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In geotechnical engineering, ground movement caused by excavations is a challenging issue. The excessive differential settlement generated by soil movement induced by wall deflection may cause damage to nearby structures. A detailed literature review on the general deformation behavior of deep excavation support systems is presented in this paper. Many factors, such as normalized horizontal deflection (δh-max/He%), vertical displacement (δv-max/He%), δvmax/δhmax ratio, settlement influence zone (Do), etc., can play significant roles in describing the deflection behavior of the excavation system. A descriptive analysis of the reviewed data was carried out. The concluded δh-max/He% values range between 0.17 to 1.5, with a mean value of 0.58 for soft clay, while in the case of sands and stiff clay soils δh-max/He% value ranges between 0.07 to 0.40, with a mean value of 0.20. δv-max/He% values range between 0.13 to 1.10, with a mean value of 0.49 for soft soil, while its value ranges between 0.02 to 1.10, with a mean value of 0.24 in the case of sands and stiff clay soils. The settlement influence zone (Do) reaches a mean distance of 2.3He, which falls within Do=1.5-3.5He in the case of soft clays, while Do reaches a mean distance of 2.0He and 3.0He in the case of sands and other stiff clay soils, respectively. The relationship between system stiffness and excavation-induced wall and ground movements was discussed. Unfortunately, the literature review offers limited data regarding system stiffness, the 3-D nature of excavation support systems, excavation processes, and time effects.

Abstract The analysis of the current situation of water distribution networks is based on all the alternatives that are technically feasible and implemented from the decision-maker's point of view. Taking the water distribution network of Tourville city as a case study, this paper combines a set of Decision Support Systems (DSS) including the Geographic Information Systems (GIS), multi-criteria analysis and hydraulic simulation models to establish a multi-criteria decision-making aid method for renovation and rehabilitation of water distribution networks. This combination creates an expert management system based on multi-criteria decision making that strengthens the optimization of the management of water distribution networks in terms of renovation and rehabilitation. After dividing the water distribution network into three emergency levels, it was concluded that 26% of the network is in urgent need of rehabilitation.

Objective: Numerous communication support systems based on reminiscence therapy have been developed. However, when using communication support systems, the emotional assessment of older people is generally conducted using verbal feedback or questionnaires. The purpose of this study is to investigate the feasibility of using Electroencephalography (EEG) signals for automatic emotion recognition during RT for older people.Participants: Eleven older people (mean 71.25, SD 4.66) and seven young people (mean 22.4, SD 1.51) participated in the experiment.Methods: Old public photographs were used as material for reminiscence therapy. The EEG signals of the older people were collected while the older people and young people were talking about the contents of the photos. Since emotions change slowly and responses are characterized by delayed effects in EEG, the depth models LSTM and Bi-LSTM were selected to extract complex emotional features from EEG signals for automatic recognition of emotions.Results: The EEG data of 8 channels were inputted into the LSTM and Bi-LSTM models to classify positive and negative emotions. The recognition highest accuracy rate of the two models were 90.8% and 95.8% respectively. The four-channel EEG data based Bi-LSTM also reached 94.4%.Conclusion: Since the Bi-LSTM model could tap into the influence of “past” and “future” emotional states on the current emotional state in the EEG signal, we found that it can help improve the ability to recognize positive and negative emotions in older people. In particular, it is feasible to use EEG signals without the necessity of multimodal physiological signals for emotion recognition in the communication support systems for reminiscence therapy when using this model.

Decision support technology has become a key link in modern information strategy. With the deepening of research, introduced expert systems have been introduced into decision support systems. In this way, decision support systems gradually become more uncertain and capable of handling uncertainties. The development direction of decision support system is typically based on qualitative analysis. Intelligent decision support system is a system that combines decision support system with artificial intelligence technology. This study attempts to assess in an innovative way the relationship between financing constraints, entrepreneurship, and agricultural firms. The most recently proposed intelligent decision support system, AI-assisted Intelligent Decision Support System (AIIDSS), is used to predict the impact of entrepreneurship on corporate performance. The paper constructs an entrepreneurship index from five aspects: innovation, competitiveness, human capital accumulation, management capability, and adventurous spirit. The method intends to construct the Kaplan–Zingales (KZ) index to evaluate financing constraints. Through an empirical study, it was found that entrepreneurship can significantly promote the growth of listed agricultural companies. The study can drastically reduce the difficulties involved in financing constraints normally faced by agricultural companies. The impact paths include increasing agricultural company operating cash flow, improving stock liquidity, and increasing debt financing. The research suggests that if listed agricultural companies are to improve financing constraints, entrepreneurs must improve their own competitiveness and management capabilities. This will help in reasonably controlling research and development investment besides the impulse to take risks. As the growth of an enterprise relies on considering the determinants of financing constraints, this research provides an effective investigation technique. Moreover, the findings of the study will help entrepreneurs, particularly agricultural companies, to bear most of the risks and to avail most of the opportunities.

Clinical Decision Support Systems (CDSSs) signify the framework shift in the medical sector in the modern age. CDSSs are utilized in augmenting healthcare facilities in the process of making complex clinical decisions. Since the first application of CDSSs in the 80s, the framework has witnessed significant transformation. The frameworks are now administered through electronic healthcare records with complex capacities. Irrespective of these complex advancements, there are existing questions concerning the impacts of CDSSs on service providers, healthcare costs, and patients’ records. There are many published texts concerning the success stories of CDSSs, but significant setbacks have proved that CDSSs are not without any potential risks. In this research, we provide critical analysis on the application of CDSSs in clinical setting, integrating various forms, present application cases with proven effectiveness, potential harms and common pitfalls. We therefore conclude with evidence-centered recommendation for mitigating the issues of CDSSs maintainability, evaluation, implementation and designing.