at 550 °C, the resultant CMS membrane exhibited both high gas permeability and gas selectivities compared to its un-brominated network PI precursor derived CMS membrane. This is due to the bromination/debromination and carbonization enable the CMS membrane possesses large d-spacing values (11.06 Å), high BET surface areas (538.57 m2/g), and high percentage (∼71 %) of ultra-micropores. For instance, 6F-TA/TA-Br-550/30 membrane exhibits great CO2/CH4 separation ability, with a CO2 permeability of 6300 Barrer and a CO2/CH4 selectivity of 46.11, which is suppressed the latest 2019 trade-off curves. We anticipate that the present bromination/debromination and carbonization could provide a fresh perspective on the rational design of network PI precursor and its derived CMS membrane with excellent gas separation performance.

How economically and environmentally viable are multiple dams in the upper Cauvery Basin, India? A hydro-economic analysis using a landscape-based hydrological model

Abstract. The construction of dams threatens the health of watershed ecosystems. The purpose of this study is to show how multiple dams in a basin can impact hydrological flow regimes and subsequently aquatic ecosystems that depend on river flows. The approach assesses the ecosystem services (ESs), including the tradeoffs between economic and ecological services due to altered flow regimes. It uses a previously developed model that integrates a landscape-based hydrological model with a reservoir operations model on a basin scale. The approach is novel because not only does it offer the analysis of alterations in ecosystem services on a daily scale when pre-dam data are unavailable but also allows for dams to be synthetically placed anywhere in the river network and the corresponding alterations in flow regimes to be simulated in a flexible manner. As a proof of concept, we analyse the economic and ecological performances of different spatial configuration of existing reservoirs instead of synthetically placed reservoirs in the upper Cauvery River basin in India. Such a study is timely and conducted for the first time, especially in light of calls to assess the cascade of reservoirs in India and regions elsewhere where pre-dam data are unavailable. The hydrological impact of different configurations of reservoirs is quantified using indicators of hydrologic alteration (IHAs). Additionally, the production of two major ecosystem services that depend on the flow regime of the river, as indicated by irrigated agricultural production and the normalized fish diversity index (NFDI), is estimated, and a tradeoff curve, i.e. a production possibility frontier, for the two services is established. Through the lens of the indices chosen for the ecosystem services, the results show that smaller reservoirs on lower-order streams are better for the basin economy and the environment than larger reservoirs. Cultivating irrigated crops of higher value can maximize the value of stored water and, with lower storage, generate a better economic value than cultivating lower-value crops while reducing hydrological alterations. The proposed approach, especially when simulating synthetic spatial configurations of reservoirs, can help water and river basin managers to understand the provision of ecosystem services in hydrologically altered basins, optimize dam operations, or even prioritize dam removals with a goal of achieving a balanced provision of ecosystem services.

The influence of debromination and TR on the microstructure and properties of CMSMs

One unique challenging in achieving advanced carbon molecular sieve membranes (CMSMs) is tailoring their gas separation properties from the precursor. To clarify this, we designed two homo-polyimides with a bromine (PI-Br) and a hydroxyl (PI-OH) groups in the ortho position of imide group, and carbonized them into CMSMs at 550 °C (PI-Br-550 and PI-OH-550). There are debromination (510 °C) and carbonization occurred for PI-Br-550 whereas the PI-OH underwent thermally rearrangement (TR, 450 °C) and then pyrolysis to CMSM. After carbonization, both CMSMs showed excellent gas separation properties, with their CO2/CH4 separation performance surpassed the latest 2019 pure-gas and 2018 mixed-gas trade-off curves, additionally, the PI-Br-550 demonstrated a relatively smaller CO2 permeability (12462 vs 14,253 Barrer), lower PCO2 increment ratio (199 vs 1480), and less CO2/CH4 dropping ratio (69.7 to 45.5 vs 87.5 to 36.1) than the PI-OH-550 from their precursors. Their XPS, Raman, and WXRD characterization results indicated that debromination boosts the gas separation performance of CMSM by creating more ultra-micropore “plate” (pyrrolic-N of 36.0 % vs 19.0 %) and smaller d-spacing (3.89 vs 4.15 Å) than the TR precursor, which boosts the permeability of CMSM is due to the high stability of PBO intermediates that retarded the decomposition of the polymer main chian and caused some big pores. The above results provided a good route to get highly efficient CMSM by molecular designing of their precursors.

Plasma microRNA-15a/16-1-based machine learning for early detection of hepatitis B virus-related hepatocellular carcinoma

Background and aimsHepatocellular carcinoma (HCC), which is prevalent worldwide and has a high mortality rate, needs to be effectively diagnosed. We aimed to evaluate the significance of plasma microRNA-15a/16-1 (miR-15a/16) as a biomarker of hepatitis B virus-related HCC (HBV-HCC) using the machine learning model. This study was the first large-scale investigation of these two miRNAs in HCC plasma samples. MethodsUsing quantitative polymerase chain reaction, we measured the plasma miR-15a/16 levels in a total of 766 participants, including 74 healthy controls, 335 with chronic hepatitis B (CHB), 47 with compensated liver cirrhosis, and 310 with HBV-HCC. The diagnostic performance of miR-15a/16 was examined using a machine learning model and compared with that of alpha-fetoprotein (AFP). Lastly, to validate the diagnostic efficiency of miR-15a/16, we performed pseudotemporal sorting of the samples to simulate progression from CHB to HCC. ResultsPlasma miR-15a/16 was significantly decreased in HCC than in all control groups (P < 0.05 for all). In the training cohort, the area under the receiver operating characteristic curve (AUC), sensitivity, and average precision (AP) for the detection of HCC were higher for miR-15a (AUC = 0.80, 67.3%, AP = 0.80) and miR-16 (AUC = 0.83, 79.0%, AP = 0.83) than for AFP (AUC = 0.74, 61.7%, AP = 0.72). Combining miR-15a/16 with AFP increased the AUC to 0.86 (sensitivity 85.9%) and the AP to 0.85 and was significantly superior to the other markers in this study (P < 0.05 for all), as further demonstrated by the detection error tradeoff curves. Moreover, miR-15a/16 impressively showed potent diagnostic power in early-stage, small-tumor, and AFP-negative HCC. A validation cohort confirmed these results. Lastly, the simulated follow-up of patients further validated the diagnostic efficiency of miR-15a/16. ConclusionsWe developed and validated a plasma miR-15a/16-based machine learning model, which exhibited better diagnostic performance for the early diagnosis of HCC compared to that of AFP.

Privacy-Preserving Gaze Data Streaming in Immersive Interactive Virtual Reality: Robustness and User Experience.

Eye tracking is routinely being incorporated into virtual reality (VR) systems. Prior research has shown that eye tracking data, if exposed, can be used for re-identification attacks [14]. The state of our knowledge about currently existing privacy mechanisms is limited to privacy-utility trade-off curves based on data-centric metrics of utility, such as prediction error, and black-box threat models. We propose that for interactive VR applications, it is essential to consider user-centric notions of utility and a variety of threat models. We develop a methodology to evaluate real-time privacy mechanisms for interactive VR applications that incorporate subjective user experience and task performance metrics. We evaluate selected privacy mechanisms using this methodology and find that re-identification accuracy can be decreased to as low as 14% while maintaining a high usability score and reasonable task performance. Finally, we elucidate three threat scenarios (black-box, black-box with exemplars, and white-box) and assess how well the different privacy mechanisms hold up to these adversarial scenarios. This work advances the state of the art in VR privacy by providing a methodology for end-to-end assessment of the risk of re-identification attacks and potential mitigating solutions. f.

Crossover Flux and Ionic Resistance Metrics in Polysulfide-Permanganate Redox Flow Battery Membranes

A survey of 23 commercially available cation exchange membranes was performed for the downselection of membranes for use in a polysulfide-permanganate redox flow battery (pS-Mn RFB). The survey measured the flux of permanganate ions across a 0.1 mol L−1 concentration gradient as well as the membrane resistance in a 0.5 mol L−1 sodium chloride solution. The membranes exhibited the characteristic flux/resistance trade-off observed in most classes of membranes. To connect the individual membrane testing to how the membranes will perform in a device, cell performance data in a pS-Mn RFB was collected for three membranes from the survey. The coulombic, voltaic, and energy efficiency at low cycle counts aligned with the predictions from the membrane flux and resistance survey results. The study also identified three membranes—Fumapem F-930-RFS, Fumapem FS-715-RFS, and Aquivion E98-09S—that outperformed most other membranes regarding their position on the flux-resistance trade-off curve, indicating them to be good candidates for further testing.

Multimodal biometric user authentication using improved decentralized fuzzy vault scheme based on Blockchain network

Biometric security has emerged as a major concern in the field of Biometric Systems. The existing centralized Biometric Cryptosystems approach still suffers from privacy issues and network-based attacks. In particular, the Biometric Cryptosystems are susceptible to Attack via Record Multiplicity, brute force and collusion attacks. To address these issues, this paper proposes a multimodal biometric user authentication scheme using a decentralized fuzzy vault based on Blockchain technology. In this scheme, the facial biometric images and dorsal hand images together form the multimodal biometric. The proposed security framework is implemented in two phases. In the first phase, multimodal biometric user authentication using an improved fuzzy vault scheme is implemented. In the second phase, a privacy preservation technique using a decentralized fuzzy vault biometric system based on the Blockchain network is implemented. The performance of the proposed framework is verified on the biometric datasets consisting of both genuine and imposter users, and a comparative analysis with the existing state-of-the-art approaches is also presented. The Detection Error Trade-off (DET) curve and the Receiver Operating Characteristic (ROC) curve, obtained for verifying the efficacy of the proposed framework, are also presented. The proposed framework yielded an accuracy of 99.8% and an Equal Error Rate of 0.08. Furthermore, to validate the biometric template protection, the security analysis of the proposed scheme based on irreversibility, unlinkability, and revocability criteria is described. The proposed framework achieves significant improvement in terms of a low False Acceptance Rate (0.08), and a high True Acceptance Rate (0.96).

Significantly enhanced gas separation properties of membranes by debromination and thermal rearrangement simultaneously

One important challenge in advanced gas separation membranes is breaking the trade-off effect between gas permeability and selectivity. Here, we developed a method by combining the debromination and thermal rearrangement (TR) techniques together to significantly enhance the gas separation properties of the resulting membrane for the first time. First, we designed a co-polyimide (DBOH) containing both hydroxyl and bromine (50/50) groups in the ortho position of the imide group. After thermal treatment at 450 °C, the TR and debromination happened simultaneously as proved by TG-MS, FT-IR and XPS results, the resulting DBPO showed a larger d-spacing (7.03–9.71 Å), a 18-fold enhanced BET surface area, and 2-fold improved in ultra-microporosity (<7 Å) than its DBOH precursor. Hence, DBPO demonstrated not only an over 100 times improved CO2 permeability (7933 vs 76 Barrer) but also keep a high CO2/CH4 selectivity of 31, and the overall performance surpassed the latest 2019 trade-off curve. Besides, wonderful anti-plasticization and mixed-gas separation properties were also observed for DBPO, which showed a CO2 permeability of 3324 Barrer and CO2/CH4 selectivity of 14.7 even under the upstream CO2/CH4 mixed-gas pressure of 435 psi (1 psi = 6894.8 Pa), outperforming the latest 2018 CO2/CH4 mixed-gas upper limit. It showed 100 times improved CO2 permeability while maintain selectivity of DBPO is attributed to the significantly enhanced ultra-microporosity that increases the diffusion and solubility coefficient, whereas maintain their diffusion and solubility selectivity. Conclusively, the debromination coupled with TR provides a novel direction for designing advanced TR membranes.

Maximum Sensitivity Constrained Graphical Controller Tuning for a DC–DC Boost Converter Loaded With a CPL

Owing to the negative impedance instability of the constant power load (CPL), oscillations are induced in the output voltage of the boost converter which may lead to voltage collapse. This paper proposes a graphical strategy to tune a PID controller for the boost converter loaded with a CPL. The idea of root crossing boundaries is applied to compute the all stability region. Robustness and relative stability of the system are addressed by obtaining the desired maximum sensitivity region (DMSR) within the all stability region. Required controller parameters are obtained by finding the centroid of the DMSR. A novel technique to select the derivative gain of the PID controller from the trade-off curve between disturbance rejection and noise suppression abilities, is also proposed. Experimental results with comparison are provided to show the effectiveness of the presented scheme.

Pareto-optimal front generation for the bi-objective JIT scheduling problems with a piecewise linear trade-off between objectives

This paper proposes a novel method of Pareto front generation from a set of piecewise linear trade-off curves typically encountered in bi-objective just-in-time (JIT) scheduling problems. We have considered the simultaneous minimization of total weighted earliness and tardiness (TWET) and total flowtime (TFT) objectives in a single-machine scheduling problem (SMSP) with distinct job due dates allowing inserted idle times in the schedules. An optimal timing algorithm (OTA) is presented to generate the trade-off curve between TWET and TFT for a given sequence of jobs. The proposed method of Pareto front generation generates a Pareto-optimal front constituted of both line segments and points. Further, we employ a simple local search method to generate sequences of jobs and their respective trade-off curves, which are trimmed and merged to generate the Pareto-optimal front using the proposed method. Computational results obtained using problem instances of different sizes reveal the efficiency of the proposed OTA and the Pareto front generation method over the state-of-the-art methodologies adopted from the literature.

Advancing the optimization of urban–rural ecosystem service supply-demand mismatches and trade-offs

ContextIntensified human activities have disrupted landscape patterns, causing a reduction in the supply of ecosystem services (ESs) and an increase in demand, especially in urban agglomerations. This supply-demand imbalance will eventually lead to unsustainable landscapes and needs to be optimized.ObjectiveBased on ES supply-demand mismatch and trade-off relationships across urban–rural landscapes, this study explored which ESs need to be optimized and identified priority restoration regions of ESs that require optimization to promote landscape sustainability in Beijing-Tianjin-Hebei urban agglomeration.MethodsA methodological framework for ES supply-demand optimization in urban–rural landscapes was developed. urban–rural landscapes were identified using Iso cluster classification tool. ES supply was quantified using biophysical models and empirical formulas, and demand was quantified through consumption and expectations. Restoration Opportunities Optimization Tool was then adopted to identify priority regions.ResultsFrom 2000 to 2020, most of ES supply were lowest in urban areas and highest in rural areas, while demand exhibited the opposite. Although supply was increasing, it did not match demand. ES deficits were dominant in urban areas; both deficits and trade-offs were dominant in urban–rural fringe; and trade-offs were dominant in rural areas. There were 13,175 km2 of priority regions distributed in urban–rural landscapes, and their spatial heterogeneity was influenced by ES deficits and trade-offs.ConclusionDifferences in ESs supply-demand relationships affected the necessity of optimizing ESs zoning in urban–rural landscapes. Assigning weights reasonably according to trade-off curves to determine priority regions could facilitate both efficient use of resources and sustainable ES management for urban–rural regions.

Multi-objective Colliding Bodies Optimization Algorithm for the Obnoxious p-median Problems

The obnoxious p-median problem consists of locating p facilities among a set of sites such that the sum distance from any demand to its nearest facility and the dispersion among facilities are maximized. In this paper, the multi-objective colliding bodies optimization algorithm (MOCBO) is utilized to obtain the trade-off curve of the obnoxious p-median problems. The performance of the developed optimization method is investigated for locating obnoxious facilities through two case studies to maximize the two conflicting objectives. The performance of the MOCBO algorithm is further compared with those of the MPSO and NSGA-II algorithms representative of the state of the art in the field of multi-objective optimization. In this study, the MOCBO algorithm showed suitable convergence performance and generalization abilities compared to the MPSO and NSGA-II algorithms.

Iterative Rule Extension for Logic Analysis of Data: An MILP-Based Heuristic to Derive Interpretable Binary Classifiers from Large Data Sets

Data-driven decision making is rapidly gaining popularity, fueled by the ever-increasing amounts of available data and encouraged by the development of models that can identify nonlinear input–output relationships. Simultaneously, the need for interpretable prediction and classification methods is increasing as this improves both our trust in these models and the amount of information we can abstract from data. An important aspect of this interpretability is to obtain insight in the sensitivity–specificity trade-off constituted by multiple plausible input–output relationships. These are often shown in a receiver operating characteristic curve. These developments combined lead to the need for a method that can identify complex yet interpretable input–output relationships from large data, that is, data containing large numbers of samples and features. Boolean phrases in disjunctive normal form (DNF) are highly suitable for explaining nonlinear input–output relationships in a comprehensible way. Mixed integer linear programming can be used to obtain these Boolean phrases from binary data though its computational complexity prohibits the analysis of large data sets. This work presents IRELAND, an algorithm that allows for abstracting Boolean phrases in DNF from data with up to 10,000 samples and features. The results show that, for large data sets, IRELAND outperforms the current state of the art in terms of prediction accuracy. Additionally, by construction, IRELAND allows for an efficient computation of the sensitivity–specificity trade-off curve, allowing for further understanding of the underlying input–output relationship. History: Accepted by Andrea Lodi, Area Editor for Design &amp; Analysis of Algorithms–Discrete. Funding: This work was supported by the Netherlands Organization for Scientific Research (Nederlandse Organisatie voor Wetenschappelijk Onderzoek) [Grant VI.VENI.192.043]. Supplemental Material: The online supplement is available at https://doi.org/10.1287/ijoc.2021.0284 .

Grid-patterned trench insulated gate bipolar transistor for 1.2 kV hybrid/plug-in hybrid electric vehicle silicon power devices

This paper reports on the newly developed silicon insulated gate bipolar transistor (IGBT) for Toyota’s fifth generation power devices. The designed IGBTs with a grid-patterned trench provide a lower on-state voltage (V ON ) while maintaining a wide mesa width. A successful 15% reduction of V ON was accomplished by enhancement of the two-dimensional hole storage effect. Furthermore, the grid-patterned trench IGBT merged with Schottky and multi-layered anode technology demonstrated a significant improvement in the trade-off curve between the reverse recovery current (I rr ) and V ON in a reverse conducting IGBT without a lifetime control process. As a result, the grid-patterned trench IGBT has contributed to reduce power loss and volume in the new power control units.

Joint Multi-item Multi-supplier Sustainable Lot-Sizing Model Applying Combined BWM, TOPSIS, Possibilistic Programming, and ϵ-constraint Method

Procurement decisions are critical in sustainable supply chain (SC) transformation. Until now, mainly deterministic supplier selection and lot-sizing models have been suggested. Moreover, it is not easy to make a SC sustainable solely by focusing on carbon footprint. Other sustainability factors, such as social, water, and waste management need to be considered. Therefore, the present study proposed a three-stage multiobjective, multisupplier, multiperiod joint supplier selection, and lot-sizing model, considering carbon footprint, water footprint, solid and liquid waste, and use of recycled materials in a stochastic environment. The model optimizes three objectives (cost, carbon emission, and social sustainability). The model considers different model parameters uncertain, such as costs, emission, solid waste, liquid waste, recycled material, quality rejection, and capacity. The present study suggests quantifying social sustainability and further using it in the lot-sizing model. The study has applied three stages. First, best–worst method and TOPSIS are used to assess suppliers' social scores. Second, the proposed possibilistic lot-sizing model uses the suppliers' social scores. In the third stage, the ϵ-constraint approach is used to generate tradeoff curves. The model generates optimal solutions for various levels of uncertainty, which are utilized to develop cost, emission, and social tradeoff curves.

Optimization of water-energy-food nexus via an integrated system of solar-assisted desalination and farming

This paper introduces a systematic framework for water-energy-food (WEF) nexus with focus on integrating water desalination (via reverse osmosis and thermal desalination), energy generation (from fossil-based power plants and solar panels), and crop production. Specifically, the paper provides generally applicable and new contributions in formulating WEF nexus problems as multi-objective, multi-period optimization problems with data-driven models based on field work. The paper also established a systematic method for integrating and reconciling farming methods, water technologies, energy-resource selection and operation, carbon footprint, and economic factors. A superstructure representation is used to embed the candidate options and configurations. An optimization formulation is developed to determine the optimal system configuration, water and energy uses and sources, and crop mix selection. A multi-period model is developed to account for the seasonal variability. The optimization model is solved under different carbon-footprint cuts to establish a tradeoff curve between the economic and environmental objectives. To generate the necessary experimental data for a meaningful case study, a year-long field study was carried out to generate experimental data for three fields were constructed in Kuwait using open-field, agrivoltaic, and greenhouse farming methods. The field data were extracted and coupled with the optimization formulation to apply the proposed approach to a case study. First, the optimization formulation was solved to maximize the net profit with no limits on the carbon footprint. The solution of this relaxed formulation gives a maximum profit of 2.127 MM USD/y and an annual emission of 2242 tonne CO2eq. Next, the optimization formulation was solved with the objective of minimizing the total annual emissions. The result was a minimum annual emission of 363 tonne CO2eq that corresponded to 1.366 MM USD/y of profit. The ɛ-constraint method was employed to establish tradeoffs between the economic and environmental objectives. The solutions also provided valuable information on the percentage contribution of solar energy and water sources.

A Hybrid Bald Eagle-Crow Search Algorithm for Gaussian mixture model optimisation in the speaker verification framework

A mutualism of nature-inspired algorithms, the Bald Eagle Search Algorithm (BESA) with the Crow Search Algorithm (CSA), is proposed for Gaussian Mixture Model optimisation in the speaker verification framework. BESA simulates bald eagles’ hunting behaviour, whereas CSA simulates crows’ food-hiding behaviour. BESA excels in exploitation but has limitations in exploration, whereas CSA showcases strong exploration capabilities but faces challenges in effective exploitation. BESA’s hunting characteristic combines crows’ memory update to generate a Hybrid Bald Eagle-Crow Search Algorithm (HBE-CSA). The ability of the proposed HBE-CSA is tested on clean speech utterances from the Librispeech dataset and eight real-life noisy conditions from the AURORA dataset at four different Signal-to-Noise Ratios (SNRs). The comparative analysis shows that the proposed HBE-CSA converges with higher log-likelihood than individual search algorithms and the state-of-the-art Expectation Maximisation algorithm. Detection error trade-off curves validate the results of convergence behaviour, indicating that best speaker models are built using the proposed HBE-CSA. The noise analysis shows the smallest equal error rate for the proposed HBE-CSA at different SNRs than competing algorithms. The proposed HBE-CSA holds promise for optimising models not only in speaker verification but also across other areas of speech processing.

Evaluating Risk Prediction with Data Collection Costs: Novel Estimation of Test Tradeoff Curves.

The test tradeoff curve helps investigators decide if collecting data for risk prediction is worthwhile when risk prediction is used for treatment decisions. At a given benefit-cost ratio (the number of false-positive predictions one would trade for a true positive prediction) or risk threshold (the probability of developing disease at indifference between treatment and no treatment), the test tradeoff is the minimum number of data collections per true positive to yield a positive maximum expected utility of risk prediction. For example, a test tradeoff of 3,000 invasive tests per true-positive prediction of cancer may suggest that risk prediction is not worthwhile. A test tradeoff curve plots test tradeoff versus benefit-cost ratio or risk threshold. The test tradeoff curve evaluates risk prediction at the optimal risk score cutpoint for treatment, which is the cutpoint of the risk score (the estimated risk of developing disease) that maximizes the expected utility of risk prediction when the receiver-operating characteristic (ROC) curve is concave. Previous methods for estimating the test tradeoff required grouping risk scores. Using individual risk scores, the new method estimates a concave ROC curve by constructing a concave envelope of ROC points, taking a slope-based moving average, minimizing a sum of squared errors, and connecting successive ROC points with line segments. The estimated concave ROC curve yields an estimated test tradeoff curve. Analyses of 2 synthetic data sets illustrate the method. Estimating the test tradeoff curve based on individual risk scores is straightforward to implement and more appealing than previous estimation methods that required grouping risk scores. The test tradeoff curve helps investigators decide if collecting data for risk prediction is worthwhile when risk prediction is used for treatment decisions.At a given benefit-cost ratio or risk threshold, the test tradeoff is the minimum number of data collections per true positive to yield a positive maximum expected utility of risk prediction.Unlike previous estimation methods that grouped risk scores, the method uses individual risk scores to estimate a concave ROC curve, which yields an estimated test tradeoff curve.

A Comprehensive Model for Assessing Synergistic Revenue–Cost for the Joint Operation of a Complex Multistakeholder Reservoir System

The joint operation of a multiobjective multistakeholder reservoir system enhances the revenues of downstream-compensated reservoirs at the expense of increasing the operation cost of upstream-compensating reservoirs. Challenges in quantifying the synergistic revenue–cost tradeoffs with incomplete information arise from difficulties in multistakeholder, high-dimensional, and combinational joint optimal operation modeling. This study proposed an equivalent aggregated reservoir multiobjective operation and synergistic revenue–cost assessment model. The proposed methodology includes three parts. Module I constructs revenue indexes covering energy production, water supply, ecological protection, and shipping objectives and uses the maximum outflow change degree as a surrogate “cost” index. Module II defines “aggregated reservoirs” that aggregate upstream reservoirs within the same river system as a single reservoir, reducing model complexity with the least information. Module III evaluates the revenue–cost tradeoffs under various operation scenarios. The following conclusions were derived from a 27-reservoir system: (1) The model complexity was reduced by 67.18% with precision preserved. (2) Key compensating reservoirs are identified via tradeoff curves, which are reservoirs controlling high streamflow with large storage. (3) Upstream compensating reservoirs homogenize the inflows of downstream-compensated reservoirs to increase the downstream synergistic revenue by sacrificing upstream benefit. The proposed method provides a new approach for revenue–cost estimation via the joint optimal operation of a multistakeholder-reservoir system.

The LET trilemma: Conflicts between robust target coverage, uniform dose, and dose-averaged LET in carbon therapy.

Linear energy transfer (LET) is closely related to the biological effect of ionizing radiation. Increasing the dose-averaged LET (LETd ) within the target volume has been proposed as a means to improve clinical outcome for hypoxic tumors. However, doing so can lead to reduced robustness to range uncertainty. To quantify the relationship between robust target coverage, target dose uniformity, and LETd , we employ robust optimization using dose-based and LETd -based functions and allow varying amounts of target non-uniformity. Robust carbon therapy optimization is used to create plans for phantom cases with increasing target sizes (radii 1, 3, and 5cm). First, the influence of respectively range and setup uncertainty on the LETd in the target is studied. Second, we employ strategies allowing overdosage in the clinical target volume (CTV) or gross tumor volume (GTV), which enable increased LETd in the target. The relationship between robust target coverage and LETd in the target is illustrated by tradeoff curves generated by optimization using varying weights for the LETd -based functions. As the range uncertainty used in the robust optimization increased from 0% to 5%, the near-minimum nominal LETd decreased by 17%-29% (9-21keV/µm) for the different target sizes. The effect of increasing setup uncertainty was marginal. Allowing 10% overdosage in the CTV enabled 9%-29% (6-12keV/µm) increased near-minimum worst case LETd for the different target sizes, compared to uniform dose plans. When 10% overdosage was allowed in the GTV only, the increase was 1%-20% (1-8keV/µm). There is an inherent conflict between range uncertainty robustness and high LETd in the target, which is aggravated with increasing target size. For large tumors, it is possible to simultaneously achieve two of the three qualities range robustness, uniform dose, and high LETd in the target.