REMARKS ON THE ASYMPTOTIC BEHAVIOR OF SCALAR AUXILIARY VARIABLE (SAV) SCHEMES

We introduce a time semi-discretization of a damped wave equation by a SAV scheme with second order accuracy. The energy dissipation law is shown to hold without any restriction on the time step. We prove that any sequence generated by the scheme converges to a steady state (up to a subsequence). We notice that the steady state equation associated to the SAV scheme is a modified version of the steady state equation associated to the damped wave equation. We show that a similar result holds for a SAV fully discrete version of the Cahn-Hilliard equation and we compare numerically the two steady state equations

A Memetic Algorithm with Reinforcement Learning for Sociotechnical Production Scheduling

The following interdisciplinary article presents a memetic algorithm with applying deep reinforcement learning (DRL) for solving practically oriented dual resource constrained flexible job shop scheduling problems (DRC-FJSSP). From research projects in industry, we recognize the need to consider flexible machines, flexible human workers, worker capabilities, setup and processing operations, material arrival times, complex job paths with parallel tasks for bill of material (BOM) manufacturing, sequence-dependent setup times and (partially) automated tasks in human-machine-collaboration. In recent years, there has been extensive research on metaheuristics and DRL techniques but focused on simple scheduling environments. However, there are few approaches combining metaheuristics and DRL to generate schedules more reliably and efficiently. In this paper, we first formulate a DRC-FJSSP to map complex industry requirements beyond traditional job shop models. Then we propose a scheduling framework integrating a discrete event simulation (DES) for schedule evaluation, considering parallel computing and multicriteria optimization. Here, a memetic algorithm is enriched with DRL to improve sequencing and assignment decisions. Through numerical experiments with real-world production data, we confirm that the framework generates feasible schedules efficiently and reliably for a balanced optimization of makespan (MS) and total tardiness (TT). Utilizing DRL instead of random metaheuristic operations leads to better results in fewer algorithm iterations and outperforms traditional approaches in such complex environments.Comment: This article has been accepted by IEEE Access on June 30, 202

Theoretical and Numerical Analysis of a Class of Nonlinear Elliptic Equations

19 pagesIn this paper we show the existence of weak solutions for a nonlinear elliptic equations with arbitrary growth of the non linearity and data measure. A numerical algorithm to compute a numerical approximation of the weak solution is described and analyzed. In a first step a super-solution is computed using a domain decomposition method. Numerical examples are presented and commented

Theoretical and Numerical Analysis of a Class of Nonlinear Elliptic Equations

In this paper we show the existence of weak solutions for a nonlinear elliptic equations with arbitrary growth of the non linearity and data measure. A numerical algorithm to compute a numerical approximation of the weak solution is discribed and analysed. In a first step a super-solution is computed using a domain decomposition method. Numerical examples are presented and commented

Posterior Reversible Encephalopathy Syndrome in Two Omani Children with Underlying Renal Diseases

Posterior reversible encephalopathy syndrome (PRES) is a neurological condition with a combination of clinical and radiological features. Clinical symptoms include headaches, confusion, seizures, disturbed vision or an altered level of consciousness. Classic magnetic resonance imaging (MRI) findings indicate subcortical and cortical oedema, affecting mainly the posterior cerebral region. We report two paediatric cases of PRES with underlying renal diseases presenting at the Sultan Qaboos University Hospital in Muscat, Oman, in April 2010 and August 2011. The first case was an 11-year-old girl diagnosed with systemic lupus erythematosus and the second was a six-and-a-half-year-old boy on peritoneal dialysis due to multi-drug-resistant nephrotic syndrome. Both patients were hypertensive and treated with blood pressure control medications. No residual neurological dysfunction was noted in the patients at a one-year follow-up and at discharge, respectively. The role of hypertension in paediatric PRES cases, among other important risk factors, is emphasised. Additionally, MRI is an important diagnostic and prognostic tool. Prompt diagnosis and aggressive management is fundamental to preventing permanent neurological damage

Raising the Diversity of Ugi Reactions Through Selective Alkylations and Allylations of Ugi Adducts

We report here selective Tsuji-Trost type allylation of Ugi adducts using a strategy based on the enhanced nucleophilicity of amide dianions. Ugi adducts derived from aromatic aldehydes were easily allylated at their peptidyl position with allyl acetate in the presence of palladium catalysts. These substitutions were compared to more classical transition metal free allylations using allyl bromides

Clinical Study Age of 40 Years or Younger Is an Independent Risk Factor for Locoregional Failure in Early Breast Cancer: A Single-Institutional Analysis in Saudi Arabia

Background. This study was undertaken to evaluate the impact of prognostic factors on the locoregional failure-free survival of early breast cancer patients. Methods. In this single-institutional study, 213 breast cancer patients were retrospectively analysed. Fiftyfive of 213 patients were ≤40 years of age at diagnosis. The impact of patient-or treatment-related factors on the locoregional failure-free survival was assessed using the Kaplan-Meier method. The simultaneous impact of factors on the locoregional failure-free survival was assessed using the Cox proportional hazards regression analysis. Results. The median follow-up time of the censored patients was 22 months (mean 28 months, range 3-92 months). On univariate analysis, statistically significant factors for the locoregional failure-free survival were the age (≤40 versus >40 years), T stage (Tis, T0-2 versus T3-4), molecular tumor type (luminal A versus luminal B, Her2neu overexpression, or triple negative), and lymphovascular status (LV0 versus LV1). On multivariate analysis, age and T stage remained statistically significant. Conclusions. Being 40 years or younger has a statistically significant independent adverse impact on the locoregional failure-free survival of patients with early breast cancer

Optimizing the number of fog nodes for finite fog radio access networks under multi-slope path loss model

Fog Radio Access Network (F-RAN) is a promising technology to address the bandwidth bottlenecks and network latency problems, by providing cloud-like services to the end nodes (ENs) at the edge of the network. The network latency can further be decreased by minimizing the transmission delay, which can be achieved by optimizing the number of Fog Nodes (FNs). In this context, we propose a stochastic geometry model to optimize the number of FNs in a finite F-RAN by exploiting the multi-slope path loss model (MS-PLM), which can more precisely characterize the path loss dependency on the propagation environment. The proposed approach shows that the optimum probability of being a FN is determined by the real root of a polynomial equation of a degree determined by the far-field path loss exponent (PLE) of the MS-PLM. The results analyze the impact of the path loss parameters and the number of deployed nodes on the optimum number of FNs. The results show that the optimum number of FNs is less than 7% of the total number of deployed nodes for all the considered scenarios. It also shows that optimizing the number of FNs achieves a significant reduction in the average transmission delay over the unoptimized scenarios

Using artificial intelligence to improve body iron quantification: A scoping review

This scoping review explores the potential of artificial intelligence (AI) in enhancing the screening, diagnosis, and monitoring of disorders related to body iron levels. A systematic search was performed to identify studies that utilize machine learning in iron-related disorders. The search revealed a wide range of machine learning algorithms used by different studies. Notably, most studies used a single data type. The studies varied in terms of sample sizes, participant ages, and geographical locations. AI's role in quantifying iron concentration is still in its early stages, yet its potential is significant. The question is whether AI-based diagnostic biomarkers can offer innovative approaches for screening, diagnosing, and monitoring of iron overload and anemia.Open Access funding provided by the Qatar National Library.Scopu

Association of serum leptin and ghrelin levels with smoking status on body weight: a systematic review and meta-analysis

Background and aimsSmoking cigarettes is a major global health problem that affects appetite and weight. The aim of this systematic review was to determine how smoking affected plasma leptin and ghrelin levels.MethodsA comprehensive search of PubMed, Scopus, Web of Science, and Ovid was conducted using a well-established methodology to gather all related publications.ResultsA total of 40 studies were included in the analysis of 11,336 patients. The overall effect showed a with a mean difference (MD) of −1.92[95%CI; −2.63: −1.20] and p = 0.00001. Subgroup analysis by study design revealed significant differences as well, but with high heterogeneity within the subgroups (I2 of 82.3%). Subgroup by sex showed that there was a significant difference in mean difference between the smoking and non-smoking groups for males (MD = −5.75[95% CI; −8.73: −2.77], p = 0.0002) but not for females (MD = −3.04[95% CI; −6.6:0.54], p = 0.10). Healthy, pregnant, diabetic and CVD subgroups found significant differences in the healthy (MD = −1.74[95% CI; −03.13: −0.35], p = 0.01) and diabetic (MD = −7.69[95% CI, −1.64: −0.73], p = 0.03). subgroups, but not in the pregnant or cardiovascular disease subgroups. On the other hand, the meta-analysis found no statistically significant difference in Ghrelin serum concentration between smokers and non-smokers (MD = 0.52[95% CI, −0.60:1.63], p = 0.36) and observed heterogeneity in the studies (I2 = 68%).ConclusionThis study demonstrates a correlation between smoking and serum leptin/ghrelin levels, which explains smoking’s effect on body weight.Systematic review registrationhttps://www.crd.york.ac.uk/ prospero/display_record.php, identifier (Record ID=326680)