Decision-making Process Research Articles

Navigating shared decision-making after the Life-Sustaining Treatment Decision Act: a qualitative study of in-depth interviews with terminal cancer patients, families, and healthcare professionals.

End-of-life decision-making, particularly relating to withholding life-sustaining treatment (LST), is a complex and emotionally charged process involving healthcare professionals, patients, and caregivers. This qualitative study explored the decision-making process in South Korea, where cultural norms and ethical considerations influence the dynamics of shared decision-making (SDM). In-depth interviews were conducted with healthcare professionals, patients, and caregivers using a grounded theory approach to elucidate the themes and processes underlying SDM for LST. This study used the "6C" framework, which could reflect the intricacies of the SDM process. The results suggest that healthcare professionals face emotional challenges and an ethical dilemma in disclosing prognoses and discussing LST withholding, often deferring such discussions until the condition of patients worsens. Cultural factors, such as collectivist values and societal taboos surrounding death, influence decision-making dynamics, highlighting the need for tailored interventions and cultural competence in healthcare settings. The proposed "6C" framework provides insights into addressing current challenges in SDM and emphasizes the importance of cultural norms and ethical obligations in end-of-life decision-making. Further research is warranted to examine the SDM process in diverse cultural contexts and develop interventions to enhance patient and family involvement in the decision-making process for LST.

A Review on PDF Malware Detection: Toward Machine Learning Modeling with Explainability Analysis

The "PDF Malware Detection: Toward Machine Learning Modeling with Explainability Analysis" project aims to develop a machine learning model to detect malware embedded within PDF files, focusing on enhancing both detection accuracy and transparency. By leveraging explainable AI techniques, the model seeks not only to identify malicious PDFs but also to provide insights into the decision-making process, offering a clear understanding of the features contributing to the detection of potential threats. This project combines cybersecurity and machine learning to improve the safety of digital documents in a user-comprehensible way.

Obstacles to the Full Realization and Adoption of Artificial Intelligence (AI)

Artificial Intelligence (AI) holds transformative potential across various sectors, yet its journey to full realization and widespread adoption is impeded by several significant obstacles. These challenges span technical, ethical, societal, research and development, implementation, and regulatory domains, necessitating a comprehensive and multi-faceted approach to address them.Technical Challenges: AI systems require vast amounts of high-quality, labeled data, which is often unavailable or of poor quality. AI models also struggle to generalize beyond specific tasks, and their opaque decision-making processes hinder trust. Scaling AI to handle large datasets and interactions, while ensuring robustness and security against adversarial attacks, poses major technical hurdles.Ethical and Societal Challenges: AI can perpetuate and amplify biases, leading to unfair outcomes and privacy issues. AI-driven automation risks job displacement, necessitating measures for worker support and retraining. The rapid development of AI outstrips regulatory bodies' abilities to create appropriate frameworks, complicating responsible deployment.Research and Development Challenges: AI struggles to integrate information from different modalities and lacks human-like common sense and reasoning. Improving AI's learning efficiency to reduce its dependence on vast data is a significant research focus.Implementation Challenges: Integrating AI into legacy systems, addressing the talent shortage, and managing the high costs of AI development and deployment are substantial barriers.Ethical and Regulatory Uncertainty: The absence of universally accepted ethical guidelines and varying regulatory landscapes create uncertainty and potential misuse.Addressing these multifaceted obstacles requires a concerted effort from researchers, developers, policymakers, and society to create robust, fair, and transparent AI systems, unlocking AI's full potential and societal benefits.

Fashioning of the Homo Economicus (Economic Citizen): Reflections on Migration and Integration

ABSTRACT This article critically explores what is meant by integration and the expectations that come with it, and how the ‘right’ kind of economic citizen (homo economicus) gets produced through social work practice with immigrants. This article engages in critical reflection on a participant’s narrative that derives from a larger study that investigated structural and individualistic factors that describe how Ethiopian immigrants living in Toronto, Canada negotiate the decision-making process to returning to their homeland. At the macro-level, the study explored the role that broader processes of social, political and economic transformation (globalization) play whilst at the micro-level exploring the aspirations, capabilities and individual agency to migrate out and how their experiences of settlement shape the return-thinking process. The participant accounting analyzed in this article probes the expansion of immigrant selection in Canada (Author), the policy of multiculturalism as Canada’s most recognized policy as it pertains to integration, and how they coalesce and shape settlement work with immigrants. This article, through the participant accounting, engages the question of what is meant by integration – and whether integration is the new assimilation – and concludes with a discussion on how to begin to address concerns that surround social work practice with immigrants.

Project management challenges in client‐consultant decision‐making: A case for the Ghanaian mining construction industry

AbstractThis study explores the complex decision‐making (DM) dynamics between clients and consultants, a crucial area often neglected in existing research. Utilizing a Multi‐Perspective Approach combined with Multi‐Criteria Decision Analysis, we examine DM challenges such as cost overruns, project delays, and compromised work quality stemming from ineffective DM practices. Key findings highlight the pervasive fear of financial repercussions from poor decisions, which impedes decisive and transparent DM. We offer practical recommendations for managers, propose strategies to strengthen DM processes, enhance project execution frameworks and insights to improve operational efficiency and competitive advantages for Ghanaian mining construction sector stakeholders. This study significantly contributes to the literature on project management by offering targeted solutions and advancing knowledge on DM practices.

Factors affecting emergency medical dispatchers decision making in stroke calls - a qualitative study.

Emergency Medical Communication Centers (EMCC) have a key role in the prehospital chain-of-stroke-survival by recognizing stroke patients and reducing prehospital delay. However, studies on EMCC stroke recognition report both substantial undertriage and overtriage. Since mis-triage at the EMCC challenges the whole chain-of-stroke-survival, by occupying limited resources for non-stroke patients or failing to recognize the true stroke patients, there is a need to achieve a more comprehensive understanding of the dispatchers' routines and experiences. The aim of this study was to explore factors affecting EMCC dispatcher's decision-making in stroke calls. A qualitative exploratory study, based on individual semi-structured interviews of 15 medical dispatchers from EMCC Oslo, Norway. Interviews were conducted during August and October 2022 and analyzed using the principles of thematic analysis. We identified four themes: [1] Pronounced stroke symptoms are easy to identify [2]. Non-specific neurological symptoms raise suspicion of acute stroke but are difficult to differentiate from other medical conditions [3]. Consistent use of the Criteria Based Dispatch (CBD) protocol may increase EMCC overtriage [4]. Contextual conditions at EMCC can affect dispatchers' decision-making process and the ability for experiential learning. Medical dispatchers at the EMCC perceive vague and non-specific stroke symptoms, such as dizziness, confusion or altered behaviour, challenging to differentiate from symptoms of other less time-critical medical conditions. They also perceive the current CBD protocol in use as less supportive in assessing such symptoms. High workload and strict EMCC response time interval requirements hinder the gathering of essential patient information and the ability to seek guidance in cases of doubt, potentially exacerbating both EMCC undertriage and overtriage. The absence of feedback loops and other strategies for experiential learning in the EMCC hampers the medical dispatcher's ability to evaluate their own assessments and improve dispatch accuracy.

Enhancing misogyny detection in bilingual texts using explainable AI and multilingual fine-tuned transformers

AbstractGendered disinformation undermines women’s rights, democratic principles, and national security by worsening societal divisions through authoritarian regimes’ intentional weaponization of social media. Online misogyny represents a harmful societal issue, threatening to transform digital platforms into environments that are hostile and inhospitable to women. Despite the severity of this issue, efforts to persuade digital platforms to strengthen their protections against gendered disinformation are frequently ignored, highlighting the difficult task of countering online misogyny in the face of commercial interests. This growing concern underscores the need for effective measures to create safer online spaces, where respect and equality prevail, ensuring that women can participate fully and freely without the fear of harassment or discrimination. This study addresses the challenge of detecting misogynous content in bilingual (English and Italian) online communications. Utilizing FastText word embeddings and explainable artificial intelligence techniques, we introduce a model that enhances both the interpretability and accuracy in detecting misogynistic language. To conduct an in-depth analysis, we implemented a range of experiments encompassing classic machine learning methodologies and conventional deep learning approaches to the recent transformer-based models incorporating both language-specific and multilingual capabilities. This paper enhances the methodologies for detecting misogyny by incorporating incremental learning for cutting-edge datasets containing tweets and posts from different sources like Facebook, Twitter, and Reddit, with our proposed approach outperforming these datasets in metrics such as accuracy, F1-score, precision, and recall. This process involved refining hyperparameters, employing optimization techniques, and utilizing generative configurations. By implementing Local Interpretable Model-agnostic Explanations (LIME), we further elucidate the rationale behind the model’s predictions, enhancing understanding of its decision-making process.

"I think all of us should have […] much better training in ethics." Ethical challenges in policy making during the COVID-19 pandemic: Results from an interview study with Swiss policy makers and scientists.

The COVID-19 pandemic posed many unprecedented challenges to health care systems and public health efforts worldwide. Policy making and science were deeply intertwined, in particular with regard to the justification of health policy measures. In this context, ethical considerations were often at the core of decision-making trade-offs. However, not much is known about the actual ethical challenges encountered by policy makers and scientists involved in policy advice. With this study, we therefore aim to explore the ethical challenges during COVID-19-related political decision-making in Switzerland as perceived by policy makers and scientists involved in policy making. We also explore the role ethics advice had during the pandemic response and what can be learned for future public health crises. We conducted thirteen qualitative expert interviews with policy makers and scientists involved in decision-making on COVID-19 policy responses in Switzerland on the regional and national level. We used inductive content analysis to analyse the interviews. Among the multitude of ethical challenges highlighted, interviewees perceived making trade-offs between the common good vs. the individual good and between economic welfare vs. health of the population, as well as proportionality of the policy measures, and the capacity of the public to accept uncertainty as central. Interviewees had diverging opinions on whether ethical considerations were sufficiently raised and discussed on the Swiss policy level during the COVID-19 pandemic. Among the reasons why ethics was not sufficiently discussed, they mentioned a lack of time in the fast-paced dynamic of the pandemic, ethics as a complex subject area, the interconnectedness between ethics and law, too much focus on few topics (mostly on vaccination-related ethical questions), and power relationships, such as dominance of medical professionals over ethicists. They evaluated ethics support to have been adequately present in the decision-making process, but wished for ethics training, involvement of the public in the discourse and for accompanying communication to build trust among the population for the future. The study provides empirical insights into the ethical considerations of COVID-19 policy making in practice in Switzerland. It can help to develop ethics assistance for future crises and inform ethical health policy and decision-making not only in Switzerland, but also in other countries.

'For Youth by Youth': Distributive Leadership in Action With a Youth Codesign Team.

Integrated youth services are an emerging delivery model in Canada that addresses siloed and fragmented youth mental health and other services. Youth engagement is viable for developing integrated youth services when purposefully built. However, it is not always clear how youth are involved in service transformation as decision-makers, and it requires an exploration of how to work with youth authentically and intentionally in the codesign process. This study reflects on the development of HOMEBASE, a network of integrated youth service delivery in Saskatchewan, Canada, and documents the process of actively and authentically engaging with youth through distributive leadership in the codesign process. Youth are actively and eagerly willing to participate in the codesign process of developing integrated services when there is a shared responsibility, and they are authentically involved and informed within the decision-making process. This requires time to form trust, build relationships and provide youth with low-pressure environments to foster healthy debates. By utilizing a distributive leadership approach, the Youth Codesign Team has been engaged in various levels of decision-making. By following these guiding principles, policymakers, youth development workers and researchers can engage youth in meaningful ways to improve the design and development of integrated care. Five youths from the HOMEBASE Provincial Youth Co-Design Team collaborated in writing this article based on their experiences of being engaged at varying levels of decision-making in a distributive leadership approach to building integrated youth services.

A Consensus Reaching Process for Product Design Decision-Making by Integrating Intuitionistic Fuzzy Sets and Trust Network

A Consensus Reaching Process for Product Design Decision-Making by Integrating Intuitionistic Fuzzy Sets and Trust Network

Factors associated with access and utilization of sexual abuse emergency healthcare services among street children in Zomba, Malawi: a qualitative study.

Sexual abuse among street children is a problem that renders a burden of sexually transmitted diseases, HIV infection, and early pregnancy. Literature shows that globally 95 million children experience sexual abuse with 1 in 5 girls and 1 in 7 boys exposed to sexual abuse before 18years of age in Malawi. Malawi adopted the World Health Organization guidelines for providing emergency health services for victims of sexual abuse, which include HIV Post Exposure Prophylaxis (PEP) and Emergency Contraceptive Pills (ECP) within 72h of exposure, Sexually Transmitted Infections (STI) treatment, and psychosocial services. However, there are challenges associated with the services that limit access and utilization among street children. This study explored the factors associated with access and utilization of sexual abuse emergency healthcare services among street children in Zomba. This was a cross-sectional phenomenological qualitative study conducted in Zomba City from 2022 to 2023 using in-depth and key informant interviews. We purposively selected street children between 10 to 17years who have been exposed to sexual abuse on the streets and social actors working with street children. The study employed a thematic analysis approach. The study found that street children did not utilize sexual abuse emergency health services. The major factors associated with utilization included the knowledge of sexual abuse, its associated health risks and sexual abuse emergency health services, and perceptions of utilizing sexual abuse emergency health services. The barriers to utilization of sexual abuse emergency health services included perceived shame, fear, discrimination, prolonged treatment process, and attitude of the health service providers. Sexual abuse and its health risks continue to be a challenge among street children. The absence of adequate connections and secure environments for street children to report sexual abuse and seek help without facing judgment has created significant obstacles for them in accessing sexual abuse emergency health services. To curb this challenge it is crucial for successful interventions to specifically address the health needs of street children and involve them in the decision-making processes related to their interventions.

Radiomics-based predictive model for preoperative risk classification of gastrointestinal stromal tumors using multiparametric magnetic resonance imaging: aretrospective study.

The aim of this study was to develop and assess aradiomics model utilizing multiparametric magnetic resonance imaging (MRI) for the prediction of preoperative risk assessment in gastrointestinal stromal tumors (GISTs). An analysis was performed retrospectively on agroup of 121 patients who received ahistological diagnosis of GIST. They were then divided into two sets, with85 in the training set and 36 in the validation set through random partitioning. Radiomics features from five MRI sequences, totaling 600 per patient, were extracted and subjected to feature selection utilizing arandom forest algorithm. The discriminatory efficacy of the models was evaluated through receiver operating characteristic (ROC) and precision-recall (P-R) curve analyses. Model calibration was assessed via calibration curves. Subgroup analysis was performed on GISTs with apathological maximum diameter equal to or less than 5 cm. Furtherly, Kaplan-Meier (K-M) curves and log-rank tests were used to compare the differences in survival status among different groups. Cox regression analysis was employed to identify independent prognostic factors and to construct aprognostic prediction model. The clinical model (ModelC) displayed limited predictive efficacy in the context of GIST. Conversely, aradiomics model (ModelR) incorporating five parameters exhibited robust discriminative capabilities across both the training and validation sets, yielding area under the ROC curve (AUC) values of 0.893 (95% confidence interval [CI]: 0.807-0.949) and 0.855 (95% CI: 0.732-0.978), respectively. The F1max scores derived from the P‑R curves were 0.741 and 0.842 for the training and validation sets, respectively. Noteworthy was the exclusion of the two-dimensional tumor diameter and tumor location when constructing ahybrid model (ModelCR) that amalgamated radiomics and clinical features. ModelR demonstrated asubstantially enhanced discriminative capacity in the training set compared with ModelC (p < 0.005). The net reclassification improvement (NRI) corroborated the superior performance of ModelR over ModelC, thereby enhancing diagnostic accuracy and clinical applicability. Patients in the high-risk group had significantly worse recurrence-free survival (RFS, p < 0.001) and overall survival (OS, p = 0.004), and the radiomics signature is an independent risk factor for RFS. The extended model incorporating the radiomics signature outperformed the baseline model in terms of risk assessment accuracy (p < 0.001). Our investigation underscores the value of integrating radiomics analysis in conjunction with machine learning algorithms for prognostic risk stratification in GIST, presenting promising implications for informing clinical decision-making processes as well as optimizing management strategies.

Digital Architectural Form Generation Through Pixel System-Driven Image Feature Information

This study analyzes valuable information (image feature information) from pixel-based images to systematize the form generation process utilizing this information. Information in architecture is mainly used as an analytical tool for functional design optimization. However, the Fourth Industrial Revolution and advances in information and communication technology have positioned information as a core value-creating tool. Consequently, there is a growing need to develop methodologies for effectively applying information to architectural design generation. This study combines digital technology with images, which directly influence architectural design, transforming them into numerical information. By systematizing the three-dimensional form generation process using low-level image feature information, it analyzes the relationship between the characteristics of valuable information and the generated forms. This approach helps identify the interrelationships between the characteristics of valuable information and the generated forms, the complementarity needed to mitigate the limitations of valuable information, and the potential for architectural applications of the three-dimensional form generation methodology utilizing image information. This approach reduces labor and enhances architectural aesthetics by instantly generating diverse early design alternatives. Such research can provide a more logical basis for avoiding intuitive judgments during the design decision-making process. It can also lay the groundwork for information-centric design systems in architecture.

Emotions and decision-making in boardrooms—a systematic review from behavioral strategy perspective

This systematic review examines the influence of emotions on strategic decision-making in business context, from the perspective of behavioral strategy. We examined 1,227 articles from two databases (Web of Science and PsycInfo), and after applying the inclusion and exclusion criteria, the final sample resulted in 43 articles. Our systematic review focuses on the role of emotions in strategic decision-making as well as the decision-making process itself. This systematic review explores research using a variety of approaches and a combination of theoretical and empirical perspectives brought by the literature. It aims to address three main questions: how board members’ emotions influence their decision-making; what insights behavioral strategy provides on the emotional aspect of strategic decision-making; and what are the main theories linking emotions to strategic decision-making in the business context. The results demonstrate how emotions can affect the quality of decisions and imply that conflict resolution and emotional intelligence are relevant skills for making strategic decisions. This analysis supports the need for incorporating emotional insights into strategic planning methods by considering agreeable and divergent points of view.

Factors Associated With Biologic Therapy After Ileal Pouch-Anal Anastomosis in Patients With Ulcerative Colitis.

Patients with ulcerative colitis (UC) undergoing proctocolectomy and ileal pouch-anal anastomosis (IPAA) may eventually require biologic therapy. Factors associated with biologic therapy after IPAA have not been previously studied. All patients with UC after total proctocolectomy and IPAA who were followed at Rabin Medical Center comprehensive pouch clinic and who consented to prospective observational follow-up were included. The primary outcome was the initiation of biologic therapy after IPAA. Cox proportional hazard models were used to evaluate potential associations. Out of 400 patients receiving their care at the pouch clinic, 148 patients consented to prospective observational follow-up and constituted the study cohort. The median age at diagnosis was 21 years and the age at IPAA was 30 years. Median time-to-biologic therapy initiation post-IPAA was 9.2 years, with 34 patients (23%) initiating biologic therapy: Associated factors for initiating biologic therapy post-IPAA were preoperative treatment with biologic therapy and immunomodulatory therapy (hazard ratio [HR] 6.1 and 3.6, respectively, P < .001); Arab descent (HR 5.3, P < .001); heterozygosity of NOD2 variant rs2066845 (HR 5.1, P = .03); past smoking status (HR 2.3, P = .03); 3-stage IPAA (HR 2.3, P = .02); immediate postoperative complications (HR 2.1, P = .033); and pediatric-onset UC (HR 2.1, P = .03). None of the patients undergoing IPAA due to dysplasia (n = 27) required biologic therapy. Several demographic, disease-related, surgery-related, and genetic factors associated with post-IPAA biologic therapy were identified. Physicians treating patients with UC undergoing colectomy should incorporate these factors into their decision-making process. These patients may benefit from closer postoperative follow-up, and earlier initiation of biologic therapy should be considered.

Do you love traveling to the beach or mountain? Predicting personality traits and choice behaviour

ABSTRACT Several personality-predicting tools are being used by managers in their strategic decision-making process. The most common are the Big Five model and the Myers – Myers-Briggs Type Indicator model, respectively. This study deviates and tests a novel personality prediction model. Specifically, it utilizes the TF-IDF (Term Frequency-Inverse Document Frequency) algorithm as a feature extraction method to map personality traits based on consumers’ textual data from the Kaggle MBTI Personality Type dataset. This study uses realistic data gathered from traveller reviews and comments on hotel stays from TripAdvisor’s widely recognised platform to predict different personality traits and their travel destination preferences, i.e. beach or mountain. The contribution of this study lies in the identification of multiple dimensions of peoples’ personality traits and their relationship with their preferences for travel locations. This proposed trait classification mechanism can assist marketing managers in effectively carrying out customer segmentation, setting realistic goals, and optimizing marketing strategies.

Impacts of Climate Change: Examining Precipitation, Temperature Anomalies, and Effects on Water Resources in Turkey

Climate change is a significant global issue that affects our everyday lives and water resources. One of the most critical impacts of climate change is on our water resources, which are vital for sustaining life and supporting various ecosystems. Understanding the complex relationship between climate change and water resources is crucial for developing effective mitigation and adaptation strategies. This study aims to investigate the impact of climate change on water resources and to shed light on the importance of addressing this issue. Water is a limited resource, and its availability and quality directly affect human well-being, agriculture, energy production, and ecosystem health. However, climate change alters the patterns and dynamics of precipitation, temperature, and evaporation, leading to significant changes in water availability and distribution. Increasing global temperatures cause glaciers and ice caps to melt, progressively depleting freshwater resources. Moreover, changing precipitation patterns are leading to more frequent and severe droughts in some regions, while others experience increased rainfall and flooding events. The effects of these changes are profound. As water scarcity becomes more widespread, competition for limited resources intensifies, potentially leading to conflicts and social unrest. Given the importance of water resources and the undeniable impact of climate change on their availability and quality, it is imperative that we deepen our understanding of this complex relationship. This study aims to assess the socio-economic and environmental impacts of climate change on water resources. By doing so, it seeks to provide policymakers, scientists, and communities with information that will guide decision-making processes and direct effective climate change adaptation and mitigation strategies. In conclusion, the impact of climate change on water resources cannot be overstated. This study aims to contribute to our understanding of this critical issue by highlighting the urgency of addressing climate change and its consequences on water resources and sustainability. This study examines the effects of changing precipitation patterns and increasing temperatures due to climate change on underground and surface water resources in Turkey. By conducting this research, we hope to pave the way for informed decision-making and encourage collective responsibility to protect our water resources for future generations.

Swarm Intelligence and Unmanned Systems: The Potentıal Impact of The Principles of Swarm Intelligence and Collective Behaviour in Nature On Unmanned Systems and Autonomous Organizational Structures

This paper examines the potential implications of the principles of swarm intelligence and collective behavior in nature for unmanned systems and autonomous organizational structures. Swarm intelligence is inspired by natural systems in which individual units interact according to simple rules to form a complex and organized whole. These principles can be observed in a wide range of situations, from the synchronized flight of flocks of birds to the harmonized swimming behavior of schools of fish. The study emphasizes that swarm intelligence principles have the potential to create more flexible, resilient and efficient systems with decentralized control mechanisms and autonomous decision-making processes. Furthermore, it is suggested that these approaches can find applications in many fields, from military operations to agricultural and environmental monitoring, from disaster response to urban planning. The study provides a detailed analysis of swarm behavior in nature and discusses how these behaviors can be emulated and optimized in unmanned systems. In this context, the potential impacts of swarm intelligence and collective behavior principles on unmanned systems are evaluated in terms of increasing their adaptability, optimizing energy efficiency and maximizing mission success. It is also argued that these principles can contribute to making unmanned systems more resilient to contingencies and changing environmental conditions. This theoretical study suggests that swarm intelligence principles can provide innovative solutions for future unmanned systems and autonomous organizational structures.

Navigating Concurrent Disasters: Lessons learned from a Hospital Evacuation Amidst a Pandemic and an Earthquake.

The concurrent challenges of the COVID-19 pandemic and a significant earthquake in Izmir on October 30, 2020, presented a unique scenario for disaster management and response. This study focuses on the impact of the earthquake, which resulted in 117 fatalities, including 1 due to drowning, and injured 1034 individuals, alongside widespread structural damage including to the Izmir Democracy University Buca Seyfi Demirsoy Training and Research Hospital. The objective is to assess the activation and implementation of the hospital disaster plan amidst the ongoing pandemic. Through a retrospective evaluation of all actions undertaken as per the Hospital Disaster Emergency Plan within the Disaster Management cycle, this study examines the decision-making process for the hospital evacuation on October 30, 2020, the evacuation of COVID-19 patients, and the strategies employed to increase hospital capacity. Of 216 patients hospitalized at the time of the earthquake, 65 were transferred to other facilities under COVID-19 protocols. The prolonged nature of pandemics and the likelihood of secondary disasters underscore the importance of comprehensive risk assessments and dynamic disaster planning, considering simultaneous multiple hazards. This study suggests the inclusion of multi hazard scenarios and diverse evacuation methods by using types of ambulances, such as ground, helicopter, and boat.

Pseudo-trajectory inference for identifying essential regulations and molecules in cell fate decisions.

Cell fate decision is crucial in biological development and plays fundamental roles in normal development and functional maintenance of organisms. By identifying key regulatory interactions and molecules involved in these fate decisions, we can shed light on the intricate mechanisms underlying the cell fates. This understanding ultimately reveals the fundamental principles driving biological development and the origins of various diseases. In this study, we present an overarching framework which integrates pseudo-trajectory inference and differential analysis to determine critical regulatory interactions and molecules during cell fate transitions. To demonstrate feasibility and reliability of the approach, we employ the differentiation networks of hepatobiliary system and embryonic stem cells as representative model systems. By applying pseudo-trajectory inference to biological data, we aim to identify critical regulatory interactions and molecules during the cell fate transition processes. Consistent with experimental observations, the approach can allow us to infer dynamical cell fate decision processes and gain insights into the underlying mechanisms which govern cell state decisions.