Minimal Set Research Articles

A novel improved structural controllability method on complex temporal networks based on temporal ACO algorithm

The controllability of complex temporal networks is an area of research focused on understanding how to guide or influence the behaviour of dynamic. Structural controllability is considered as one of the most prominent network controllability methods. Structural controllability uses the maximum matching algorithm to find the minimum set of control nodes. The maximum matching algorithm on temporal networks is a class of NP-hard problems. In this paper, a novel method based on temporal ACO algorithm is proposed to solve the maximum matching problem in structural controllability. The ACO algorithm has been adapted to temporal networks. The results of implementing the proposed method on real-world datasets demonstrate that the ACO algorithm has a good performance and has converged to the optimal solution with high speed. The results demonstrate that the proposed method has higher efficiency in finding driver nodes and algorithm execution speed compared to the basic structural controllability.

Risk aversion can promote cooperation

Abstract Cooperative dynamics are central to our understanding of many phenomena in living and complex systems. However, we lack a universal mechanism to explain the emergence of cooperation. We present a novel framework for modelling social dilemma games with an arbitrary number of players by combining reaction networks, methods from quantum mechanics applied to stochastic complex systems, game theory and stochastic simulations of molecular reactions. Using this framework, we propose a novel and robust mechanism for cooperation based on risk aversion that leads to cooperative behaviour in population games. Rather than individuals seeking to maximise payouts in the long run, individuals seek to obtain a minimum set of resources with a given level of confidence and in a limited time span. We show that this mechanism can lead to the emergence of new equilibria in a range of social dilemma games.

Developing a core outcome set for assessing interventions and care for parents after neonatal death in high-income countries (iCHOOSE Neonatal study): protocol for a mixed-methods study

IntroductionNeonatal death exerts long-lasting impact on parents’ mental health, finances and relationships, and the wider family. There is national and international momentum to evaluate interventions to support parents after the...

Building global collaborative research networks in paediatric critical care: a roadmap.

Paediatric critical care units are designed for children at a vulnerable stage of development, yet the evidence base for practice and policy in paediatric critical care remains scarce. In this Health Policy, we present a roadmap providing strategic guidance for international paediatric critical care trials. We convened a multidisciplinary group of 32 paediatric critical care experts from six continents representing paediatric critical care research networks and groups. The group identified key challenges to paediatric critical care research, including lower patient numbers than for adult critical care, heterogeneity related to cognitive development, comorbidities and illness or injury, consent challenges, disproportionately little research funding for paediatric critical care, and poor infrastructure in resource-limited settings. A seven-point roadmap was proposed: (1) formation of an international paediatric critical care research network; (2) development of a web-based toolkit library to support paediatric critical care trials; (3) establishment of a global paediatric critical care trial repository, including systematic prioritisation of topics and populations for interventional trials; (4) development of a harmonised trial minimum set of trial data elements and data dictionary; (5) building of infrastructure and capability to support platform trials; (6) funder advocacy; and (7) development of a collaborative implementation programme. Implementation of this roadmap will contribute to the successful design and conduct of trials that match the needs of globally diverse paediatric populations.

Advancements in Interoperability: Achieving Anatomic Pathology Reports That Adhere to International Standards and Are Both Human-Readable and Readily Computable.

Over the past 50 years, multiple pathology organizations worldwide have evolved in cancer histopathology reporting from subjective, narrative assessments to structured, synoptic formats using controlled vocabulary. These reporting protocols include the required data elements that represent the minimum set of evidence-based, clinically actionable parameters necessary to convey the diagnostic, prognostic, and predictive information essential for patient care. Despite these advances, the synoptic reporting protocols were not harmonized across the various pathology organizations. Cancer pathology continues to be widely reported and stored in free-text format, or without encoded data such that it is neither computable nor interoperable across organizations. In 2020, SNOMED International created the Cancer Synoptic Reporting Working Group (CSRWG). This resulted in international collaboration across multiple pathology organizations. CCRWG's mission was to use SNOMED Clinical Terms (CT) concepts to represent the required content within the College of American Pathologists (CAP) and International Collaboration on Cancer Reporting (ICCR) published pathology reporting protocols. In late 2023, the CSRWG published over 1,300 new or revised SNOMED CT concepts to represent all required pathology cancer data elements for adult and pediatric solid tumors in both CAP and ICCR using the semantic principles of the SNOMED-CT concept model. Thus, computability and interoperability would be broadly established. This work brings to fruition the longstanding desire for an international, interoperable, human- and machine-readable cancer pathology report for use in patient care, health care quality improvement, population health, public health surveillance, and translational and clinical trial research. The following report describes the project, its methods, and applications in the stated use cases.

SA-Net: Leveraging Spatial Correlations Spatial-Aware Net for Multi-Perspective Robust Estimation Algorithm

Robust estimation aims to provide accurate and reliable parameter estimations, particularly when data are affected by noise or outliers. Traditional methods like random sample consensus (RANSAC) struggle with handling outliers because they treat all observations as equally important. A series of advanced deep learning methods have recently emerged, which use deep learning techniques to estimate the probability of each sample being selected, prioritizing higher confidence for observations that are closer to the ground truth model. However, optimizing solely based on proximity to the ground truth model does not guarantee higher-quality estimations. Meanwhile spatial relationships between the data points in the minimum sampled set also influence the accuracy of the final estimated model. To address these issues, we propose Spatial-Aware Net (SA-Net), a dual-branch neural network that integrates both confidence and spatial encodings. SA-Net employs a confidence distribution encoder to learn the confidence distribution and a spatial distribution encoder to capture spatial correlations between point features. By incorporating multi-perspective sampling, the minimum sample set can be selected based on different spatial distributions in the output of the neural network, and applying Chamfer Loss constraints, our approach improves model optimization and effectively mitigates suboptimal solutions. Extensive experiments demonstrate that SA-Net outperforms the state of the art across various real-world robust estimation tasks.

Identifying Patient Subgroups in the Heterogeneous Chronic Pain Population Using Cluster Analysis.

Chronic pain is an ill-defined disease with complex biopsychosocial aspects, posing treatment challenges. We hypothesized that treatment failure results, at least partly, from limited understanding of diverse patient subgroups. We aimed to identify subgroups using psychological variables, allowing for more tailored interventions. In a retrospective cohort study, we extracted patient-reported data from two Dutch tertiary multidisciplinary outpatient pain clinics (2018-2023) for unsupervised hierarchical clustering. Clusters were defined by anxiety, depression, pain catastrophizing, and kinesiophobia. Sociodemographics, pain characteristics, diagnosis, lifestyle, health-related quality of life and treatment efficacy were compared among clusters. A prediction model was built utilizing a minimum set of questions to reliably assess cluster allocation. Among 5,466 patients with chronic pain, three clusters emerged. Cluster 1 (n=750) was characterized by high psychological burden, low health-related quality of life, lower educational levels and employment rates, and more smoking. Cluster 2 (n=1,795) showed low psychological burden, intermediate health-related quality of life, higher educational levels and employment rates, and more alcohol consumption. Cluster 3 (n=2,909) showed intermediate features. Pain reduction following treatment was least in cluster 1 (28.6% after capsaicin patch, 18.2% after multidisciplinary treatment), compared to >50% for both treatments in clusters 2 and 3. A model incorporating 15 psychometric questions reliably predicted cluster allocation. In conclusion, our study identified distinct chronic pain patient clusters through 15 psychological questions, revealing one cluster with notably poorer response to conventional treatment. Our prediction model, integrated in a web-based tool, may help clinicians improve treatment by allowing patient-subgroup targeted therapy according to cluster allocation. PERSPECTIVE: Hierarchical clustering of chronic pain patients identified three subgroups with similar pain intensity and diagnoses but distinct psychosocial traits. One group with higher psychological burden showed poorer treatment outcomes. A web-based tool using this model could help clinicians tailor therapies by matching interventions to specific patient subgroups for improved outcomes.

On distance k-domination number of graphs under product operations

For a positive integer k, a subset S ⊆ V(G) is said to be a k-dominating set of G if every vertex v ∈ V(G)\S is at a distance of at most k from some vertex of S. The cardinality of the minimum k-dominating set of G is the k-domination number, denoted by γk(G). In this article, we determine the distance k-domination number for two graphs under some product operations in terms of its factor graphs.

Cooperative Multi-Agent Planning Framework for Fuel Constrained UAV-UGV Routing Problem

Unmanned Aerial Vehicles (UAVs), adept at aerial surveillance, are often constrained by their limited battery capacity. Refueling on slow-moving Unmanned Ground Vehicles (UGVs) can significantly enhance UAVs’ operational endurance. This paper explores the computationally complex problem of cooperative UAV-UGV routing for vast area surveillance, considering speed and fuel constraints. It presents a sequential multi-agent planning framework aimed at achieving feasible and optimally satisfactory solutions. By considering the UAV fuel limit and utilizing a minimum set cover algorithm, we determine UGV refueling stops. This, in turn, facilitates UGV route planning as the first step. Through a task allocation technique and energy-constrained vehicle routing problem modeling with time windows (E-VRPTW), we then achieve the UAV route in the second step of the framework. The effectiveness of our multi-agent strategy is demonstrated through the implementation on 30 different task scenarios across three different scales. This work provides significant insight into the collaborative advantages of UAV-UGV systems and introduces heuristic approaches to bypass computational challenges and swiftly reach high-quality solutions.

The effect of moderate to late preterm birth on lung function over the first 5 years of life in a South African birth cohort

BackgroundPreterm birth is associated with increased mortality and morbidity, particularly due to lung disease. Low-middle income countries (LMIC) have the highest rates of preterm birth. Infants born extremely preterm rarely survive, so the largest burden is amongst moderate to late preterm (MLP) infants. The long-term health impact on MLP children in LMIC is poorly understood.AimTo assess the effect of MLP birth on lung function trajectories from birth to 5 years in the birth cohort Drakenstein Child Health Study.MethodsChildren were followed from birth with lung function measurements at 6 weeks and annually until 5 years. Exposures were longitudinally collected from the antenatal period through 5 years of age. Lung function tests included spectral and intra-breath oscillometry, tidal breathing and multiple breath washout. Mixed-effect models adjusted using a minimum set of variables were used.ResultsOf 923 children, 119 (13%) were born MLP [32-<37 weeks gestation; median gestational age (GA) 35 weeks] and 19 (2%)<32 weeks gestation. Children born MLP had altered lung function trajectories. Spectral oscillometry demonstrated a 13% reduction in compliance (0.87, 95% CI: 0.82; 0.92), 5% increase in resistance at end expiration (1.05, 95% CI: 1.01;1.09) and 1.74 Hz (95% CI: 1.10; 2.39) increase in resonance frequency over 5 years compared to term children. The proportion of children hospitalised for lower respiratory tract infections (LRTI) was higher in preterm born children (29 (23.6%) MLP; 18 (94.7%) extreme to very preterm; 109 (14%) term born children;p<0.01). LRTI and tobacco smoke exposure were associated with impaired lung function, but the impact of MLP on lung function was independent of these.ConclusionMLP born children have impaired lung function trajectories over the first 5 years of life, highlighting the importance of strengthening maternal health, perinatal care and strategies to reduce tobacco smoke exposure and early life LRTI.

Supporting endocrine therapy adherence in women with breast cancer: findings from the ROSETA pilot fractional factorial randomized trial.

Adherence to adjuvant endocrine therapy (AET) in women with breast cancer is poor. Multicomponent intervention packages are needed to address adherence barriers. Optimizing these packages prior to definitive evaluation can increase their effectiveness, affordability, scalability, and efficiency. To pilot procedures for an optimization-randomized controlled trial (O-RCT) of the 'Refining and Optimizing Strategies to support Endocrine Therapy Adherence' (ROSETA) intervention. This was a multisite individually randomized external pilot trial using a 24-1 fractional factorial design (ISRCTN10487576). Breast cancer survivors prescribed AET were recruited from 5 hospitals and randomized to one of 8 conditions, each comprising a combination of 4 intervention components set to "on" or "off" (SMS messages, information leaflet, guided self-help, and self-management website). We set criteria to inform the decision to progress to an O-RCT for consent rate, component adherence, and availability of outcome measures, with predefined cutoffs for "green" (proceed), "amber" (minor changes), and "red" (major changes). Among 141 eligible patients, 54 (38.3%) consented (green range). At least 50.0% of participants adhered to the minimum threshold set for each intervention component (green range). Data for one of the 3 medication adherence measures were available (amber range). Most (86.8%) participants were satisfied with their trial experience. Exploratory analysis indicated some evidence of a negative main effect of the information leaflet on medication adherence (adjusted mean difference = 0.088, 95% CI, 0.018, 0.158). Progression to a fully powered O-RCT of the ROSETA intervention package is feasible, but review of medication adherence measures is required.

A parallel algorithm for minimum weight set cover with small neighborhood property

A parallel algorithm for minimum weight set cover with small neighborhood property

The Songdo consensus: Development of minimum reporting standards for studies of intervention in idiopathic anal fistula using a modified nominal group technique.

Cryptoglandular anal fistulas carry a substantial burden to quality of life. Surgery is the only effective curative treatment but requires balancing fistula healing against pain, wounds and continence impairment. Sphincter-preserving procedures do exist but demonstrate variable rates of success. A lack of consistency and precision in outcome reporting and methodological quality hinders effective evidence-based decision-making. We aimed to establish a series of minimum reporting standards for interventional studies in idiopathic anal fistula, to eradicate low-quality studies, thus providing a consistent baseline of useful evidence. An international group of 16 experts participated in a modified nominal group technique consensus. The nominal question was: 'What should be the minimum set of reporting standards for studies of intervention in idiopathic anal fistula?' The process was conducted between May and June 2023, culminating in a hybrid in-person/virtual meeting that took place at the Songdo International Proctology Symposium in June 2023. Initial idea generation resulted in 37 statements within the first round. Themes included variable reporting of follow-up and incontinence. Participants indicated their agreement via a 9-point Likert scale. Any statement achieving >70% consensus was retained. Subsequent group discussion condensed the list to 11 statements for further voting and a final minimum set of 12 reporting standards was created. To date, this is the first study dedicated to developing minimum reporting standards for interventional studies in idiopathic anal fistula using a modified nominal group technique. These standards will instruct researchers in producing meticulous, high-quality studies that are accurate, transparent and reproducible.

Development of a core outcome set of quality indicators for enhanced recovery after surgery in minimally invasive cardiac heart valve surgery by interprofessional Delphi consensus.

A core outcome set (COS) giving indicators of the quality of the process for minimally invasive valve surgery embedded into enhanced recovery after surgery (ERAS) protocols should be developed. Using web-based questionnaires, a Delphi process with three rounds was conducted from January to December 2022. Prior to the rounds, a systematic database search was performed identifying potential quality parameters. Experts for the panel were selected reflecting the interprofessional nature of the ERAS protocol. In the first round, participants could make suggestions of indicators in the pre-, intra- or postoperative and rehabilitative phase. These suggestions form together with the indicators of the literature a first indicator list. In the second round, participants could rate the relevance of the indicators resulting in a condensed indicator list. The third round was performed for further condensation based on importance ranking of the remaining indicators. Three studies could be included in the systematic literature search providing a list of 22 indicators. Twenty-one experts participated in the Delphi survey. The experts named 315 indicators in the first round. After condensation in two further rounds, the final COS consisted of 24 indicators in the categories structure, process, outcome and complications. A consensual minimum set of quality measurements during pre-, intra- and postoperative and rehabilitation phase for patients with minimally invasive heart surgery is now available for enhancing the quality of clinical practice and facilitating comparisons across different ERAS programs.

Development of Genomic SSR Markers for Assessing Genetic Diversity in Korean Native Fallopia multiflora

Fallopia multiflora, a perennial herb in the Polygonaceae family belonging to the genus Fallopia Adanson, is traditionally used as a Chinese herbal medicine. However, there is still confusion about the botanical origin of the species and the phylogenetic relationship between the cultivars and the wild relatives. To develop an efficient identification method, a molecular analysis was performed using SSR markers. The genetic diversity of the F. multiflora genetic resources has been assessed by using 10 locally collected accessions, including varieties and landraces. We screened 100 pairs of SSR primers and selected 71 successfully amplified SSR markers, in which one SSR was found to be a monomorphic marker. The results indicated that the number of alleles (NA) ranged from 2 to 10, with an average of 4.1 alleles. The major allele frequency (MAF) spanned from 0.20 to 0.90, the observed heterozygosity (HO) ranged from 0 to 0.80, and the polymorphic information content (PIC) varied between 0.16 and 0.86. Clustering analysis using an unweighted pair group mean algorithm (UPGMA) with all 70 SSR markers revealed three clusters among the F. multiflora accessions. Furthermore, seven minimum marker set combinations were identified and proved useful for variety identification. Therefore, these SSR markers could be valuable for various applications, including cultivar identification and assessing the purity of F. multiflora populations. Three genetic groups of F. multiflora should be considered as independent units for conservation and germplasm management of the species.

An Efficient Algorithm for Power Dominating Set

AbstractThe problem Power Dominating Set (PDS) is motivated by the placement of phasor measurement units to monitor electrical networks. It asks for a minimum set of vertices in a graph that observes all remaining vertices by exhaustively applying two observation rules. Our contribution is twofold. First, we determine the parameterized complexity of PDS by proving it is W[P]-complete when parameterized with respect to the solution size. We note that it was only known to be W[2]-hard before. Our second and main contribution is a new algorithm for PDS that efficiently solves practical instances. Our algorithm consists of two complementary parts. The first is a set of reduction rules for PDS that can also be used in conjunction with previously existing algorithms. The second is an algorithm for solving the remaining kernel based on the implicit hitting set approach. Our evaluation on a set of power grid instances from the literature shows that our solver outperforms previous state-of-the-art solvers for PDS by more than one order of magnitude on average. Furthermore, our algorithm can solve previously unsolved instances of continental scale within a few minutes.

Diagnosability-optimization design of fixed-wing unmanned aerial vehicle based on causal matching and tree seed algorithm

With mission intensity and difficulty escalating, the future market demand for UAV reliability and safety grows dramatically. So, this paper proposes an optimal design strategy based on causal matching and the Tree Seed Algorithm (TSA) based on structural analysis. First, a novel algorithm is designed for finding the minimum set of consistency relations for diagnosability analysis. The complexity of this algorithm is at a polynomial level, which is a significant improvement over previous algorithms with exponential complexity. A causal consistency search algorithm is also innovatively proposed for causal diagnosability analysis considering the causal constraints of the dynamic variables. Secondly, a diagnosability optimization strategy based on TSA is designed to balance the diagnosability requirements and the design cost of consistency relations. This strategy can satisfy the system’s diagnosability demand under different causal constraints with minimum consistency relations. Finally, a fixed-wing UAV model is established to analyze the diagnosability under different causal constraints qualitatively. Based on the TSA, consistency relations with the minimum integrated diagnosis cost and the best diagnosis performance are preferred.

An Effective and Robust Approach Based on Malatya Centrality Algorithm for Interpreting Cheminformatics Graphs Using Maximum Clique

Cheminformatics graphs are derived by transforming the atomic nodes and bonds of chemical compounds into graph structures and are used to analyze the chemical and structural properties of molecules. In this study, an effective and robust approach based on the Malatya Centrality Algorithm is proposed for identifying the maximum clique in cheminformatics graphs. The proposed method transforms cheminformatics graphs by taking their complement and calculates the Malatya centrality values for these graphs. Using these values, the minimum independent set is identified in the complemented graph, which corresponds to the set of nodes forming the maximum clique in the original graph. The study demonstrates, through tests on various cheminformatics graphs, including enzyme and molecular graphs, that maximum clique and chromatic number values provide significant insights into the structural properties of these graphs. Notably, the maximum clique value was often calculated as 2 for bipartite graphs. Additionally, it was observed that enzyme graphs exhibit maximum clique and chromatic number values that are optimal or near-optimal, with some graphs possessing perfect graph properties. The proposed approach offers an effective and robust solution for structural analysis in cheminformatics graphs.

Reliability analysis of subsea control system using FMEA and FFTA

Reliability technology plays a significant role in ensuring the safe operation of the subsea control system. To perform a comprehensive analysis of the reliability of complex systems, a combination of Failure Mode and Effects Analysis (FMEA) and Fuzzy Fault Tree Approach (FFTA) is introduced. Firstly, the FMEA method is used to analyze the potential failure modes and causes of system failure by completing the qualitative analysis of system reliability from the perspective of multi-factor failure modes. And the risk matrix diagram is applied to determine the degree of harm of different failure modes to the system. Then, the system reliability is quantitatively analyzed using FFTA, and a fault tree model is established by dividing the system into "system-subsystem-component" and solving for the minimum cut set. In addition, the failure probability of the top-level event is quantitatively calculated by introducing fuzzy set theory, and the probabilistic importance of the bottom-level event is analyzed to find out the weak points of each subsystem. Finally, a qualitative and quantitative reliability analysis is conducted by using FMEA-FFTA method for subsea control system. Effective measures should be taken to focus on preventive protection and regular testing for the high risk, medium–high risk and medium risk modes for subsea control system.

Optimal PI controller parameter setting for torque ripple minimization in SVPWM–DTC based BLDC motor drive using sine cosine algorithm

Abstract This paper focuses on the selection of the most suitable values for the PI controller parameters to minimize the discrepancy between the desired reference values and the actual values of speed and torque in the context of Space Vector Pulse Width Modulation-Direct Torque Control (SVPWM-DTC) of Brushless DC motors (BLDCM). To effectively fine-tune the PI controller, the criteria of Integral of Squared Speed Error (ISSE), Integral of Squared Torque Error (ISTE), and Integral of Squared Flux Error (ISFE) are taken into account as the objective functions across various scenarios. Nevertheless, when it comes to optimizing multiple objective functions, the most optimal approach is to reduce their linear combination. This paper employs JAYA and Sine Cosine algorithms (SCA) for the calibration of the gain parameters of the PI Controller. To assess the effectiveness of the proposed optimal PI controller for SVPWM-DTC of BLDCM, simulations are conducted. According to the findings of the simulations, the Sine Cosine technique yields a remarkable improvement by reducing the torque ripple and controlling the speed peaks overshoot reducing it to 61.6% and 11.9% respectively, and the JAYA algorithm.