Real Case Research Articles

Optimal Survival Trees: A Dynamic Programming Approach

Survival analysis studies and predicts the time of death, or other singular unrepeated events, based on historical data, while the true time of death for some instances is unknown. Survival trees enable the discovery of complex nonlinear relations in a compact human comprehensible model, by recursively splitting the population and predicting a distinct survival distribution in each leaf node. We use dynamic programming to provide the first survival tree method with optimality guarantees, enabling the assessment of the optimality gap of heuristics. We improve the scalability of our method through a special algorithm for computing trees up to depth two. The experiments show that our method's run time even outperforms some heuristics for realistic cases while obtaining similar out-of-sample performance with the state-of-the-art.

HSMM multi-observations for prognostics and health management

An efficient maintenance policy allows for determining the current state of a system (diagnosis phase) and its future state (prognosis phase). We show in this paper that Markovian methods allow for obtaining many efficient indicators for the expert. To characterize the quality and robustness of these methods, we compared the Hidden Semi-Markov Model (HSMM) with the Hidden Markov Model (HMM). Several learning and decoding methods were included in the competition. A real case study was used as a particularly interesting working tool. The Remaining Useful Life (RUL) has also been included in this work.

Detection and pre-localization of anomalous consumption events in water distribution networks through automated, pressure-based methodology

Anomalous water-consumption events (AEs) can significantly impact the functioning of water distribution networks, and their prompt identification can improve the service provided by water utilities. This study proposes a new methodology for AE detection and pre-localization in water distribution networks relying exclusively on pressure-data collected in the field, which are exploited to evaluate differential-pressure trends for all possible pressure-sensors couples located in the WDN. In greater detail, AEs are detected and pre-localized by analysing differential-pressure trends over time. The level of deviation of these trends from the standard is considered to provide information about (i) AE alert levels and (ii) the area of the network where the AE is most likely to occur. The application of the methodology to two real case studies featuring different characteristics in terms of residential and industrial users demonstrated method effectiveness in detecting and pre-localizing individual and simultaneous AEs of different magnitude and occurring at different times of the day, providing useful information about the presence of AEs without the need for hydraulic models, and allowing the evaluation of their effects in terms of piezometric head alteration in the different areas of the system.

Abstract 6779: Modality agnostic tumor models of cancer which can more accurately define drug efficacy and mechanism to help develop more effective therapeutics

Abstract Introduction: In the path to clinic, researchers require robust tools to effectively evaluate the capacity of novel therapeutic modalities to generate anti-tumor responses. Using human-relevant, translational ex-vivo models is widely considered to reduce attrition during drug discovery and development for a more effective prediction of clinical outcomes during preclinical testing of drug candidates. Methods: The three levels of our patient derived tumoroid model platform, COMPASS (Custom Organoid Modeling Platform for Accurate SolutionS) was built to be versatile modality-agnostic human on human model system for pre-clinical testing. COMPASS 1 has stroma with epithelium; COMPASS 2 has stroma, epithelium, and immune components (from PBMCs) and COMPASS 3 has stroma, epithelium, immune components, and oxygenation. For widespread adaptation and utilization of our COMPASS platform we encourage extensive characterization by articulating the context of use to help build confidence in cases where limited knowledge and only few real-life cases exist. Results: Our proof-of-concept examples articulating the context of use demonstrate the modality-agnostic adaptability our COMPASS platform brings to the field of preclinical testing. We encourage a collaborative approach with researchers to support their testing needs to build confidence in our model to enable transition from existing screens, assays, and animal models. We envision preclinical use of COMPASS to help stratify patients for clinical trials, identify subpopulations that need dose modification and predict the response of patients and/or their risk of adverse effects. Conclusion: Due to its greater physiological relevance and ability to encompass downstream processes in a time and cost-efficient way, industry and academia should deliberate the impact platform such as COMPASS would have on their workflow and consider adapting to using these models routinely. Citation Format: Preethi Samuel, Damieanus Odour Ochola, Huckie Del Mundo, Bhuvanesh Dave. Modality agnostic tumor models of cancer which can more accurately define drug efficacy and mechanism to help develop more effective therapeutics [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2024; Part 1 (Regular Abstracts); 2024 Apr 5-10; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2024;84(6_Suppl):Abstract nr 6779.

Methodology of Eco-Design and Software Development for Sustainable Product Design

In the face of the growing social recognition of environmental awareness and emerging regulations in countries where targets include the reduction of the CO2 footprint in the industrial sector, several companies are facing the challenge of introducing environmental impacts as new key design criteria. To successfully launch new products with optimized environmental impact, it is crucial to apply Life Cycle Assessment (LCA) during the design phase. However, the design process of any product is a process where materials, production processes, concepts, and various design factors are constantly changing, which requires an agile LCA calculation for its effective inclusion during the iterative design process. This paper presents an eco-design methodology, based on the adaptation of the LCA method to the changing design environment, through the adaptation of LCA stages to the design process, the customization of environmental databases to the product of the company, and the development of a software tool for its application during the earlier design phases. This methodology assists designers to save efforts during the calculation process, with different integration levels of environmental data, according to LCA phases established by ISO 14040 and ISO 14044. The effectiveness of this methodology will be shown with a real case study.

Asm2Seq: Explainable Assembly Code Functional Summary Generation for Reverse Engineering and Vulnerability Analysis

Reverse engineering is the process of understanding the inner working of a software system without having the source code. It is critical for firmware security validation, software vulnerability research, and malware analysis. However, it often requires a significant amount of manual effort. Recently, data-driven solutions were proposed to reduce manual effort by identifying the code clones on the assembly or the source level. However, security analysts still have to understand the matched assembly or source code to develop an understanding of the functionality, and it is assumed that such a matched candidate always exists. This research bridges the gap by introducing the problem of assembly code summarization. Given the assembly code as input, we propose a machine-learning-based system that can produce human-readable summarizations of the functionalities in the context of code vulnerability analysis. We generate the first assembly code to function summary dataset and propose to leverage the encoder-decoder architecture. With the attention mechanism, it is possible to understand what aspects of the assembly code had the largest impact on generating the summary. Our experiment shows that the proposed solution achieves high accuracy and the Bilingual Evaluation Understudy (BLEU) score. Finally, we have performed case studies on real-life CVE vulnerability cases to better understand the proposed method’s performance and practical implications.

Navigating the Indeterminate Zone: Surgeons' Decision-Making Factors in Treating Vertebral Metastases with Spinal Instability Scores of 7–12

BackgroundThe Spinal Instability Neoplastic Score (SINS) classification system, is a validated and the most widely accepted instrument for defining instability in vertebral metastasis (VM), in which lesions scoring between 7 – 12 are defined as indeterminate and the treatment is controversial. This study aimed to determine which variables more frequently are considered by spine surgeons for choosing between the conservative and the surgical treatment of VMs among patients with an indeterminate SINS. Material and MethodsA single-round online survey was conducted in ten spine surgeons with expertise in the management of VMs from our AO Spine Region. In this survey, each surgeon independently reviewed demographic and cancer-related variables of 36 real-life cases of patients with vertebral metastases scored between 7 – 12 in the SINS. Bivariate and multivariate analyses were performed to identify significant SINS and non-SINS variables influencing the decision-making on surgical treatment. ResultsThe most commonly variables considered important, were the SINS element ‘mechanical pain’, rated important for 44.4% of the cases, ‘lesion type’ for 36.1%, ‘degree of vertebral collapse’ and the non-SINS factor ‘tumor histology’ rated for 13.9% of cases. By far the factor most commonly rated unimportant was ‘posterior element compromise’ (in 72.2% of cases). ConclusionsSurgeons relied on mechanical pain and type of metastatic lesion for treatment choices. Vertebral collapse, spinal malalignment, and mobility were less influential. Spinal mobility was a predictor of surgical versus non-surgical treatment. The only variables not identified either by surgeons themselves or as a predictor of surgery selection was the presence/degree of posterolateral/posterior element involvement.

A Bi-Objective Home Health Care Routing and Scheduling Problem under Uncertainty.

Home health care companies provide health care services to patients in their homes. Due to increasing demand, the provision of home health care services requires effective management of operational costs while satisfying both patients and caregivers. In practice, uncertain service times might lead to considerable delays that adversely affect service quality. To this end, this paper proposes a new bi-objective optimization problem to model the routing and scheduling problems under uncertainty in home health care, considering the qualification and workload of caregivers. A mixed-integer linear programming formulation is developed. Motivated by the challenge of computational time, we propose the Adaptive Large Neighborhood Search embedded in an Enhanced Multi-Directional Local Search framework (ALNS-EMDLS). A stochastic ALNS-EMDLS is introduced to handle uncertain service times for patients. Three kinds of metrics for evaluating the Pareto fronts highlight the efficiency of our proposed method. The sensitivity analysis validates the robustness of the proposed model and method. Finally, we apply the method to a real-life case and provide managerial recommendations.

An innovative method for accurate NBA player performance forecasting and line-up optimization in daily fantasy sports

AbstractThis study presents a novel approach for predicting NBA players' performance in Fantasy Points (FP) by developing individualized models for 203 players, using advanced basketball metrics from season 2011–2012 up to season 2020–2021 from reliable sources. A two-step evaluation and validation process secured validity, while applying linear optimization methodology, considering constraints such as salary and player position to recommend an eight-player line-up for Daily Fantasy Sports (DFS). Four scenarios with 14 machine learning models and meta-models with a blending approach with an ensembling methodology were evaluated. Using individual per-player modeling, standard and advanced features, and different timespans resulted in accurate, well-established, and well-generalized predictions. Standard features improved MAPE results by 1.7–1.9% in the evaluation and 0.2–2.1% in the validation set. Additionally, two model selection cases were developed, with average scoring MAPEs of 28.90% and 29.50% and MAEs of 7.33 and 7.74 for validation sets. The most effective models included Voting Meta-Model, Random Forest, Bayesian Ridge, AdaBoost, and Elastic Net. The research demonstrated practical application using predictions in a real-life DFS case evaluated in a DFS tournament on a specific match day. Among 11,764 real users, our Daily Line-up Optimizer ranked in the top 18.4%, and profitable line-ups reached the top 23.5%. This unique approach proves the proposed methodology's effectiveness and emphasizes its profitability, as the optimizer process delivers positive results.

Iterative learning robust optimization - with application to medium optimization of CHO cell cultivation in continuous monoclonal antibody production

In the presence of uncertainty, the optimum obtained based on a nominal identified model can neither provide any performance guarantee nor ensure that critical constraints are satisfied, which is crucial for e.g., bioprocess applications characterized by a high degree of complexity combined with costly experiments. Hence, uncertainty should be considered in the optimization and, furthermore, experiments designed to reduce the uncertainty most important for optimization. Herein, we propose a general framework that combines model-based robust optimization with optimal experiment design. The proposed framework can take advantage of prior knowledge in the form of a mechanistic model structure, and the importance of this is demonstrated by comparing it to more standard black-box models typically employed in learning. Through optimal experiment design, we repeatedly reduce the uncertainty most relevant for optimization so as to maximize the potential for improving the worst-case performance by balancing between exploration and exploitation. This makes the proposed method an efficient model-based robust optimization framework, especially in cases with limited experimental resources. The main part of the paper focuses on the case with modeling uncertainty that can be reduced with the availability of more experimental data. Towards the end of the paper, we consider extending the method to also include inherent uncertainty, such as input uncertainty and unmeasured disturbances. The effectiveness of the method is illustrated through a realistic simulation case study of medium optimization of Chinese hamster ovary cell cultivation in continuous monoclonal antibody production, where the metabolic network consists of 23 extracellular metabolites and 126 reactions.

Towards a new framework to assess agri-food value chains’ sustainability – The case of chestnut value chain

Food systems rely on natural resources for production causing their depletion. Sustainability assessment can encourage farms and agri-food companies to improve sustainability performances. Sustainability assessment frameworks and tools differ in their purposes, scope, methods of application, and required time for execution; however, most of them do not fit with value chains, or they do not cover all sustainability dimensions. Our objective is to propose a holistic framework to assess sustainability at agri-food value chains level. The proposed framework combines the Sustainability Assessment of Food and Agriculture systems (SAFA) (El Hage, 2012) [1] and The Agri-food Evaluation Framework (TEEB) [2]. It incorporates the concepts of Socio-Ecological Systems, Assemblage, and Social Practices. It integrates system dynamics by emphasising human and natural capital stocks and their users. We explain in detail the methodological steps we followed to construct and to apply this new framework to two case studies in Italy and France. The new framework was applied to real-life case studies and has shown its effectiveness and demonstrates its potential for widespread use in similar scenarios.

Climatic scenario-based integrated recurrent ensemble model for energy demand forecasting

This study presents an improved approach to estimate future energy demand based on climate scenarios, upstream documents, and Machine Learning (ML). To accurately predict future weather conditions, a Regional Climate Model (RCM) is developed. The RCM incorporates an ensemble approach that combines the power of recurrent neural network and recurrent random forest algorithms to analyze relevant data. The demand model incorporates correction coefficients that are derived from behavioral and variations in income, coupled with an ensemble and recurrent ML models. Four scenarios named from the distinctive wildlife are considered for Tehran, I. R. Iran as a real case study for megacities. The average temperature in the Caracal, Striped hyena, and Balochistan black bear scenarios is projected to be 0.91 °C, 2.01 °C, and 2.86 °C higher than the Persian cheetah scenario between 2015 and 2100. Similarly, natural gas demands are estimated to be 0.93, 4.91, and 3.38 times higher than the Persian cheetah scenario by 2050, respectively. The electricity demand is also 14, 3.98, and 3.50 times higher in the Caracal, Striped hyena, and Balochistan black scenarios compared to the Persian cheetah.This study has developed scenarios delineating future temperature conditions, as well as revealing how energy demand changes within climate change. The results of this study precisely identify the influential features for predicting future energy demand and temperature, indicating that utilizing recurrent models is preferable for forecasting future temperature and energy demand.These changes in the energy demand have significant implications for investment in the energy supply sector as well as the greenhouse gas emissions and the air pollution levels. Therefore, it is crucial to prioritize climate scenarios that lead to a decrease in energy demand to reach sustainable zero-emission cities.

The effect of autonomous team role selection in flexible projects

Today, most software projects are flexible, such as agile, extreme, or hybrid. In addition, this approach is increasingly being adopted for other types of projects. Most software project scheduling problems (SPSPs), more broadly, project scheduling problems (PSPs) and resource allocation methods, assume a fixed project structure. If we use these traditional methods, we cannot model and therefore understand why certain flexible, e.g., agile, policies are essential and how they help project management. This research shows how a matrix-based project planning approach can model team roles and synergies between employees. Extending the synergy-based software project scheduling problem, this study shows the effects of autonomous team role selection, highlighted in agile projects. The proposed model can manage (1) flexible projects, (2) synergies between employees, and (3) the difference between hard skills and soft skills. The proposed method is evaluated in a real-life case study. The proposed method is compared with existing, nonsynergy-based project scheduling methods. We found that positive synergy between employees can reduce the makespan of the project, and the best solution is given in autonomous team role selection.

Gradient‐based eigenvalue optimization for electromagnetic cavities with built‐in mode matching

AbstractShape optimization with respect to eigenvalues of a cavity plays an important role in the design of new resonators or in the optimization of existing ones. This paper proposes a gradient‐based optimization scheme, which is enhanced with closed‐form shape derivatives of the system matrices. Based on these, accurate derivatives of eigenvalues, eigenmodes, and the cost function can be computed with respect to the geometry, which significantly reduces the computational effort of the optimizer. The work is demonstrated by applying it to the 9‐cell TESLA cavity, for which the design parameters of the computational model are considered as optimization variables to match the design criteria for devices in realistic use cases. Since eigenvalues may cross during the shape optimization of a cavity, a new algorithm based on an eigenvalue matching procedure is proposed, to ensure the optimization of the desired mode in order to also enable successful matching along large shape variations.

A two-stage decision model for sustainable-resilient supplier selection and order allocation under uncertain environment

PurposeThe paper aims to propose an innovative two-stage decision model to address the sustainable-resilient supplier selection and order allocation (SSOA) problem in the single-valued neutrosophic (SVN) environment.Design/methodology/approachFirst, the sustainable and resilient performances of suppliers are evaluated by the proposed integrated SVN-base-criterion method (BCM)-an acronym in Portuguese of interactive and multi-criteria decision-making (TODIM) method, with consideration of the uncertainty in the decision-making process. Then, a novel multi-objective optimization model is formulated, and the best sustainable-resilient order allocation solution is found using the U-NSGA-III algorithm and TOPSIS method. Finally, based on a real-life case in the automotive manufacturing industry, experiments are conducted to demonstrate the application of the proposed two-stage decision model.FindingsThe paper provides an effective decision tool for the SSOA process in an uncertain environment. The proposed SVN-BCM-TODIM approach can effectively handle the uncertainties from the decision-maker’s confidence degree and incomplete decision information and evaluate suppliers’ performance in different dimensions while avoiding the compensatory effect between criteria. Moreover, the proposed order allocation model proposes an original way to improve sustainable-resilient procurement values.Originality/valueThe paper provides a supplier selection process that can effectively integrate sustainability and resilience evaluation in an uncertain environment and develops a sustainable-resilient procurement optimization model.

Reliability, validity and acceptability of an online clinical reasoning simulator for medical students: An international pilot

Introduction Clinical reasoning skills are essential for decision-making. Current assessment methods are limited when testing clinical reasoning and management of uncertainty. This study evaluates the reliability, validity and acceptability of Practicum Script, an online simulation-based programme, for developing medical students’ clinical reasoning skills using real-life cases. Methods In 2020, we conducted an international, multicentre pilot study using 20 clinical cases with 2457 final-year medical students from 21 schools worldwide. Psychometric analysis was performed (n = 1502 students completing at least 80% of cases). Classical estimates of reliability for three test domains (hypothesis generation, hypothesis argumentation and knowledge application) were calculated using Cronbach’s alpha and McDonald’s omega coefficients. Validity evidence was obtained by confirmatory factor analysis (CFA) and measurement alignment (MA). Items from the knowledge application domain were analysed using cognitive diagnostic modelling (CDM). Acceptability was evaluated by an anonymous student survey. Results Reliability estimates were high with narrow confidence intervals. CFA revealed acceptable goodness-of-fit indices for the proposed three-factor model. CDM analysis demonstrated good absolute test fit and high classification accuracy estimates. Student survey responses showed high levels of acceptability. Conclusion Our findings suggest that Practicum Script is a useful resource for strengthening students’ clinical reasoning skills and ability to manage uncertainty.

Swash-Zone Formula Evaluation of Morphological Variation in Haeundae Beach, Korea

In this study, a swash-zone model, using Larson and Wamsley formula (LW07), was combined into the Telemac-2D model system to examine the performance of modeling swash-zone processes through comparisons with field observation data. The experimental site was the Haeundae Beach in South Korea where Typhoon Phanfone occurred in October 2014, and bathymetric surveys were performed before and after the typhoon. Hydrodynamic data were also measured to validate the modeled data. The performance of LW07 was tested by running the model in two modes, with and without LW07. First, the model was run to simulate the shoreline response to an imaginary coastal breakwater. The result showed a clear discrepancy between the two modes as the sediments were considerably cumulated behind the breakwater in the case with the swash-zone formula (LW07) in the wide range along the shoreline behind the breakwater, indicating that the sediments more actively and rapidly responded to the shadowing by the breakwater with LW07. The model was also run for a realistic case from August to October 2014, which included the typhoon’s period during 2–6 October. The results showed that the morphological changes at both ends of the beach in the swash zone were simulated with higher accuracy with LW07, supporting the effectiveness of LW07 in simulating the short-term morphological changes induced by the typhoon attack. In particular, the successful simulation of the sand accumulation at the end sides of the beach’s swash zone indicates that LW07 was effective in estimating not only the cross-shore transport but also longshore transport, which was likely due to the characteristics of LW07 that calculated sand transport in both directions. The enhanced modeling performance with LW07 was likely due to the adjustment of the sediment transport rate to the instantaneous changes in the local beach slope, which could successfully control the erosion/accretion process in the swash zone more realistically.

Using Innovative Tools and Lean Writing Workflow Processes to Accelerate Regulatory Document Writing

Lean writing workflow processes can streamline development of complex documents, including protocols and other regulatory submissions. This paper outlines standard development timelines and describes useful lean writing workflow methods and tools to reduce protocol development timelines without sacrificing document quality. We present the results of a survey completed by 75 Medical writers (MW) that reveal the strategies used during document preparation and use real case scenarios (protocol development) that illustrate how innovative tools and approaches can be efficiently used to create a lean writing workflow process. Software tools (e.g., proofreading assistance, citation manager, collaborative authoring and reviewing) were widely accepted and used during protocol development according to the MWs surveyed. Most of the surveyed writers (62%) hypothesized a reduction in production time by about 25%. Efficient collaborative work across different time zones had a marked impact on shortening timelines while maintaining production of high-quality documents. Real case scenarios were used to illustrate how innovative tools and approaches can positively impact timelines and efficiently create a lean writing workflow process regardless of therapeutic area. The real case scenarios showed that protocol development time can be decreased by about 26% without compromising the quality of the document. This shows that software tools, templates, checklists, and collaborative writing were more impactful than hypothesized by the MWs included in the survey. With clear workflow processes, roles, and responsibilities backed by innovative software applications, template guidelines, and collaborative work processes, MWs can overcome common development challenges and improve their lean regulatory writing skills.

Human rights in international law, state responsibilities and accountability mechanisms: a case study of Iran

ABSTRACT This research presents a critical theoretical and practical re-examination of the link between human rights' issues and politics at national and international levels. The study of human rights is an inherently multidisciplinary enterprise. The paper provides an insight into the relations between diplomacy and human rights and discusses how the latter are exploited for political and commercial aims. This study focuses on the case and constitution of the Islamic Republic of Iran (IRI), and the problem of hostage taking. An internationally-oriented study and based on theoretical arguments and an empirical research approach-notably by using several real cases of ordinary individuals of foreign and dual nationalities who became hostages and the subject of political games and financial conflicts between Iran and the Western countries-the paper highlights the issue of hostage taking of individuals by a state as a political policy and prisoner swaps involving financial and commercial deals. The paper provides evidence of the substantial suffering of the hostages and their families. The paper makes an academic contribution to the literature-the unlawful act of hostage taking involving a state has rarely been discussed -and addresses the legal vacuum in international law and human rights conventions regarding the topic.

The Adoption of Artificial Intelligence in Bureaucratic Decision-making: A Weberian Perspective

This work questions AI´s role in bureaucratic decision-making. The Weberian conception of bureaucracy, based around the concept of an ideal bureaucracy in which authority is distributed, delegated, clearly delimited and hierarchical and that enshrines the following of formal rules, task specialization through division of labor, legal certainty and a predilection for efficiency in recordable and accountable decisions can serve as a framework to orient how governments should approach the adoption of artificial intelligence (AI) given the many problems associated with its careless deployment. Using theoretical analysis, this work explains Weberian ideas of bureaucracy and contrasts them with real-life cases of implementation of AI in bureaucratic decision-making, often with detrimental results for society. After identifying and framing issues related to AI, e.g., lack of transparency, attempts to shift accountability from humans to technology, the exacerbation of bias and potential for systemic discrimination, the paper proposes Weberian prescriptions that should help public administration make careful decisions about the adoption of AI and the consequences of its implementation. The article also engages with Weber critically, rejecting the notion that public administrators do not engage in politics and asserting that AI decision-making is necessarily political as well, as it entails exercising power over citizens.