Set Of Problems Research Articles

An optimization framework for solving large scale multidemand multidimensional knapsack problem instances employing a novel core identification heuristic

By applying the core concept to solve a binary integer program (BIP), certain variables of the BIP are fixed to their anticipated values in the optimal solution. In contrast, the remaining variables, called core variables, are used to construct and solve a core problem (CP) instead of the BIP. A new approach for identifying CP utilizing a local branching (LB) alike constraint is presented in this article. By including the LB-like constraint in the linear programming relaxation of the BIP, this method transfers batches of variables to the set of core variables by analyzing changes to their reduced costs. This approach is sensitive to problem hardness because more variables are moved to the core set for hard problems compared to easy ones. This novel core identification approach is embedded in a multi-stage framework to solve the multidemand, multidimensional knapsack problems (MDMKP), where at each stage, more variables are added to the previous stage CP. The default branch and bound of CPLEX20.10 is used to solve the first stage, and a tabu search algorithm is used to solve subsequent stages until all variables are added to CP in the last stage. The new framework has shown equivalent to superior results compared to the state-of-the-art algorithms in solving large MDMKP instances having 500 and 1,000 variables.

Solution to the problem of setting up medical points and road maintenance in mountainous areas based on optimization

Assume there are 100 villages in a mountainous area. To improve the medical standards of the local residents, three medical points need to be established in several villages, and the roads need to be repaired. This paper modifies the Floyd algorithm to derive all the shortest paths between nodes. Based on the Prim algorithm, a minimum spanning tree model is constructed to find the road maintenance method with the lowest cost; a nested greedy algorithm is used to construct an optimization model under multiple variables, effectively solving the problem of setting up medical points and road maintenance in mountainous areas.

Gradient estimates for Yamabe type equations under different curvature conditions and applications

The aim of this paper is to give various aspects of gradient estimates for positive solutions to the Yamabe type equation ut=Δfu+au+buα. We study the problem in many different settings. As result, we obtain several types of gradient estimates for the solution. When the manifold is complete without boundary, we study gradient estimates of Shi type, and of Hamilton type. Moreover, we also derive gradient estimates if the manifold is evolved under the (k,m)-super Perelman-Ricci flows. Besides, we prove gradient estimates under the integral Ricci curvature bounds. Finally, when the manifold has compact boundary, we investigate gradient estimates with Dirichlet boundary conditions. Our theorems generalize and improve many previous works.

Quantum computational finance for martingale asset pricing in incomplete markets

A derivative is a financial asset whose future payoff is a function of underlying assets. Pricing a financial derivative involves setting up a market model, finding a martingale (“fair game”) probability measure for the model from the existing asset prices, and using that probability measure to price the derivative. When the number of underlying assets and/or the number of market outcomes in the model is large, pricing can be computationally demanding. In this work, we first formulate the pricing problem in a linear algebra setting, including the realistic setting of incomplete markets where derivatives do not have a unique price. We show that the problem can be solved with a variety of quantum techniques such as quantum linear programming and the quantum linear systems algorithm. While in previous works the martingale measure is assumed to be given, here it is extracted from market variables akin to bootstrapping, a common practice among financial institutions. We discuss the quantum zero-sum game algorithm and the quantum simplex algorithm as viable subroutines. For quantum linear systems solvers, we formalize a new market assumption milder than market completeness, which allows the potential for large speedups. Towards prototype use cases, we conduct numerical experiments motivated by the Black–Scholes–Merton model.

Comparing the performance of metaheuristics on the Transit Network Frequency Setting Problem

The Transit Network Frequency Setting Problem (TNFSP), an NP-Hard combinatorial optimization problem, has been frequently addressed in previous investigations, most of which employ metaheuristics. However, previous studies have not investigated to determine the best metaheuristic and the most effective parameter values for the TNFSP. This study aims to fill this research gap by comprehensively comparing five well-known metaheuristics, namely, Artificial Bee Colony, Differential Evolution, Firefly Algorithm, Genetic Algorithm, and Particle Swarm Optimization, on the TNFSP. The best parameter configurations for employed metaheuristics are determined using the brute-force method. The comparative results not only highlight the significance of metaheuristic selection in addressing the TNFSP but also reveal the importance of choosing the most suitable parameter values for these metaheuristics. Furthermore, the metaheuristics are performed on the transit networks obtained from previously published studies for comparison purposes. The results demonstrate that the metaheuristics achieve better frequency sets than the original frequency sets of the used transit networks.

Recommending for a Multi-Sided Marketplace: A Multi-Objective Hierarchical Approach

We propose a multi-objective hierarchical recommender that solves the multi-objective recommendation problem in a hierarchical setting and was deployed on Uber Eats.

Optimal distribution of limited resource in a context of uncertainty

Object of research: methods of rational distribution of limited resource in a context of uncertainty. It is assumed that the system consists of a set of independently functioning elements, and effectiveness of these elements depends on the level of investment. The complexity of solving the problem using traditional methods depends on and is determined by the type of analytical description of production functions of the system elements. Significant difficulties arise if parameters of production functions are uncertain quantities, specified, for example, in terms of fuzzy mathematics. This circumstance emphasizes the relevance of studying approaches to solving the set problem of resource distribution for the case when its parameters are not clearly defined, and this circumstance also determines the goal of the work. Problems arising from the goal consist in developing mathematical models and methods for rational resource distribution for the main types of production functions with their parameters being fuzzy numbers of (L –R) type. To solve these problems, a method is proposed that transforms the original fuzzy problems into clear ones that can be solved using standard constrained optimization technologies. An important result of the research consists in the fact that the proposed method is universal, that is, it is applied in the same way to solve problems of resource distribution in systems wherein production functions of the elements can be of arbitrary nature. Refs.: 11 titles.

Factors Affecting Nursing Students' Perception of Workplace Bullying.

Workplace bullying is a critical and prevalent issue that causes serious problems in healthcare settings. However, there is little research on the factors affecting nursing students' perception of workplace bullying despite their forthcoming transition into the nursing profession. Therefore, this study aimed to identify the factors related to nursing students' perception of workplace bullying in Korea. A cross-sectional study was conducted among 242 nursing students who had experienced clinical practice. The survey questionnaire included general characteristics, perceived susceptibility and severity of bullying, and perception of workplace bullying. Data were analyzed using multiple regression analysis. In this study, the significant factors affecting nursing students' perception of workplace bullying included bullying experience in clinical practice and the perceived severity of bullying. Therefore, it is crucial for nursing managers and instructors to have a clear understanding of the bullying situations experienced by nursing students during clinical practice. We should promote the perception of workplace bullying through indirect experiences such as systematic education about workplace bullying for nursing students, which may prevent workplace bullying in clinical practice and work environments. Furthermore, a comprehensive and multifaceted approach is necessary to effectively prevent workplace bullying in clinical practice and work environments. This study reveals that systemic and persistent education and intervention to bullying may improve nursing students' perception of workplace bullying and prevent workplace bullying in clinical practice and work environments. Furthermore, this study provides basic data on the prevention and management of bullying in nursing students' clinical practice.

Utilizing Integrated Think-Pair-Share and SSCS Techniques to Enhance Problem-Solving Skills in Mathematical Set among 10th Grade Students

This study aimed to investigate the impact of integrating Think-Pair-Share (TPS) and Search Solve Create Share (SSCS) techniques on the problem-solving skills of 10th-grade students within the framework of set theory, while also assessing participants' satisfaction with these methods. Conducted using a one-group experimental research design, the study involved 37 10th-grade students selected through cluster random sampling from a public school in Mahasarakham province, Thailand. Ethical considerations were meticulously observed throughout the study. Instruments utilized included a TPS and SSCS learning management plan, a Mathematics Set Problem Solving Ability Test, a Satisfaction Questionnaire, and Rubric Scoring. Data analysis employed percentage, mean score, standard deviation, and a one-sample t-test comparing to a predetermined criterion of 75% of the maximum score for each test. The results demonstrated that the integrated TPS and SSCS techniques were effective in developing students' problem-solving abilities and provided satisfying learning experiences. This underscores the efficacy of collaborative learning techniques, particularly in enhancing problem-solving skills. Additionally, the study highlights the potential of integrating various collaborative methods to achieve favorable outcomes in mathematics education.

The epidemiology and risk factors for postnatal complications among postpartum women and newborns in southwestern Uganda: A prospective cohort study.

Sub-Saharan Africa accounts for two-thirds of the global burden of maternal and newborn deaths. Adverse outcomes among postpartum women and newborns occurring in the first six weeks of life are often related, though data co-examining patients are limited. This study is an exploratory analysis describing the epidemiology of postnatal complications among postpartum women and newborns following facility birth and discharge in Mbarara, Uganda. This single-site prospective cohort observational study enrolled postpartum women following facility-based delivery. To capture health information about both the postpartum women and newborns, data was collected and categorized according to domains within the continuum of care including (1) social and demographic, (2) pregnancy history and antenatal care, (3) delivery, (4) maternal discharge, and (5) newborn discharge. The primary outcomes were readmission and mortality within the six-week postnatal period as defined by the WHO. Multivariable logistic regression was used to identify risk factors. Among 2930 discharged dyads, 2.8% and 9.0% of women and newborns received three or more postnatal visits respectively. Readmission and deaths occurred among 108(3.6%) and 25(0.8%) newborns and in 80(2.7%) and 0(0%) women, respectively. Readmissions were related to sepsis/infection in 70(88%) women and 68(63%) newborns. Adjusted analysis found that caesarean delivery (OR:2.91; 95%CI:1.5-6.04), longer travel time to the facility (OR:1.54; 95%CI:1.24-1.91) and higher maternal heart rate at discharge (OR:1.02; 95%CI:1.00-1.01) were significantly associated with maternal readmission. Discharge taken on all patients including maternal haemoglobin (per g/dL) (OR:0.90; 95%CI:0.82-0.99), maternal symptoms (OR:1.76; 95%CI:1.02-2.91), newborn temperature (OR:1.66; 95%CI:1.28-2.13) and newborn heart rate at (OR:1.94; 95%CI:1.19-3.09) were risk factors among newborns. Readmission and death following delivery and discharge from healthcare facilities is still a problem in settings with low rates of postnatal care visits for both women and newborns. Strategies to identify vulnerable dyads and provide better access to follow-up care, are urgently required.

Most Hospital-Acquired Complications among Older Adults Are Associated with Frailty: The South-Western Sydney Frailty and Hospital-Acquired Complications Study

BackgroundPeople live longer, and frailty has become an important problem in the acute hospital setting. Increasingly the association between frailty and hospital-acquired complications has been reported. However, the overall burden of frailty in this setting has not been described. Therefore, we undertook this study to describe the association between frailty and the risk of hospital-acquired complications among older adults across our five acute hospitals and to estimate the overall burden of frailty attributable to these complications.MethodsConsecutive admissions among women and men aged ≥ 65 years across our local health district’s five acute hospitals, between January 2010 and December 2020, were included to investigate the association between the number of cumulative frailty deficit items and hospital-acquired complications and infections. The numbers of cumulative frailty deficits are presented in four groups (0–1 item, 2 items, 3 items, and 4–13 items). Individual events such as falls, delirium, pressure injuries, thromboembolism, malnutrition, and multiple types of infections are also presented. The overall burden of frailty was estimated using a population-attributable-risk approach.ResultsDuring the study period there were 4,428 hospital-acquired complications, among 120,567 older adults (52% women). The risk of any hospital-acquired complication (HAC) or any hospital-acquired infection (HAI) increased as the cumulative number of frailty deficits increased. For the 0–1 deficit item group versus the 4–13 items group, the risk of any HAC increased from 5.5/1000 admissions to 80.0/1000 admissions, and for any HAI these rates were 6.2/1000 versus 58.2/1000, respectively (both p-values < 0.001). The 22% (27,144/120,567) of patients with 3 or more frailty deficit items accounted for 63% (2,774/4,428) of the combined hospital-acquired complications and infections. We estimated that the population-attributable risks of any hospital-acquired complication or infection were 0.54 and 0.47, respectively.ConclusionWe found that an increasing number of cumulative frailty deficit items among older patients are associated with a higher risk of hospital-acquired complications or infections. Importantly, frail older adults account for most of these adverse events.

The Efficacy of the Sensorimotor Training Program on Sensorimotor Development, Auditory and Visual Skills of Schoolchildren Aged 5–8 Years

Abstract Background Around 800 million young children worldwide have cognitive-developmental limitations due to issues related to biological, environmental, and psychosocial factors. These problems lead to educational challenges, limited skill development, and higher unemployment rates. Therefore, timely interventions addressing the underlying problems in institutional settings are critically important. Objective The authors created the “Sensorimotor Training Program” (STP) as a critical intervention to develop skills essential for starting school. This experimental study aimed to investigate the impact of the STP in an institutional setting, targeting the specific auditory and visual skills crucial for kindergarten and primary school learning. Methods The STP comprises 120 training sessions focused on sensorimotor maturation. Seven hundred and seventy-two children aged 5–8 participated in the study, with 704 in the experimental and 68 in the control group, each containing a relatively balanced ratio of boys to girls. The study spanned six to eight months, with three to five weekly sessions. Results The intervention resulted in significant improvements in sensorimotor development in the experimental compared to the control group [p &lt; .001, effect size (d) = .483; auditory skills r = .605 p &lt; .001, d = .366; visual skills r = .542, p &lt; .001, d = .294]. The intervention group also improved compared to its baseline measurements. Conclusion These results show that implementing the STP in school settings can improve sensorimotor development, impacting auditory and visual skills in children aged 5–8. These intervention-based improvements are above and beyond biological maturation.

Financial Hedging of Operational Risk Constraints: A General Framework

We consider the operation of a non-financial corporation and present a novel risk-management framework that supports the joint optimization of the firm’s operational and financial decisions. Specifically, we study the problem of maximizing the firm’s operating profits when these profits depend on movements in the financial markets, and where the decision maker—the firm’s manager—is risk averse. Critically, our framework (i) imposes risk aversion through a generic class of risk constraints, and (ii) considers explicitly the differences that can exist between the shareholders’ and manager’s beliefs about future market risks. Together, these aspects allow us to remove speculation so that financial instruments can only be used for hedging purposes. We analyze our problem in an incomplete financial market setting and consider different informational assumptions. Within this framework, we characterize the optimal financial hedging strategy so that the problem reduces to a constrained maximization over operational policies. Our results show how financial hedging can be effectively used to expand the set of admissible operating policies, and identify conditions under which the risk constraint can be completely ignored when solving for an optimal operating policy. We illustrate our methodology in the context of an exporter firm which faces exchange rate risk in trading.

Estimation of real‐time reachable set on nonlinear time‐delay switched singular systems with state jump

AbstractThis article addresses the real‐time reachable set problem on nonlinear time‐delay switched singular systems with state jump, which is subject to initial state and peak‐bound disturbance. Initially, based on the system state decomposition method, a state jump lemma is proposed, which combines the nonlinear function and the system state at the switching instant. Then, a real‐time bounded lemma is developed, premised on using the idea of average dwell‐time and analyzing subinterval function changes. Afterwards, the sufficient conditions for estimating the real‐time and nonreal‐time reachable set are established, which ensures that the reachable state of system can be restricted within a real‐time or nonreal‐time closed set. Finally, simulation results of the typical Direct Current boost chopper are performed to exhibit the effectiveness of the devised estimation scheme for the real‐time reachable set of system.

Runtime Analysis of Single- and Multi-Objective Evolutionary Algorithms for Chance Constrained Optimization Problems with Normally Distributed Random Variables.

Chance constrained optimization problems allow to model problems where constraints involving stochastic components should only be violated with a small probability. Evolutionary algorithms have been applied to this scenario and shown to achieve high quality results. With this paper, we contribute to the theoretical understanding of evolutionary algorithms for chance constrained optimization. We study the scenario of stochastic components that are independent and normally distributed. Considering the simple single-objective (1+1) EA, we show that imposing an additional uniform constraint already leads to local optima for very restricted scenarios and an exponential optimization time. We therefore introduce a multi-objective formulation of the problem which trades off the expected cost and its variance. We show that multi-objective evolutionary algorithms are highly effective when using this formulation and obtain a set of solutions that contains an optimal solution for any possible confidence level imposed on the constraint. Furthermore, we prove that this approach can also be used to compute a set of optimal solutions for the chance constrained minimum spanning tree problem. In order to deal with potentially exponentially many trade-offs in the multi-objective formulation, we propose and analyze improved convex multi-objective approaches. Experimental investigations on instances of the NP-hard stochastic minimum weight dominating set problem confirm the benefit of the multi-objective and the improved convex multi-objective approach in practice.

Adaptive 3D Mesh Steganography Based on Feature-Preserving Distortion.

Current 3D mesh steganography algorithms relying on geometric modification are prone to detection by steganalyzers. In traditional steganography, adaptive steganography has proven to be an efficient means of enhancing steganography security. Taking inspiration from this, we propose a highly adaptive embedding algorithm, guided by the principle of minimizing a carefully crafted distortion through efficient steganography codes. Specifically, we tailor a payload-limited embedding optimization problem for 3D settings and devise a feature-preserving distortion (FPD) to measure the impact of message embedding. The distortion takes on an additive form and is defined as a weighted difference of the effective steganalytic subfeatures utilized by the current 3D steganalyzers. With practicality in mind, we refine the distortion to enhance robustness and computational efficiency. By minimizing the FPD, our algorithm can preserve mesh features to a considerable extent, including steganalytic and geometric features, while achieving a high embedding capacity. During the practical embedding phase, we employ the Q-layered syndrome trellis code (STC). However, calculating the bit modification probability (BMP) for each layer of the Q-layered STC, given the variation of Q, can be cumbersome. To address this issue, we design a universal and automatic approach for the BMP calculation. The experimental results demonstrate that our algorithm achieves state-of-the-art performance in countering 3D steganalysis.

Silicone dressing combined with topical oxygen therapy alleviates incontinence-associated dermatitis via NF-κB p65/STAT1 signaling pathway.

Incontinence-associated dermatitis (IAD) is a tough problem in clinical settings, not only increasing the risk of complications like catheter-related urinary tract infections and pressure ulcers in elderly and critically ill patients, but also prolonging hospital stays, raising hospital costs, and possibly leading to medical disputes. This study is aimed to evaluate the therapeutic effect of silicone dressing combined with topical oxygen therapy on IAD in a rat model. An IAD rat model induced by synthetic urine with trypsin was established. Hematoxylin & eosin staining was carried out to examine skin histology. Using immunofluorescence, the microvessel density in the affected skin tissues was determined. ELISA was performed to measure the concentrations of inflammatory cytokines and angiogenic factors in serum. The mRNA expression of EGF, PDGF, and VEGF was detected via qRT-PCR. Western blotting was employed to determine NF-κB p65/STAT1 pathway-related protein levels. Compared to single therapy, silicone dressing combined with topical oxygen therapy could significantly reduce the severity of IAD, improve skin histology, inhibit inflammation, and promote angiogenesis in IAD rat models. Additionally, the results showed that relatively speaking, the combined therapy suppressed the NF-κB p65/STAT1 signaling pathway more effectively. These findings indicated that silicone dressing combined with topical oxygen therapy can alleviate IAD through promoting wound healing and inhibiting inflammation via NF-κB p65/STAT1 signaling pathway in a rat model, which provided a theoretical basis for the prevention and treatment of IAD in clinic.

ATM-R: An Adaptive Tradeoff Model With Reference Points for Constrained Multiobjective Evolutionary Optimization.

The goal of constrained multiobjective evolutionary optimization is to obtain a set of well-converged and well-distributed feasible solutions. To achieve this goal, a delicate tradeoff must be struck among feasibility, diversity, and convergence. However, balancing these three elements simultaneously through a single tradeoff model is nontrivial, mainly because the significance of each element varies in different evolutionary phases. As an alternative approach, we adapt distinct tradeoff models in various phases and introduce a novel algorithm named adaptive tradeoff model with reference points (ATM-R). In the infeasible phase, ATM-R takes the tradeoff between diversity and feasibility into account, aiming to move the population toward feasible regions from diverse search directions. In the semi-feasible phase, ATM-R promotes the transition from "the tradeoff between feasibility and diversity" to "the tradeoff between diversity and convergence." This transition is instrumental in discovering an adequate number of feasible regions and accelerating the search for feasible Pareto optima in succession. In the feasible phase, ATM-R places an emphasis on balancing diversity and convergence to obtain a set of feasible solutions that are both well-converged and well-distributed. It is worth noting that the merits of reference points are leveraged in ATM-R to accomplish these tradeoff models. Also, in ATM-R, a multiphase mating selection strategy is developed to generate promising solutions beneficial to different evolutionary phases. Systemic experiments on a diverse set of benchmark test functions and real-world problems demonstrate that ATM-R is effective. When compared to eight state-of-the-art constrained multiobjective optimization evolutionary algorithms, ATM-R consistently demonstrates its competitive performance.

A better LP rounding for feedback arc set on tournaments

We present a randomized algorithm to approximate the feedback arc set problem on weighted tournaments, a classic well-studied NP-hard problem. Our algorithm is based on rounding its standard linear programming relaxation. It improves the previously best-known LP rounding algorithm by achieving an approximation factor of 2.127 (best known was 2.5). As a result, we have found a better upper bound for the integrality gap of the corresponding LP.

Hyperspectral Tensor Completion Using Low-Rank Modeling and Convex Functional Analysis.

Hyperspectral tensor completion (HTC) for remote sensing, critical for advancing space exploration and other satellite imaging technologies, has drawn considerable attention from recent machine learning community. Hyperspectral image (HSI) contains a wide range of narrowly spaced spectral bands hence forming unique electrical magnetic signatures for distinct materials, and thus plays an irreplaceable role in remote material identification. Nevertheless, remotely acquired HSIs are of low data purity and quite often incompletely observed or corrupted during transmission. Therefore, completing the 3-D hyperspectral tensor, involving two spatial dimensions and one spectral dimension, is a crucial signal processing task for facilitating the subsequent applications. Benchmark HTC methods rely on either supervised learning or nonconvex optimization. As reported in recent machine learning literature, John ellipsoid (JE) in functional analysis is a fundamental topology for effective hyperspectral analysis. We therefore attempt to adopt this key topology in this work, but this induces a dilemma that the computation of JE requires the complete information of the entire HSI tensor that is, however, unavailable under the HTC problem setting. We resolve the dilemma, decouple HTC into convex subproblems ensuring computational efficiency, and show state-of-the-art HTC performances of our algorithm. We also demonstrate that our method has improved the subsequent land cover classification accuracy on the recovered hyperspectral tensor.