A Reinforcement Learning Motivated Algorithm for Process Optimization

In process scheduling problems there are several processes and resources. Any process consists of several tasks, and there may be precedence constraints among them. In our paper we consider a special case, where the precedence constraints form short disjoint (directed) paths. This model occurs frequently in practice, but as far as we know it is considered very rarely in the literature. The goal is to find a good resource allocation (schedule) to minimize the makespan. The problem is known to be strongly NP-hard, and such hard problems are often solved by heuristic methods. We found only one paper which is closely related to our topic, this paper proposes the heuristic method HH. We propose a new heuristic called QLM which is inspired by reinforcement learning methods from the area of machine learning. As we did not find appropriate benchmark problems for the investigated model. We have created such inputs and we have made exhaustive comparisons, comparing the results of HH and QLM, and an exact solver using CPLEX. We note that a heuristic method can give a “near optimal” solution very fast while an exact solver provides the optimal solution, but it may need a huge amount of time to find it. In our computational evaluation we experienced that our heuristic is more effective than HH and finds the optimal solution in many cases and very fast

Dr. Kuthy Béla entomológiai gyűjteménye - Dr. Béla Kuthy’s entomological collection I.

Dr. Kuthy Béla (1873-1946) was a physician and an excellent entomologist who lived in Kiskushalas and studied the local fauna between 1920 and 1946. The authors redetermined his insect collection to publish the faunistical data of Odonata, Orthoptera Mantoptera, Raphidioptera, Neuroptera, Hymenoptera, Trichoptera, and Macrolepidoptera. This collection has a great significance from historical, faunistical and nature conservation points of view

The value of PLA2R antigen and IgG subclass staining relative to anti-PLA2R seropositivity in the differential diagnosis of membranous nephropathy

The diagnostic performance of PLA2R and IgG subclass staining of kidney biopsies relative to anti-PLA2R seropositivity in the differentiation of primary and secondary membranous nephropathy (pMN, sMN) was examined. Besides PLA2R staining - which has a lower specificity than anti-PLA2R antibody serology - there is insufficient knowledge to decide which IgG1-4 subtype immunohistological patterns (IgG4-dominance, IgG4-dominance/IgG1-IgG4-codominance or IgG4-dominance/IgG4-codominance with any IgG subtype) could be used to distinguish between pMN and sMN.87 consecutive Hungarian patients (84 Caucasians, 3 Romas) with the biopsy diagnosis of MN were classified clinically as pMN (n = 63) or sMN (n = 24). The PLA2R and IgG subclass staining was part of the diagnostic protocol. Anti-PLA2R antibodies were determined by an indirect immunofluorescence test in 74 patients with disease activity.For pMN, the sensitivity of anti-PLA2R seropositivity was 61.1%, and the specificity was 90.0%; and similar values for PLA2R staining were 81.0%, and 66.7%, respectively. In all stages of pMN, IgG4-dominance was the dominant subclass pattern, while the second most frequent was IgG3/IgG4-codominance. The sensitivity and specificity scores were: IgG4-dominance 52.2% and 91.7%, IgG4-dominance/IgG3-IgG4-codominance 76.2% and 87.5%, IgG4-dominance/IgG1-IgG4-codominance 64.2% and 75%, and IgG4-dominance/codominance with any IgG subclass 92.1% and 70.8%, respectively. Anti-PLA2R seropositivity, glomerular PLA2R, and IgG4-dominance/codominance significantly correlated with each other. The IgG4 subclass was rarely encountered in sMN.In our series, IgG4-dominance had the highest specificity in the differentiation of MN, just as high as that for anti-PLA2R seropositivity. The specificity values of PLA2R staining and IgG4-dominance/codominance with any IgG subclass or IgG4-dominance/IgG1-IgG4 codominance were ≤ 75%. Apart from IgG4 dominance, IgG4-dominance/IgG3-IgG4-codominance also had good statistical value in differentiating pMN from sMN. As IgG subclass switching during the progression of pMN was not the feature of our cohort, pMN in Hungarian patients is presumed to be an IgG4-related disorder right from the start. Although anti-PLA2R seropositivity has become the cornerstone for diagnosing pMN, if a kidney biopsy evaluation is conducted, besides the staining of PLA2R antigen, the evaluation of IgG subclasses provides relevant information for a differential diagnosis. Even in cases with IgG4-dominance, however, malignancy should be thoroughly checked

Clinical benefits of oral capecitabine over intravenous 5-fluorouracyl regimen in case of neoadjuvant chemoradiotherapy followed by surgery for locally advanced rectal cancer

Background: During the last decade, one of the most important treatment options for locally advanced, potencially resectable rectal tumours was neoadjuvant chemoradiotherapy (CRT) followed by surgery

Single-Molecule Imaging Reveals Rapid Estradiol Action on the Surface Movement of AMPA Receptors in Live Neurons

Gonadal steroid 17β-estradiol (E2) exerts rapid, non-genomic effects on neurons and strictly regulates learning and memory through altering glutamatergic neurotransmission and synaptic plasticity. However, its non-genomic effects on AMPARs are not well understood. Here, we analyzed the rapid effect of E2 on AMPARs using single-molecule tracking and super-resolution imaging techniques. We found that E2 rapidly decreased the surface movement of AMPAR via membrane G protein-coupled estrogen receptor 1 (GPER1) in neurites in a dose-dependent manner. The cortical actin network played a pivotal role in the GPER1 mediated effects of E2 on the surface mobility of AMPAR. E2 also decreased the surface movement of AMPAR both in synaptic and extrasynaptic regions on neurites and increased the synaptic dwell time of AMPARs. Our results provide evidence for understanding E2 action on neuronal plasticity and glutamatergic neurotransmission at the molecular level

Interaction of Temperature and Light in the Development of Freezing Tolerance in Plants

Abstract Freezing tolerance is the result of a wide range of physical and biochemical processes, such as the induction of antifreeze proteins, changes in membrane composition, the accumulation of osmoprotectants, and changes in the redox status, which allow plants to function at low temperatures. Even in frost-tolerant species, a certain period of growth at low but nonfreezing temperatures, known as frost or cold hardening, is required for the development of a high level of frost hardiness. It has long been known that frost hardening at low temperature under low light intensity is much less effective than under normal light conditions; it has also been shown that elevated light intensity at normal temperatures may partly replace the cold-hardening period. Earlier results indicated that cold acclimation reflects a response to a chloroplastic redox signal while the effects of excitation pressure extend beyond photosynthetic acclimation, influencing plant morphology and the expression of certain nuclear genes involved in cold acclimation. Recent results have shown that not only are parameters closely linked to the photosynthetic electron transport processes affected by light during hardening at low temperature, but light may also have an influence on the expression level of several other cold-related genes; several cold-acclimation processes can function efficiently only in the presence of light. The present review provides an overview of mechanisms that may explain how light improves the freezing tolerance of plants during the cold-hardening period

Efficient Pre-Solve Algorithms for the Schwerin and Falkenauer_U Bin Packing Benchmark Problems for Getting Optimal Solutions with High Probability

Bin Packing is one of the research areas of Operations Research with many industrial applications, as well as rich theoretical impact. In this article, the authors deal with Bin Packing on the practical side: they consider two Bin Packing Benchmark classes. These benchmark problems are often used to check the “usefulness”, efficiency of algorithms. The problem is well-known to be NP-hard. Instead of introducing some exact, heuristic, or approximation method (as usual), the problem is attacked here with some kind of greedy algorithm. These algorithms are very fast; on the other hand, they are not always able to provide optimal solutions. Nevertheless, they can be considered as pre-processing algorithms for solving the problem. It is shown that almost all problems in the considered two benchmark classes are, in fact, easy to solve. In case of the Schwerin class, where there are 200 instances, it is obtained that all instances are solved by the greedy algorithm, optimally, in a very short time. The Falkenauer U class is a little bit harder, but, here, still more than 91% of the instances are solved optimally very fast, with the help of another greedy algorithm. Based on the above facts, the main contribution of the paper is to show that pre-processing is very useful for solving such kinds of problems