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The Kaplan-Meier curves of OS for the reclassified response and non-response patients in original groups of TCGA

Gene expression profile of genes in GPS-64

The Kaplan-Meier curves of DFS or OS for the reclassified response and non-response patients in original groups of GSE72970.

This paper proposes a group decision making model with individual preferences to investigate the multicriteria ABC inventory classification problem. We first propose a preliminary group decision matrix by means of identifying the individual preference among the classification criteria as a typical decision maker; then analyze the endogenous individual preference structure in terms of exploring the preferential difference and preferential priority, a revised group decision matrix is therefore presented; thirdly, make the use of SMAA-2 to derive the holistic acceptability index for classifying the SKUs. A satisfaction index is developed to manifest the merits of proposed method. A numerical illustration using the data drawn from previous studies is performed to indicate the applicability and feasibility of our method.

This paper proposes a composite indicator methodology to rank National Olympic Committees (NOCs) by considering only the medals won at both Summer and Winter Olympic games. The lexicographic and sum ranking systems at both Summer and Winter Olympics are comprehensively investigated to create the preliminary decision matrix for constructing composite indicators. Then we investigate the preferential difference and preferential priority for better reflecting the preference structures of the sub-indicators, and thereby obtain a revised decision matrix. The SMAA-2 is employed to implement the composite indicator construction based upon the revised decision matrix. An empirical study is performed to demonstrate the applicability of our methodology, using the data set that is composed of 28 NOCs who have participated in both the Rio 2016 Summer Olympic Games and the PyeongChang 2018 Winter Olympics. The Spearman’s rank correlation coefficient and the satisfaction index are calculated to justify the merits of the proposed methodology.

Social manufacturing is conceptualised as a new type of networked manufacturing paradigm that supports the organisation of socialised manufacturing resources (SMRs) to satisfy the growth of persona...

PurposePhysical gatherings at social events have been found as one of the main causes of COVID-19 transmission all over the world. Smartphone has been used for contact tracing by exchanging messages through Bluetooth signals. However, recent confirmed cases found in venues indicated that indirect transmission of the causative virus occurred, resulting from virus contamination of common objects, virus aerosolization in a confined space or spread from inadequate ventilation environment with no indication of human direct or close contact observed.Design/methodology/approachThis paper presents a novel cyber-physical architecture for spatial temporal analytics (iGather for short). Locations with time windows are modeled as digital chromosomes in cyberspace to represent human activity instances in the physical world.FindingsResults show that the high spatial temporal correlated but indirect tracing can be realized through the deployment of physical hardware and spatial temporal analytics including mobility and traceability analytics. iGather is tested and verified in different spatial temporal correlated cases. From a management perspective of mobilizing social capacity, the venue plays not only a promotion role in boosting the utilization rates but also a supervision-assisted role for keeping the venue in a safe and healthy situation.Social implicationsThis research is of particular significance when physical distancing measures are being relaxed with situations gradually become contained. iGather is able to help the general public to ease open questions: Is a venue safe enough? Is there anyone at a gathering at risk? What should one do when someone gets infected without raising privacy issues?Originality/valueThis study contributes to the existing literature by cyber-physical spatial temporal analytics to trace COVID-19 indirect contacts through digital chromosome, a representation of digital twin technology. Also, the authors have proposed a venue-oriented management perspective to resolve privacy-preserving and unitization rate concerns.

Conventional DEA models tend to allocate the fixed resources to multiple decision-making units (DMUs) and treat the allocated resource as an extra input for every single DMU. However, the existing DEA resource allocation (DEA-RA) methods are applicable exclusively to the DMUs with exact values of inputs and outputs. A lack of precision for the input or output data of DMUs, such as the interval data, would cause a failure of the existing methods to allocate resources to DMUs. In order to resolve this problem, three DEA-RA models are proposed in this paper for different scenarios of decision-making. All of the proposed DEA-RA models are based on a set of common weights. Finally, the proposed models are empirically tested and validated through three examples. As revealed by the results, our proposed models are capable of providing a more fair and practical initial allocation scheme for decision makers.

This paper is concerned with proposing a new mechanism to re-construct the published composite indicators that are conventionally aggregated in terms of equal weighting scheme, by means of taking all possible preferences among the indicators into account. Regarding to each preference, we apply a sophisticated mathematical transformation to formulate an interval metric. Inspired by the collective choice theory that integrates individual preference into social preference, an interval decision matrix is therefore formulated with preference as column. An interval-valued TOPSIS procedure in conjunction with Shannon entropy objective weights is applied to re-build composite indicators. The proposed methodology is illustrated by modifying the value measure of health systems. The Spearman’s rank correlation coefficients between Access, Satisfaction, Efficiency and two versions of value measure are calculated to perform the comparisons and demonstrate the superiority of our methodology.

Bevacizumab is the molecular-targeted agent used for the antiangiogenic therapy of metastatic colorectal cancer. But some patients are resistant to bevacizumab, it needs an effective biomarker to predict the prognosis and responses of metastatic colorectal cancer (mCRC) to bevacizumab therapy. In this work, we developed a qualitative transcriptional signature to individually predict the response of bevacizumab in patients with mCRC. First, using mCRC samples treated with bevacizumab, we detected differentially expressed genes between response and nonresponse groups. Then, the gene pairs, consisting of at least one differentially expressed gene, with stable relative expression orderings in the response samples but reversal stable relative expression orderings in the nonresponse samples were identified, denoted as pairs-bevacizumab. Similarly, we screened the gene pairs significantly associated with primary tumor locations, donated as pairs-LR. Among the overlapped gene pairs between the pairs-bevacizumab and pairs-LR, we adopted a feature selection process to extract gene pairs that reached the highest F-score for predicting bevacizumab response status in mCRC as the final gene pair signature (GPS), denoted as 64-GPS. In two independent datasets, the predicted response group showed significantly better overall survival than the nonresponse group (P = 6.00e−4 in GSE72970; P = 0.04 in TCGA). Genomic analyses showed that the predicted response group was characterized by frequent copy number alternations, whereas the nonresponse group was characterized by hypermutation. In conclusion, 64-GPS was an objective and robust predictive signature for patients with mCRC treated with bevacizumab, which could effectively assist in the decision of clinical therapy.

Digital twin models are computerized clones of physical assets or systems and have attracted much attention from academia and industries. Digital twin applications focus on smart manufacturing syst...

The progression of cancer is accompanied by the acquisition of stemness features. Many stemness evaluation methods based on transcriptional profiles have been presented to reveal the relationship between stemness and cancer. However, instead of absolute stemness index values—the values with certain range—these methods gave the values without range, which makes them unable to intuitively evaluate the stemness. Besides, these indices were based on the absolute expression values of genes, which were found to be seriously influenced by batch effects and the composition of samples in the dataset. Recently, we have showed that the signatures based on the relative expression orderings (REOs) of gene pairs within a sample were highly robust against these factors, which makes that the REO-based signatures have been stably applied in the evaluations of the continuous scores with certain range. Here, we provided an absolute REO-based stemness index to evaluate the stemness. We found that this stemness index had higher correlation with the culture time of the differentiated stem cells than the previous stemness index. When applied to the cancer and normal tissue samples, the stemness index showed its significant difference between cancers and normal tissues and its ability to reveal the intratumor heterogeneity at stemness level. Importantly, higher stemness index was associated with poorer prognosis and greater oncogenic dedifferentiation reflected by histological grade. All results showed the capability of the REO-based stemness index to assist the assignment of tumor grade and its potential therapeutic and diagnostic implications.

As a new composite indicator for capturing the value delivered by health systems of developing and developed economies, the Value Measure is the arithmetic average of access, satisfaction and efficiency, which are all made up of various components and scaled from 0 to 100. In light of the possibility of compensation associated with the arithmetic average, this paper proposes a new methodology to modify the Value Measure in a social choice framework. Specifically, we first take into account all possible individual preferences among access, satisfaction and efficiency; then calculate the pessimistic (worst case) and optimistic (best case) results, which are balanced by a “coefficient of optimism”; the CRITIC method is lastly employed to determine a set of common weights associated with individual preferences, for modifying the Value Measure released by Royal Philips. The superiority of proposed methodology is verified by means of computing and comparing Spearman’s rank correlation coefficients. The validity of our methodology is demonstrated using the data drawn from Future Health Index 2018.

Developed by the World Economic Forum in collaboration with Accenture, the energy architecture performance index is constructed by means of calculating the arithmetic average of economic growth and development, environmental sustainability and energy access and security. However, this scheme is highly affected by the extreme values, not the Pareto optimal, and loses association with weights. This paper addresses these issues in terms of taking into account all possible individual preferences among the above three indicators, then aggregating them into a collective choice result. Specifically, an individual preference is characterized by an importance order of the indicators, which can be mathematically denoted by a set of ranked weights. The pessimistic and optimistic results under certain individual preference are obtained in closed form to encompass all possible weighting scenarios. An ideal point approach is employed to implement the aggregation. An empirical study with 23 emerging countries is presented to demonstrate the effectiveness of our methodology.

In practice, a variety of composite indicators are created using the arithmetic average of different sub-indicators. However, this scheme is typically criticized of the possibility of compensation. For this reason, this paper re-constructs these composite indicators by means of a new methodology. Comprehensive individual judgements among the sub-indicators have been considered to determine the maximum total utilities. Then the best–worst method is introduced to determine the preference associated with various individual judgements. An illustration of Value Measure of health systems is presented to demonstrate the validity and usefulness of our methodology.

Histological grading (HG) is an important prognostic factor of colorectal adenocarcinoma (CRAC): the high-grade CRAC patients have poorer prognosis after tumor resection. Especially, the high-grade stage II CRAC patients are recommended to receive adjuvant chemotherapy. Due to the subjective nature of HG assessment, it is difficult to achieve consistency among pathologists, which brings patients uncertain grading outcomes and inappropriate treatments. We developed a qualitative transcriptional signature based on the within-sample relative expression orderings (REOs) of gene pairs to discriminate high-grade and low-grade CRAC. Using the stage II-III CRAC samples, we detected gene pairs with stable REOs in the high-grade samples and reversal stable REOs in the low-grade samples, and retained the gene pairs whose specific REO patterns were significantly associated with the disease-free survival of patients by univariate Cox regression model. Then, we used a forward-backward searching procedure to extract gene pairs with the highest concordance index as the final grading signature. Finally, 9 gene pairs (9-GPS) were developed to divide CRAC patients into high-grade and low-grade groups. With the signature, there were more differential expression characteristics between reclassified high-grade and low-grade groups. Significant difference of prognosis between the classified two group patients could be seen in four independent datasets. Additionally, genomic analyses showed that the classified high-grade groups were characterized by hypermutation while classified low-grade groups were characterized by frequent copy number alternations. In conclusion, the 9-GPS can provide an objective and robust grading assessment for CRAC patients, which could assist clinical treatment decision.

BackgroundMicrosatellite instability (MSI) accounts for about 15% of colorectal cancer and is associated with prognosis. Today, MSI is usually detected by polymerase chain reaction amplification of specific microsatellite markers. However, the instability is identified by comparing the length of microsatellite repeats in tumor and normal samples. In this work, we developed a qualitative transcriptional signature to individually predict MSI status for right-sided colon cancer (RCC) based on tumor samples.ResultsUsing RCC samples, based on the relative expression orderings (REOs) of gene pairs, we extracted a signature consisting of 10 gene pairs (10-GPS) to predict MSI status for RCC through a feature selection process. A sample is predicted as MSI when the gene expression orderings of at least 7 gene pairs vote for MSI; otherwise the microsatellite stability (MSS). The classification performance reached the largest F-score in the training dataset. This signature was verified in four independent datasets of RCCs with the F-scores of 1, 0.9630, 0.9412 and 0.8798, respectively. Additionally, the hierarchical clustering analyses and molecular features also supported the correctness of the reclassifications of the MSI status by 10-GPS.ConclusionsThe qualitative transcriptional signature can be used to classify MSI status of RCC samples at the individualized level.

As a high-energy-consumption and high-CO2-emission industry in China, the transportation sector has been under increasing pressure to improve its performance. This paper develops a novel parallel DEA approach to measure Chinese transportation sector’s energy and environmental performance (EEP) over all possible weights, which is to avoid the risk of using the extreme or the most favorable weights in performance evaluation. In our method, the transportation sector is consisted of two parallel subsystems (passenger transportation and freight transportation) with shared inputs and undesirable shared outputs. All possible weights are considered in the EEP evaluation, then the EEP of a transportation sector is represented by a ranking interval. Finally, the proposed approach is applied to the transportation sectors in 30 Chinese provinces. Results show that the best and the worst ranking of most provinces vary greatly. Besides, the EEP of most provinces is hard to dominate others strictly, but the general tendency is the EEP of eastern provinces better than western provinces. Furthermore, the EEP difference of some adjacent provinces with similar features is distinct. These findings are not all the same as previous studies, which verifies the necessity of considering all possible weights in performance evaluation. Therefore, our method provides a new point of view in performance evaluation and can give more robust results for decision makers.

Firms with omni-channel multi-echelon distribution networks (OMDC) integrate all of their sale channels and distribution echelons to achieve an effective matching of multi-type orders and a shared inventory. However, the high operational cost caused by insufficient use of inventory resources and unreasonable order allocation restricts the sustainable development of firms. To this end, the joint decisions of inventory optimization (IO) and order allocation (OA) are explored to achieve cost-efficient and sustainable omni-channel operations. Specifically, from the perspective of IO, an inventory integrated policy is proposed for online and offline inventory sharing within nodes and lateral transshipment between nodes; from the aspect of OA, an order allocation mechanism is designed for the minimum cost under the influence of multiple factors (inventory replenishment, holding, order delivery distance and time) among different echelon nodes. A joint optimization model of an inventory and orders is therefore developed and is subsequently solved using the Genetic Algorithm (GA). Results obtained show that the proposed strategy has a better performance with regards to operational cost and customer service level and is also more sustainable than alternative strategies. The proposed joint strategy provides a relatively novel approach to realize flexible and sustainable omni-channel distribution systems.

Nowadays sustainability has received global attention in supply chain. To evaluate the sustainability level, environmental, social, and governance (ESG) reporting is widely adopted especially for the listed companies. However, due to the lack of data authentication, consistency, and transparency, the ESG-based sustainability evaluation is still inadequate. To address those challenges, this paper proposes a blockchain-based ESG reporting framework for facilitating the sustainability evaluation of listed company. Firstly, a fact-telling blockchain gateway is designed to facilitate the raw data authentication issues. It plays the role of light node to transfer the data of smart infrastructure/devices to the blockchain network, which satisfies both privacy and transparency. Secondly, a versioning smart contract mechanism is developed to verify the consistency between the raw data and the final ESG report. Thirdly, a token-based sustainability evaluation mechanism is used to evaluate the behaviors of listed company in the sustainable supply chain. The discussion analyzes the benefits and potential obstacles of utilizing the proposed framework in the ESG reporting preparation, generation, and publication. The results of this study contribute a reliable approach to facilitate the sustainability-level evaluation of listed company.

We consider price competition with a linear demand function and compare three scenarios. To compare the impact of revenue-sharing or wholesale price contracts on manufacturers, retailers and supply chains. In the three common cases, the maximum profit of retailers, manufacturers and supply chains was analyzed and compared. We consider the interaction between the various links of the supply chain, and through different strategies, achieve the optimal decision of wholesale price and order quantity. By comparing the profit trends between manufacturers, retailers and supply chains, and the difference in profits, we can get different strategies for the supply chain in the two competing supply chains. And the impact of the profits of retailers and manufacturers is significant.

In this paper, a multi-period buffer space hedging coordination between a building contractor (BC) and a logistics provider (LP) is investigated. In order to facilitate the application of the construction lead-time hedging (CLTH) strategy, adopted by the BC, extra buffer space needs to be reserved at LP’s intermediate warehouse for contingency usage. This strategy is defined as ‘buffer space hedging (BSH)’ and it increases the pressure of LP on involving extra storage and maintenance cost. Two coordination mechanisms are adopted for solving this BSH problem. One scheme is by introducing a cost-sharing term. A Nash game model is studied to find the individual optimal decisions. Another scheme is a cooperative game model with proper side-payments. We show that adjusting the BSH amount for each review period benefits the supply chain. Both coordination mechanisms enable a win–win outcome. Especially, if system resources are sufficient, the cooperative game outperforms the Nash game. Numerical experiments fur...

Published since 2006 on a biennial basis by the Sustainable Society Foundation, the Sustainable Society Index (SSI) calculates the sustainability level of various geographical entities (e.g., countries or regions) by aggregating three dimensions of wellbeing, namely, Human Wellbeing, Environmental Wellbeing and Economic Wellbeing, with equal weights. An aggregation with equal weights is likely to overlook the discriminating power of each dimension of the SSI. In this study, we propose the use of ranked weights and Shannon entropy to overcome this shortcoming of the SSI. Specifically, we take into account all possible preferences among various dimensions, use a sophisticated mathematical transformation to calculate the least and most favorable sustainability levels under certain preferences, adopt the geometric mean values of the extreme levels to formulate a new preference-as-column decision matrix, and employ Shannon entropy to determine a set of common weights associated with each preference. We then calculate the Spearman's rank correlation coefficients to re-evaluate the published SSI and demonstrate the added-value of our logic. A case study of regional SSI reveals the applicability and superiority of the proposed method.

A variety of published composite indicators, i.e., Energy Trilemma Index and Sustainable Society Index, are commonly aggregated with equal weights. However, this plausible scheme is criticized as eclecticism and ignores the discriminating power of the different indicators. Differing from the traditional methods that assign weights to each indicator for the purpose of aggregation, this paper proposes a new mechanism to construct composite indicators using ranked weights and stochastic multicriteria acceptability analysis. More specifically, this study comprehensively consider all possible preferences among the indicators. Under each preference, we develop a sophisticated mathematical transformation to calculate the least and most favorable scores of each entity, which formulates the lower and upper bounds of the intervals. Then an interval decision matrix, alternatively described as a stochastic decision problem, is formulated to construct the composite indicators. Holistic acceptability indices are generated and regarded as a new composite indicator, which is capable of providing a comprehensive and robust composite indicator with more discriminating power. We apply the proposed method to modify the regional sustainable society index and present the obtained results and comparisons.

The World Energy Council annually releases the Energy Trilemma Index to measure the country-level energy performance. However, the preferences among the trilemma can change from country to country, which always is an undetermined issue and full of controversy. This paper comprehensively considers all possible preferences, and formulates interval evaluation results under certain preference. Such formulations are motivated by the observations that it is difficult to reach a consensus about the weights associated with the trilemma, since different weight elicitation methods inevitably produce different weighting schemes. Therefore, we propose an interval decision making problem and apply a Stochastic Multicriteria Acceptability Analysis to present a holistic measurements of the country-specific energy performance. This differs from the conventional wisdom that assigns exact values to corresponding weights, but explores the weight space to make each country the most preferred one. Our analysis is demonstrated by measuring the energy performance of top 10 countries based on 2015 Energy Trilemma Index.

This paper extends the Ng-model [Ng, 2007] for multiple criteria ABC inventory classification based upon Shannon entropy. The proposed approach determines the common weights associated with all criteria importance rankings, and provides a comprehensive scoring scheme by aggregating all rankings of the criteria importance. A numerical illustration is presented to compare the model with previous studies.

This paper introduces a new procedure for handling the multicriteria ABC inventory classification problem using stochastic multicriteria acceptability analysis. All possible preferences among the evaluation criteria have been considered. Due to the fact that even under certain preference, it is difficult to reach a group consensus on the exact weight values along with each criterion, we calculate preference-specific intervals under each preference and then formulate a stochastic decision-making problem. To tackle this problem, we consider different distribution functions of the intervals and then compute the holistic acceptability indices to classify stock-keeping units. The results derived from our method are compared to the previous results to show the robustness and superiority of our method.

This paper combines the theory of teams and data envelopment analysis (DEA) to design a mechanism to optimally allocate resources in public healthcare. A statutory authority and the public hospitals under its governance are interpreted as a team, the members of which seek to operate efficiently under the shared institutional constraint that public healthcare is a public good. The individual public hospital exploits DEA to maximize own-payoff, subject to the team-condition that the payoff of each other public hospital does not fall and thereby subtract from the external effects created by the public supply of healthcare. The resulting team-DEA solution, which is shown to be both an individually-efficient and team-satisficing equilibrium and to be computable in terms of a convergent algorithm, can then be applied by the authority to determine the optimal allocation of resources in public healthcare. A case based on Chinese data is presented to illustrate the team-DEA model’s ready operationalization and computation.

This paper discusses schemes for allocation of emission permits (AEP) among a group of manufacturing companies, aiming at controlling the total emissions of the group while maintaining efficient production. Game theory and data envelopment analysis have been integrated in different ways for constructing two variants of AEP models. The first situation is where all members in the group are cooperative and a reasonable permit allocation scheme maximizes the overall payoff of the group. The second situation is where group members are non-cooperative and each member makes every effort to selfishly maximize its own payoff. The decision maker allocates permits to firms according to their non-cooperative game equilibrium scores. Proposed models are applied to study a group of paper mills to analyze their payoffs. The results show that the methods proposed in this work can provide reasonable allocation results for all firms. In addition, although our allocation methods adopt the principle of maximizing the payoff of the firm, the efficiency of each firm from current output and input levels is still a factor that determines the permit allocation.

Histological grading (HG) is an important prognostic factor of colorectal adenocarcinoma (CRAC): the high-grade CRAC patients have poorer prognosis after tumor resection. Especially, the high-grade stage II CRAC patients are recommended to receive adjuvant chemotherapy. Due to the subjective nature of HG assessment, it is difficult to achieve consistency among pathologists, which brings patients uncertain grading outcomes and inappropriate treatments. We developed a qualitative transcriptional signature based on the within-sample relative expression orderings (REOs) of gene pairs to discriminate high-grade and low-grade CRAC. Using the stage II-III CRAC samples, we detected gene pairs with stable REOs in the high-grade samples and reversal stable REOs in the low-grade samples, and retained the gene pairs whose specific REO patterns were significantly associated with the disease-free survival of patients by univariate Cox regression model. Then, we used a forward-backward searching procedure to extract gene pairs with the highest concordance index as the final grading signature. Finally, 9 gene pairs (9-GPS) were developed to divide CRAC patients into high-grade and low-grade groups. With the signature, there were more differential expression characteristics between reclassified high-grade and low-grade groups. Significant difference of prognosis between the classified two group patients could be seen in four independent datasets. Additionally, genomic analyses showed that the classified high-grade groups were characterized by hypermutation while classified low-grade groups were characterized by frequent copy number alternations. In conclusion, the 9-GPS can provide an objective and robust grading assessment for CRAC patients, which could assist clinical treatment decision.

The clinical applicability of the whole-exome sequencing (WES) in estimating tumor mutational burden (TMB) is currently limited by high cost, time-consuming and tissue availability. And given to the differences in the mutational landscapes among different types of cancer, we aimed to develop a cancer-specific signature to estimate TMB for right-sided colon cancer patients (RCC). Using WES data of 315 RCC patients, we identified the exons in which the number of mutational sites of the coding DNA sequences associated with TMB through linear regression analysis. Then, among these exons, we extracted a signature composed by 102 exons (~0.13 Mbp) through a heuristic selection procedure. The TMB estimated by the signature was highly correlated with those calculated by WES in the discovery dataset (R2=0.9869) and three independent validation datasets (R2=0.9351, R2=0.8063 and R2=0.9527, respectively). And the performance of the signature was superior to a colorectal-specific TMB estimation model contained 22 genes (~0.24 Mbp). Moreover, between TMB-high and TMB-low RCC patients, there were significantly differences in the frequencies of microsatellite instability status, CpG island methylator phenotype, BRAF, KRAS and POLE/POLD1 mutation status (p

Additional file 2: Table S1. The mutation status of the four MMR genes of the five signature-disconfirmed RCC in the TCGA dataset. Table S2. The performances of the 10-GPS in LCCs and CRCs of the independent datasets. Table S3. The Composition of 6-GPS. Table S4. The performances of the 6-GPS in LCCs of the independent datasets.(DOCX 18 kb)