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With the development of data mining techniques, user-generated data has become a valuable resource in diverse research areas. In product design research, many studies have been utilizing user data to discover implications for new product design. However, previous works focused on analyzing existing features, whereas companies also need strategies for new features. Some studies discovered new feature ideas from user data but did not provide design implications. This paper addresses the above limitation by extracting comprehensive design implications for both features from user data. The method first defines the lists of existing/new features and collects spec data for these features. Then, it constructs customer choice sets based on the online review and spec data. Regarding spec values, this study presents a newness merit function that reflects the changing value of new features and applies it to the choice sets. The final stage trains a neural network model based on choice sets and conducts SHAP (SHapley Additive exPlanations) on the model. The method draws design implications by further analyzing the resultant SHAP values. The suggested methodology was tested on real-world datasets. The result provides design guidance, including strategies for new features and recommended spec ranges for existing features. This article validates the result by showing that the obtained design implications are consistent with previous market research for product features. • Customer choice sets are constructed based on online reviews. • A newness merit function quantifies the value of new features in choice sets. • The analysis of SHAP values provides design guidance for new products. • Recommended spec values for existing features are presented. • Effective strategies for new features are suggested. [ABSTRACT FROM AUTHOR]

Rationale and Objectives: The study goal was to test whether the improved accuracy in quantitative dynamic contrast-enhanced magnetic resonance imaging measurement using a point-of-care portable perfusion phantom (P4) leads to better stratification of prostate cancer grade.Materials and Methods: A prospective clinical study was conducted recruiting 44 patients scheduled for multi-parameter MRI prostate exams. All participants were imaged with the P4 placed under their pelvic regions. Tissue sampling was carried out for 25 patients at 22 ± 18 (mean ± SD) days after multi-parameter MRI. On histologic examination, a total of 31 lesions were confirmed as prostate cancer. Tumors were classified into low grade (n = 14), intermediate grade (n = 10), and high grade (n = 7). Tumor perfusion was assessed by volume transfer constant, Ktrans, before and after P4-based error correction, and the Ktrans of low, intermediate and high-grade tumors were statistically compared.Results: After P4-based error correction, the Ktrans of low, intermediate, and high-grade tumors were 0.109 ± 0.026 min-1 (95% CI: 0.0094 to 0.124 min-1), 0.163 ± 0.049 min-1 (95% CI: 0.129 to 0.198 min-1) and 0.356 ± 0.156 min-1 (95% CI: 0.215 to 0.495 min-1), respectively, with statistically significant difference among the groups (low vs intermediate: p = 0.002; intermediate vs high: p = 0.002; low vs high: p < 0.001). The sensitivity and specificity of Ktrans value, 0.14 min-1, to detect the clinically significant prostate cancer were 88% and 93%, respectively, after P4 based error correction, but those before error correction were 88% and 86%, respectively.Conclusion: The P4 allows to reduce errors in quantitative dynamic contrast-enhanced magnetic resonance imaging measurement, enhancing accuracy in stratification of prostate cancer grade. [ABSTRACT FROM AUTHOR]

This technical note describes how to modify a population-based arterial input function to incorporate variation among the individuals. In DCE-MRI, an arterial input function (AIF) is often distorted by pulsated inflow effect and noise. A population-based AIF (pAIF) has high signal-to-noise ratio (SNR), but cannot incorporate the individual variation. AIF variation is mainly induced by variation in cardiac output and blood volume of the individuals, which can be detected by the full width at half maximum (FWHM) during the first passage and the amplitude of AIF, respectively. Thus pAIF scaled in time and amplitude fitting to the individual AIF may serve as a high SNR AIF incorporating the individual variation. The proposed method was validated using DCE-MRI images of 18 prostate cancer patients. Root mean square error (RMSE) of pAIF from individual AIFs was 0.88 ± 0.48 mM (mean ± SD), but it was reduced to 0.25 ± 0.11 mM after pAIF modification using the proposed method ( p < 0.0001). [ABSTRACT FROM AUTHOR]

To achieve a “green profit” in their businesses, original equipment manufacturers (OEMs) who produce both new and remanufactured products must optimize their pricing and production decisions. They must determine the buyback price and takeback quantity of end-of-life products (i.e., supply) as well as the selling prices and production quantities of new and remanufactured products (i.e., demand). Detailed production plans for matching the supply and demand should be optimized as well. This paper addresses the lack of a model to deal with buyback pricing, sales pricing, and production planning in an integrated manner. Considering their mutual dependence, the total profit cannot be maximized without optimizing all three simultaneously. This paper presents a model for integrated pricing and production planning for a line of new and remanufactured products in a competitive market. A mixed-integer programming model is proposed that assumes a buyback program as a takeback strategy and optimizes the buyback prices, selling prices, and detailed production plans simultaneously. A transition matrix is used to coordinate pricing and production planning reflecting the design of products. The main objective is to maximize the total profit, but the model also considers how much environmental impact can be avoided by remanufacturing. With the help of the model, OEMs can identify an optimal line of new and remanufactured products that can maximize their total profit while achieving environmental-impact saving greater than a target. By enforcing incrementally increasing environmental targets, OEMs can also explore multiple green profit opportunities that can create greater profits and increased environmental-impact savings than producing new products only. To demonstrate the proposed model, this paper presents a case study with a smartphone example. [ABSTRACT FROM AUTHOR]

The usage modeling in life cycle assessment (LCA) is rarely discussed despite the magnitude of environmental impact from the usage stage. In this paper, the usage modeling technique, predictive usage mining for life cycle assessment (PUMLCA) algorithm, is proposed as an alternative of the conventional constant rate method. By modeling usage patterns as trend, seasonality, and level from a time series of usage information, predictive LCA can be conducted in a real time horizon, which can provide more accurate estimation of environmental impact. Large-scale sensor data of product operation is suggested as a source of data for the proposed method to mine usage patterns and build a usage model for LCA. The PUMLCA algorithm can provide a similar level of prediction accuracy to the constant rate method when data is constant, and the higher prediction accuracy when data has complex patterns. In order to mine important usage patterns more effectively, a new automatic segmentation algorithm is developed based on change point analysis. The PUMLCA algorithm can also handle missing and abnormal values from large-scale sensor data, identify seasonality, and formulate predictive LCA equations for current and new machines. Finally, the LCA of agricultural machinery demonstrates the proposed approach and highlights its benefits and limitations. [ABSTRACT FROM AUTHOR]

Introduction We recently validated monoclonal antibody (mAb) 376.96 as an effective carrier for targeted α-particle radioimmunotherapy (RIT) with 212 Pb in ovarian cancer mouse models. In this study, we tested the binding of radiolabeled mAb 376.96 to human pancreatic ductal adenocarcinoma (PDAC) cells and localization in xenografts in immune-deficient mice and evaluated 212 Pb-labeled 376.96 ( 212 Pb-376.96) for PDAC therapy. Methods In vitro Scatchard assays assessed the specific binding of 212 Pb-376.96 to human PDAC3 adherent differentiated cells and non-adherent cancer initiating cells (CICs) dissociated from tumorspheres. In vitro clonogenic assays were used to measure the proliferation of adherent PDAC3 cells and CIC-enriched tumorspheres treated with 212 Pb-376.96 or the irrelevant isotype-matched 212 Pb-F3-C25. Mice bearing patient derived pancreatic cancer Panc039 xenografts were i.v. injected with 0.17–0.70 MBq 212 Pb-376.96 or isotype control 212 Pb-F3-C25, and used for biodistribution and tumor growth inhibition studies. Mice bearing orthotopic PDAC3 xenografts were i.v. co-injected with 99m Tc-376.96 and 125 I–F3-C25 and used for biodistribution studies. Results 212 Pb-376.96 specifically bound to PDAC3 adherent and dissociated tumorsphere CICs; K d values averaged 9.0 and 21.7 nM, respectively, with 10 4 –10 5  binding sites/cell. 212 Pb-376.96 inhibited the clonogenic survival of PDAC3 cells or CICs dissociated from tumorspheres 3–6 times more effectively than isotype-matched control 212 Pb-F3-C25. Panc039 s.c . tumors showed significantly higher uptake of 212 Pb-376.96 (14.0 ± 2.1% ID/g) compared to 212 Pb-F3-C25 (6.5 ± 0.9% ID/g, p < .001) at 24 h after dosing. Orthotopic PDAC3 tumors showed significantly higher uptake of 99m Tc-376.96 (6.4 ± 1.8% ID/g) compared to 125 I–F3-C25 (3.9 ± 0.9% ID/g, p < .05) at 24 h after dosing. Panc039 tumor growth was significantly inhibited by 212 Pb-376.96 compared to 212 Pb-F3-C25 or non-treated control tumors (p < .05). Conclusion Our results provide evidence for the efficacy of B7-H3 targeted RIT against preclinical models of pancreatic ductal adenocarcinoma (PDAC) and support future studies with 212 Pb-376.96 in combination with chemotherapy to potentiate efficacy against PDAC. [ABSTRACT FROM AUTHOR]

Abstract: The promise of product and design analytics has been widespread and more engineering designers are attempting to extract valuable knowledge from large-scale data. This paper proposes a new demand modeling technique, Demand Trend Mining (DTM), for Predictive Life Cycle Design. The first contribution of this work is the development of the DTM algorithm for predictability. In order to capture hidden and upcoming trends of product demand, the algorithm combines three different models: decision tree for large-scale data, discrete choice analysis for demand modeling, and automatic time series forecasting for trend analysis. The DTM dynamically reveals design attribute pattern that affects demands. The second contribution is the new design framework, Predictive Life Cycle Design (PLCD), which connects the DTM and data-driven product design. This new optimization-based model enables a company to optimize its product design by considering the pre-life (manufacturing) and end-of-life (remanufacturing) stages of a product simultaneously. The DTM model interacts with the optimization-based model to maximize the total profit of a product. For illustration, the developed model is applied to an example of smart-phone design, assuming that used phones are taken back for remanufacturing after one year. The result shows that the PLCD framework with the DTM algorithm identifies a more profitable product design over a product life cycle when compared to traditional design approaches that focuses on the pre-life stage only. [Copyright &y& Elsevier]

• Selection of oxygen source is one of the important factors to improve ferroelectricity. • Heavy water (deuterium water, D 2 O) is used as a tracer to pinpoint the origin of hydrogen. • Using a hydrogen-free oxygen source such as O 3 in the atomic layer deposition process improves the ferroelectric properties. Ferroelectric Hf 0.5 Zr 0.5 O 2 (HZO) thin films are mostly deposited with a thickness of less than 10 nm by an atomic layer deposition (ALD) process. Since the oxygen source used in the ALD process affects the residues in the deposited HZO films, the choice of oxygen source can be one of the important factors to improve ferroelectricity. From this point of view, the ferroelectric properties of 10-nm-thick ALD-HZO films according to the oxygen source (O 3 , H 2 O, and D 2 O) were comprehensively analyzed in this study. Heavy water (deuterium water, D 2 O) was used as a tracer to pinpoint the origin of hydrogen that could be derived from unreacted metal precursors or unreacted hydroxyl groups. As a result, it was revealed that the decrease in ferroelectric polarization and increase in leakage current observed in the H 2 O- and D 2 O-based HZO capacitors compared to the O 3 -based HZO capacitor were due to the oxygen source. These results highlight the importance of using O 3 as a hydrogen-free oxygen source in the ALD process to achieve better ferroelectricity. [ABSTRACT FROM AUTHOR]

China's livestock and poultry industries have been experiencing a transformation over recent decades, transitioning from family-size farms to larger, confined animal feeding operations. This development has significantly improved animal production capacity and reduced costs but has also created new challenges to manure management. One important concern is the conflicting interests of environmental protection and economic welfare between policymakers and manure utilization practitioners. In this study, a regional manure utilization chain (RMUC) model was developed by recognizing optimal logistic configurations for manure and manure-based products between animal feeding operations, centralized processing facilities, and crop farms. We then use RMUC model to quantify the impact of management practices to the animal manure utilization chain of Hangzhou, China in the context of sustainable development. The RMUC model implemented an analytical target cascading structure with a multi-objective optimization algorithm to generate a set of Pareto-optimal configurations for discussing the regional economic costs and greenhouse gas (GHG) emission considering the practitioners' operational decisions to the designated manure management practices. A comparative analysis quantified and prioritized the manure management practices (solid/liquid separation, manure reduction strategies); estimated economic and GHG emission credits of manure composition measurements; and indicated economic and GHG emission benefits of electric vehicles and the secondary infrastructures on manure distribution. The results showed sustainable metrics of the manure utilization improvement, including private costs, regional benefits, and the global impact of GHG emissions. The RMUC model demonstrated the compromise between practitioners' interests and public sustainability benefits given a certain level of constraints in decision process. Our analysis is an example of implementing computational models to deal with agricultural systematic problems with social, environmental, and economic concerns. [Display omitted] • Regional manure utilization between animal-crop production systems. • Collaborative optimization structure to recognize manure logistics configuration. • Dynamics of private actions and public sustainable goals in manure utilization chain. • Quantification of sustainable trajectories to animal manure managements. • Decision supports to all practitioners when making policies and management strategies of animal manure recirculation. [ABSTRACT FROM AUTHOR]

This study aimed to investigate if a deep learning model trained with a single institution's data has comparable accuracy to that trained with multi-institutional data for segmenting kidney and cyst regions in magnetic resonance (MR) images of patients affected by autosomal dominant polycystic kidney disease (ADPKD). We used TensorFlow with a Keras custom UNet on 2D slices of 756 MRI images of kidneys with ADPKD obtained from four institutions in the Consortium for Radiologic Imaging Studies of Polycystic Kidney Disease (CRISP) study. The ground truth was determined via a manual plus global thresholding method. Five models were trained with 80 % of all institutional data (n = 604) and each institutional data (n = 232, 172, 148, or 52), respectively, and validated with 10 % and tested on an unseen 10 % of the data. The model's performance was evaluated using the Dice Similarity Coefficient (DSC). The DSCs by the model trained with all institutional data ranged from 0.92 to 0.95 for kidney image segmentation, only 1–2 % higher than those by the models trained with single institutional data (0.90–0.93).In cyst segmentation, however, the DSCs by the model trained with all institutional data ranged from 0.83 to 0.89, which were 2–20 % higher than those by the models trained with single institutional data (0.66–0.86). The UNet performance, when trained with a single institutional dataset, exhibited similar accuracy to the model trained on a multi-institutional dataset. Segmentation accuracy increases with models trained on larger sample sizes, especially in more complex cyst segmentation. • The model trained on a single institution was comparable to that trained with a multi-institutional dataset for kidneys. • The model's accuracy with the single-institutional dataset largely varied over different institutes for cyst segmentation. • The MRI images obtained from a single institute may be utilized for automatic total kidney volume (TKV) determination. [ABSTRACT FROM AUTHOR]

Manure management is a concern for many livestock and poultry producers all around the world. Manure is generated, processed, transported, and utilized in various ways. Manure management requires the coordination of animal feeding operations (AFOs), centralized processing facilities (CPFs), and crop farms. Such a manure utilization chain is more than an individual farm scale, and it is a complex nexus between different production systems. In this study, the manure utilization chain, which recognizes manure management behaviors at different units of a region, was proposed to ensure sustainable manure utilization for distributed animal farms. The goal of this study was to develop a regional manure utilization chain (RMUC) model to minimize annual manure utilization costs by identifying the optimal manure flow patterns among AFOs, CPFs, and crop farms. The model was implemented to evaluate the manure utilization chain in Hangzhou, China. The results showed that the average solid manure logistics cost was CNY 20/ton (1 CNY ~ 0.14 USD), and the average slurry manure utilization cost was CNY 25.4/ton when the manure nutrients were adequately distributed. If the solid manure processing capacities of CPF were optimized, the average solid manure logistics cost would be reduced to CNY 8/ton. This paper also discusses the cost of executing the manure land application setbacks (the minimum distance required between manure application areas and sensitive areas). If Hangzhou followed manure land application restrictions of Illinois, U. S, the slurry manure utilization cost (CNY 65.8/ton) would be 2.59 times greater than the cost (CNY 25.4/ton) in the current scenario. Manure management would be more similar to other waste management and rely on centralized strategy instead of individual farm management. Unlabelled Image • Holistic spatial planning and evaluation of animal manure utilization • Integration of animal-crop production systems • Collaborative optimization structure to recognize manure logistics configuration • Economic impacts of regional manure management and utilization decisions [ABSTRACT FROM AUTHOR]

One of the most significant challenges in existing livestock production is the negative impact of animal waste on the environment. Accumulative manure produced in intensive swine feeding operations (ISFO) cannot be efficiently utilized in a sustainable and economical way. A successful manure management system should maximize the overall economic benefits while satisfying the environmental requirements. To address the manure management problem in a region that lacks adequate land for manure spreading, this project presents a novel modeling approach (Analytic target cascading, ATC) to optimize the design and operation of a swine manure management system by formulating economic objectives, engineering objectives and environmental objectives into individual tasks. This modeling structure simplifies the formulation of a systematic problem, decomposes "all-in-one" model into small tasks, and integrates the professional assessment models into optimal design. We organized the local agricultural information (swine production, crop production) and treatment operational data into parameters and constraints, then optimized the design capacities of main components, operations of manure management and crop management sequentially through updating the targets and responses in each iteration. To explore the viability of the proposed models and solution methodology, a case study in Hangzhou, China (a swine farm with Anaerobic Digestion process + Ectopic Fermentation) is designed using ATC approach. Additionally, the scenario analyses are discussed to provide further insights of opportunities and risks. Our analysis will improve the ability to deal with agricultural systematic problems with social, environmental and economic agreements. [ABSTRACT FROM AUTHOR]

Introduction Novel therapies that effectively kill both differentiated cancer cells and cancer initiating cells (CICs), which are implicated in causing chemotherapy-resistance and disease recurrence, are needed to reduce the morbidity and mortality of ovarian cancer. These studies used monoclonal antibody (mAb) 376.96, which recognizes a B7-H3 epitope expressed on ovarian cancer cells and CICs, as a carrier molecule for targeted α-particle radioimmunotherapy (RIT) in preclinical models of human ovarian cancer. Methods mAb 376.96 was conjugated to the chelate 2-(4-isothiocyanotobenzyl)-1,4,7,10-tetraaza-1,4,7,10-tetra-(2-carbamoylmethyl)-cyclododecane (TCMC) and radiolabeled with 212 Pb, a source of α-particles. In vitro Scatchard assays determined the specific binding of 212 Pb-376.96 to adherent differentiated or non-adherent CIC-enriched ES-2 and A2780cp20 ovarian cancer cells. Adherent ovarian cancer cells and non-adherent CIC-enriched tumorspheres treated in vitro with 212 Pb-376.96 or the irrelevant isotype-matched 212 Pb-F3-C25 were assessed for clonogenic survival. Mice bearing i.p. ES-2 or A2780cp20 xenografts were injected i.p. with 0.17–0.70 MBq 212 Pb-376.96 or 212 Pb-F3-C25 and were used for in vivo imaging, ex vivo biodistribution, and therapeutic survival studies. Results 212 Pb-376.96 was obtained in high yield and purity (>98%); K d values ranged from 10.6–26.6 nM for ovarian cancer cells, with 10 4 –10 5 binding sites/cell. 212 Pb-376.96 inhibited the clonogenic survival of ovarian cancer cells up to 40 times more effectively than isotype-matched control 212 Pb-F3-C25; combining 212 Pb-376.96 with carboplatin significantly decreased clonogenic survival compared to either agent alone. In vivo imaging and biodistribution analysis 24 h after i.p. injection of 212 Pb-376.96 showed high peritoneal retention and tumor tissue accumulation (28.7% ID/g in ES-2 ascites, 73.1% ID/g in A2780cp20 tumors); normal tissues showed lower and comparable uptake for 212 Pb-376.96 and 212 Pb-F3-C25. Tumor-bearing mice treated with 212 Pb-376.96 alone or combined with carboplatin survived 2–3 times longer than mice treated with 212 Pb-F3-C25 or non-treated controls. Conclusion These results support additional RIT studies with 212 Pb-376.96 for future evaluation in patients with ovarian cancer. [ABSTRACT FROM AUTHOR]