Set Of Problems Research Articles

Routing and scheduling optimization for urban air mobility fleet management using quantum annealing

The growing integration of urban air mobility (UAM) for urban transportation and delivery has accelerated due to increasing traffic congestion and its environmental and economic repercussions. Efficiently managing the anticipated high-density air traffic in cities is critical to ensure safe and effective operations. In this study, we propose a routing and scheduling framework to address the needs of a large fleet of UAM vehicles operating in urban areas. Using mathematical optimization techniques, we plan efficient and deconflicted routes for a fleet of vehicles. Formulating route planning as a maximum weighted independent set problem enables us to utilize various algorithms and specialized optimization hardware, such as quantum annealers, which has seen substantial progress in recent years. Our method is validated using a traffic management simulator tailored for the airspace in Singapore. Our approach enhances airspace utilization by distributing traffic throughout a region. This study broadens the potential applications of optimization techniques in UAM traffic management.

Enumeration Approach to Atom-to-Atom Mapping Accelerated by Ising Computing.

Chemical reactions are regarded as transformations of chemical structures, and the question of which atoms in the reactants correspond to which atoms in the products has attracted chemists for a long time. Atom-to-atom mapping (AAM) is a procedure that establishes such correspondence(s) between the atoms of reactants and products in a chemical reaction. Currently, automatic AAM tools play a pivotal role in various chemoinformatics tasks. However, achieving accurate automatic AAM for complex or unknown reactions within a reasonable computation time remains a significant challenge due to the combinatorial nature of the problem and the difficulty in applying appropriate reaction rules. In this study, we propose a rule-free AAM algorithm, which enumerates all atom-to-atom correspondences that minimize the number of bond cleavages and formations during the reaction. To reduce the computational burden associated with the combinatorial optimization (i.e., minimizing bond changes), we introduce Ising computing, a computing paradigm that has gained significant attention for its efficiency in solving hard combinatorial optimization problems. We found that our Ising computing framework outperforms conventional combinatorial optimization algorithms in terms of computation times, making it feasible to solve the AAM problem without reaction rules in an acceptable time. Furthermore, our AAM algorithm successfully found the correct AAM solution for all problems in a benchmark data set. In contrast, conventional AAM algorithms based on chemical heuristics failed for several problems. Specifically, these algorithms either failed to find the optimal solution in terms of bond changes, or they identified only one optimal solution, which was incorrect when multiple optimal solutions exist. These results emphasize the importance of enumerating all optimal correspondences that minimize bond changes, which is effectively achieved by our Ising-computing framework.

Double Burden of Malnutrition: A Narrative Review of its Drivers and Mitigation Strategies in Urban Settings

Background: Double burden of malnutrition (DBM) is an emerging public health problem in urban settings where both undernutrition and obesity exist. This complex phenomenon is caused by socioeconomic disparities, urbanization, and dietary transitions, with women and children being proportionately affected. This study reviews the drivers of DBM and evaluates suitable mitigation strategies. Methods: A comprehensive review was performed using studies published between 2014 and 2024. Peer-reviewed articles examining the drivers, interventions, and outcomes of DBM within urban contexts were the target inclusion criteria. Data were extracted on location, target populations, methodologies, key drivers, interventions and outcomes which were then synthesized using thematic analysis. Results: Eighteen studies from Sub-Saharan Africa, Southeast Asia, and Latin America were included. Drivers known to be common among the reviewed studies include income inequality, poor dietary diversity, and cultural food practices that are further worsened by urbanization and sedentary lifestyles. By region, effective mitigation strategies included maternal nutrition literacy programs in Southeast Asia and Sub-Saharan Africa and taxation policies against unhealthy foods in Latin America. Gaps were identified in the areas of policy implementation and program scalability. Conclusion: Integrated, double-duty actions are needed to address the double burden of malnutrition in urban contexts. However, policy reform, community-based nutritional programs and educational initiatives targeted at vulnerable populations, are essential. Longitudinal and gender-specific research should be prioritized in assessing DBM’s trajectory and informing future sustainable interventions. The findings from this study offer actionable insights for policymakers and public health practitioners working to eliminate DBM in urban settings.

Popular Arborescences and Their Matroid Generalization

Consider a directed, rooted graph \(G=(V\cup\{r\},E)\) where each vertex in \(V\) has a partial order preference over its incoming edges. The preferences of a vertex naturally extend to preferences over arborescences rooted at \(r\) . We present a polynomial-time algorithm that decides whether a given input instance admits a popular arborescence, i.e., one for which there is no “more popular” arborescence. In fact, our algorithm solves the more general popular common base problem in the intersection of two matroids: we are given an arbitrary matroid \(M=(E,\mathcal{I})\) and a partition matroid \(M_{\text{part}}\) over \(E\) , where partition classes correspond to a set \(V\) of agents with \(|V|={\rm rank}(M)\) and each agent has a partial order preference over its associated partition class; the problem asks for a common base of \(M\) and \(M_{\text{part}}\) such that there is no “more popular” common base. Our algorithm is combinatorial, and can be regarded as a primal–dual algorithm. It searches for a solution along with its dual certificate, a chain of subsets of \(E\) , witnessing its popularity. Our generalized results, expressed in terms of matroids, demonstrate that the identification of agents with vertices of the graph in the popular arborescence problem is not essential. We also study the related popular common independent set problem. For the case with weak rankings, we formulate the popular common independent set polytope, and thus show that a minimum-cost popular common independent set can be computed efficiently. By contrast, we prove that it is \(\mathsf{NP}\) -hard to compute a minimum-cost popular arborescence, even when rankings are strict.

Antisymmetric maximum principles and Hopf’s lemmas for the Logarithmic Laplacian, with applications to symmetry results

Abstract We prove antisymmetric maximum principles and Hopf-type lemmas for linear problems described by the Logarithmic Laplacian. As application, we prove the symmetry of solutions for semilinear problems in symmetric sets, and a rigidity result for the parallel surface problem for the Logarithmic Laplacian.

The sum-product problem for small sets

The sum-product problem for small sets

Integer programs with bounded subdeterminants and two nonzeros per row

We give a strongly polynomial-time algorithm for integer linear programs defined by integer coefficient matrices whose subdeterminants are bounded by a constant and that contain at most two nonzero entries in each row. The core of our approach is the first polynomial-time algorithm for the weighted stable set problem on graphs that do not contain more than k vertex-disjoint odd cycles, where k is any constant. Previously, polynomial-time algorithms were only known for k =0 (bipartite graphs) and for k =1. We observe that integer linear programs defined by coefficient matrices with bounded subdeterminants and two nonzeros per column can be also solved in strongly polynomial-time, using a reduction to b -matching.

Study on characters associations and path coefficient analysis for yield and quality traits of parthenocarpic cucumber genotypes in poly-net house conditions

Parthenocarpy in cucumber is a complex trait regulated by various genes and phytohormones. It is an important feature which helps to overcome the problem of poor fruit setting in cucumbers. The present research was conducted to estimate the variability, correlation and path analysis in 15 yield and quality traits using 30 advance breeding parthenocarpic lines including standard check ‘Punjab Kheera 1’. The genetic variability studies reported high estimates of phenotypic and genotypic coefficient of variation for number of fruits set per node and number of fruits per plant which signified the improvement of these traits with selection. High to moderate heritability and genetic gains were observed for fruit length, fruit diameter, fruit weight, internodal length, vine length, number of female flowers per node, number of fruits set per node, number of fruits per plant, fruit yield per plant, and vitamin C content of fruit. The correlation and path analyses provided a clear picture that there was direct and positive effect of fruit weight, number of fruits per plant, number of fruits set per node, fruit length, vitamin C, flesh to seed cavity ratio, and pistil length on fruit yield per plant.

Exploring geometric reasoning development in early civil engineering education through structured problem-solving

ABSTRACT Geometric reasoning is an indispensable skill within the domain of civil engineering, where professionals are required to comprehend and manipulate geometric figures for the creation of stable and functional structures. This paper evaluates the geometric reasoning abilities of four groups of civil engineering students who engaged with a complex geometric problem: constructing a parallelogram using three distinct triangular shapes. To decode the students’ reasoning processes, we employed theoretical frameworks from the didactics of mathematics, focusing particularly on geometric reasoning, discursive organization, and the integrated processes of discursive apprehension and operational engagement in solving and rationalizing problems tied to the geometry of triangles. The findings of this study reveal that introducing challenging problems in educational settings facilitates the enhancement of geometric reasoning. Furthermore, it fosters the assimilation and application of concepts and properties across diverse contexts, thereby encouraging advanced levels of mathematical thought. Additionally, the strategic use of guiding questions significantly improves argumentative skills and the accurate employment of mathematical terminology.

Resource‐Efficient Adaptive Variational Quantum Algorithm for Combinatorial Optimization Problems

AbstractVariational quantum algorithms (VQAs) are quantum‐classical hybrid algorithms that are promising for the near future. The Quantum Approximate Optimization Algorithm (QAOA) is a representative VQA for solving combinatorial optimization problems. However, the parameterized quantum circuit (PQC) of QAOA still has room for improvement. The existing method, called ADAPT‐QAOA, has improved the PQC of QAOA, but the circuit depth remains deep. A Resource‐Efficient Adaptive VQA (RE‐ADAPT‐VQA) that utilizes gates from a new gate pool is proposed to construct a PQC. RE‐ADAPT‐VQA incrementally integrates parameterized quantum gates based on the gradient until the predefined stopping criteria are satisfied, and a rollback mechanism is proposed to ensure that the circuit remains shallow. The algorithm is experimentally simulated to solve Max‐Cut problem and the maximum independent set problem. The results show that RE‐ADAPT‐VQA significantly reduces circuit depth, single‐qubit gates, and CNOT gates compared to existing methods, while maintaining the same level of energy error.

Emergency Care for Refugee Patients at Suceava Hospital, Romania: Challenges and Insights from the First Year of the Russian-Ukrainian Conflict.

The ongoing military conflict in Ukraine has had a devastating impact on children's health, exposing them to a range of illnesses. The aim of this study was to analyze the most common medical conditions among Ukrainian children since the beginning of the conflict, with a focus on identifying and understanding these problems in a wartime setting. To assess the health status of affected children, we collected data from 422 pediatric patients who presented to the emergency department. The analysis included reviewing medical records, documenting the nature of illness, treatments administered, and the need for hospitalization. Preliminary results indicate that interstitial pneumonia, contusions, gastroenterocolitis, and traumatic brain injury were the most common conditions. Of the 422 children studied, 80% received appropriate care without hospitalization, while 20% were admitted for further evaluation. Interstitial pneumonia was diagnosed in 23% of patients, highlighting the vulnerability of the respiratory system under conflict conditions. Contusions were predominant among musculoskeletal injuries, accounting for 81% of cases, and gastroenterocolitis was diagnosed in 46% of patients, reflecting the impact of poor living conditions.

Production scheduling with multi-robot task allocation in a real industry 4.0 setting

The demand for efficient Industry 4.0 systems has driven the need to optimize production systems, where effective scheduling is crucial. In smart manufacturing, robots handle material transfers, making precise scheduling essential for seamless operations. However, research often oversimplifies the Robotic Flexible Job Shop problem by focusing only on transportation time, ignoring resource allocation and robot diversity. This study addresses these gaps, tackling a Multi-Robot Flexible Job Shop (MRFJS) scheduling problem with limited buffers. It involves non-identical parallel machines and robots with varying capabilities overseeing material handling under blocking conditions. The case study is based on a real Industry 4.0 scenario, where the layout restricts each robotic arm’s access, requiring strategic buffer placement for part transfers. A Mixed-Integer Programming (MILP) model aims to minimize makespan, followed by a new Genetic Algorithm (GA) using Roy and Sussman’s Alternative Graph. Computational tests on various scales and real data from a manufacturing plant demonstrate the GA’s efficacy in solving complex scheduling problems in real-world production settings. Based on the data, the Proposed Genetic Algorithm (PGA), with an average Relative Deviation (ARD) of 0.25%, performed approximately 34% better compared to the Basic Genetic Algorithm (BGA), with an average ARD of 0.38%. This percentage indicates that the PGA significantly outperforms in solving complex scheduling problems.

Wannabe Bounded Treewidth Graphs Admit a Polynomial Kernel for Directed Feedback Vertex Set

In the Directed Feedback Vertex Set (DFVS) problem, given a digraph D and a positive integer k , the goal is to check if there exists a set of at most k vertices whose deletion from D results in a directed acyclic graph. The existence of a polynomial kernel for DFVS , parameterized by the solution size k , is a central open problem in Kernelization. In this paper, we give a polynomial kernel for DFVS parameterized by k plus the size of a treewidth- η modulator (of the underlying undirected graph), where η is any fixed positive integer. Since the status of the existence of a polynomial kernel for DFVS (parameterized by the solution size) is open for a very long time now, and it is known to not admit a polynomial kernel when the parameter is the size of a treewidth-2 modulator, solution size plus the size of the treewidth- η modulator makes for an interesting choice of parameter to study. In fact, the polynomial kernelization complexity of DFVS parameterized by the size of the undirected feedback vertex set (treewidth-1 modulator) in the underlying undirected graph, has already been studied in literature. Our choice of parameter strictly encompasses previous positive kernelization results on DFVS . Our result is based on a novel application of the tool of important separators embedded in state-of-the-art machinery such as protrusion decompositions.

Clinical presentation with consequences and laboratory diagnosis of multi and extensive drug resistance in typhoid cases

Background: Enteric fever is among the major problem in the health care settings of developing countries like Pakistan. Unjudicial use and over the counter availability of antibiotics lead to the emergence of multi drug resistant and extensive drug-resistant strains of salmonella outbreak which demonstrate severity of disease of the disease. Our objective is to determine the clinical presentation, consequences and antibiotic susceptibility patterns of Multi Drug Resistant (MDR) typhoid and Extensive drug resistance (XDR) typhoid cases. Methods: The study was conducted in the department of Microbiology, Dow Diagnostic Reference and Research Laboratory, Dow University of Health Sciences. All laboratory proven Salmonella positive cultures samples were selected from laboratory that were either fully or partially treated, or were taking anti-Salmonella. Clinical trends along with clinical outcomes were assessed from clinical presentation. Furthermore, Demographic details were noted. Detailed history about the presenting illness with examination findings and medications were recorded. Results: A total of 333 patients had positive blood culture for enteric fever, in which maximum number of XDR cases 84% (n=281) were observed in blood cultures followed by MDR salmonella in 16 % (n=52). Meropenem and azithromycin didn’t show any resistance in our study. Conclusion: Our study has demonstrated that pediatric population is more vulnerable for developing salmonella infection. Carbapenems and azithromycin are among the last choices for the treatment of XDR cases. Analysis of our results imply stress on the implementation of preventive measures for spread of enteric fever including proper diagnosis, vaccination and good sanitation system

An Intelligent System for Classifying Patient Complaints Using Machine Learning and Natural Language Processing: Development and Validation Study.

Accurate classification of patient complaints is crucial for enhancing patient satisfaction management in health care settings. Traditional manual methods for categorizing complaints often lack efficiency and precision. Thus, there is a growing demand for advanced and automated approaches to streamline the classification process. This study aimed to develop and validate an intelligent system for automatically classifying patient complaints using machine learning (ML) and natural language processing (NLP) techniques. An ML-based NLP technology was proposed to extract frequently occurring dissatisfactory words related to departments, staff, and key treatment procedures. A dataset containing 1465 complaint records from 2019 to 2023 was used for training and validation, with an additional 376 complaints from Hangzhou Cancer Hospital serving as an external test set. Complaints were categorized into 4 types-communication problems, diagnosis and treatment issues, management problems, and sense of responsibility concerns. The imbalanced data were balanced using the Synthetic Minority Oversampling Technique (SMOTE) algorithm to ensure equal representation across all categories. A total of 3 ML algorithms (Multifactor Logistic Regression, Multinomial Naive Bayes, and Support Vector Machines [SVM]) were used for model training and validation. The best-performing model was tested using a 5-fold cross-validation on external data. The original dataset consisted of 719, 376, 260, and 86 records for communication problems, diagnosis and treatment issues, management problems, and sense of responsibility concerns, respectively. The Multifactor Logistic Regression and SVM models achieved weighted average accuracies of 0.89 and 0.93 in the training set, and 0.83 and 0.87 in the internal test set, respectively. Ngram-level term frequency-inverse document frequency did not significantly improve classification performance, with only a marginal 1% increase in precision, recall, and F1-score when implementing Ngram-level term frequency-inverse document frequency (n=2) from 0.91 to 0.92. The SVM algorithm performed best in prediction, achieving an average accuracy of 0.91 on the external test set with a 95% CI of 0.87-0.97. The NLP-driven SVM algorithm demonstrates effective classification performance in automatically categorizing patient complaint texts. It showed superior performance in both internal and external test sets for communication and management problems. However, caution is advised when using it for classifying sense of responsibility complaints. This approach holds promises for implementation in medical institutions with high complaint volumes and limited resources for addressing patient feedback.

Practice of self-medication to manage oral health issues in a community setting of Nepal

BackgroundSelf-medication has been defined as the practice of self-diagnosis and medication use without seeking professional healthcare advice. Its prevalence for alleviating dental problems in Nepali communities has not been explored. This study was conducted to assess self-medication for oral health problems in a community setting in Nepal.MethodsA community-based cross-sectional study was conducted in two conveniently selected wards of Baraha Municipality of Sunsari district in August 2021 using a pre-validated questionnaire in the Nepali language. The prevalence of self-medication for oral health problems was assessed. The chi-square and Mann-Whitney U tests were used to check its association with demographic variables.ResultsThe prevalence of self-medication was found to be 43.3%. The most common health problem leading to self-medication was toothache (78.5%). Around half of the respondents (55.1%) replied achieving temporary relief after its use while around a quarter (25.1%) thought that self-medication was effective in taking care of their problems. Three-quarters of the respondents (75.7%) knew that they had to visit a dentist if their problems persisted. The annual family income was significantly associated with the practice of self-medication for oral health problems. (p = 0.013)ConclusionSelf-medication was found to be frequently utilized by the people of Baraha municipality with almost every second person with oral health problems reporting using it to solve their problems. This phenomenon is disproportionately seen in those with lower economic status.Clinical trial numberNot applicable.

Weighted Q-learning for optimal dynamic treatment regimes with nonignorable missing covariates

ABSTRACT Dynamic treatment regimes (DTRs) formalize medical decision-making as a sequence of rules for different stages, mapping patient-level information to recommended treatments. In practice, estimating an optimal DTR using observational data from electronic medical record (EMR) databases can be complicated by nonignorable missing covariates resulting from informative monitoring of patients. Since complete case analysis can provide consistent estimation of outcome model parameters under the assumption of outcome-independent missingness, Q-learning is a natural approach to accommodating nonignorable missing covariates. However, the backward induction algorithm used in Q-learning can introduce challenges, as nonignorable missing covariates at later stages can result in nonignorable missing pseudo-outcomes at earlier stages, leading to suboptimal DTRs, even if the longitudinal outcome variables are fully observed. To address this unique missing data problem in DTR settings, we propose 2 weighted Q-learning approaches where inverse probability weights for missingness of the pseudo-outcomes are obtained through estimating equations with valid nonresponse instrumental variables or sensitivity analysis. The asymptotic properties of the weighted Q-learning estimators are derived, and the finite-sample performance of the proposed methods is evaluated and compared with alternative methods through extensive simulation studies. Using EMR data from the Medical Information Mart for Intensive Care database, we apply the proposed methods to investigate the optimal fluid strategy for sepsis patients in intensive care units.

On Exceptional Sets for Waring-Goldbach Problems with Unlike Powers

On Exceptional Sets for Waring-Goldbach Problems with Unlike Powers

The Erdős distinct subset sums problem in a modular setting

The Erdős distinct subset sums problem in a modular setting

Data-centric strategies for deep-learning accelerated salt interpretation

Abstract Deep learning (DL) has become the most effective machine learning solution for addressing and accelerating complex problems in various fields, from computer vision and natural language processing to many more. Training well-generalized DL models requires large amounts of data which allows the model to learn the complexity of the task it is being trained to perform. Consequently, performance optimization of the deep-learning models is concentrated on complex architectures with a large number of tunable model parameters, in other words, model-centric techniques. To enable training such large models, significant effort has also gone into high-performance computing and big-data handling. However, adapting DL to tackle specialized domain-related data and problems in real-world settings presents unique challenges that model-centric techniques do not suffice to optimize. In this paper, we tackle the problem of developing DL models for seismic imaging using complex seismic data. We specifically address developing and deploying DL models for salt interpretation using seismic images. Most importantly, we discuss how looking beyond model-centric and leveraging data-centric strategies for optimization of DL model performance was crucial to significantly improve salt interpretation. This technique was also key in developing production quality, robust and generalized models.