Scenario Planning Research Articles

AlphaFold and Docking Approaches for Antibody-Antigen and Other Targets: Insights From CAPRI Rounds 47-55.

Accurate modeling of the structures of protein-protein complexes and other biomolecular interactions represents a longstanding and important challenge for computational biology. The Critical Assessment of PRedicted Interactions (CAPRI) experiment has served for over two decades as a key means to assess and compare current approaches and methods through blind predictive scenarios, highlighting useful strategies, and new developments. Here we describe the performance of our laboratory's team in recent CAPRI rounds, which included submissions for 10 modeling targets. Our team utilized a range of docking and modeling approaches, including ZDOCK, Rosetta, and ZRANK, to model, refine, and score protein-protein and protein-DNA complexes. For recent targets we utilized adaptations of AlphaFold to generate models, leading to near-native models for an antibody-peptide target, and a highly accurate (but low ranked) model for an antibody-MHC complex. These results underscore the utility of AlphaFold-based protocols for predictive protein complex modeling, including for immune recognition, and highlight considerations regarding the use of AlphaFold confidence metrics in model selection.

Automated Volumetric Milling Area Planning for Acoustic Neuroma Surgery via Evolutionary Multi-Objective Optimization.

Mastoidectomy is critical in acoustic neuroma surgery, where precise planning of the bone milling area is essential for surgical navigation. The complexity of representing the irregular volumetric area and the presence of high-risk structures (e.g., blood vessels and nerves) complicate this task. In order to determine the bone area to mill using preoperative CT images automatically, we propose an automated planning method using evolutionary multi-objective optimization for safer and more efficient milling plans. High-resolution segmentation of the adjacent risk structures is performed on preoperative CT images with a template-based approach. The maximum milling area is defined based on constraints from the risk structures and tool dimensions. Deformation fields are used to simplify the volumetric area into limited continuous parameters suitable for optimization. Finally, a multi-objective optimization algorithm is used to achieve a Pareto-optimal design. Compared with manual planning on six volumes, our method reduced the potential damage to the scala vestibuli by 29.8%, improved the milling boundary smoothness by 78.3%, and increased target accessibility by 26.4%. Assessment by surgeons confirmed the clinical feasibility of the generated plans. In summary, this study presents a parameterization approach to irregular volumetric regions, enabling automated milling area planning through optimization techniques that ensure safety and feasibility. This method is also adaptable to various volumetric planning scenarios.

New venture risk management: Theoretical framework and research perspectives

ABSTRACT Managing risk is crucial for start-ups, which often struggle due to their small size, newness, and fast-paced growth. This study highlights strategies to address these issues, focusing on practical solutions. Building early financial reserves, like emergency funds or access to credit lines, can reduce the impact of unexpected setbacks. Start-ups should implement clear risk management protocols, including market research, to validate product–market fit before launching. Rigorous product testing ensures customer needs are met and regulatory compliance is achieved, minimizing costly mistakes. Flexible organizational structures, such as agile teams and adaptable processes, enable start-ups to respond quickly to market changes and growth demands. Long-term planning, such as scenario planning or creating contingency strategies, is essential to sustain growth. This research offers actionable guidance for founders, emphasizing lean, targeted risk management to optimize resources. Policy makers can use these insights to support start-ups through programs that reduce barriers, provide mentorship, and offer financial safety nets, boosting overall success rates and resilience in dynamic market conditions.

Accelerating spatial planning in Indonesia: Critical points and improvement initiatives for detailed spatial plan

The policy to accelerate the design of the Detailed Spatial Plan regulation document (RDTR) is a strategic step to enhance ease of doing business and promote sustainable development in Indonesia. Targeting 2036 RDTR sites nationwide, the initiative relies on various policy interventions and technical approaches. However, as of 8 January 2024, only 399 RDTRs (19.59%) were enacted after four years of implementation. This underperformance suggests the need to examine factors influencing the process, including issues at each stage of the RDTR design business process. While often overlooked due to its perceived irrelevance to the core substance of planning, analyzing the process is crucial to addressing operational and procedural challenges. This research identifies critical issues arising from the preparation to the enactment stage of RDTR regulations and proposes necessary policy changes. Using an explanatory approach, the study employs methods such as Analytic Hierarchy Process (AHP), post-review analysis, stakeholder analysis, business process evaluation, and scenario planning. Results show several impediments, including challenges related to commitment, technical and substantive issues, managerial coordination, policy frameworks, ICT support, and data availability. These findings serve as inputs for the development of business process improvement scenarios and reengineering schemes based on Business Process Management principles.

Spatiotemporal modeling of long-term PM2.5 concentrations and population exposure in Greece, using machine learning and statistical methods.

The lack of high-resolution, long-term PM2.5 observations in Greece and the Eastern Mediterranean hampers the development of spatial models that are crucial for providing representative exposure estimates to health studies. This work presents a spatial modeling approach to address this gap and assess PM2.5 spatial variability for the first time on a national level in Greece, by integrating in situ observations, meteorology, emissions and satellite AOD data among others. A high-resolution (1km2) gridded dataset of PM2.5 concentrations across Greece from 2015 to 2022 was developed, and seven statistical, machine learning, and hybrid models were evaluated under different prediction scenarios. Random Forest (RF) models demonstrated superior performance, (R2=0.73, MAE=2.2μgm-3), validated against ground-based measurements. Winter months consistently showed the highest PM2.5 levels, averaging 16.8μgm-3, over the domain, due to residential biomass burning (BB) and limited atmospheric dispersion. Summer months had the lowest concentrations, averaging 10.3μgm-3, while substantial decreases nationwide were observed during the 2020 COVID-19 lockdown. Population exposure analysis indicated that the entire Greek population was exposed to long-term PM2.5 concentrations exceeding the WHO air quality guideline (AQG) of 5μgm-3. Moreover, the dataset revealed elevated PM2.5 levels across several regions of mainland Greece. Notably, 70% to 90% of the population experience levels exceeding 10μgm-3 in Central and Northern regions of continental Greece like Thessaly, Central Macedonia, and Ioannina. The Ioannina region, which is severely impacted by residential BB, recorded pollution levels up to five times the WHO AQG highlighting the urgent need for targeted interventions. The high-resolution RF model's superior performance for monthly average concentrations, compared to the Copernicus Atmosphere Monitoring Service (CAMS) dataset, renders it a reliable tool for long-term PM2.5 assessment in Greece that can support air quality management and health studies.

Forecasting for Vaccinated COVID-19 Cases using Supervised Machine Learning in Healthcare Sector

Machine learning (ML)-based forecasting techniques have demonstrated significant value in predicting postoperative outcomes, aiding in improved decision-making for future tasks. ML algorithms have already been applied in various fields where identifying and ranking risk variables are essential. To address forecasting challenges, a wide range of predictive techniques is commonly employed. Research indicates that ML-based models can accurately predict the impact of COVID-19 on Jordan's healthcare system, a concern now recognized as a potential global health threat. Specifically, to determine COVID-19 risk classifications, this study utilized three widely adopted forecasting models: support vector machine (SVM), least absolute shrinkage and selection operator (LASSO), and linear regression (LR). The findings reveal that applying these techniques in the current COVID-19 outbreak scenario is a viable approach. Results indicate that LR outperforms all other models tested in accurately forecasting death rates, recovery rates, and newly reported cases, with LASSO following closely. However, based on the available data, SVM exhibits lower performance across all predictive scenarios.

Air Corridor Planning for Urban Drone Delivery: Complexity Analysis and Comparison via Multi-Commodity Network Flow and Graph Search

Urban drone delivery, a rapidly evolving sector, holds the potential to enhance accessibility, address last-mile delivery issues, and alleviate ground traffic congestion in cities. Effective Unmanned Aircraft System Traffic Management (UTM) is essential to scale drone delivery. A critical aspect of UTM involves planning a city-wide network with spatially-separated air corridors (air routes). Most existing works have focused on routing problems or air traffic management. Compared to these problems, the air corridor planning problem requires much higher spatial and temporal resolutions and presents computational challenges due to the scale, complexity, and density of urban airspace, along with the coupling issues of multi-path planning. Therefore, we conducted this research to understand the complexity and computational resources required to optimally solve the air corridor planning problem. In this paper, we use a minimum-cost Multi-Commodity Network Flow (MCNF) model, a mathematical model, to model the problem and demonstrate the complexity of air corridor planning through the complexity of MCNF. We then apply Gurobi’s and GLPK’s integer programming (IP) solvers to find optimal solutions. Additionally, we present two existing multi-path graph search algorithms, the Sequential Route Network Planning (SRP) algorithm and the Distributed Route Network Planning (DRP) algorithm, to address this corridor planning problem. Numerical experiments conducted at various scales and settings using IP solvers and graph search algorithms indicate that finding an optimal solution requires significant computational resources and yields only a slight improvement in optimality compared to graph search algorithms. Thus, air corridor planning is complex both theoretically and numerically, and graph search algorithms can provide a feasible solution with good enough optimality for corridor planning in real-world scenarios. Moreover, the multi-path graph search algorithms can easily incorporate side constraints that are known to be impossible to solve with polynomial algorithms, making it more practical for real-world applications. Finally, we demonstrate the application of SRP and DRP in real-world 3D urban scenarios.

The Singularity of “Blind Spots” as a Self-Organization of Uncertainties and Risks in a Digitalized Society

Topicality. Digitalization of society is accompanied by deep changes in social, economic and political relations, generating uncertainties and risks. The growth of technology, particularly artificial intelligence, is transforming the nature of information, communication and management, creating "blind spots" that require insight and systemic analysis. The purpose of the article. To identify and investigate the mechanisms of the formation of "blind spots" as a phenomenon that characterizes the self-organization of uncertainties and risks in a digitalized society. Special attention is paid to illusions of cognitive perception and their impact on social interactions. Research methodology. The methodological basis includes the concept of singularity, the ideas of scenario planning by Pierre Wack, of the singularity by Ray Kurzweil and the theory of self-organization of social systems. Metaphors of "black swan", "gray rhinoceros" and other images are used to structure risks and uncertainties. The main conclusions of the discussion. The article emphasizes the importance of critical thinking, a universal approach to education and the introduction of ethical norms for the use of artificial intelligence as key aspects in countering the formation of "blind spots" and overcoming risks in the modern digital environment.

HMSFU: A hierarchical multi‐scale fusion unit for video prediction and beyond

AbstractVideo prediction is the process of learning necessary information from historical frames to predict future video frames. Learning features from historical frames is a crucial step in this process. However, most current methods have a relatively single‐scale learning approach, even if they learn features at different scales, they cannot fully integrate and utilise them, resulting in unsatisfactory prediction results. To address this issue, a hierarchical multi‐scale fusion unit (HMSFU) is proposed. By using a hierarchical multi‐scale architecture, each layer predicts future frames at different granularities using different convolutional scales. The abstract features from different layers can be fused, enabling the model not only to capture rich contextual information but also to expand the model's receptive field, enhance its expressive power, and improve its applicability to complex prediction scenarios. To fully utilise the expanded receptive field, HMSFU incorporates three fusion modules. The first module is the single‐layer historical attention fusion module, which uses an attention mechanism to fuse the features from historical frames into the current frame at each layer. The second module is the single‐layer spatiotemporal fusion module, which fuses complementary temporal and spatial features at each layer. The third module is the multi‐layer spatiotemporal fusion module, which fuses spatiotemporal features from different layers. Additionally, the authors not only focus on the frame‐level error using mean squared error loss, but also introduce the novel use of Kullback–Leibler (KL) divergence to consider inter‐frame variations. Experimental results demonstrate that our proposed HMSFU model achieves the best performance on popular video prediction datasets, showcasing its remarkable competitiveness in the field.

ОЦЕНКА СОСТОЯНИЯ И КАЧЕСТВА ГИДРОГЕОХИМИЧЕСКИХ ПОКАЗАТЕЛЕЙ АРТЕЗИАНСКИХ ПОДЗЕМНЫХ ВОД ЖАМБЫЛСКОЙ ОБЛАСТИ

The results of a study assessing the condition and quality of groundwater in the Talas, Sarysu, and Moiynkum districts of Zhambyl region are presented. Fieldwork conducted in 2023 in these districts showed exceedances of chemical indicators of groundwater at wells No. 1, 8, 27, 61, and 66. The scientific work included field research and chemical-analytical studies of water samples from self-flowing wells to determine their suitability for domestic and drinking water supply, as well as for land irrigation. To improve efficiency, geoinformation support was provided. The analysis and generalization of the collected materials were carried out using a geographic information system (GIS) for developing predictive scenarios for the operation and protection of self-flowing wells. Based on the research results, both an analysis and an assessment were made of the potential for the fountain (artesian) exploitation of pressurized groundwater. Recommendations were given on the use of groundwater resources for the sustainable development of rural areas. Using GIS technologies, a geoinformation database of self-flowing wells and confined artesian aquifers of the territory was created for the first time, enabling more effective groundwater management and protection.

The Bounce‐Back of Business Travel. An Exploration Through the Lens of Complex Systems Theory

ABSTRACTDuring the Covid‐pandemic, the expectation emerged that the digitalization of business processes would have a permanent character. In particular, business travel would be replaced, to a large extent, by virtual meetings. This article uses complex systems theory to study business travel to Amsterdam before and after the Covid‐pandemic, analyzing a large dataset of hotel check‐ins and check‐outs from 55% of the city's hotels during 2019, 2022, and 2023. The data were explored to detect changes in business traveler behavior and subjected to tests proposed by Baggio and Sainaghi to assess the dynamics of nonlinear complex tourism systems. Despite the pandemic's disruption, our findings suggest that business travel dynamics continue to evolve similarly to pre‐pandemic trends, with notable shifts in markets of origin, longer stays, and a move toward midscale hotels and bleisure stays. The study also highlights a dissonance between actual travel behavior and the desire to reduce business travel for environmental or social reasons. By applying complex systems theory, this research enhances our understanding of the resilience and adaptability of business travel, emphasizing the need for strategic foresight and scenario planning in the hospitality and tourism sectors to navigate post‐pandemic complexities.

A Comprehensive Study of Recent Path-Planning Techniques in Dynamic Environments for Autonomous Robots.

This paper presents a comprehensive review of path planning in dynamic environments. This review covers the entire process, starting from obstacle detection techniques, through path-planning strategies, and also extending to formation control and communication styles. The review discusses the key trends, challenges, and gaps in current methods to emphasize the need for more efficient and robust algorithms that can handle complex and unpredictable dynamic environments. Moreover, it discusses the importance of collaborative decision making and communication between robots to optimize path planning in dynamic scenarios. This work serves as a valuable resource for advancing research and practical applications in dynamic obstacle navigation.

N‐Factorial Scenarios: A Systems‐Theoretical Approach to Scenario‐Planning

ABSTRACTScenarios are among the most popular techniques for managing the uncertainty and complexity of the future. Even the more sophisticated scenario designs, however, often reduce the future to a narrow set of typically only two key factors that are arranged into a four‐square matrix representing four distinct yet interrelated scenarios. Consequently, scenarios have been criticised for being simplistic or reductionist by design. In this article, we address these criticisms by proposing a basic design for n‐factorial scenarios. Following a short discussion of the procedures and limitations of classical scenario design, we draw on the example of a standard 2 × 2 matrix titled ‘Four Scenarios for the Digital Transformation’ to illustrate the limitations of the standard approach and demonstrate the potential of a digital approach to scenario building. We conclude that standard scenario planning is often characterised by a systematic omission of potentially critical scenarios, which our proposed digital approach can detect and map out.

Developing land-use planning scenarios in Türkiye to reduce water-induced soil erosion

Summary Because soil erosion constrains agricultural productivity and overuse of soils exacerbates erosion, land use can only be sustained through the implementation of land evaluation. We studied five land-use scenarios including erosion-reducing land terracing and contour farming using ILSEN modelling. These scenarios’ rates of soil loss were determined using the revised universal soil loss equation (RUSLE) method. We found that all the erosion-reducing scenarios reduced soil loss compared to the current land use of the study area; in the non-agricultural land use, soil erosion was reduced 4.25 times. The model is expected to inform reduction of soil erosion in geographies characterized by rugged topography.

Development of an optimal route planning algorithm using hot zones

The authors in the article address the problem of planning optimal routes in a dynamic environment using OpenStreetMap (OSM) geographic data for the territory of Ukraine. Amid conflict situations and active hostilities, there is a need to adapt classical route-finding algorithms to account for specific terrain constraints. The main aim of the research is to develop an adaptive A* algorithm that considers high-risk areas ("hot zones") which can significantly impact the efficiency and safety of the calculated route. To achieve this goal, the authors conducted an analysis of existing shortest path search algorithms using the classic A* algorithm and its heuristic function, which considers the distance between route points. The study explores a modified version of the A* algorithm that includes an additional cost factor for "hot zones." The proposed approach introduces an adaptive multiplier that restricts route planning through dangerous areas. To validate the proposed method, OSM data containing only basic road information, without military restrictions, was used. This limitation highlights the importance of additional analysis of factors such as safety and risk in route planning. The results demonstrate the effectiveness of the developed adaptive A* algorithm in constructing routes in a dynamic environment, particularly with the ability to avoid dangerous ("hot") zones. The study's conclusions confirm that adding a cost factor in "hot zones" improves route accuracy and enhances travel safety. The developed algorithm can be applied to automated route planning in scenarios where it is crucial to consider variable risk factors and specific terrain conditions.

Class-specific feature selection for enhancing explainability in ensemble classification models

AbstractFeature selection techniques aim at finding a relevant subset of features that perform equally or better than the original set of features at explaining the behavior of data. Typically, features are extracted from feature ranking or subset selection techniques, and the performance is measured by classification or regression tasks. However, while selected features may not have equal importance for the task, they do have equal importance for each class. The fundamental idea of the class-specific concept resides in the understanding that the significance of each feature can vary from one class to another. This contrasts with the traditional class-independent approach, which evaluates the importance of attributes collectively for all classes. For example, in tumor prediction scenarios, each type of tumor (class) may be associated with a distinct subset of relevant features. These features possess significant discriminatory power, enabling the differentiation of one tumor type from others (classes). This class-specific perspective offers a more effective approach to classification tasks by recognizing and leveraging the unique characteristics of each class. A novel deep one-versus-each strategy is introduced, which offers advantages from the point of view of explainability (feature selection) and decomposability (classification). In addition, the class-specific relevance matrix is presented, from which some more sophisticated classification schemes can be derived, such as the three-layer class-specific scheme. These schemes have the great advantage to combine independent classification units (e.g., neural networks) that use a reduced number of features to target each class. The potential for further advancements in this area is wide and will open new horizons for exploring novel research directions in interdisciplinary fields, particularly in complex, multiclass hyperdimensional contexts (e.g., in genomics).

Integrated Simulation of Groundwater Flow and Nitrate Transport in an Alluvial Aquifer Using MODFLOW and MT3D: Insights into Pollution Dynamics and Management Strategies

This study employs an integrated numerical modeling approach using MODFLOW and MT3D to simulate groundwater flow and nitrate transport in the alluvial aquifer of Hennaya plain. The groundwater flow model was calibrated and validated against observed hydraulic heads, showing excellent agreement in both steady-state and transient conditions, with a correlation coefficients (R2) of 0.99 and 0.987, respectively. Meticulous calibration yielded adjusted hydraulic conductivity values between 10−1 and 10−11 m/s, with effective porosity ranging from 0.03 to 0.34 and total porosity values varying from 0.29 to 0.38 across the aquifer. Water budget analysis revealed that the aquifer’s primary recharge occurs from the southern side. Nitrate transport modeling indicated that advection is the dominant process, with contaminants migration predominantly occurring from south to north, following the groundwater flow direction. Pollution levels were found to decrease gradually with distance from sources, confirming agricultural activities and sewage disposal as primary contributors to nitrate contamination. Predictive scenarios over a 40-year period explored various management strategies, which suggest that maintaining current nitrogen input rates will lead to continued increases in nitrate pollution, while a 50% reduction in agricultural inputs could significantly improve groundwater quality. However, even with substantial reductions, nitrate concentrations are not expected to reach levels safe for drinking within the simulation timeframe. This study underscores the need for immediate and sustained action to address nitrate pollution in the Hennaya Plain aquifer, emphasizing the importance of stringent nitrogen management practices, particularly in the agricultural sector.

Development of strategy formulation with scenario planning approach case study on Zakat House

Development of strategy formulation with scenario planning approach case study on Zakat House

Aircraft Flight Autonomous Decision-Making Method Based on Target Predicted Trajectory and Markov Decision Process

In this paper, in order to enhance the autonomous operation capabilities of aircraft and ensure their operational safety and efficiency, we propose an autonomous decision-making framework based on target motion state prediction combined with the Markov Decision Process, namely IMM-MDP architecture. Firstly, our own aircraft utilizes the IMM algorithm to achieve the state prediction of the target; building upon the existing algorithmic structure, the proposed framework improves model sets within the IMM algorithm by incorporating climb and turn models and refines the motion modes of aircraft during the cruise phase, gathering corresponding sub-models to predict different motion modes and enhance the prediction accuracy of the target. Secondly, we adopt the Markov Decision Process as the autonomous decision-making method for the own aircraft, proposing a method by which to calculate the optimal decision sequence based on the prediction scenarios of the IMM algorithm; that is, in both multi-step prediction and single-step prediction scenarios, the payoff values of different action strategies at each decision moment are calculated to obtain the optimal decision sequence. The experimental results show that the IMM algorithm with the improved model set, described in this paper, is more accurate than the IMM algorithm and prediction results of the current statistical model, as described in the literature. We also set up a multi-aircraft operation scenario, comparing the IMM-MDP decision framework proposed in this paper with the Monte Carlo and MPC decision models, thus demonstrating that the proposed framework provides better decisions while ensuring safety. Due to the target state prediction and updating, this method also demonstrates better real-time performance and practicality.

Effect of repair factors on multi-level optimization of maintenance planning for corroded pipelines considering different failure modes

The use of pipelines for transporting fluids is essential and has been growingsignificantly in the oil and gas industry. Although pipelines are one of the safestmethods to transport these materials, it is necessary to consider the risks of failures inthe design, as such incidents can cause considerable damage to the population, theenvironment, and the infrastructure. In this context, the aging of the pipeline networkhas been extensively studied, and corrosion is one of the main concerns regardingstructural integrity. Therefore, adequate maintenance planning is essential to ensure thesafety and cost-effectiveness of the sector, as effective pipeline integrity managementcan reduce expenses and maintain safety standards throughout the operational life of thepipeline. In this scenario, inspection and maintenance planning research has becomeincreasingly relevant; recent studies have utilized reliability analysis through MonteCarlo simulations to define optimal maintenance schedules for corroded pipelines,aiming to minimize operational costs associated with pipeline management. This paperinvestigates the influence of repair factors on the estimated total costs of inspectionschedules optimized for a given number of inspections, considering only one failuremode and the combination of these (small leak, burst, and rupture). For this, acombination of methodologies already validated and presented in previous works isused, in which a multilevel optimization is carried out by predefining an upper limit forthe maximum failure probability, which will act as a constraint on the optimizationproblem. The reliability analysis will be performed using Monte Carlo simulations,which are commonly used in the literature. Finally, preliminary results indicated that thefailure mode strongly influences the final cost, so the results considering only onefailure mode are expected to differ considerably.