Set Of Problems Research Articles

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

Background: 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. Method: 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. Results: 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. Conclusions: 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

Fixed-parameter algorithms for Fair Hitting Set problems

Fixed-parameter algorithms for Fair Hitting Set problems

A Structured L-BFGS Method with Diagonal Scaling and Its Application to Image Registration

AbstractWe devise an L-BFGS method for optimization problems in which the objective is the sum of two functions, where the Hessian of the first function is computationally unavailable while the Hessian of the second function has a computationally available approximation that allows for cheap matrix–vector products. This is a prototypical setting for many inverse problems. The proposed L-BFGS method exploits the structure of the objective to construct a more accurate Hessian approximation than in standard L-BFGS. In contrast with existing works on structured L-BFGS, we choose the first part of the seed matrix, which approximates the Hessian of the first function, as a diagonal matrix rather than a multiple of the identity. We derive two suitable formulas for the coefficients of the diagonal matrix and show that this boosts performance on real-life image registration problems, which are highly non-convex inverse problems. The new method converges globally and linearly on non-convex problems under mild assumptions in a general Hilbert space setting, making it applicable to a broad class of inverse problems. An implementation of the method is freely available.

The Effect of Increasing the Body's Core Temperature and Improving Blood Flow by Using Far-Infrared Rays Emitted from Functional Loess Bio-Balls.

Low-energy far-infrared rays (FIRs) are widely used in the treatment of wounds, lymphedema, and various vascular diseases, and various types of products that emit infrared rays are being used at home for patients with blood flow-related diseases without experimental evidence. Blood flow and epidermal temperature were measured while applying conductive heat and FIRs via an electric mat (non-intervention) or a loess bio-ball mat (intervention). In the control group (n = 30), there was a minimal change in blood flow and epidermal temperature in the right and left middle fingers (LMF, RMF) as the mat temperature gradually increased. In the experimental group (n = 30), when the mat temperature increased from 25 °C to 50 °C, the blood flow increased by 39.80% in the LMF and by 41.83% in the RMF. In addition, the epidermal temperature increased by 8.78% in the LMF and by 8.44% in the RMF. The FIRs emitted from loess bio-balls can be applied to alleviate symptoms not only in patients with blood flow problems in medical settings but also in people who complain of discomfort due to blood flow disorders or cold hands and feet during their daily life and sleep.

Living Labs: Knowledge Infrastructures to Forge a New Social Contract of Science?

Living Labs (LLs) are heralded as new and inclusive platforms for collective and transformative knowledge production and innovation management with actors from science, practice and society. This begs the question what kinds of social relations emerge between these actors especially as the leading institutions vary, in other words, whether LLs give rise to distinct knowledge infrastructures and new forms of social contracting (of science). A comparative analysis focusing on structural and power-related aspects of four LL case studies with different leading institutions (university, industry, city and civil society) show specific features of social ordering while performing social contracts with regard to the leading institution. The cases result, on the one hand, in rather closed off spaces in which science and innovation for and in society is enacted by technology-oriented industry and university LLs. These are embedded in and solve set problems for society. On the other hand, in less confined spaces science and innovation with and by society is conducted in city and civil society run LLs. Here, a more communal contracting towards defining and solving societal problems is established. A reflexive approach to structuring and governing LLs in practice and a more robust theoretical foundation would therefore be beneficial.

Perspectives on Industry-Sponsored Capstone Projects Within Mechanical and Materials Engineering at Western University

Capstone projects that are conducted in final year study within Mechanical and Materials Engineering at Western University, traditionally, have been either student-originated, faculty-originated, or facilities-originated. Capstone projects provide students with an opportunity to apply what they have learned in their studies to an actual problem in a team setting. Recently, the capstone project course had experienced revisions with a desire to increase the number of industry and community-sponsored projects. Industry-sponsored capstone projects can provide value, not only to the sponsoring company, but also to the participants who are afforded the opportunity to work on a relevant industry problem. There are intrinsic benefits for students having worked on a project that can, depending on the outcome, be used by the industry sponsor to add value to their operations. Particularly, where student capstone projects are externally funded, there exists the expectation that the project outcomes will be in a usable form. In recognizing the value of industry-sponsored capstone projects, we have implemented a rigorous method of capstone project oversight based upon the experience of other academic institutions, existing literature, and employing conventional project management strategies and tools. This paper discusses some of the challenges associated with industry-driven capstone projects and some manners in which we have attempted to address them.

Epidemiological and genetic characteristics of clinical carbapenemase-producing Enterobacterales isolates from Batna hospitals in Algeria

BackgroundCarbapenemase-producing Enterobacterales isolates are associated with significant mortality and have emerged as a major problem in healthcare settings worldwide.ObjectiveOur aim was to investigate the epidemiological and genotypic characteristics of carbapenemase-positive Enterobacterales isolates from patients hospitalised in three hospitals in the city of Batna, Algeria.MethodsBetween 2016 and 2019, a total of 5,316 clinical isolates were obtained. The collected isolates were identified using the VITEK-2 system. Demographic and microbiological data were collected as well as the antimicrobial susceptibility testing, phenotypic and molecular characterisation of carbapenemase and mcr-1 genes were performed.ResultsOut of the 5,316 isolates, 201 were carbapenem-resistant Enterobacterales isolates and 179 of them (89.05%) were positive for the production of carbapenemase, of which Klebsiella pneumoniae, Escherichia coli and Enterobacter cloacae were the most common. The blaOXA−48−like gene alone was detected in 147 isolates (82.12%) moreover, the blaNDM gene was detected in ten isolates (5.59%). Dual and triple combinations of carbapenemase genes were also observed here for the first time in Algeria: blaVIM and blaOXA−48−like; blaKPC, blaVIM and blaOXA−48−like; blaVIM and blaNDM; blaKPC, blaNDM and blaVIM; blaNDM and blaOXA−48−like genes. In addition, resistance to both colistin and carbapenem antibiotics was detected in eight isolates, however none of them was positive for the mcr-1 gene.ConclusionThis is the first study reporting the detection of carbapenemase genes in Klebsiella aerogenes, Enterobacter sakazakii, Raoultella ornithinolytica, Serratia ficaria, and Serratia marcescens and specific carbapenemase gene combinations in Algeria. The present study revealed that blaOXA−48−like were found to be the predominant carbapenemase genes in Batna hospitals.

Bifurcation for a sharp interface generation problem

As opposed to the widely studied bifurcation phenomena for maps or PDE problems, we are concerned with bifurcation for stationary points of a nonlocal variational functional defined not on functions but on sets of finite perimeter, and involving a nonlocal term. This sharp interface model (1.2), arised as the \Gamma -limit of the FitzHugh–Nagumo energy functional in a (flat) square torus in {\mathbb{R}}^{2} of size T , possesses lamellar stationary points of various widths with well-understood stability ranges and exhibits many interesting phenomena of pattern formation as well as wave propagation. We prove that when the lamella loses its stability, bifurcation occurs, leading to a two-dimensional branch of nonplanar stationary points. Thinner nonplanar structures, achieved through a smaller T , or multiple layered lamellae in the same-sized torus, are more stable. To the best of our knowledge, bifurcation for nonlocal problems in a geometric measure theoretic setting is an entirely new result.

Individual, household, and community-level determinants of undernutrition among pregnant women in the northern zone of the Sidama region, Ethiopia: A multi-level modified Poisson regression analysis.

In Ethiopia, maternal undernutrition is a major public health concern. However, comprehensive evidence is lacking in the southern part of Ethiopia, specifically the household and community-level related determinants of undernutrition. Besides, the evidence about the prevalence and determinants of undernutrition is not yet documented in the current study setting. Thus, this study aimed to determine the prevalence of undernutrition and identify its determinants among pregnant women in Hawela Lida district of the Sidama region, Ethiopia. A community-based cross-sectional study was conducted on a sample of 515 pregnant women from June 1-25, 2024. A multi-stage sampling method was utilized to select eligible pregnant women. We collected data using a structured and pretested interviewer-administrated questionnaire and an anthropometric measurement. Data were collected using the Open Data Kit smart phone device and exported it to Stata version 17 for further processing and analysis. A multi-level mixed-effects modified Poisson regression analysis with robust variance was used to account for confounders and between and with cluster effects. The prevalence of undernutrition among pregnant women was 41.7% (95% CI: 37.3-45.6). The prevalence of undernutrition was associated with planned pregnancy (adjusted prevalence ratio [APR]: 0.80; 95% CI: 0.66-0.98), household food insecurity (APR: 1.64; 95% CI: 1.26-2.13), inadequate dietary diversity (APR: 1.79; 95% CI: 1.43-2.25), and women's poor knowledge of nutrition (APR: 1.68; 95% CI: 1.32-2.12) at individual levels. The identified determinants of undernutrition at the community level were low community literacy rates (APR: 4.62; 95% CI: 1.13-18.79) and low community wealth status (APR: 1.91; 95% CI: 1.10-3.31). Two in five pregnant women had an undernutrition problem in the study setting. Individual and community-level determinants contributed to the high prevalence of undernutrition. Thus, any prevention and control approaches to undernutrition should consider inter-sectorial collaboration to account for determinants at various levels. Besides, any program must emphasize the delivery of nutrition education about dietary diversity, particularly targeting pregnant mothers who have poor knowledge of nutrition and unplanned pregnancy at the individual level. Moreover, creating a small business reform for the community with low wealth status using agricultural extension workers must be considered.

On The Assembly Line Balancing Problem: A Simplified Perspective With The Precedence Matrix

The assembly line balancing problem (ALBP) has been an attractive research area for decades; however, the industrial application of the research findings remains limited. This can be attributed to the complexity of solution methods, restrictive assumptions, and the numerous variants of the problem in the real-world settings. This article describes using the precedence matrix as the basis for developing simplified, more practical analysis frameworks for the ALBP. We introduce algorithms to construct the precedence matrix from basic assembly problem data and implement it within a spreadsheet model, and to utilize this matrix in assigning the assembly tasks to workstations while ensuring assignment feasibility. The structure and potential uses of the precedence matrix-based framework are presented in detail. Furthermore, we compare selected line balancing priority rules using the proposed framework to demonstrate its effectiveness. The results suggest that the precedence matrix model is a straightforward, efficient tool for line balancing analysis, that can offer substantial support to both industry practitioners and academic researchers.

Violence risk prediction in mental health inpatient settings using the Dynamic Appraisal of Situational Aggression

Introduction/BackgroundAggression and violence are common problems in healthcare settings and affects both patients and healthcare staff. The Dynamic Appraisal of Situational Aggression (DASA) is an assessment tool to guide assessment for short term risk in inpatient settings. There have been no large-scale studies examining the performance of the DASA across different clinical settings. Our objective is to examine the performance of the DASA using a large longitudinal patient sample on different clinical units. A secondary objective was to examine alterative risk categories of the DASA.MethodsAll consecutive mental health hospital admissions to a large hospital in Toronto, Canada between 2016 and 2019 were included. Time-to-event analysis and Receiver Operating Characteristics Area Under the Curve (AUC) was conducted with the outcome variable being the occurrence of the first violent incident or first restraint event.ResultsWe included 3819 patients, of which 17% had at least one violent incident. We analysed 88,124 DASA scores and found a significant association with violence (HR 1.79 (95% CI), AUC 0.73). We found that the AUCs were similar for subspecialized forensic, schizophrenia and acute care units (0.71, 0.73 and 0.75 respectively), and lower for geriatric units (0.66). We propose new violence risk categories based on the frequency of violence at each score.DiscussionHigher DASA scores are associated with higher risk of violent incidents in both forensic and non-forensic inpatient psychiatric units. The proposed violence risk groups help rule out patients at low risk of violence and may help identify patients who would most benefit from interventions to reduce violence.

The Markov chain embedding problem in a one-jump setting

Abstract The embedding problem of Markov chains examines whether a stochastic matrix $\mathbf{P} $ can arise as the transition matrix from time 0 to time 1 of a continuous-time Markov chain. When the chain is homogeneous, it checks if $ \mathbf{P}=\exp{\mathbf{Q}}$ for a rate matrix $ \mathbf{Q}$ with zero row sums and non-negative off-diagonal elements, called a Markov generator. It is known that a Markov generator may not always exist or be unique. This paper addresses finding $ \mathbf{Q}$ , assuming that the process has at most one jump per unit time interval, and focuses on the problem of aligning the conditional one-jump transition matrix from time 0 to time 1 with $ \mathbf{P}$ . We derive a formula for this matrix in terms of $ \mathbf{Q}$ and establish that for any $ \mathbf{P}$ with non-zero diagonal entries, a unique $ \mathbf{Q}$ , called the ${\unicode{x1D7D9}}$ -generator, exists. We compare the ${\unicode{x1D7D9}}$ -generator with the one-jump rate matrix from Jarrow, Lando, and Turnbull (1997), showing which is a better approximate Markov generator of $ \mathbf{P}$ in some practical cases.

Gradient corrections to the quantum effective action

We derive the quantum effective action up to second order in gradients and up to two-loop order for an interacting scalar field theory. This expansion of the effective action is useful to study problems in cosmological settings where spatial or time gradients are important, such as bubble nucleation in first-order phase transitions. Assuming spacetime dependent background fields, we work in Wigner space and perform a midpoint gradient expansion, which is consistent with the equations of motion satisfied by the propagator. In particular, we consider the fact that the propagator is non-trivially constrained by an additional equation of motion, obtained from symmetry requirements. At one-loop order, we show the calculations for the case of a single scalar field and then generalise the result to the multi-field case. While we find a vanishing result in the single field case, the one-loop second-order gradient corrections can be significant when considering multiple fields. As an example, we apply our result to a simple toy model of two scalar fields with canonical kinetic terms and mass mixing at tree-level. Finally, we calculate the two-loop one-particle irreducible (1PI) effective action in the single scalar field case, and obtain a nonrenormalisable result. The theory is rendered renormalisable by adding two-particle irreducible (2PI) counterterms, making the 2PI formalism the right framework for renormalization when resummed 1PI two-point functions are used in perturbation theory.

On the existence of solutions to adversarial training in multiclass classification

Abstract Adversarial training is a min-max optimization problem that is designed to construct robust classifiers against adversarial perturbations of data. We study three models of adversarial training in the multiclass agnostic-classifier setting. We prove the existence of Borel measurable robust classifiers in each model and provide a unified perspective of the adversarial training problem, expanding the connections with optimal transport initiated by the authors in their previous work [21]. In addition, we develop new connections between adversarial training in the multiclass setting and total variation regularization. As a corollary of our results, we provide an alternative proof of the existence of Borel measurable solutions to the agnostic adversarial training problem in the binary classification setting.

Admissions Within the First Year After Kidney Transplantation: Evaluation of Risk Factors, Causes, and Outcomes

Objectives: The aim of this study was to evaluate causes of hospital admission within the 1st year after kidney transplantation in a public hospital. Methods: This is a retrospective cohort. Data were collected by medical record review. Inclusion criteria were age older than 18 years and kidney transplant. Exclusion criteria were double transplant, primary graft failure, and in-hospital death after transplant procedure. Outcomes included 4-year patient survival (primary outcome), 1-year graft failure, and graft function in 1 year. Results: A total of 130 patients were evaluated. There was a predominance of male sex, glomerulonephritis, history of hemodialysis, first kidney transplant, and deceased donor kidneys. Hospital admission in the 1st year occurred in 71.5%. There were three deaths at the first admission (two had cardiovascular causes). The most frequent causes of admission were infections, rejection, and surgical complications. Less common causes included elective procedures such as peritoneal dialysis catheter removal. There was no statistically significant association between categorical and continuous variables studied and the occurrence of hospital admission. Also, hospital admission did not influence primary outcome or 1-year graft failure rate. However, it was associated with lower graft function at 1 year (both by serum creatinine and by estimated glomerular filtration rate – p < 0.05). Conclusion: There was a high incidence of hospital admission, and first admissions resulted in death in three patients (noninfectious causes). Considering the contribution of elective procedures to the high incidence of admission, we propose that hospital managers focus on improving the ability to solve such problems in the outpatient setting.

Informed Down-Sampled Lexicase Selection: Identifying productive training cases for efficient problem solving.

Genetic Programming (GP) often uses large training sets and requires all individuals to be evaluated on all training cases during selection. Random down-sampled lexicase selection evaluates individuals on only a random subset of the training cases allowing for more individuals to be explored with the same amount of program executions. However, sampling randomly can exclude important cases from the down-sample for a number of generations, while cases that measure the same behavior (synonymous cases) may be overused. In this work, we introduce Informed Down-Sampled Lexicase Selection. This method leverages population statistics to build down-samples that contain more distinct and therefore informative training cases. Through an empirical investigation across two different GP systems (PushGP and Grammar-Guided GP), we find that informed down-sampling significantly outperforms random down-sampling on a set of contemporary program synthesis benchmark problems. Through an analysis of the created down-samples, we find that important training cases are included in the down-sample consistently across independent evolutionary runs and systems. We hypothesize that this improvement can be attributed to the ability of Informed Down-Sampled Lexicase Selection to maintain more specialist individuals over the course of evolution, while still benefiting from reduced per-evaluation costs.

Automatic Temperature Parameter Tuning for Reinforcement Learning Using Path Integral Policy Improvement.

In this article, we propose a novel variant of path integral policy improvement with covariance matrix adaptation ( [Formula: see text] - [Formula: see text] ), which is a reinforcement learning (RL) algorithm that aims to optimize a parameterized policy for the continuous behavior of robots. [Formula: see text] - [Formula: see text] has a hyperparameter called the temperature parameter, and its value is critical for performance; however, little research has been conducted on it and the existing method still contains a tunable parameter, which can be critical to performance. Therefore, tuning by trial and error is necessary in the existing method. Moreover, we show that there is a problem setting that cannot be learned by the existing method. The proposed method solves both problems by automatically adjusting the temperature parameter for each update. We confirmed the effectiveness of the proposed method using numerical tests.