DHHFL-MABAC Approach based on DHHFLDM-LCME for Multi-criteria Group Decision Making Involving Sewage Treatment Company Selection

With the continuous acceleration of urban construction and development, ecological governance has become an important part of our green life. Therefore, how to select the appropriate governance company in the complex decision-making environment is very important. A double hierarchy hesitant fuzzy linguistic multi-attributive border approximation area method (DHHFL-MABAC) is proposed in this paper, which is based on distance measure and comprehensive weight. DHHFL-MABAC method not only considers the potential loss, but also has simple calculation and stable results. The double hierarchy hesitant fuzzy linguistic distance measure based on least common multiple (DHHFLDM-LCME) is proposed, which reduces the loss of original information and makes the result more accurate. Binary contrast method is proposed and combined with entropy weight to obtain comprehensive weight, which makes the determined weight more reasonable. Finally, this method is applied to the case selected by the sewage treatment company and proved its effectiveness.

Antipsychotic Use in Pregnancy: Patient Mental Health Challenges, Teratogenicity, Pregnancy Complications, and Postnatal Risks

Pregnant women constitute a vulnerable population, with 25.3% of pregnant women classified as suffering from a psychiatric disorder. Since childbearing age typically aligns with the onset of mental health disorders, it is of utmost importance to consider the effects that antipsychotic drugs have on pregnant women and their developing fetus. However, the induction of pharmacological treatment during pregnancy may pose significant risks to the developing fetus. Antipsychotics are typically introduced when the nonpharmacologic approaches fail to produce desired effects or when the risks outweigh the benefits from continuing without treatment or the risks from exposing the fetus to medication. Early studies of pregnant women with schizophrenia showed an increase in perinatal malformations and deaths among their newborns. Similar to schizophrenia, women with bipolar disorder have an increased risk of relapse in antepartum and postpartum periods. It is known that antipsychotic medications can readily cross the placenta, and exposure to antipsychotic medication during pregnancy is associated with potential teratogenicity. Potential risks associated with antipsychotic use in pregnant women include congenital abnormalities, preterm birth, and metabolic disturbance, which could potentially lead to abnormal fetal growth. The complex decision-making process for treating psychosis in pregnant women must evaluate the risks and benefits of antipsychotic drugs.

AI-Assisted Dynamic Modeling for Data Management in a Distributed System

Data management is an administrative mechanism that involves the acquisitions, validations, storage, protection, and processing of data needed by its users to ensure that data are accessible, reliable, and timely. It is a challenging task to manage protections for information properties. With the emphasis on distributed systems and Internet-accessible systems, the need for efficient information security management is increasingly important. In the paper, artificial intelligence-assisted dynamic modeling (AI-DM) is used for data management in a distributed system. Distributed processing is an effective way to enhance the efficiency of database systems. Therefore, each distributed database structure’s functionality depends significantly on its proper architecture in implementing fragmentation, allocation, and replication processes. The proposed model is a dynamically distributed internet database architecture. This suggested model enables complex decision-making on fragmentation, distribution, and duplication. It provides users with links from anywhere to the distributed database. AI-DM has an improved allocation and replication strategy where no query performance information is accessible at the initial stage of the distributed database design. AI-DM findings show that the proposed database model leads to the reliability and efficiency of the enhanced system. The final results are obtained by analyzing the dynamic modeling ratio is 87.6%, increasing decision support ratio is 88.7%, the logistic regression ratio is 84.5%, the data reliability ratio is 82.2%, and the system ratio is 93.8%.

Drifting the goals archetype: a systemic study

PurposeManagerial decision-making is an area of interest to both academia and practitioners. Researchers found that managers often fail to manage complex decision-making tasks and system thinkers assert that generic structures known as systems archetypes help them to a great deal in handling such situations. In this paper, it is demonstrated that decision makers resort to lowering of goal (quick-fix) in order to resolve the gap between the goal and current reality in the “drifting the goals” systems archetype.Design/methodology/approachA real-life case study is taken up to highlight the pitfalls of “drifting the goals” systems archetype for a decision situation in the Indian two-wheeler industry. System dynamics modeling is made use of to obtain the results.FindingsThe decision makers fail to realize the pitfall of lowering the goal to resolve the gap between the goal and current reality. It is seen that, irrespective of current less-than-desirable performance, managers adopting corrective actions other than lowering of goals perform better in the long run. Further, it is demonstrated that extending the boundary and experimentation results in designing a better service system and setting benchmarks.Practical implicationsThe best possible way to avoid the pitfall is to hold the vision and not lower the long term goal. The managers must be aware of the pitfalls beforehand.Originality/valueSystems thinking is important in complex decision-making tasks. Managers need to embrace long-term perspective in decision-making. This paper demonstrates the value of systems thinking in terms of a case study on the “drifting the goals” systems archetype.

Co-Optimizing Battery Storage for Energy Arbitrage and Frequency Regulation in Real-Time Markets Using Deep Reinforcement Learning

Battery energy storage systems (BESSs) play a critical role in eliminating uncertainties associated with renewable energy generation, to maintain stability and improve flexibility of power networks. In this paper, a BESS is used to provide energy arbitrage (EA) and frequency regulation (FR) services simultaneously to maximize its total revenue within the physical constraints. The EA and FR actions are taken at different timescales. The multitimescale problem is formulated as two nested Markov decision process (MDP) submodels. The problem is a complex decision-making problem with enormous high-dimensional data and uncertainty (e.g., the price of the electricity). Therefore, a novel co-optimization scheme is proposed to handle the multitimescale problem, and also coordinate EA and FR services. A triplet deep deterministic policy gradient with exploration noise decay (TDD–ND) approach is used to obtain the optimal policy at each timescale. Simulations are conducted with real-time electricity prices and regulation signals data from the American PJM regulation market. The simulation results show that the proposed approach performs better than other studied policies in literature.

‘Kyidug Manyom’: Transnational Migration, Belonging and Social Change in Mustang, Nepal

This paper explores transnational migration in and from Mustang, Nepal, a high-altitude region of the Himalayas, to understand how people who migrate and return reconstruct a sense of belonging to their birthplace. Narrative ethnography forms the core of this paper as we discuss the stories of four individuals from Mustang to explore the complex decision making around migration and the act of returning, permanently and cyclically. We build on theories of ‘transnationalism’ and ‘belonging,’ and emphasize the circular nature of migration, to argue that migratory journeys involve a continued evaluation of the social and economic realities of contemporary life at ‘home’—highlighting intergenerational tensions, ideas around cultural preservation, and a dynamic understanding of belonging in the context of a transnational community. Although financial need continues to be a primary driving force behind migration trends in Mustang, this paper acknowledges other factors that shape migration such as, family pressure and intergenerational tensions, and the infrastructural and technological developments that have made travel and communication easier and more reliable. Despite the widespread depopulation of Nepal’s highlands, we argue that many Mustangis who migrate remain committed to Mustang’s socioeconomic future, and nurture a connection to their ancestral homeland even as their transnational aspirations pull them away.

Anatomically-based skeleton kinetics and pose estimation in freely-moving rodents

Forming a complete picture of the relationship between neural activity and body kinetics requires quantification of skeletal joint biomechanics during behavior. However, without detailed knowledge of the underlying skeletal motion, inferring joint kinetics from surface tracking approaches is difficult, especially for animals where the relationship between surface anatomy and skeleton changes during motion. Here we developed a videography-based method enabling detailed three-dimensional kinetic quantification of an anatomically defined skeleton in untethered freely-behaving animals. This skeleton-based model has been constrained by anatomical principles and joint motion limits and provided skeletal pose estimates for a range of rodent sizes, even when limbs were occluded. Model-inferred joint kinetics for both gait and gap-crossing behaviors were verified by direct measurement of limb placement, showing that complex decision making behaviors can be accurately reconstructed at the level of skeletal kinetics using our anatomically constrained model.

Longshot or Favorite: The Ending Effect in Investment Decisions

The ending effect describes the phenomenon that at the end of a series of repeated risky decision-making tasks, participants become more likely to engage in risk-taking behavior. Past research has suggested that the ending effect might be caused by a motivational shift induced by changes in time perception. Previous studies mainly tested this phenomenon in a binary decision-making setting (e.g., a decision-making task usually includes two alternatives). However, none of these prior studies included safe options and risky options that differed in risk levels. To address this knowledge gap, the present study replicated the ending effect in a repeated decision-making task that included both a safe option and risky options that differed in risk levels (N = 104). We found that at the end of the decision-making task, participants became more likely to engage in risk-taking and to favor the option with the highest risk. Further, we found that the investment likelihood and investment amount of high-risk options both increased significantly at the ending. In addition, a shift in favoring the safe option emerged in the noninformed condition at the end. We also found that the emotional motivation in the last round could predict the increased preference for high-risk at the ending. This study extended previous findings on the ending effect by adopting a more complex decision-making scenario and, more broadly, helped further our understanding of the psychological consequences of perceived endings.

A smart tech lever to augment caregivers' touch and foster vulnerable patient engagement and well-being

PurposeSome population groups face precarious health, reflecting their vulnerability, in terms of lack of agency or control. Smart technologies promise to transform people's lives from the enhanced connectedness, greater computational processing and more complex decision-making they can achieve. This study aims to investigate how smart technology can mitigate vulnerability and improve well-being.Design/methodology/approachThe research group, of three scholars and three managers, pursued an action research methodology with an iterative process of planning, action and learning. The authors conducted three related action studies: (1) adopting smart technologies, (2) fostering patient engagement and (3) assessing well-being.FindingsThe adoption of sensors and wearable devices had positive impacts for both patients and caregivers. Technologies highlighted their meaning as resources to support actors' (caregivers' and vulnerable patients') activities. Smart devices as resources get integrated, stimulate change and enable new practices. For caregivers, such innovative solutions help improve their knowledge of patients and their ability to act efficiently; for vulnerable patients, they fostered engagement in daily activities to improve well-being.Originality/valueThe paper delineates an overall model (SEVP) that describes how the integration of high-tech and high touch enables patient engagement to mitigate vulnerability and improve well-being.

Age Differences in Complex Decision Making and Judgment

This chapter discusses age differences in complex decision making and judgment, particularly the role of motivational factors and individual differences. Literature on the influence of age-related changes in cognition and motivation on search and performance in complex decision making is reviewed. The role of financial incentives, need for cognition, and need for cognitive closure is discussed, including the age-related influence of motivational factors on the performance of sequential decision-making tasks. Additionally, the role of feedback as a factor producing superior performance of older adults in a decision-making task is introduced. Moreover, novel research findings regarding connections between intellectual helplessness and information and communication technologies in older adults are presented. Lastly, individual differences in numeracy and intellectual helplessness in mathematics as predictors of age-related differences in performance of multiattribute tasks are described.