Solution Approach Research Articles

Models and applications of stochastic programming with decision‐dependent uncertainty in power systems: A review

AbstractStochastic programming is a competitive tool in power system uncertainty management. Traditionally, stochastic programming assumes uncertainties to be exogenous and independent of decisions. However, there are situations where statistical features of uncertain parameters are not constant but dependent on decisions, classifying such uncertainties as decision‐dependent uncertainty (DDU). This is particularly the case with future power systems highly penetrated by multi‐source uncertainties, where planning or operation decisions might exert unneglectable impacts on uncertainty features. This paper reviews the stochastic programming with DDU, especially those applied in the field of power systems. Mathematical properties of diversified types of DDU in stochastic programming are introduced, and a comprehensive review on sources and applications of DDU in power systems is presented. Then, focusing on a specific type of DDU, that is, decision‐dependent probability distributions, a taxonomy of available modelling techniques and solution approaches for stochastic programming with this type of DDU and different structural features are presented and discussed. Eventually, the outlook of two‐stage stochastic programming with DDU for future power system uncertainty management is explored, including both exploring the applications and developing efficient modelling and solution tools.

Metaheuristics-based multistage iterative optimization framework for decomposed high-speed rail systems planning problem

High-speed rail (HSR) systems planning and decision-making is a large-scale, multistage, intricate problem, where each stage represents a complex sub-problem. The solution approach to the multistage problem needs information exchange across the stages. A non-optimal planning and decision at any stage could yield a sub-optimal HSR system. This study proposes a novel multistage iterative HSR station location and alignment optimization framework (HSLAOF) to solve the main problem by decomposing it into the identification of (a) HSR potential regions and corridor, (b) station location, (c) shortest sequence/corridor of stations, and (d) collision-free alignment (horizontal and vertical) sub-problems. It integrates these sub-problems, which are solved separately using customized artificial intelligence- and motion planning-based metaheuristics such as particle swarm optimization for station location, ant colony optimization for shortest sequence of stations, path planner method for horizontal alignment and exploration, and exploitation-based ant colony optimization for vertical profile. In HSLAOF, the information from the upper and lower stages of the optimization process are exchanged and combined for a holistic optimized HSR system. Application of HSLAOF in the Mumbai-Ahmedabad HSR project yielded a solution that has 14.24 % lower total system cost, no environmental impact, 5.82 % better socio-economic impact, 21.14 % lesser noise and vibration impacted population, and 2.79 % higher station location utility than the one proposed by the experts. For predefined station location, the HSLAOF yielded alignment with 13.90 % lower total system cost and 24.00 % less noise and vibration impacted population than the one developed by experts. It required about 150 min to obtain a solution for each iteration.

Organic fluorophores in developing latent fingerprints: an up-to-date review

AbstractFingerprints have been established as legitimate and critical forensic science evidence for identifying criminals beginning as early as the twentieth century. This article details the different types of fingerprints, the broad range of surfaces, and various development techniques available for on-site latent fingerprint collection, with fingerprint fidelity being of prime significance. Fluorescent imaging of latent finger-marks benefits from enhanced sensitivity, selectivity, and reduced reliance on instruments. The review focuses on the topical developments reported on small-molecule organic fluorophores which could be used to develop latent prints through powder, solution, and fuming approaches. These inexpensive luminophores extend wide emission profiles that span the visible spectrum to visualize the latent prints with exceptional resolution, free from any interference originating from the background surface. Further, the critical challenges and prospective scope for future research developments to improve the detection of latent fingerprints using small-molecule fluorophores are also discussed. Graphical abstract

Enhancing the accessibility of regionalization techniques through large language models: a case study in conversational agent guidance

The concept of regions has long been crucial for understanding and managing Earth’s phenomena, leading to regionalization, aggregating smaller areas into larger, contiguous, and homogeneous regions to achieve specific goals. Open-source regionalization is gaining traction because it reduces dependence on commercial software and fosters wider adoption in analysis and decision-making. However, these packages, often designed by experts for specialized tasks, can be challenging to understand and utilize due to domain-specific jargon and functionalities, especially for unfamiliar users. A prevalent disconnect must be addressed: How can we make a complex optimization approach available to a broad audience with various backgrounds? This study introduces RegionDefiner, a Large Language Modeling (LLM)-powered conversational agent, to comprehensively understand the functionality, inputs, outputs, and potential applications of regionalization problems. We selected it as an illustrative example due to its wide-ranging potential for delineating study regions in various applications. RegionDefiner is designed to guide users in framing their problems, collecting necessary data, and implementing solution approaches in a straightforward and user-centric manner. The experiments demonstrate that RegionDefiner interprets and presents the results in an understandable way for all audiences, thus bridging the gap between intricate computations and practical problem-solving needs.

Deep Learning-Driven Analysis of a Six-Bar Mechanism for Personalized Gait Rehabilitation

Abstract Recent advances in robotics and artificial intelligence have highlighted the potential for the integration of computational intelligence in enhancing the functionality and adaptability of robotic systems, particularly in rehabilitation. Designing robotic exoskeletons for the lower limb rehabilitation of post-stroke patients requires frequent adjustments to accommodate individual differences in leg anatomy. This complex engineering challenge necessitates a deep understanding of human physiology, robotics, and optimization to develop adaptive robotic systems and also to swiftly quantify the required adjustments and implement them for each patient. The conventional approaches, which mostly rely on heuristics and manual tuning, often struggle to achieve optimal results. This paper presents a novel method that integrates a genetic algorithm (GA) with a deep learning approach to generate a gait trajectory of the ankle joint from a six-bar linkage mechanism of fixed dimensions. Later, using the same approach, the inverse kinematics solution for this mechanism is also devised whereby, the set of the link dimensions of the six-bar linkage mechanism is obtained for the given gait trajectory of an individual to achieve customization. We simulated the kinematic behavior of the six-bar linkage mechanism within defined mechanical constraints and utilized the generated data for training a feedforward neural network and long short-term memory models. The proposed model, when trained, can produce accurate lengths for the desired gait trajectories in the sagittal plane and vice-versa which further validates our proposed approach for inverse kinematics solution.

CaF2 Nanoparticle-Induced γ-CsPbI2.81Br0.19 Heterogeneous Crystallization for High-Efficiency Flexible All-Inorganic Perovskite Solar Cells.

All-inorganic CsPbI3 films necessitate higher annealing temperatures for high-quality crystallization. Consequently, the conventional low-temperature solution approach often results in poor crystallization in flexible CsPbI3 films, significantly degrading the optoelectronic performance and stability of flexible perovskite solar cells (f-PSCs). Herein, a heterogeneous CaF2 nanocrystal seed-induced strategy has been successfully utilized to achieve enhanced crystallization of a flexible CsPbI2.81Br0.19 film. Due to their good lattice match with the perovskite material, CaF2 nanoparticles can decrease the critical Gibbs free energy of CsPbI2.81Br0.19 perovskite nucleation, thereby accelerating γ-phase CsPbI2.81Br0.19 crystallization at low temperatures. This leads to an improved crystalline quality of the flexible perovskite film at low temperatures, which minimizes defects and enhances the stability of f-PSCs. The CsPbI2.81Br0.19 f-PSCs achieved a champion power conversion efficiency of 15.03% and demonstrated mechanical stability, retaining 98.1% of their initial efficiency even after 60 000 bending cycles with a curvature radius of 5 mm.

A Graph-Refinement Algorithm to Minimize Squared Delivery Delays Using Parcel Robots

In recent years, parcel volumes have reached record highs, prompting the logistics industry to explore innovative solutions to meet growing demand. In densely populated areas, delivery robots offer a promising alternative to traditional truck-based delivery systems. These autonomous electric robots operate on sidewalks and deliver time-sensitive goods, such as express parcels, medicine and meals. However, their limited cargo capacity and battery life require a return to a depot after each delivery. This challenge can be modeled as an electric vehicle-routing problem with soft time windows and single-unit capacity constraints. The objective is to serve all customers while minimizing the quadratic sum of delivery delays and ensuring each vehicle operates within its battery limitations. To address this problem, we propose a mixed-integer quadratic programming model and introduce an enhanced formulation using a layered graph structure. For this layered graph, we present two solution approaches based on relaxations that reduce the number of nodes and arcs compared to the expanded formulation. The first approach, Iterative Refinement, solves the current relaxation to optimality and refines the graph when the solution is infeasible for the expanded formulation. This process continues until a proven optimal solution is obtained. The second approach, Branch and Refine, integrates graph refinement into a branch-and-bound framework, eliminating the need for restarts. Computational experiments on modified Solomon instances demonstrate the effectiveness of our solution approaches, with Branch and Refine consistently outperforming Iterative Refinement across all tested parameter configurations.

The Identity of Motherhood Transformed by Social Media: A Phenomenological Study

Objective: This qualitative study was conducted to examine the effect of social media use on women's mothering roler and childcare attitudes. Methods: The study group for this research was determined using the criterion sampling method, which is a purposeful sampling method. The sample consisted of 27 women who actively use social media and have children between the ages of 0-6. Data were collected using an “Introductory Information Form” and a “Semi-structured Interview Form”. Data analysis was conducted using Colaizzi's phenomenological interpretation method. Results: The mean age of the women who participated in the study was 32.03+4.89 years. It was determined that 96.3% of the women used Instagram, 63% used Youtube, and 40.7% used Twitter, and they spent an average of 1-3 hours daily on these sites. It was determined that the mothers who participated in the study mostly sought help from their own mothers and close environment (70.4%), web browsers (22.2%), social media (14.8%) and health personnel (7.4%), respectively, as a solution approach when they encountered a situation or problem they did not know about child care. It was determined that the working mothers (59.3%) who participated in the study had a sense of guilt and inadequacy due to the perception of super motherhood created by the social media accounts they followed, and therefore, they were more lenient, soft and ignored the error in the care approach of their own children. Conclusion: Guidance for parents on accessing reliable health information and proactive use of social media by health care providers to promote healthy decisions is an important requirement of our age.

Optimal resource allocation for rapid convergence to stable healthy state in epidemic spreading models

The networked Susceptible-Infected-Susceptible (SIS) model has been widely investigated as a model for the spread of epidemics within networked systems. In networks with SIS dynamics and unstable healthy states, a critical question is how to distribute compensatory curing resources (with constrained total cost) among individuals, ensuring that the network converges to a healthy state as fast as possible. This paper introduces a novel approach to this problem by developing an algorithm for the optimal allocation of compensatory curing resources required by agents in a given network. This solution approach has been made possible by reformulating the original convergence rate optimization problem and an additional constraint guaranteeing a minimum convergence rate as a standard semidefinite programming problem. The applicability of the proposed algorithm to arbitrary undirected topologies and other variations of the SIS model, including the one with a weighted cost function, has been demonstrated. In the case of symmetry preserving curing and infection rates, it has been shown that the problem over a given network with a symmetric topology can be reduced to a smaller network with each orbit acting as a node. Additionally, for networks with one or two orbits, the problem has been addressed analytically, and several examples have been included. Based on two different scenarios inspired by the SARS outbreak in Hong Kong and the COVID-19 outbreak in the USA, it is shown that the algorithm's optimal results outperform the uniform distribution of additional curing resources. The paper also explores how optimal performance metrics change with the given upper limit on the total amount of additional curing resources.

Alzheimer's dementia in people with Down syndrome : Results of guideline-assisted expert interviews on healthcare deficits in the diagnostics and treatment as well as solution approaches

People with Down syndrome have agenetically increased risk of developing early onset Alzheimer's dementia. An interview study with healthcare providers, patient representatives and employees in residential and work facilities was conducted to identify deficits in the healthcare process and approaches to overcoming them. In this study 14 semi-structured interviews were conducted and analyzed using qualitative content analysis. Alack of knowledge and experience on the part of medical service providers in dealing with and providing medical care for people with Down syndrome was identified as akey challenge. In addition, the diagnosis of dementia in people with Down syndrome is difficult for various reasons (including lack of appropriate diagnostic tools in standard care and lack of time or financial resources). Doubts were expressed about the efficacy of antidementia medications and the reasons for the increased use of sedatives were discussed. Attentive observation of behavior and involvement of caregivers, regular review and reduction of polypharmacy and the use of alternative behavior modification techniques were mentioned as possible solutions. The identified deficits in the medical care of the target population and the approaches to solving them will be incorporated into the development of health policy recommendations in order to optimize the care situation of those affected in the long term.

Levy solution and state-space approach for hygro-thermo-magnetic bending of a cross-ply laminated plate on Kerr foundation under various loadings

Levy solution and state-space approach for hygro-thermo-magnetic bending of a cross-ply laminated plate on Kerr foundation under various loadings

Dynamic collaborative truck-drone delivery with en-route synchronization and random requests

Coordinated truck and drone delivery is gaining popularity in logistics as it can greatly reduce operation costs. However, existing studies on related operations management problems typically ignore the following important features: (i) the random appearance of requests, which require operators to dynamically respond to the requests; and (ii) the decisions of optimal launch and retrieval locations for trucks and drones instead of fixed to customer locations, which can significantly impact the overall time costs. To tackle these challenges, this study investigates the dynamic collaborative truck-drone routing problem with randomly arriving requests and synchronization on routes. We model the problem as a Markov Decision Process (MDP) and solve the MDP via a reinforcement learning (RL) approach. The proposed RL approach determines: (i) whether each request should be serviced upon arrival, (ii) which truck or drone should be assigned for the request, and (iii) the optimal en-route take-off and landing positions for paired trucks and drones. We further employ a framework of decentralized learning and centralized dispatching in RL to increase performance. Numerical experiments are conducted to assess the proposed solution approach on instances generated based on both the Solomon dataset and real-world operational data of a logistics operator in Singapore over several benchmark algorithms under various battery endurance levels of drones and distinct transportation scenarios including node-based dynamic collaborative truck-drone routing problem, dynamic non-collaborative truck and drone routing problem, and dynamic vehicle routing problem. The results show that our RL solution outperforms the benchmark algorithm in total profit by an average of 28.03 %, and our en-route takeoff and landing scenario outperforms the benchmark scenarios in total profit by an average of 8.43 % in multi-day instances. Additionally, compared to the traditional node-based landing scenario, employing our en-route takeoff and landing strategy can save 0.9 h/(drone*day) of waiting time on average.

Fast heuristics for the time‐constrained immobile server problem

AbstractWe propose easy‐to‐implement heuristics for time‐constrained applications of a problem referred to in the literature as the facility location problem with immobile servers, stochastic demand, and congestion, the service system design problem, or the immobile server problem (ISP). The problem is typically posed as one of allocating capacity to a set of M/M/1 queues to which customers with stochastic demand are assigned with the objective of minimizing a cost function composed of a fixed capacity‐acquisition cost, a variable customer‐assignment cost, and an expected‐waiting‐time cost. The expected‐waiting‐time cost results in a nonlinear term in the objective function of the standard binary programming formulation of the problem. Thus, the solution approaches proposed in the literature are either sophisticated linearization or relaxation schemes, or metaheuristics. In this study, we demonstrate that an ensemble of straightforward, greedy heuristics can rapidly find high‐quality solutions. In addition to filling a gap in the literature on ISP heuristics, new stopping criteria for an existing cutting plane algorithm are proposed and tested, and a new mixed‐integer linear model requiring no iterating algorithm is developed. In many cases, our heuristic approach finds solutions of the same or better quality than those found by exact methods implemented with expensive, state‐of‐the‐art mathematical programming software, in particular a commercial nonlinear mixed‐integer linear programming solver, given a five‐minute time limit.

Advantages of general parametric solution Approach in dynamics courses

Problems of dynamics are treated using a general parametric solution approach. The advantages of treating problems in the parametric way is discussed. The approach has many advantages over the numerical calculations starting from the beginning: 1) Solutions are expressed in the general form suitable for algorithmic programming 2) Unit check on the correctness of the results are easier 3) Effect of each input parameter on the desired output parameter can be easily observed 4) Results can be evaluated for the limiting cases to recognize the soundness of the solutions. 5) Mathematical principles can be more exploited on the solutions 6) Solutions give more insight on the design principles. The ideas are outlined using three sample problems. The advantages of the approach have been tested on Mechanical Engineering Sophomore students for over 16 years with success.

Selection of Health Insurance Policy: Using Analytic Hierarchy Process and Combined Compromised Solution Approach Under Spherical Fuzzy Environment

The process of health insurance policy selection is a critical decision with far–reaching financial implications. The complexity of health insurance policy selection necessitates a structured approach to facilitate informed decision-making amidst numerous criteria and provider options. This study addresses the health insurance policy selection problem by employing a comprehensive methodology integrating Spherical Fuzzy Analytic Hierarchy Process (SF–AHP) and Combined Compromise Solution (CoCoSo) Algorithm. Eight experienced experts, four from academia and industry each, were engaged, and eleven critical factors were identified through literature review, survey, and expert opinions. SF–AHP was utilized to assign weights to these factors, with Claim settlement ratio (C9) deemed the most significant. Subsequently, CoCoSo Algorithm facilitated the ranking of insurance service providers, with alternative A6 emerging as the superior choice. The research undertakes sensitivity analysis, confirming the stability of the model across various scenarios. Notably, alternative A6 consistently demonstrates superior performance, reaffirming the reliability of the decision-making process. The study’s conclusion emphasizes the efficacy of the joint SF–AHP and CoCoSo approach in facilitating informed health insurance policy selection, considering multiple criteria and their interdependencies. Practical implications of the research extend to individuals, insurance companies, and policymakers. Individuals benefit from making more informed choices aligned with their healthcare needs and financial constraints. Insurance companies can tailor policies to customer preferences, enhancing competitiveness and customer satisfaction. Policymakers gain insights to inform regulatory decisions, promoting fair practices and consumer protection in the insurance market. This study underscores the significance of a structured approach in navigating the intricate health insurance landscape, offering practical insights for stakeholders and laying a foundation for future research advancements.

A new solution approach to proportion delayed and heat like fractional partial differential equations

The importance of fractional partial differential equations (FPDEs) may be observed in many fields of science and engineering. On the same hand their solutions and the approaches for the same are also very important to notice due to the effectiveness of the methods and accuracy of the results. This work discusses the diverse estimated analytic description of fractional partial differential equations (with proportion delay and heat like equation), applying the Iterative Laplace Transform Method. The specified method represents a significant advancement in the tool case of applied mathematicians and scientists. Its ability to efficiently and accurately solve complex differential equations, especially FPDEs. Here in this work, the solution of four test problems of FPDEs related to proportion delay and heat like equations is obtained for testing the validity and asset of the Iterative Laplace Transform Method. Further their numerical and graphical interpretations are also mentioned.

Maximal extractable value: Current understanding, categorization, and open research questions

In traditional financial markets, front-running is a well-structured phenomenon. It represents a form of privileged actors utilizing knowledge or power advantages to extract undue profit at the cost of other stakeholders. Various mitigation strategies have emerged, ranging from market design to regulatory measures. More recently, a similar and substantially richer variety of means to gain unethical profit from power asymmetries has appeared in the context of blockchain-based decentralized applications. This phenomenon is called “maximal extractable value” (MEV). Despite the decentralized nature and inherent transparency of blockchain ledgers, MEV is particularly prevalent and challenging to mitigate. While related work in computer science and algorithmic game theory has already identified several different ways in which MEV manifests in decentralized finance (DeFi) and outlined partial solution approaches, a discussion of its impacts in the information systems (IS) domain is still absent. A holistic definition of MEV and how it can be exploited is necessary for the discussion of its potential implications for blockchain-based IS for businesses and public institutions. This paper conducts a systematic literature review to close this gap. It consolidates the diverging definitions of MEV and provides a categorization of the different ways in which it can manifest. As such, we synthesize and review the existing state of knowledge on MEV and point to undiscovered areas relevant to decentralized electronic markets in the form of a research agenda.

A HYBRID MEREC-TOPSIS APPROACH FOR THE IMPROVEMENT OF PHYSICAL VAPOUR DEPOSITED TITANIUM CARBO-NITRIDE COATING

Titanium carbo-nitride (TiCN) comes under the metal nitride group which can provide a combined property of titanium carbide (TiC) and titanium nitride (TiN) coating. TiCN Coating possess an excellent mechanical and tribological, properties which leads its application in many industries particularly in the Automotive and aerospace industries. TiCN films can be very hard and strong based on the composition and synthesis process. Physical Vapor Deposition (PVD) technique is widely used for the preparation of thin film coating because of its ability to precisely control the composition and thickness of the coatings. The properties of the developed coating significantly affect by the PVD process parameters and their levels. Multi-Criteria Decision Making (MCDM) methods are widely used in many sectors for the selection of process parameters based upon some specific criteria. In the present work TiCN coatings were synthesized by using one of the most widely used PVD method called magnetron sputtering. L16 orthogonal array was used as a design of experiment for the experimental work by varying the sputtering process parameters like bias voltage, N2 flow rate and substrate to target distance. After the synthesis, the developed films were tested using atomic force microscopy (AFM), Scanning electron microscopy (SEM), and Nanoindentation. From the characterization three response parameters such as hardness (H), Modulus of elasticity (E) and surface roughness (Ra) of the coating has been considered. A hybrid method based on improved removal effects of criteria- technique for order performance by similarity to ideal solution (MEREC-TOPSIS) approach has been considered for the for the process parameters selection. The results have also been compared with other weight calculation techniques. In all the cases it is observed that experiment no 16 and 15 have got the 1st and second rank respectively. However, experiment no 1 is the last rank in all the cases. From the correlation analysis it is found a strong positive correlation between MEREC-TOPSIS and other methods. The method provides a significant advancement in the selection of optimal parameters, ensuring enhanced coating properties crucial for industrial applications.

Electrification of transportation: A hybrid Benders/SDDP algorithm for optimal charging station trading

This paper examines the electrification of transportation as a response to environmental challenges caused by fossil fuels, exploring the potential of battery electric vehicles and hydrogen fuel cell vehicles as alternative solutions. However, a significant barrier to their widespread adoption is the limited availability of charging infrastructure. Therefore, this study proposes the development of comprehensive charging stations capable of accommodating both battery and hydrogen vehicles to address this challenge. The energy is purchased from the day-ahead and intraday auction-based electricity markets, where the electricity price is subject to uncertainty. Therefore, a two-stage stochastic programming model is formulated while the price scenarios are generated utilizing a k-means clustering algorithm. Given the complexity of the proposed model, an efficient solution approach is developed through the hybridization of the Benders decomposition algorithm and stochastic dual dynamic programming. In the Benders master problem, day-ahead bidding variables are determined, whereas the Benders sub-problem addresses intraday bidding and charging station scheduling variables, employing stochastic dual dynamic programming to tackle its intractability. Additionally, we transform the mixed integer linear program model of the second stage problem into a linear program, confirming its validity through KKT conditions. Our model provides practical insights for making informed decisions in electricity markets based on sequential auctions. While the bidding curves submitted to the day-ahead market remain unaffected by scenarios, those submitted to the intra-day market show dependence on fluctuations in day-ahead market prices.

Adaptive scaled boundary finite element method for hydrogen assisted cracking with phase field model

This study introduces a numerical framework for analysing hydrogen assisted cracking, employing the scaled boundary finite element method. This is the first instance where the scaled boundary finite element method is employed to model hydrogen embrittlement. The phase field model is utilized to simulate defects, complemented by adaptive meshing using polytree mesh. The ability of the scaled boundary finite element method to treat polygonal elements assists in mitigating the problem of hanging nodes that arises from polytree decomposition. The adaptive framework is developed to predict crack propagation using an initial unstructured quadrilateral mesh generated from any commercial software. The hydrogen atom concentration depends on the hydrostatic stress gradient, which is calculated by interpolating nodal hydrostatic stress with scaled boundary shape functions and taking the gradient. A staggered solution approach is adopted to concurrently tackle hydrogen transport, elasticity, and phase field equations. The methodology is validated using Mode-I edge notch specimens and analysing crack propagation from corrosion pits, with results demonstrating close agreement with existing data. Subsequently, the framework is extended to address more intricate scenarios, such as crack propagation in X-shaped plates and hydrogen transmission in tanks. As the last example, more advanced image based fracture analysis of weld structure is investigated. This example studies the possibility of analysing the hydrogen diffusion directly from TEM imagery. Moreover, we investigate how hydrogen concentration influences structural failure.