Set Of Problems Research Articles

Multidrug-resistant Klebsiella pneumoniae and Klebsiella variicola isolated from patients in Portuguese hospitals: Genomic and phenotypic characterization

The emergence of antibiotic-resistant Klebsiella spp. has become a huge problem in clinical settings. In this study, we aimed to characterize the molecular and phenotypic features of 21 MDR Klebsiella isolates (n = 19 of K. pneumoniae, and n = 2 of K. variicola) isolated from patients admitted to two central hospitals in northern Portugal by using whole-genome sequencing (WGS), followed by an in vitro assessment of biofilm formation and in vivo evaluation of the pathogenicity in a Galleria mellonella larval model. Our findings showed a high prevalence of O1/O2 serotypes (14/21; 67 %) among the isolates tested, which is consistent with previous reports from Portugal. In contrast, a wide variety of K locus serotypes was found, where ST15-KL19 (4/21; 19 %) associated to serotype O1/O2v2 was the dominant one. Within the O1/O2v2 serotype, a ST10-KL151 Klebsiella variicola (strain H97) harboured a high number of virulence genes. We also found statistical differences in the ability to produce biofilm biomass within the strains, with a ST280-KL23 K. pneumoniae outcompeting nine other strains. According to our results, the most prominent serotype able to cause the death of G. mellonella was the KL105-O1/O2v2. Our findings emphasized the importance of the conducting continuous molecular surveillance in order to reveal the key molecular features to be considered in the development of novel strategies to treat Klebsiella spp.-associated infections.

The Teodorescu and the Π-operator in octonionic analysis and some applications

In the development of function theory in octonions, the non-associativity property produces an additional associator term when applying the Stokes formula. To take the non-associativity into account, particular intrinsic weight factors are implemented in the definition of octonion-valued inner products to ensure the existence of a reproducing Bergman kernel. This Bergman projection plays a pivotal role in the L2-space decomposition demonstrated in this paper for octonion-valued functions. In the unit ball, we explicitly show that the intrinsic weight factor is crucial to obtain the reproduction property and that the latter precisely compensates an additional associator term that otherwise appears when leaving out the weight factor.Furthermore, we study an octonionic Teodorescu transform and show how it is related to the unweighted version of the Bergman transform and establish some operator relations between these transformations. We apply two different versions of the Borel-Pompeiu formulae that naturally arise in the context of the non-associativity. Next, we use the octonionic Teodorescu transform to establish a suitable octonionic generalization of the Ahlfors-Beurling operator, also known as the Π-operator. We prove an integral representation formula that presents a unified representation for the Π-operator arising in all prominent hypercomplex function theories. Then we describe some basic mapping properties arising in context with the L2-space decomposition discussed before.Finally, we explore several applications of the octonionic Π-operator. Initially, we demonstrate its utility in solving the octonionic Beltrami equation, which characterizes generalized quasi-conformal maps from R8 to R8 in a specific analytical sense. Subsequently, analogous results are presented for the hyperbolic octonionic Dirac operator acting on the right half-space of R8. Lastly, we discuss how the octonionic Teodorescu transform and the Bergman projection can be employed to solve an eight-dimensional Stokes problem in the non-associative octonionic setting.

Anderson Localization of Walking Droplets

Understanding the ability of particles to maneuver through disordered environments is a central problem in innumerable settings, from active matter and biology to electronics. Macroscopic particles ultimately exhibit diffusive motion when their energy exceeds the characteristic potential barrier of the random landscape. In stark contrast, wave-particle duality causes electrons in disordered media to come to rest even when the potential is weak—a remarkable phenomenon known as Anderson localization. Here, we present a hydrodynamic active system with wave-particle features, a millimetric droplet self-guided by its own wave field over a submerged random topography, whose dynamics exhibits localized statistics analogous to those of electronic systems. Consideration of an ensemble of particle trajectories reveals a suppression of diffusion when the guiding wave field extends over the disordered topography. We rationalize mechanistically the emergent statistics by virtue of the wave-mediated resonant coupling between the droplet and topography, which produces an attractive wave potential about the localization region. This hydrodynamic analog, which demonstrates how a classical particle may localize like a wave, suggests new directions for future research in various areas, including active matter, wave localization, many-body localization, and topological matter. Published by the American Physical Society 2024

How to improve “construct, merge, solve and adapt"? Use reinforcement learning!

AbstractIn this work, we propose a new variant of construct, merge, solve, and adapt (CMSA), which is a recently introduced hybrid metaheuristic for combinatorial optimization. Our newly proposed variant, named reinforcement learning CMSA (RL-CMSA), makes use of a reinforcement learning (RL) mechanism trained online with data gathered during the search process. In addition to generally outperforming standard CMSA, this new variant proves to be more flexible as it does not require a greedy function for the evaluation of solution components at each solution construction step. We present RL-CMSA as a general framework for enhancing CMSA by leveraging a simple RL learning process. Moreover, we study a range of specific designs for the employed learning mechanism. The advantages of the introduced CMSA variant are demonstrated in the context of the far from most string and minimum dominating set problems, showing the improvement in performance and simplicity with respect to standard CMSA. In particular, the best performing RL-CMSA variant proposed is statistically significantly better than the standard algorithm for both problems, obtaining 1.28% and 0.69% better results on average respectively.

Guidelines in Designing a Universal Primer Mixture to Probe and Quantify Antibiotic-Resistant Genes Using the Polymerase Chain Reaction (PCR).

Multidrug resistance efflux pumps (MDREPs) in biofilm communities have become an increasingly expensive problem in clinical settings. Polymerase chain reaction (PCR)-based detection can be used to diagnose and characterize these genes, but this requires effective primer design to minimize false positives and negatives in test conclusions. A universal primer approach has previously been used to detect conserved core genes but not for accessory genes such as MDREPs. This study describes a guideline for the design of primers used in the detection of MDREP genes and an optimization approach for creating primers by using multiple sequence alignments to target conserved regions in silico, progressing from in silico to in vitro to generate working primers. Using thisapproach, this paper was able to generate primers to target sugE, a small multidrug resistance (SMR) protein found in microbial species. Primers were tested positively against synthetic DNA sequences but were inconsistent with DNA extracted from the organism of interest. Primer design informs the shortfalls of this detection technique and the difficulty in characterizing such genomic elements.

Quantum Limits of Covert Target Detection.

In covert target detection, Alice attempts to send optical or microwave probes to determine the presence or absence of a weakly reflecting target embedded in thermal background radiation within a target region, while striving to remain undetected by an adversary, Willie, who is co-located with the target and collects all light that does not return to Alice. We formulate this problem in a realistic setting and derive quantum-mechanical limits on Alice's error probability performance in entanglement-assisted target detection for any fixed level of her detectability by Willie. We demonstrate how Alice can approach this performance limit using two-mode squeezed vacuum probes in the regime of small to moderate background brightness, and how such protocols can outperform any conventional approach using Gaussian-distributed coherent states. In addition, we derive a universal performance bound for nonadversarial quantum illumination without requiring the passive-signature assumption.

A self‐stabilizing distributed algorithm for the 1‐MIS problem under the distance‐3 model

SummaryFault‐tolerance and self‐organization are critical properties in modern distributed systems. Self‐stabilization is a class of fault‐tolerant distributed algorithms which has the ability to recover from any kind and any finite number of transient faults and topology changes. In this article, we propose a self‐stabilizing distributed algorithm for the 1‐MIS problem under the unfair central daemon assuming the distance‐3 model. Here, in the distance‐3 model, each process can refer to the values of local variables of processes within three hops. Intuitively speaking, the 1‐MIS problem is a variant of the maximal independent set (MIS) problem with improved local optimizations. The time complexity (convergence time) of our algorithm is steps and the space complexity is bits, where is the number of processes. Finally, we extend the notion of 1‐MIS to ‐MIS for each nonnegative integer , and compare the set sizes of ‐MIS () and the maximum independent set.

Advance in Applied Cryptography Theory: Survey and New Results. Part 1. Key Cryptography

In the current paper, consisting from two parts, are presented both results already published before (but hard for access) and new once. Actuality of this work is firstly in a fact that recently has been obtained a number of new results in area of applied cryptography that are needed both in a clarification and be put into practice. This is namely the main goal of the current paper. The setting problem in the first paper part concerns to a complexity of symmetric cipher breaking while in the second part of the paper is discussed, so called, keyless cryptography, namely: wiretap channel concept, execution of communication channels which allow to provide information security without of key exchange procedure between legal correspondences. In the part widely used methods of applied mathematics, namely: algebra, number, probability and information theories. Computer simulation also used there. A novelty of the first part of work consists in the following: first of all it is clarified the sense of a key lifetime limitation for different symmetric cipher modes, secondly, it is explained an approach of cipher breaking by the use of quantum computers, finely, the key authentication for the Diffie ‒ Hellman protocol based on the mobile device pairing technology is investigated in detail. In the second part of the current paper has been presented a vulnerability of Dean ‒ Goldsmith cryptosystem under some extension of attacks. The main results of this paper are: estimation of the key lifetime of single key for symmetric cipher in CBC mode, clarifying of Grover’s algorithm breaking of symmetric ciphers by brute force attack, development of a method for authentication of Diffie ‒ Hellman values based on pre-distributed sequences, selection of ciphers which allow to execute with Shamir’s protocol without any key sharing in advance, breaking of Dean ‒ Goldsmith protocol under some conclusions, proof the fact regarding of a possible breakability of the key sharing protocol over noiseless communication channels. Practical application of paper results consists in the fact of stimulation the correct choice of ciphers and their parameters in order to provide their resistance to different attacks and more attention to algorithms of keyless cryptography.

Deep learning-based virtual staining, segmentation, and classification in label-free photoacoustic histology of human specimens

In pathological diagnostics, histological images highlight the oncological features of excised specimens, but they require laborious and costly staining procedures. Despite recent innovations in label-free microscopy that simplify complex staining procedures, technical limitations and inadequate histological visualization are still problems in clinical settings. Here, we demonstrate an interconnected deep learning (DL)-based framework for performing automated virtual staining, segmentation, and classification in label-free photoacoustic histology (PAH) of human specimens. The framework comprises three components: (1) an explainable contrastive unpaired translation (E-CUT) method for virtual H&E (VHE) staining, (2) an U-net architecture for feature segmentation, and (3) a DL-based stepwise feature fusion method (StepFF) for classification. The framework demonstrates promising performance at each step of its application to human liver cancers. In virtual staining, the E-CUT preserves the morphological aspects of the cell nucleus and cytoplasm, making VHE images highly similar to real H&E ones. In segmentation, various features (e.g., the cell area, number of cells, and the distance between cell nuclei) have been successfully segmented in VHE images. Finally, by using deep feature vectors from PAH, VHE, and segmented images, StepFF has achieved a 98.00% classification accuracy, compared to the 94.80% accuracy of conventional PAH classification. In particular, StepFF’s classification reached a sensitivity of 100% based on the evaluation of three pathologists, demonstrating its applicability in real clinical settings. This series of DL methods for label-free PAH has great potential as a practical clinical strategy for digital pathology.

Provider perceptions of young people living with HIV and unhealthy alcohol use in Southwestern Uganda: a qualitative study

BackgroundUnhealthy alcohol use is a common public health problem in HIV care settings in Africa and it affects the HIV continuum of care. In Uganda and other low-income countries, HIV care providers are a key resource in caring for young people (15–24 years) living with HIV (YPLH) with unhealthy alcohol use. Caring for YPLH largely depends on care providers’ perceptions of the problem. However, data that explores HIV care providers’ perceptions about caring for YPLH with unhealthy drinking are lacking in Uganda. We sought to describe the perceptions of HIV care providers regarding caring for YPLH with unhealthy drinking in the Immune Suppression Syndrome (ISS) Clinic of Mbarara Regional Referral Hospital in southwestern Uganda.MethodsWe used semi-structured in-depth interviews (IDIs) to qualitatively explore HIV care providers’ perceptions regarding caring for YPLH with unhealthy alcohol use. The study was conducted at the adolescent immunosuppression (ISS) clinic of Mbarara Regional Referral Hospital. Interviews were tape-recorded and transcribed verbatim. Using thematic content analysis, data from 10 interviews were analyzed.ResultsHIV care providers were concerned and intended to care for YPLH with unhealthy alcohol use. They understood that unhealthy drinking negatively impacts HIV care outcomes and used counseling, peer support, and referrals to routinely intervene. They however, did not apply other known interventions such as health education, medications and follow-up visits because these required family and institutional support which was largely lacking. Additional barriers that HCPs faced in caring for YPLH included; gaps in knowledge and skills required to address alcohol use in young patients, heavy workloads that hindered the provision of psychosocial interventions, late payment of and low remunerations, lack of improvement in some YPLH, and inadequate support from both their families and hospital management.ConclusionHIV care providers are important stakeholders in the identification and care of YPLH with unhealthy alcohol use in Southwestern Uganda. There is a need to train and skill HCPs in unhealthy alcohol use care. Such training ought to target the attitudes, subjective norms, and perceived control of the providers.

Finding the minimum [formula omitted]-weighted dominating sets using heuristic algorithms

In this work, we propose, analyze, and solve a generalization of the k-dominating set problem in a graph, when we consider a weighted graph. Given a graph with weights in its edges, a set of vertices is a k-weighted dominating set if for every vertex outside the set, the sum of the weights from it to its adjacent vertices in the set is bigger than or equal to k. The k-weighted domination number is the minimum cardinality among all k-weighted dominating sets. Since the problem of finding the k-weighted domination number is NP-hard, we have proposed several problem-adapted construction and reconstruction techniques and embedded them in an Iterated Greedy algorithm. The resulting sixteen variants of the Iterated Greedy algorithm have been compared with an exact algorithm. Computational results show that the proposal is able to find optimal or near-optimal solutions within a short computational time. To the best of our knowledge, the k-weighted dominating set problem has never been studied before in the literature and, therefore, there is no other state-of-the-art algorithm to solve it. We have also included a comparison with a particular case of our problem, the minimum dominating set problem and, on average, we achieve same quality results within around 50% of computation time.

P068 IDENTIFYING THRESHOLD BIAS IN RECORDED BLOOD PRESSURE

Background and Objective: Even when blood pressure (BP) is measured accurately, BP may be misrecorded. “Threshold bias” occurs when BP measurements are intentionally or unintentionally recorded at values just below BP control goals and may occur due to incentives to improve BP control or to avoid medication titration. Our objectives were to 1) examine the potential for threshold bias in datasets with and without a BP control goal from two countries and 2) propose a method for detecting threshold bias. Methods: We examined the distribution of recorded BP measurements among individuals taking antihypertensive medication in clinical practice or trials with a BP goal <140/90 mmHg and population-based surveillance studies without a BP goal from Bangladesh and the US. Then, we explored whether surveillance data could be used as a reference to evaluate the likelihood of threshold bias in clinical data. We calculated the ratio of BP measurements recorded 10 mmHg below versus above thresholds of interest (i.e., the “threshold ratio,” or number of systolic BP [SBP] measurements 130-139 mmHg over 140-149 mmHg) and used bootstrapping to determine the probability of observing ratios that may indicate threshold bias. We compared the “threshold ratio” from clinical data to the probabilities determined from surveillance data. Results: Visually, we observed a cluster of measurements below SBP 140 mmHg in the clinical but not the surveillance datasets from Bangladesh and the US (Figure). Based on 10,000 random draws of 100 observations in the Bangladesh surveillance dataset, the probability of observing a “threshold ratio” of 3.56 in the clinical dataset was <2%, indicating a high probability of threshold bias. Using the same procedure in the US surveillance dataset, the probability of observing a “threshold ratio” of 2.26 in the clinical dataset was <10%, indicating potential concern. Conclusions: Threshold bias may be an underrecognized but common problem in clinical settings with a BP goal. Our proposed approach to detecting threshold bias should be tested in other settings.

Shape optimization for nonlocal anisotropic energies

In this paper we consider shape optimization problems for sets of prescribed mass, where the driving energy functional is nonlocal and anisotropic. More precisely, we deal with the case of attractive/repulsive interactions in two and three dimensions, where the attraction is quadratic and the repulsion is given by an anisotropic variant of the Coulomb potential. Under the sole assumption of strict positivity of the Fourier transform of the interaction potential, we show the existence of a threshold value for the mass above which the minimizer is an ellipsoid, and below which the minimizer does not exist. If, instead, the Fourier transform of the interaction potential is only non-negative, we show the emergence of a dichotomy: either there exists a threshold value for the mass as in the case above, or the minimizer is an ellipsoid for any positive value of the mass.

Interactions of a Deformable Wheel with a Deformable Support Surface

The article analyzes methods for solving problems on the drive systems' compacting effect on the support surface (soil). There are analyzed results of studies on the distribution of pressures and tensions in the contact surfaces depending on the propulsors' mechanical properties and the support surface's material. The article also includes an analysis of methods for formalizing contact surfaces and constructing mathematical models for determining the distribution of pressures, deformations, and tensions in the contact surfaces. Numerical methods for solving contact problems that have been used recently are not integral. To obtain such solutions, numerical FEM and DEM methods can be used for a specific problem, i.e. for a problem with a known geometric data source and specific mechanical properties of a contacting body. Based on the studies conducted, it was concluded that to understand the interaction of a deforming wheel with a deformable surface, it is necessary to use an analytical method for solving contact problems in a three-dimensional setting as the most general and productive. It’ll allow us to determine the geometrical dimensions and shapes of the contact spot, determine dependencies of the geometrical parameters of the wheel, rolling resistance, tension-strain state of the contacting surfaces depending on the loading conditions of the wheel, its mechanical properties, and the support surface.

On submodular prophet inequalities and correlation gap

Prophet inequalities and secretary problems have been extensively studied in recent years due to their elegance, connections to online algorithms, stochastic optimization, and mechanism design problems in game theoretic settings. Rubinstein and Singla [31] developed a notion of combinatorial prophet inequalities in order to generalize the standard prophet inequality setting to combinatorial valuation functions such as submodular and subadditive functions. For non-negative submodular functions they demonstrated a constant factor prophet inequality for matroid constraints. Along the way they showed a variant of the correlation gap for non-negative submodular functions.In this paper we revisit their notion of correlation gap as well as the standard notion of correlation gap and prove much tighter and cleaner bounds. Via these bounds and other insights we obtain substantially improved constant factor combinatorial prophet inequalities for both monotone and non-monotone submodular functions over any constraint that admits an Online Contention Resolution Scheme. In addition to improved bounds we describe efficient polynomial-time algorithms that achieve these bounds.

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.