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.

Cryptocurrency Price Estimation Using Hyperparameterized Oscillatory Activation Functions in LSTM Networks

Activation functions are critical components of neural networks, helping the model learn highly-intricate dependencies, trends, and patterns. Non-linear activation functions allow the model to behave as a functional approximator, learning complex decision boundaries and multi-dimensional patterns in the data. Activation functions can be combined with one another to learn better representations with the objective of improving gradient flow, performance metrics reducing training time and computational cost. Recent work on oscillatory activation functions\cite{noel2021growing}\cite{noel2021biologically} showcased their ability to perform competitively on image classification tasks using a compact architecture. Our work proposes the utilization of these oscillatory activation functions for predicting the volume-weighted average of Bitcoin on the G-Research Cryptocurrency Dataset. We utilize a popular LSTM architecture for this task achieving competitive results when compared to popular activation functions formally used.

Towards an Ontology for Impact Projection of Complex Decisions

Complex decisions are an unusual process, composed of actions. An impact is a measure of the tangible and intangible consequences of one thing on another. Impacts are interdependent, and the environment in which they are measured generates constant change for decision making. This paper proposes the impact projection’s conceptualization, organized into a meta-ontology called OntoImpact. It comprises concepts that are crucial in supporting the understanding and representation of impact projections for complex decisions. The main contribution of OntoImpact is to support decision-makers in their work tasks, besides providing bases to support the development of a complex decision system. This paper was evaluated in a case study of an emergency domain. The results show that OntoImpact provides elements that can support complex decision analysis and project impacts in a collaborative way.

Comparison of Normalization Techniques on Data Sets with Outliers

With the fast growing of data-rich systems, dealing with complex decision problems with skewed input data sets and respective outliers is unavoidable. Generally, data skewness refers to a non-uniform distribution in a dataset, i.e. a dataset which contains asymmetries and/or outliers. Normalization is the first step of most multi-criteria decision making (MCDM) problems to obtain dimensionless data, from heterogeneous input data sets, that enable aggregation of criteria and thereby ranking of alternatives. Therefore, when in presence of outliers in criteria datasets, finding a suitable normalization technique is of utmost importance. As such, in this work, we compare seven normalization techniques (Max, Max-Min, Vector, Sum, Logarithmic, Target-based, and Fuzzification) on criteria datasets, which contain outliers to analyse their results for MCDM problems. A numerical example illustrates the behaviour of the chosen normalization techniques and an (ongoing) evaluation assessment framework is used to recommend the best normalization technique for this type of criteria.

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.

Features of Acoustic Emission in Tensile Testing of Dissimilar Welded Joints of Pearlitic and Austenitic Steels

This paper presents a study of acoustic emission (AE) during the deformation of dissimilar welded joints of austenitic steel to pearlitic steel. One of the specific problems in these welded joints is the presence of decarburized and carbide diffusion interlayers, which intensively increase in width during long-term high-temperature operation. The presence of wide interlayers negatively affects the mechanical properties of welded joints. Moreover, welded defects are difficult to diagnose in welded joints containing interlayers: due to the high structural heterogeneity, interlayers create structural noises that can hinder the detection of defects such as cracks, pores, or a lack of penetration. The AE method may become a complex decision for diagnosing dissimilar welded joints due to applicability to the testing of heterogenic materials with a complex microstructure. Specimens cut from dissimilar welded joints of austenitic steel to pearlitic steel were tested by tension to rupture, with parallel AE data registration. According to the research results, the characteristic features of the AE were revealed for specimens containing defects in the form of lack of penetration as well as for specimens with diffusion interlayers. The results obtained show that the AE method can be used to test both typical welding defects and diffusion interlayers in welded joints of steels of different structural classes.

Suprathreshold perceptual decisions constrain models of confidence

Perceptual confidence is an important internal signal about the certainty of our decisions and there is a substantial debate on how it is computed. We highlight three confidence metric types from the literature: observers either use 1) the full probability distribution to compute probability correct (Probability metrics), 2) point estimates from the perceptual decision process to estimate uncertainty (Evidence-Strength metrics), or 3) heuristic confidence from stimulus-based cues to uncertainty (Heuristic metrics). These metrics are rarely tested against one another, so we examined models of all three types on a suprathreshold spatial discrimination task. Observers were shown a cloud of dots sampled from a dot generating distribution and judged if the mean of the distribution was left or right of centre. In addition to varying the horizontal position of the mean, there were two sensory uncertainty manipulations: the number of dots sampled and the spread of the generating distribution. After every two perceptual decisions, observers made a confidence forced-choice judgement whether they were more confident in the first or second decision. Model results showed that observers were on average best-fit by a Heuristic model that used dot cloud position, spread, and number of dots as cues. However, almost half of the observers were best-fit by an Evidence-Strength model that uses the distance between the discrimination criterion and a point estimate, scaled according to sensory uncertainty, to compute confidence. This signal-to-noise ratio model outperformed the standard unscaled distance from criterion model favoured by many researchers and suggests that this latter simple model may not be suitable for mixed-difficulty designs. An accidental repetition of some sessions also allowed for the measurement of confidence agreement for identical pairs of stimuli. This N-pass analysis revealed that human observers were more consistent than their best-fitting model would predict, indicating there are still aspects of confidence that are not captured by our model. As such, we propose confidence agreement as a useful technique for computational studies of confidence. Taken together, these findings highlight the idiosyncratic nature of confidence computations for complex decision contexts and the need to consider different potential metrics and transformations in the confidence computation.

Spatiotemporal dynamics of animal contests arise from effective forces between contestants

Competition among animals for resources, notably food, territories, and mates, is ubiquitous at all scales of life. This competition is often resolved through contests among individuals, which are commonly understood according to their outcomes and in particular, how these outcomes depend on decision-making by the contestants. Because they are restricted to end-point predictions, these approaches cannot predict real-time or real-space dynamics of animal contest behavior. This limitation can be overcome by studying systems that feature typical contest behavior while being simple enough to track and model. Here, we propose to use such systems to construct a theoretical framework that describes real-time movements and behaviors of animal contestants. We study the spatiotemporal dynamics of contests in an orb-weaving spider, in which all the common elements of animal contests play out. The confined arena of the web, on which interactions are dominated by vibratory cues in a two-dimensional space, simplifies the analysis of interagent interactions. We ask whether these seemingly complex decision-makers can be modeled as interacting active particles responding only to effective forces of attraction and repulsion due to their interactions. By analyzing the emergent dynamics of “contestant particles,” we provide mechanistic explanations for real-time dynamical aspects of animal contests, thereby explaining competitive advantages of larger competitors and demonstrating that complex decision-making need not be invoked in animal contests to achieve adaptive outcomes. Our results demonstrate that physics-based classification and modeling, in terms of effective rules of interaction, provide a powerful framework for understanding animal contest behaviors.

Contraceptive counselling experiences in Spain in the process of creating a web-based contraceptive decision support tool: a qualitative study

Background The choice of contraceptive method is a complex decision, and professionals should offer counselling based on the preferences, values and personal situation of the user(s). Some users are unsatisfied with the counselling received, which may, among other consequences, adversely affect method use adherence. In view of this situation, we propose exploring the experiences and needs of users and professionals for contraceptive counselling, in the context of creating a web-based contraceptive decision support tool. Methods/design Qualitative research was conducted through focus group discussions (64 users split into eight groups, and 19 professionals in two groups, in Tarragona, Spain) to explore the subjects’ experiences and needs. The data were categorized and the categories were defined and classified based on the three-step protocol or framework for Quality on Contraceptive Counseling (QCC), created by experts, which reviews the quality of interactions between user and professional during the counselling process. Results In counselling, users demand more information about the different methods, in an environment of erroneous knowledge and misinformation, which lead to false beliefs and myths in the population that are not contrasted by the professional in counselling. They complain that the method is imposed on them and that their views regarding the decision are not considered. Professionals are concerned that their lack of training leads to counselling directed towards the methods they know best. They acknowledge that a paternalistic paradigm persists in the healthcare they provide, and decision support tools may help to improve the situation. Conclusions Users feel unsatisfied and/or demand more information and a warmer, more caring approach. Professionals are reluctant to assume a process of shared decision-making. The use of a contraception DST website may solve some shortcomings in counselling detected in our environment.

‘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.