Daily Decisions Research Articles

Contradiction or Consistency: The “Double-Edged Sword” Effect of Asset Specificity on Organizational Resilience

In a turbulent environment, organizational resilience is critical to a firm's survival and long-term development. Specific assets, as a strategic resource for firms seeking a competitive advantage, have a “double-edged sword” effect on organizational resilience. There is less literature on the relationship between asset specificity and organizational resilience, and their perspectives differ. On the one hand, as the firm's “nuclear resource,” specific assets have “unique competitiveness” and assist the firm in resisting competitors and disruption. Specific assets, on the other hand, are “high locking,” making them unsuitable for the volatile environment. Its impact on resilience is contradictory, and it merits further investigation. This article investigates the impact of diverse types of asset specificity on organizational resilience and analyzes data from 302 Chinese firms. The findings show that “hard” asset specificity has a negative impact on organizational resilience, whereas “soft” asset specificity has a positive impact. As a dynamic capability, strategic flexibility mediates between specific assets and organizational resilience. Furthermore, organizational memory has a negative moderating effect on the relationship between strategic flexibility and organizational resilience. While organizational memory is useful for daily decision-making, it is ineffective for developing organizational resilience in turbulent environments. The article provides strategies for firms to invest in specific assets for the sake of developing organizational resilience.

Scope of Proactive Public Information Disclosure in Advancing Nepalese Local Governance

Proactive disclosure is a tool that organizations, especially government agencies and public institutions, use to voluntarily provide information to the public without the need for formal requests. It is taken as a fundamental right of the citizen and the heart of democracy, which ensures transparency, accountability, and open government by making information accessible to the public. Proactive disclosure is a very important provision of the Right to information (RTI) Act of 2007 that obligates the government to inform citizens regarding its daily activities, decisions, performance, and budget details proactively or on the request of citizens. Beneficiaries, governments' bodies, and experts and facilitators of RTI shall equally have the responsibility to seek, receive, and impart information that is protected by the government. The main purpose of the paper is to determine the scope of proactive disclosure of public information in advancing transparency and accountability of local governance and examine the practices of proactive disclosure of public information from the perspective of RTI Act implementation in Nepal. It has employed purposive sampling for data collection when primary data were collected in the form of information disseminated on the websites of four local bodies and secondary data from various books, journals, reports, etc. This is a really important since it has contributed to local governance being transparent and accountable towards citizens and public service delivery.

Congestion forecast framework based on probabilistic power flow and machine learning for smart distribution grids

The increase in renewable energy sources and new technologies such as electric vehicles and storage can generate uncertainties in distribution grid operations, increasing the likelihood of congestions in power lines. Distribution system operators (DSOs) face several challenges while operating their grids in such conditions. These congestions deteriorate the electrical equipment in the long term, reducing its life span. This work proposes a framework to predict grid asset congestions on a daily basis. A congestion forecast framework is proposed by combining probabilistic power flows and machine learning algorithms to support DSOs in their daily decision-making. The framework is tested on a modified IEEE-33 bus system and CINELDI MV Reference system with hourly synthetic data. The results showed that the framework is able to closely predict the congestions on the lines. Computational capabilities are reported and discussed. The study indicates that the framework is a suitable tool for day-to-day congestion predictions in smart distribution grids yielding low error in expected values.

Creative Ways to Develop Creative Potential and Networking Skills in Primary School Students

The development of students' personality, in personally plan, socially and professionally, for a favorable social integration, is achieved by accessing the diversity of school activities. But the discipline called Personal Development occupies a primordial place, focusing on the concepts: self-knowledge, emotional intelligence and self-regulated learning. The didactic approach takes into account the purpose for which it is learned and the way of action to form the student's personality. Creativity has a special importance, both for the teacher in teaching, but also for students, giving them harmony, critical thinking, novelty, flexibility in thinking and especially originality. When it comes to creativity, the student has the right to free expression without being criticized or receiving indications regarding the shortcomings of the products made. With the help of creative strategies, the development in the reflective, anticipatory and evaluative capacity can be highlighted, but also in the student's motivational plan. Also, the development of learning autonomy, for the daily decisions of each individual, the facilitation of access to counseling offered by specialists in educational institutions and more, underlines the important role of this field. The major interest in personal development, which starts at an early age, develops children's personality and later acquires an important role in social, emotional and cognitive development.

An effective approach for predicting daily stock trading decisions using fuzzy inference systems

An effective approach for predicting daily stock trading decisions using fuzzy inference systems

Involved-site Radiotherapy Delineation Dilemmas in the Treatment of Adult Hodgkin Lymphoma: Turkish Society for Radiation Oncology Hematological Oncology, Pediatric Oncology and TBI Study Group Case-based Questionnaire Review (TROD 03-005)

AimsThe International Lymphoma Radiation Oncology Group (ILROG) defined involved-site radiotherapy (ISRT) guidelines. These rules offer a certain variability that allows for autonomous decision-making in diverse clinical settings. However, this flexibility also gives rise to conflicts about the selection of treatment fields in the daily decision-making process. The aim of this study was to show the extent of interobserver variability when ILROG-ISRT recommendations were used in different clinical scenarios. Materials and methodsThe 10-question survey used in our study consisted of two parts (part A and part B) and was prepared by four senior radiation oncologists experienced in the haemato-oncology field. The results were presented by stratifying according to clinical experience (<10 years, ≥10 years). Binomial tests (one-sided) were conducted to assess whether answers for each group and the whole group reached a consensus. ResultsTwenty-six radiation oncologists, 13 of whom had less than 10 years of experience and 13 seniors, participated in the survey. Eighty per cent of respondents thought ILROG did not bring sufficient solutions for all clinical scenarios but offered solutions in some cases. In different case-based scenarios, the consensus among the respondents decreased down to 38%. Senior radiation oncologists were found to have more doubts about the adequacy of current guidelines. ConclusionsILROG guidelines allow for a high degree of variability in real-life clinical scenarios and different interpretation of the recommendations may lead to increased toxicity and recurrences. Therefore, there is a need for refinement in ISRT delineation strategies. On behalf of the Turkish Society for Radiation Oncology Hematological Oncology, Pediatric Oncology and TBI Study Group, we are planning to carry out further educational contouring sessions to detect the interobserver variability in real-life contouring cases.

EGS P26 Facilitating the Emergency Surgical Ward Round: The Route to WardNav

Abstract Background The ward round is a corner stone of surgical practice, this is no different when dealing with the acute surgical take. Constant bed pressures result in frequently needing to visit multiple wards, requiring time and effort on staff to ensure patients are reviewed. With frequent rotation of junior doctors, often the responsibility of planning the ward round falls to the ward round leader, who is also making the clinical decisions. We hypothesise a ward round planning tool available to the surgical team would result in more efficient ward rounds, freeing the team to focus on clinical decisions. Methods A survey of junior doctors (foundation doctors and core trainees) was distributed assessing their level of engagement with ward round planning while on surgical rotations at Southmead Hospital, Bristol. An initial planning tool was created in Microsoft Excel utilising a node edge graph and an algorithm based on the Hamiltonian cycle. After further survey feedback, the tool was developed into a web-based app using a version of Djikstra’s algorithm. Results 25 junior doctors responded to the initial survey over two rotations. 100% had to go to multiple wards on a round, 64% actively think about the route required although only 28% would be confident in planning the route. 96% would find a planning tool helpful. Feedback on the Excel based tool from the first cohort revealed appetite for a mobile app-based version. The resultant application was created in R. The final version allows the user to start the round on any ward and creates the optimal route through the building, visually represented on a graph with accompanying text output. Conclusions Junior surgical doctors are not confident with planning ward rounds in a large teaching hospital. An app-based ward navigation tool has the potential to remove ward round planning from daily decision making and engage the junior surgical staff.

Trust-based health decision-making recruits the neural interoceptive saliency network which relates to temporal trajectories of Hemoglobin A1C in Diabetes Type 1.

Experimental approaches in neuroeconomics generally involve monetary utility. Utility inthe health domain is relevant in diabetes because constant daily life decisions are critical for self-consequential long-term outcomes. We used fMRI to investigate self-consequent decision-making in the health and economic domains in Type 1 Diabetes Mellitus and controls (N = 50). We focused on two critical phases of decision-making: Investment and Feedback (Positive or Negative). Patients showed larger BOLD activation of limbic, and reward/dopaminergic regions in particular in the health trust game. Importantly, the worse the trajectory of metabolic control (increasing HbA1C), the higher the BOLD activity in regions of the interoceptive saliency network. This was manifested by positive correlations between brain activity during investment in anterior cingulate cortex and insula and HbA1c blood level progression. We conclude that the neural correlates of health-consequent decision-making domain involve limbic and reward related dopaminergic regions in Type 1 Diabetes Mellitus. Furthermore, the temporal trajectory of HbA1C blood levels is correlated with neural risk processing in the saliency network. Evidence for differential risk processing in the health versus the neuroeconomic context, and the discovery of a role for the saliency interoceptive network in metabolic control trajectories suggests a new perspective on the development of personalized interventions.

Research on the Application of Big Data Analysis Platform in the Enterprise Management Optimization

As companies attempt to make analysis an important data support component of daily decision-making, big data analysis technology is rapidly expanding across all industries. Although there are many software tools and libraries available to assist analysts and software engineers in developing solutions, enterprises are looking for reliable analysis platforms that can meet their specific goals and requirements. In order to minimize costs, such platforms also need to coexist with existing IT infrastructure and reuse the knowledge and resources already accumulated within the organization. To meet these requirements, this article proposes the Data Analysis Solution Engineering (DASE) framework - a knowledge driven approach supported by semantic web technology, for the design and development of requirements engineering and new data analysis platforms. It includes capturing data analysis platform requirements through a knowledge base, and enterprises learning how to use this data analysis platform to analyze all daily production data involved in the engineering data analysis platform. This article analyzes the DASE framework through knowledge modeling, requirement modeling, data architecture modeling, and platform design modeling, and demonstrates how it promotes knowledge and requirement driven data analysis platform engineering. The resulting data analysis platform is considered user-friendly, easy to maintain, and flexible in handling changes in requirements. This work contributes to the knowledge system of knowledge driven requirements engineering and data analysis platform engineering by providing customized models and reference architectures for different analytical application fields.

Klasifikasi Citra Cuaca Menggunakan Inception-V3 dan K-Nearest Neighbors

Weather imagery has a crucial role in various sectors, such as aviation, maritime and agriculture. Weather conditions have a big impact on activities in these fields and greatly influence operations. Classifying weather images can be done by analyzing weather image data, which can be used to predict the type of weather that may occur. The results of these weather predictions have significant value in daily decision making in these various sectors. One method for classifying weather images can be done by first extracting weather image features using Inception-V3 which is then calculated using the K-Nearest Neighbors method. This research uses 1748 weather images with 4 categories to carry out training which produces a model with Accuracy 91%, F1 91%, Recall 91%, Precision 91%, and uses 8 weather images with 4 categories to carry out testing which produces classifications with all correct values. every image.

Bitcoin and carbon dioxide emissions: Evidence from daily production decisions

Environmental externalities from cryptomining may be large, but have not been linked causally to mining incentives. We exploit daily variation in Bitcoin price as a natural experiment for an 86 megawatt waste coal-fired power plant with on-site cryptomining. We find that carbon emissions respond swiftly to mining incentives, with price elasticities of 0.69–0.71 in the short-run and 0.33–0.40 in the longer run. A $1 increase in Bitcoin price leads to $3.11–$6.79 in external damages from carbon emissions alone, well exceeding cryptomining’s value added (using a $190 social cost of carbon, but ignoring increased local air pollution). As cryptomining requires ever more computing power to mine a given number of blocks, our study highlights both the revitalization of US fossil assets and the need for financial industry accounting to incorporate cryptomining externalities.

Artificial Intelligence Reveals Distinct Prognostic Subgroups of Muscle-Invasive Bladder Cancer on Histology Images.

Muscle-invasive bladder cancer (MIBC) is a highly heterogeneous and costly disease with significant morbidity and mortality. Understanding tumor histopathology leads to tailored therapies and improved outcomes. In this study, we employed a weakly supervised learning and neural architecture search to develop a data-driven scoring system. This system aimed to capture prognostic histopathological patterns observed in H&E-stained whole-slide images. We constructed and externally validated our scoring system using multi-institutional datasets with 653 whole-slide images. Additionally, we explored the association between our scoring system, seven histopathological features, and 126 molecular signatures. Through our analysis, we identified two distinct risk groups with varying prognoses, reflecting inherent differences in histopathological and molecular subtypes. The adjusted hazard ratio for overall mortality was 1.46 (95% CI 1.05-2.02; z: 2.23; p = 0.03), thus identifying two prognostic subgroups in high-grade MIBC. Furthermore, we observed an association between our novel digital biomarker and the squamous phenotype, subtypes of miRNA, mRNA, long non-coding RNA, DNA hypomethylation, and several gene mutations, including FGFR3 in MIBC. Our findings underscore the risk of confounding bias when reducing the complex biological and clinical behavior of tumors to a single mutation. Histopathological changes can only be fully captured through comprehensive multi-omics profiles. The introduction of our scoring system has the potential to enhance daily clinical decision making for MIBC. It facilitates shared decision making by offering comprehensive and precise risk stratification, treatment planning, and cost-effective preselection for expensive molecular characterization.

Perspectives of Health and Eating Behaviors in Marathon and Half-Marathoners

Introduction: Marathon and half-marathon runners are a growing population of athletes who have unique training and nutritional demands that put them at risk for health issues. The purpose of this study was to investigate perspectives regarding sport-related health and factors impacting eating behaviors in adult marathoners and half-marathoners. Methods: The Runners Health Choice Questionnaire was deployed online and distributed via a snowball sampling approach. Participants were adult runners who had completed a marathon or half-marathon in the previous three years and planned to participate in another marathon or half marathon in the next 24 months. Collegiate athletes were excluded. Results: Five hundred male and female marathon and half-marathon runners ages 18-79 completed the survey. The relationship between sex and diet classification was significant (χ2 [1, 500]= 7.2194, p= 0.0072); males were significantly more likely to select an atypical diet when compared to females. Time to prepare meals (83.40%) and training run/race that day (81.00%) had a moderate to high impact on daily meal decisions. If provided new nutrition education, 77.24% of runners reported being very likely or somewhat likely to change their diet. Conclusions: Health and eating behaviors and decisions are complex and impacted by several variables: time, training, and an athlete's age and sex. However, runners have a desire to be healthy and a willingness to change with new nutritional information.

One-year follow-up of a multifocal intraocular lens with optimized elevated phase shift.

To evaluate the standard outcomes of a multifocal intraocular lens (MIOL) with optimized elevated phase shift (EPS). ("Masked by journal requirement"). Retrospective observational. 41 patients, consecutively operated on cataracts or refractive lens exchange with the implantation of the Liberty 640PM (EPS 2.0) and followed during 12-months, were included in the analysis. Retrieved variables were visual acuities at far, intermediate and near distances, defocus curves (VADC), and prediction error of four formulas optimized for IOL Master 500 and Pentacam AXL Wave. Patient reported outcomes were also obtained for assessing spectacle independence, satisfaction, bothersome to dysphotopsia, difficulties in daily life tasks and decision to be operated with the same MIOL. Median monocular efficacy with best distance correction was 0, 0.1 and 0.1 logMAR at far, intermediate and near distances, respectively, with patients achieving binocularly a median of 0 logMAR at the three distances. VADC showed a depth of field of 3 D above 0.2 logMAR with a median increase of 0.07 logMAR from -1.5 D to -2.5 D. Complete spectacle independence was achieved at far distance whereas 97.6% and 85.4% was achieved at intermediate and near distances, respectively. 7.3% of patients were bothered by dysphotopsia and 92.6% of patients were likely to be operated again. EPS 2.0 restored patient's vision in the full range of field with a nearly monotone decrease of visual performance from far to near, achieving high rates of spectacle independence at all distances and with low positive dysphotopsia rates (ClinicalTrials.gov Identifier: NCT05735990).

The evolving role of cardiopulmonary exercise testing in ischemic heart disease - state of the art review.

Cardiopulmonary exercise testing (CPET) is the gold standard for directly assessing cardiorespiratory fitness (CRF) and has a relatively new and evolving role in evaluating atherosclerotic heart disease, particularly in detecting cardiac dysfunction caused by ischemic heart disease. The purpose of this review is to assess the current literature on the link between cardiovascular (CV) risk factors, cardiac dysfunction and CRF assessed by CPET. We summarize the basics of exercise physiology and the key determinants of CRF. Prognostically, several studies have been published relating directly measured CRF by CPET and outcomes allowing for more precise risk assessment. Diagnostically, this review describes in detail what is considered healthy and abnormal cardiac function assessed by CPET. New studies demonstrate that cardiac dysfunction on CPET is a common finding in asymptomatic individuals and is associated with CV risk factors and lower CRF. This review covers how key CPET parameters change as individuals transition from the asymptomatic to the symptomatic stage with progressively decreasing CRF. Finally, a supplement with case studies with long-term longitudinal data demonstrating how CPET can be used in daily clinical decision making is presented. In summary, CPET is a powerful tool to provide individualized CV risk assessment, monitor the effectiveness of therapeutic interventions, and provide meaningful feedback to help patients guide their path to improve CRF when routinely used in the outpatient setting.

'Eating is like experiencing a gamble': A qualitative study exploring the dietary decision-making process in adults with inflammatory bowel disease.

For adults with inflammatory bowel disease (IBD), they experience many challenges in dietary decision-making. Thus, this study examined the perspective and experiences of adults with IBD in dietary decision-making. This study aimed to explore the perception and consideration of people with IBD in their daily dietary decisions through monitoring, interpretation and action during the decision-making process. A qualitative study of individuals affected by IBD was conducted through semistructured interviews. Twenty patients were recruited from four tertiary hospitals in Nanjing, China, and each participant completed a semistructured interview. The majority of participants reported on the process and experience of dietary decision-making. Key themes were categorised into three stages: (1) assessing needs, preferences and food cues (monitor); (2) moving from experience to expertise (interpret) and (3) balancing expectations amidst limitations (act). The majority of participants reported that their decisions were shaped by assessing current disease status and food cues. Those interviewed with IBD were willing to make tradeoffs for bowel stability, but their decisions were also influenced by past dietary experiences and traditional Chinese beliefs. The lack of awareness of dietary guidelines was a significant barrier to healthy eating decisions. Positive or negative feelings accompanied dietary decisions. Although most people with IBD change their diet after diagnosis, the changes made are often inconsistent with existing dietary recommendations. Several factors can influence the dietary decision-making process. This study will help assess the experiences of people with IBD in dietary decision-making to encourage the formation of targeted dietary health and well-being interventions. Knowledge of nutrition and diet should be provided in education and training programmes for IBD management. The first three authors of this paper were the lead researchers in this study's design. These authors were mentored by patient researchers who also contributed to the manuscript, and the research process was co-lead and directed by other patient participants and consultants. The results of this paper were directly obtained from patient participants.

The Procedural Politicking Tug of War: Law-Versus-Management Disputes in Contexts of Democratic Backsliding

Abstract The legitimacy of democracy and civil rights is based upon laws and administrative procedures. The presence of a legal framework and its application by bureaucrats in their daily decisions are prerequisites for the democratic rule of law. This explains why, in contexts of democratic backsliding, legal frameworks are under attack. Scholars observed the role of public administration in processes of democratic backsliding, but there is still a gap in understanding the disputes around the legal framework. Here, we analyze the conflicts between politicians and bureaucrats around the legal framework in a context of democratic backsliding. Analyzing the case of Brazil under Bolsonaro’s Government, we draw on 164 interviews with bureaucrats to understand how both bureaucrats and politicians dispute the legitimacy, uses, and interpretations of the legal framework to attack or protect democratic institutions and civil rights. On one side, bureaucrats defend themselves and their legitimacy through existing rules and procedures. On the other side, politicians change or reinterpret the rules to fragilize bureaucrats’ decisions. In this process, both politicians and bureaucrats learn how to improve their strategies around the uses of legal frameworks. These findings contribute to understanding how the dynamics around the legal framework explain processes of democratic backsliding.

Decision-making and Utility Theory

Decision-making is a process that would affect people's lives from simple daily decisions even to major life decisions. The understanding of judgment is critical for making better decisions as well as improving individual and collective outcomes. This paper would focus on historical decision theory, utility theory, prospective theory, and their applications in real life. The utility theory of the decision-making theory is a crucial part of the decision-making theories, as well as the prospect theory in the later paper. According to prospect theory and utility theory, people would make decisions based on the relative importance of the options' predicted values, and biases and heuristics would influence their choices. Both theories have applications in many fields such as economics, psychology, and medicine. They are used to form models and predict consumer behavior or financial decisions. Peoples lives are strongly associated with different decision makings, from simple to complex. The decision-making process is essential to human existence and fully connect to humans. By figuring out the decision-making processes, people can identify the factors that would influence the choices and make adjustments based on them. The application of the theories could be more practical. Thus, it is crucial to analyze or quantify it for more stable and reasonable choice-making.

New memory-based hybrid model for middle-term water demand forecasting in irrigated areas

The energy demand and their associated costs in pressurized irrigation networks together with water scarcity are currently causing serious challenges for irrigation district’s (ID) managers. Additionally, most of the new water distribution networks in IDs have been designed to be operated on-demand complexing ID managers the daily decision-making process. The knowledge of the water demand several days in advance would facilitate the management of the system and would help to optimize the water use and energy costs. For an efficient management and optimization of the water-energy nexus in IDs, longer term forecasting models are needed. In this work, a new hybrid model (called LSTMHybrid) combining Fuzzy Logic (FL), Genetic Algorithm (GA), LSTM encoder-decoder and dense or full connected neural networks (DNN) for the one-week forecasting of irrigation water demand at ID scale has been developed. LSTMHybrid was developed in Python and applied to a real ID. The optimal input variables for LSTMHydrid were mean temperature (°C), reference evapotranspiration (mm), solar radiation (MJ m−2) and irrigation water demand of the ID (m3) from 1 to 7 days prior to the first day of prediction. The optimal LSTMHybrid model selected consisted of 50 LSTM cells in the encoder submodel, 409 LSTM cells in the decoder submodel and three hidden layers in the DNN submodel with 31, 96 and 128 neurons in each hidden layer, respectively. Thus, LSTMHybrid had a total of 1.5 million parameters, obtaining a representativeness higher than 94 % and an accuracy around of 20 %.

A simulation model for predicting hospital occupancy for Covid-19 using archetype analysis

COVID-19 pandemic has sent millions of people to hospitals worldwide, exhausting on many occasions the capacity of healthcare systems to provide care patients required to survive. Although several epidemiological research works have contributed a variety of models and approaches to anticipate the pandemic spread, very few have tried to translate the output of these models into hospital service requirements, particularly in terms of bed occupancy, a key question for hospital managers. This paper proposes a tool for predicting the current and future occupancy associated with COVID-19 patients of a hospital to help managers make informed decisions to maximize the availability of hospitalization and intensive care unit (ICU) beds and ensure adequate access to services for confirmed COVID-19 patients. The proposed tool uses a discrete event simulation approach that uses archetypes (i.e., empirical models of trajectories) extracted from empirical analysis of actual patient trajectories. Archetypes can be fitted to trajectories observed in different regions or to the particularities of current and forthcoming variants using a rather small amount of data. Numerical experiments on realistic instances demonstrate the accuracy of the tool’s predictions and illustrate how it can support managers in their daily decisions concerning the system’s capacity and ensure patients the access the resources they require.