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Inflammatory bowel disease increases the risks of human colorectal cancer. In this study, the effects of Salvia miltiorrhiza extract (SME) on chemically-induced colitis in a mouse model were evaluated. Chemical composition of SME was determined by HPLC analysis. A/J mice received a single injection of AOM 7.5 mg/kg. After one week, these mice received 2.5% DSS for eight days, or DSS plus SME (25 or 50 mg/kg). DSS-induced colitis was scored with the disease activity index (DAI). Body weight and colon length were also measured. The severity of inflammatory lesions was further evaluated by colon tissue histological assessment. HPLC assay showed that the major constituents in the tested SME were danshensu, protocatechuic aldehyde, salvianolic acid D, and salvianolic acid B. In the model group, the DAI score reached its highest level on Day 8, while the SME group on both doses showed a significantly reduced DAI score (both p < 0.01). As an objective index of the severity of inflammation, colon length was significantly shorter in the model group than the vehicle group. Treatment with 25 and 50 mg/kg of SME inhibited the shortening of colon in a dose-related manner (p < 0.05 and p < 0.01, respectively). SME groups also significantly reduced weight reduction (p < 0.05). Colitis histological data supported the pharmacological observations. Thus, Salvia miltiorrhiza could be a promising candidate in preventing and treating colitis and in reducing the risks of inflammation-associated colorectal cancer.

Clinical decision support systems rooted in machine learning have grabbed much attention due to the use of huge datasets for performing analysis and accurate diagnosis of diseases. Our recent research contributed to the existence of research by having a complete assessment of decision support systems used within the clinical context to diagnose cardiovascular diseases. The researchers separately compiled and analyzed aspects related to clinical decision support systems (CDSS) for cardiac disorders. The primary intention of this systematic review is to point out and critically evaluate the Machine Learning (ML)-based PubMed, Clinical Trial.gov, and Cochrane libraries, an analysis of 400 different studies (1990 to 24 January 2021) is made in which 25 papers met the inclusion criterion. The technique incorporated here NVIVO 10 software gathers and analyzes data. Search filters such as “Machine Learning”, “Clinical decision support systems”, and “heart disease prediction” are used for heart diseases. The results were obtained within clinics in 55% of such investigations, whereas experimental setups were seen in 25% of the studies. The remaining 20% research did not report on the methodologies’ relevance and effectiveness in healthcare situations. The given findings point to CDSS ability to provide accurate interpretations and accurate visual peer representations. The key finding of this analysis report is that although CDSS might be employed in clinical settings it needs to be properly trained with non-ambiguous real-time clinical data. The creation of fuzzy complexes that can conclude on clinical deviations many of which are practical and imposed and tracked in real time is a major focus of future Machine Learning-based CDSS research.

According to the literature about customer satisfaction and loyalty, it is possible to define knowledge-based system to support management decision-making in the organizations. Nevertheless, the problem as to how much the context impacts on correlation has not been investigated in the literature. This paper focuses on developing of Decision Support System (DSS) taking into account correlations among statistical factors, i.e., expert knowledge, and customers’ opinions depending on several contextual features, e.g., culture, location, in order to build context-sensitive simulation environment. The proposed work defines a general system design workflow to tailor knowledge-based DSS by using a fuzzy model to quantify correlations among variables in a given context. We explore ontologies to represent correlations among statistical factors, e.g., Calculative Commitment, Quality of Service. We apply fuzzy data analysis techniques to train fuzzy classifier on the customer’s opinions collected by survey. Finally, synergistic usage of Description Logic and Fuzzy Theory allows the implementation of a simulation environment that supports the management team to tune business strategies. The framework has been instantiated for a case study to support public administration at the University of the Salerno.

The high level of complexity of tourism supply chain and the inherent risks that exist in the demand and supply of resources are viewed as major limiting factors in achieving high level performance. Though emerging literature on risk management in tourism industry or its equivalent exists, progress in this area is uneven, as most research focuses on this problem from the traditional single business risk management perspective, without considering the entire range of different suppliers involved in the provision and consumption of tourism products. This study applies risk management theory to a new research perspective, which is tourism supply chain management (SCM). This paper develops a framework for the design of a multi-agent-based decision support system (DSS) based on multi-agent theory and technique, in order to manage disruptions and mitigate risks in tourism supply chain.

In the present competitive and disruptive arena, big data analytics has emerged as a revolutionary approach enabling sound decision making leading to enhanced organisational performance. However, extant studies on big data analytics in organisational perspective is limited, specifically in the case of SMEs. The tenet is that organisations bank on superior decisionmaking capabilities through data-driven insights, like big data analytics. So, it is imperative to explore the intertwined themes from organisational perspectives. In this backdrop, this chapter addresses the key concerns with focus on the enablers and deterrents in big data and its analytics in the context of SMEs and mainly to decipher the ways it contributes to their enhanced organisational performance It investigates the moderating role of big data analytics on the relationship between decision making rationality and organisational performance. So, it adopts a crosssectional research approach, based on primary data collected from the SMEs firms in Delhi NCR. The key finding of the study emanating from the regression and interaction effect of big data analytics reinforce the use of big data analytics as the moderator, which affects the relationship between decision making and organisational performance. Thus, it reinstates that the use of rational decision making model in the organisation to result in higher performance. The chapter thus presents important insights for developing data-driven insights using the BDA in context of SMEs for driving organisational performance.

In modern manufacturing companies, new product design is a matter of great importance that can directly affect their profitabilities. Nowadays, customers demand higher quality products, lower prices, and better performance in delivery time. The intense competition of companies in global markets stimulates a significant change in the way products are designed, manufactured and delivered. These situations are forcing to designers and manufacturing engineers to consider the use of tools for the process of new product design. In this paper, we propose an intelligent decision support system (DSS) based on a proposed distributed multi-agent architecture. This DSS implements a New Product Design Multicriteria Methodology based on consumer preferences. The system is composed by elements of Marketing Decision Support Systems, agent technologies, multi-objective evolutionary algorithms and multicriteria methods. Finally, we show a Marketing Intelligent Decision Support System prototype to support new product design decisions which is a combination of MDSS, agent technologies, multi-objective evolutionary algorithms and multicriteria ELECTRE III method.

Virtual reality, a new paradigm for relationship between humans and computers, has been recently well-known and currently investigated for practical use in the various industrial fields. Using three-dimensional computer graphics, interactive devices, and high-resolution display, a virtual world can be realized in which one can pick up imaginary objects as if they were physical world. Using this technology, Matsushita Electric Works, Ltd. has been developing several application systems for industrial use since 1990. This paper details three VR application systems operating in the real world: Virtual Space Decision Support System employing Kansei Engineering which is applied for production and sales mainly in the system kitchen business, a telepresence robot system employing semi-autonomous mobile function which is utilized for security field and a low-cost VR system employing physiological feedback mechanism which is used for health care field.