Variation In Decisions Research Articles

Multi-objective optimization of a cogeneration system based on solar energy for clean hydrogen, cooling, and electricity production

In an effort to encourage industries to switch from fossil fuels to renewable energy resources for supplying their energy demands, the exergy and financial aspects of a thermodynamic energy generation system were studied. The suggested system was modeled by MATLAB commercial software to assess the decision-making parameters affecting power generation, cooling capacity, and to produce hydrogen. The objective functions of this study were exergy efficiency and cost rate, while the temperatures at the inlet of the turbine and the evaporator, irradiated solar energy, mass flow rate, and surface area of the collector were the decision-making variables. The model was optimized via MOPSO and its results were compared with two widely utilized algorithms, namely NSGA-II and SPEA-II. The comparison results indicated that MOPSO surpassed other two optimization algorithm resulting in exergy efficiency and cost rate of 2.11 % and 21.14 $/h, respectively. According to this method, the optimum generated power was equal to 21.01 kW. Eventually, this system was utilized and evaluated in the city of Semnan, Iran. The performance results of the system in Semnan showed that the annual power output, taking into account the changes in radiation and ambient temperature, is between 316667.4 and 428080.5 kW. Also, the amount of hydrogen production is between 1503.66 and 1534.997 kg.

Normative Accounting Theory and Investment Decision in Selected Listed Manufacturing Companies of Consumer Goods in Nigeria

This study employs an ex-post facto research design to investigate the relationship between normative accounting theory and investment decisions in selected listed manufacturing companies of consumer goods in Nigeria. The research focuses on 21 consumer goods firms listed on the Nigerian Exchange Group from 2014 to 2023. Data derived from audited financial statements are analyzed using descriptive statistics and panel regression techniques, specifically the Fixed Effects Model (FE). The study finds that while variables like net worth and earnings per share (EPS) demonstrate positive influences on shareholders’ stake (SHS), their statistical significance varies. Firm age emerges as a significant factor positively affecting SHS, whereas firm size shows a negative influence, albeit not statistically significant. Normative accounting theory explains a substantial 98.4% of the variability in investment decisions, underscoring its pivotal role in guiding financial practices and enhancing transparency. The findings contribute to understanding how theoretical frameworks shape corporate strategies and investor relations in Nigeria’s Consumer Goods sector. The study recommended that the company should enhance disclosure practices to promote transparency, educate investors on normative accounting principles, ensure continuous compliance with regulatory standards, integrate sustainability reporting, benchmark against industry standards, engage stakeholders for feedback, and support further research on normative accounting theory’s impact on emerging markets.

Attributes: Selective Learning and Influence

An agent selectively samples attributes of a complex project so as to influence the decision of a principal. The players disagree about the weighting, or relevance, of attributes. The correlation across attributes is modeled through a Gaussian process, the covariance function of which captures pairwise attribute similarity. The key trade‐off in sampling is between the alignment of the players' posterior values for the project and the variability of the principal's decision. Under a natural property of the attribute correlation—the nearest‐attribute property (NAP)—each optimal attribute is relevant for some player and at most two optimal attributes are relevant for only one player. We derive comparative statics in the strength of attribute correlation and examine the robustness of our findings to violations of NAP for a tractable class of distance‐based covariances. The findings carry testable implications for attribute‐based product evaluation and strategic selection of pilot sites.

Factors associated with the adoption of technologies for avocado production systems

The growth of avocado crops has led to an increase in technological needs and research to satisfy the demands of the value chain. There is a wide range of technologies applicable for this fruit crop, and there are challenges for transferring and adopting these processes. The objective of this work was to explore the determining factors in the adoption of technologies for avocado production systems and the perception of producers about these factors. For this, we carried out a socioeconomic characterization of avocado producers in Colombia including the recognition of the perception of producers regarding technological adoption variables and an exploratory factorial analysis to evaluate the adoption factors based on the perception and technological level (TL). We found that some socioeconomic variables are related to the TL of the production systems. Meanwhile, perceptions regarding the adoption variables varied depending on the TL of the producers. Low TL presented a greater number of determinant variables in adoption decision-making. In contrast, for the medium and high levels of TL, adoption of technology was based on economic analysis. This research provides evidence for the effect of socioeconomic factors on the adoption of technologies in avocado production systems and shows how the perception of producers regarding these adoptions involves determinants associated with TL.

PREDICTORS OF DECISION MAKING FOR AGING-IN-PLACE SUPPORT IN ALZHEIMER’S DISEASE

Abstract Older adults with Alzheimer’s Disease (AD) need additional support in their lifetime, yet little is known about how the decision to accept help is made. Our goal is to better understand how older adults’ aging-in-place (AIP) decision making is impacted by worsening cognition, functional loss, social influences, and environmental factors. This longitudinal study consists of older adults from an NIA-funded cohort (LitCog) with extensive cognitive testing. The intervention, PlanYourLifespan.org (PYL), facilitates making decisions about AIP needs, including AD. After viewing PYL, subjects are surveyed every 6 months with cognitive, social, functional, health literacy, environmental and decision-making variables. Of 293 subjects, (mean age 73.5, 72.7% female, 40.4% under-represented minority) almost half (47.4%) experienced cognitive decline with 10.3% identifying worsening memory loss from the prior timepoint. Subjects were asked: If you developed AD, have you decided your living/support preferences? At 1-mo, subjects were significantly more likely to have made decisions if they had: limited health literacy (OR 4.36 [p< 0.01, 1.69-11.24]), larger social networks (OR 1.08 [p< 0.05, 1.01-1.15]), completion of a living will (OR 2.43 [p< 0.05,1.11-5.33]. At 6-mo, sufficient social support (OR 3.39 [p< 0.05, 1.19-9.70]. At 12-mo, higher social isolation (OR 1.05 [p< 0.05, 1.01-1.08]) and self-efficacy (OR 1.07 [p< 0.01, 1.04-1.11]). In the event of worsening cognition, the likelihood of making care decision is associated with external (social support) and internal (self-efficacy, health literacy) variables, which change in significance over time. Catastrophic factors (COVID-19) impacted comfortability of utilizing long-term care facilities.

An integrated approach to decision-making variables on urban water systems using an urban water use (UWU) decision-support tool

In response to pressing global challenges like climate change, rapid population growth, and an urgent need for sustainable infrastructure, cities face an immediate and crucial necessity to transition swiftly toward an integrated approach to managing urban water resources. This shift is not merely an option but an imperative, driven by the rapidly evolving urban landscape. In addressing this imperative, a crucial decision support tool that has emerged as an asset in the domain of urban water planning and management is the Urban Water Use (UWU) tool. This tool offers an integrated approach for strategic planning, promoting urban water conservation and environmental health through the investigation of interventions in urban infrastructure under different scenarios. In this study, the latest version of this UWU tool was deployed in a case study conducted in Almirante Tamandaré, Brazil. The objective was to evaluate how an integrated decision-making approach concerning urban water systems influences the efficiency and effectiveness of interventions, ultimately contributing to achieve widespread adoption, accessibility, and relevance of urban water services. The refined UWU tool evaluates a spectrum of measures across diverse scenarios, incorporating various drivers, focusing on the stakeholders' visions for the locality. These visions are composed of sustainability indicators, specifying different sets of target values and importance weights for each indicator. The approach followed in this study demonstrates how the effectiveness indexes can vary based on stakeholders' perception. Measures under Water Sensitive Urban Design and Water Demand Management strategies were deployed to simulate the response of urban water systems under three distinct scenarios, embracing the complexities of social dynamics and of climate change. The findings of the study emphasize that realizing a desired vision through selected measures relies significantly on the adoption of an integrated approach within the decision-making process. The stakeholders' perception of how indicators should be weighted while defining the vision was found to significantly impact the effectiveness range of these measures.

A potential paradigm shift in global mobility? The impact of the COVID-19 pandemic

The COVID-19 pandemic profoundly affected the global mobility (GM) policies of multinational enterprises (MNEs). Through interviews with policy decision-makers in 32 companies, we applied Interpretative Phenomenological Analysis to examine how the pandemic accelerated changes in MNE policies and the management of GM. Our framework builds on contingency theory, traditionally concerned with aligning organizational policies with macro- and meso-level contextual factors. We extend this by integrating micro-level individual variables, such as career aspirations and well-being, as contingent factors influencing GM practices. There is variation in GM responses in relation to the volume of GM activities, GM-related policies, investments in digitalization and technology, and variations in mobility types. We observed a potential paradigm shift that affords employees greater agency in their global mobility paths, thereby greater granularity to contingency theory. This nuanced approach opens new research avenues and equips organizational leaders with a comprehensive set of variables for GM decision-making. Our findings indicate lasting global mobility changes but foresee gradual implementation due to associated risks.

A Review of Optimal Design for Large-Scale Micro-Irrigation Pipe Network Systems

Micro-irrigation pipe network systems are commonly utilized for water transmission and distribution in agricultural irrigation. They effectively transport and distribute water to crops, aiming to achieve water and energy conservation, increased yield, and improved quality. This paper presents a model for the scaled micro-irrigation pipeline network system and provides a comprehensive review of the fundamental concepts and practical applications of optimization techniques in the field of pipeline network design. This paper is divided into four main sections: Firstly, it covers the background and theoretical foundations of optimal design for scaled micro-irrigation pipeline network systems. Secondly, the paper presents an optimal design model specifically tailored for scaled micro-irrigation pipeline networks. And then, it discusses various optimization solution techniques employed for addressing the design challenges of scaled micro-irrigation pipeline networks, along with real-world case studies. Finally, this paper concludes with an outlook on the ongoing research and development efforts in the field of scaled micro-irrigation pipeline network systems. In addition, this paper establishes a fundamental model for optimizing pipeline networks, to achieve minimum safe operation and total cost reduction. It considers constraints such as pipeline pressure-bearing capacity, maximum flow rate, and diameter. The decision-making variables include pipeline diameter, length, internal roughness, node pressure, future demand, and valve placement. Additionally, this paper provides an extensive overview of deterministic methods and heuristic algorithms utilized in the optimal design of micro-irrigation pipeline networks. Finally, this paper presents future research directions for pipeline network optimization and explores the potential for algorithmic improvements, integration of machine learning techniques, and wider adoption of EPANET 2.0 software. These endeavors aim to lay a strong foundation for effectively solving complex and challenging optimization problems in micro-irrigation pipeline network systems in the future.

Empowering highway network: Optimal deployment and strategy for dynamic wireless charging lanes

Amid escalating energy crises and environmental pressures, electric vehicles (EVs) have emerged as an effective measure to reduce reliance on fossil fuels, combat climate change, uphold sustainable energy and environmental development, and strive towards carbon peaking and neutrality goals. This study introduces a nonlinear integer programming model for the deployment of dynamic wireless charging lanes (DWCLs) and EV charging strategy joint optimization in highway networks. Taking into account established charging resources in highway service areas (HSAs), the nonlinear charging characteristics of EV batteries, and the traffic capacity constraints of DWCLs. The model identifies the deployment of charging facilities and the EV charging strategy as the decision-making variables and aims to minimize both the DWCL construction and user charging costs. By ensuring that EVs maintain an acceptable state of charge (SoC), the model combines highway EV charging demand and highway EV charging strategy to optimize the DWCL deployment, thus reducing the construction cost of wireless charging facilities and user charging expenses. The efficacy and universality of the model are demonstrated using the classical Nguyen–Dupius network as a numerical example and a real-world highway network in Guangdong Province, China. Finally, a sensitivity analysis is conducted to corroborate the stability of the model. The results show that the operating speed of EVs on DWCLs has the largest impact on total cost, while battery capacity has the smallest. This comprehensive study offers vital insights into the strategic deployment of DWCLs, promoting the sustainable and efficient use of EVs in highway networks.

Developing an optimal plan to improve irrigation efficiency using a risk-based central force algorithm

Abstract Losses in surface irrigation include deep percolation and runoff, which is one of the ways to increase the efficiency of furrow irrigation, using a closed-end mode in irrigation systems. This research was conducted to evaluate the effects of geometrical variables (slope and length of furrow) and flow control (inflow rate and cut-off time) on application efficiency (AE) and the uniformity of water distribution in a closed-end furrow irrigation system. The length, slope, inflow rate, and cut-off time are considered as the decision-making variables for developing the multi-objective genetic algorithm based on the non-dominated sorting. For this purpose, three irrigation furrows with the closed-end system were considered. The optimization algorithm for calculating the objective functions involves maximizing the minimum water depth and minimizing the infiltration depth in a modeling loop. The optimization algorithm was linked to the WinSRFR software to calculate the objective functions. The results showed that the best combination of inflow rate and the cut-off time for 75 mm of required water depth was 1.9 L/s/m and 150 min, respectively, which increased AE and distribution uniformity to 79 and 78%. Furthermore, the AE in the closed-end furrow irrigation system is higher (30–50%) than the open-end method in different scenarios.

Peran Tax Avoidance sebagai Moderasi: Profitabilitas, Intensitas Modal, Ukuran Perusahaan, Solvabilitas

This study investigates the moderating role of Tax Avoidance in relation to profitability, solvency, capital intensity, and firm size. Data was processed using the statistical software SPSS. The results of the analysis indicate that the correlation between Return on Assets (ROA) and Debt Asset Ratio (DAR) exhibits a significant negative association. Firm size demonstrates a significant positive correlation with DAR, while capital intensity also shows a significant positive relationship with DAR. However, the interaction between ROA and Tax Avoidance does not exhibit statistical significance in its relationship with DAR. These findings illustrate that Tax Avoidance does not act as a moderator in the relationship between ROA and DAR. The outcomes of this study offer insights into how these elements can shape a company's financial structure and underscore the importance of considering the interactions among these variables in financial decision-making.

Current practice of cancer predisposition testing in pediatric patients with CNS tumors in the United States.

An estimated 8.6% of all pediatric patients with central nervous system tumors (CNSTs) have underlying hereditary cancer predisposition (HCP). Identifying HCP affects risk assessment and medical management options for the patients and their family members. However, there is a lack of consensus on the optimal germline genetic testing (GT) approach for pediatric patients with CNSTs. As a first step in addressing the need for consensus, we surveyed oncology and genetics providers from 47 institutions in professional organizations across the United States. We investigated their current practice (e.g., GT decisions and ordering practices) when assessing pediatric patients with CNSTs for HCP. We received 60 responses from 21 pediatric oncologists, 10 neuro-oncologists, 28 genetics providers, and one neuro-oncologist/geneticist. Results demonstrate genetic counselors, followed by oncologists, most often facilitated consent, ordered testing, and selected which test to order. The most ordered test was a multi-gene panel (60%). Of 18 CNST diagnoses, choroid plexus carcinoma (CPC) was the diagnosis for which most providers (78%) reported they would offer GT. For medulloblastoma, 56% overall reported they would offer GT (64% of genetics providers, 62% of neuro-oncologists, 20% of pediatric oncologists; p=.050). Findings suggest that even for the CNSTs most commonly known to be associated with HCP regardless of family history, there was variability in providers' decisions to offer GT. The lack of consensus in GT decisions in our study indicates inconsistencies in the genetics care of pediatric patients with CNSTs, demonstrating a need for consensus guidelines to promote consistent genetics care.

Effects of customer relationship management (CRM) strategies and socio-cognitive constructs on the physical activity of individuals with arthritis over time.

Regular physical activity is important for arthritis self-management and could be promoted through tailoring community leisure and fitness centers' customer-relationship management (CRM) strategies. This study investigates the influence of two CRM strategies on individuals with arthritis reaching or maintaining two moderate-to-vigorous physical activity (MVPA) thresholds (≥150 and ≥45 minutes/week) from baseline-to-12 months and 12-to-24 months as well as mean changes in total minutes/week of MVPA. It also explores time-dependent variations in the influence of socio-cognitive variables on MVPA outcomes. Survey data from 374 participants with arthritis in a two-year randomized controlled trial (control versus two CRM strategies: IncentiveOnly and Incentive+Support) were used. Participants reported measures of physical activity participation, socio-cognitive decision-making, mental and physical wellbeing, friendship, community connectedness, sense of trust in others, and demographics. Receiving the Incentive+Support CRM strategy (versus control) increased participants' likelihood of reaching/maintaining both physical activity thresholds from 12-to-24 months (≥150 MVPA minutes/week, p < .001; ≥45 MVPA minutes/week, p < .032) but not from baseline-to-12 months. However, receiving the IncentiveOnly CRM strategy (versus control) did not predict reaching/maintaining these thresholds. Importantly, socio-cognitive decision-making variables' influence on reaching/maintaining these MVPA thresholds varied over time, suggesting CRM strategies may require further tailoring based on time-specific profiles. Perhaps because of new facility induced excitement, the mean change in total MVPA minutes/week for the control group significantly increased (26.8 minute/week, p = .014, 95% CI [5.5, 48.0]) from baseline-to-12 months, but subsequently declined by 11.4 minute/week from 12-to-24 months (p = .296, 95% CI [-32.7, 9.9]). Mean changes in total MVPA minutes/week were non-significant for those receiving IncentiveOnly content but significant for those receiving Incentive+Support content: baseline-to-12 months (38.2 minute/week increase, p = .023, 95% CI [4.9, 71.4]) and baseline-to-24-months (45.9 minute/week increase, p = .007, 95% CI [12.7, 79.1]).

Assessment of beliefs and attitude towards blood donation among the population of Tumkur district: A cross sectional study

Background: Blood donation is crucial aspect of healthcare, ensuring steady supply of safe blood for medical treatments and emergencies. Understanding the beliefs, behaviors and opinions surrounding blood donation is essential for developing effective strategies to promote voluntary blood donation. This cross-sectional study aims to assess and analyze the factors influencing blood donation in Tumkur district of Karnataka, India. Methodology: A cross-sectional community-based study was conducted in Tumkur district over a period of six months. A total of 1000 subjects were included of age 18–65 years from different communities and socioeconomic backgrounds. The structured questionnaire was asked on demographics, knowledge, misunderstandings, previous donation history, willingness and decision-making variables. Results: The study found that while a majority (70%) of the participants were aware of the importance of blood donation, only small proportion (15%) had donated blood previously. Misconceptions and fear of needles were identified as key barriers to blood donation. Moreover, religious and cultural beliefs were observed to influence willingness to donate among certain communities. Younger age, higher education and positive experiences with blood donation drives were associated with an increased likelihood of future donation. Conclusion: Religious and cultural sensitivities should be taken into account when designing donation campaigns in specific communities. Engaging educational institutions and community organizations could play a pivotal role in promoting positive attitude towards blood donation among the youth. It is imperative for healthcare authorities to collaborate with local leaders and stakeholders to develop effective strategies for increasing voluntary blood donation rates in Tumkur district. Keywords: blood donation; beliefs; behaviors; opinions; Tumkur district

Six-Degree-of-Freedom Rocket Landing Optimization via Augmented Convex–Concave Decomposition

In this paper an augmented convex–concave decomposition (ACCD) method for treating nonlinear equality constraints in an otherwise convex problem is proposed. This augmentation improves greatly the feasibility of the problem when compared to the original convex–concave decomposition approach. The effectiveness of the ACCD is demonstrated by solving a fuel-optimal six-degree-of-freedom rocket landing problem in atmosphere, subject to multiple nonlinear equality constraints. Compared with known approaches such as sequential convex programming, where conventional linearization (with or without slack variables) is employed to treat those nonlinear equality constraints, it is shown that the proposed ACCD leads to more robust convergence of the solution process and a more interpretable behavior of the sequential convex algorithm. The methodology can also be applied effectively in the case where the determination of discrete controls or decision-making variables needs to be made, such as the on–off use of reaction control system thrusters in the rocket landing problem, without the need for a mixed integer solver. Numerical results are shown for a representative, reusable rocket benchmark problem.

Optimal stochastic-probability management of resources and energy storage in energy hub considering demand response programs and uncertainties

Because the probability decision-making variables in the energy hub (EH) cause technical, economic and environmental challenges, this study presents a multi-objective planning approach based on stochastic-probability optimization algorithms. Uncertainties include the price of electricity carriers, renewable power generation, and electrical loads uncertainty (specifically electric vehicles (EVs)) based on probability distribution functions. The multi-objective optimization function includes minimizing the operation cost, minimizing the greenhouse gas emissions cost, and minimizing the EH risk cost. The stochastic-probability optimization method is programmed in both primary and secondary levels. At the primary level, the optimal operation of the EH based on the Monte Carlo method and K-means clustering is considered. To change the consumption pattern and provide a suitable platform for customer participation in optimal energy distribution, demand response (DR) programs based on energy market interactions have been modeled. At the secondary level, the EV unit optimal operation based on the Alternating direction method of multipliers (ADMM) and considering their probabilistic behavior is presented. EH risk analysis has also been developed based on the conditional value at risk (CVaR) method. The results of this study show the effectiveness of the proposed method in different scenarios and a 15.1% reduction in the total operation cost.

Property mapping of LDPE during 3D printing: evaluating morphological development with X-ray scattering

Direct digital manufacturing has been identified as one of the key tools of Industry 4.0 and it enables the creation of products directly through digital definition. Commonly known as additive manufacturing, it comprises a set of technologies that are expressively agile in small-scale productions and prototyping, in comparison to conventional mass manufacturing processes, such as injection molding of plastics. It streamlines mass customization, allows the production of highly complex objects, and has been broadly applied in several fields, from medical devices to the aerospace industry. Although a new era of design possibilities and accessibility was unveiled, most developments are focused on shape reproduction precision and the development of new feeding systems and materials. This work is focused on a shift in design for additive manufacturing, where the polymer properties, by means of the adjustment of the process conditions (extrusion rate, the write speed, and the nozzle temperature, among others), constitute a decision-making variable. In order to evaluate the morphology of semicrystalline polymers during extrusion-based 3D printing,in-situtime-resolving small and wide-angle X-ray scattering measurements were performed at the ALBA synchrotron light source in Barcelona (Spain). The goal of this research is to develop a material property mapping methodology during semicrystalline polymer melt extrusion-based 3D printing Some experiments were performed with low-density polyethylene, and we were able to confirm a correlation between the extrusion rate and writing speed of the printing with the level of preferred orientation of the chain folded lamellar crystals in the extrudate.

Surgeon-specific factors have a larger impact on decision-making for the management of proximal humerus fractures than patient-specific factors: a prospective cohort study.

There is significant variability both in how proximal humerus fractures (PHFs) are treated and the ensuing patient outcomes. The purpose of this study was to investigate which surgeon- and patient-specific factors contribute to decision-making in the treatment of adult PHFs. We hypothesized that orthopedic sub-specialty training creates inherent bias and plays an important role in management algorithms for PHFs. We performed a prospective cohort investigation in 2 groups of surgeons-traumatologists (N=25) and shoulder & elbow/sports surgeons (SES) (N=26)-and asked them to provide treatment recommendations for 30 distinct clinical cases with standardized radiographic and clinical data. This is a population-based sample of surgeons who take trauma call and treat PHFs with different sub-specializations and practice settings including academic, hospital-employed, and private. Surgeons characterized based on subspecialty (trauma vs. SES), experience level (>10 vs. ≤10-years), and employment type (hospital- vs. non-hospital-employed). Chi-square analyses, logistic mixed-effects modeling, and relative importance analysis were used to evaluate the data. Of the patient-specific factors, we found that the management of PHFs is largely dependent on initial radiographs obtained. Traumatologists were more likely to offer open reduction internal fixation (ORIF) and less likely to offer arthroplasty: 69% ORIF (traumatologists) vs. 51% ORIF (SES, P<.001), 8% arthroplasty (traumatologists) vs. 17% (SES, P<.001). Traumatologists were less likely to change from operative (either ORIF or arthroplasty) to non-operative management compared to SES surgeons when presented with additional patient demographic data. Surgeon-specific factors contributed to more than one-half of the variability in decision-making of PHF management while patient-specific factors contributed to about one-third of the variability in decision-making. As physicians strive to advance the treatment for PHFs and optimize patient outcomes, our findings highlight the complex overlap between surgeon-, fracture-, and patient-specific factors in the final decision-making process.

RISK aversion in Italian forensic and non-forensic patients with schizophrenia spectrum disorders.

Goal-directed decision-making is a central component of the broader reward and motivation system, and requires the ability to dynamically integrate both positive and negative feedback from the environment in order to maximize rewards and minimize losses over time. Altered decision-making processes, in which individuals fail to consider the negative consequences of their decisions on both themselves and others, may play a role in driving antisocial behaviour. The main study aim was to investigate possible differences in loss and risk aversion across matched patients, all with a schizophrenia spectrum disorder (SSD), but who varied according to whether they had a history of serious interpersonal violence or not, and a sample of healthy controls with no history of violence. The sample included 14 forensic and 21 non-forensic patients with SSD, and 41 healthy controls. Among the three decision-making variables under investigation, risk aversion was the only significant predictor of membership of the three groups, with greater risk aversion among non-forensic patients with SSD compared to healthy controls. No differences were observed across groups in loss aversion and choice consistency. This evidence suggests a new potential treatment target for rehabilitative measures aimed at achieving functional improvements in patients with SSD by selectively leveraging the neuro-cognitive processing of reward.

Alcohol's Role in Sexual Decision Making in First-Year College Women: An Event-Level Assessment.

Sexual decision making is often grounded in social scripts that can be detrimental to women's healthy relationship and sexual development during the transition to college. Little is known about the malleable decision-making processes and drinking behaviors that influence sexual behaviors from day-to-day. We examined whether women were more likely to engage in sexual behaviors on days they had higher intentions and willingness to engage in sex or drink alcohol. We also explored interactions between sex- and alcohol-related decision constructs. Eighty-two first-year college women completed 14-days of ecological momentary assessment, reporting on alcohol- and sex-related intentions and willingness (3x daily) and daily drinking and sexual behaviors. We found partial support for our hypotheses: intentions and willingness to have sex were positively associated with sex behaviors, but willingness to drink was negatively associated with sex behaviors. Heavy drinking was associated with sexual behavior, even when women indicated no prior willingness to engage in sexual behavior on those days. Findings highlight the need to address event-level variability in sexual decision making, with a particular focus on how alcohol impacts these processes. Further, the robust association between sexual intentions and behavior suggests intention setting may be a particularly useful sexual empowerment education tool.