A method for real power transfer allocation using multivariable regression analysis

A multivariable regression (MVR) approach is proposed to identify the real power transfer between generators and loads. Based on solved load flow results, it first uses modified nodal equation method (MNE) to determine real power contribution from each generator to loads. Then, the results of MNE method and load flow information are utilized to determine suitable regression coefficients using MVR model to estimate the power transfer. The 25-bus equivalent system of south Malaysia is utilized as a test system to illustrate the effectiveness of the MVR output compared to that of the MNE method. The error of the estimate of MVR method ranges from 0.001 4 to 0.007 9. Furthermore, when compared to MNE method, MVR method computes generator contribution to loads within 26.40 ms whereas the MNE method takes 360 ms for the calculation of same real power transfer allocation. Therefore, MVR method is more suitable for real time power transfer allocation.     

Effect of powder treatment on the crystallization behaviour and phase evolution of Al2O3–High ZrO2 nanocomposites

Al₂O₃–ZrO₂ composites containing nominally equal volume fraction of Al₂O₃ and ZrO₂ have been synthesized through combined gel-precipitation technique. Subsequently the gels were subjected to three different post gel processing treatments like ultrasonication, ultrasonication followed by water washing and ultrasonication followed by alcohol washing. It was observed that while in unwashed samples crystallization took place at low temperature, crystallization was delayed in the washed gels. The phase transition of ZrO₂ in the calcined gels followed the sequence; amorphous → cubic ZrO₂ → tetragonal ZrO₂ → monoclinic ZrO₂. On the other hand, phase transition in alumina followed the sequence amorphous to γ-Al₂O₃, the transition taking place at 650 °C. No α-Al₂O₃ could be detected even after calcination at 950 °C. However, all the sintered samples had α-Al₂O₃. In spite of high linear shrinkage (19–21%) during sintering, the sintered sample had density of only above 70% for all the four varieties of the powders. However, in spite of the low sintered density of the pellets, 31% tetragonal zirconia could be retained after sintering at 1400 °C and it reduced to about 16% at 1600 °C.     

The effect of heat treatment on high temperature mechanical properties of microalloyed medium carbon steel

In the present work, high temperature tensile properties and abrasive wear performance of a microalloyed medium carbon steel has been examined. Tensile and abrasive wear testing were carried out on as-received and heat treated specimens. The research has shown that microalloyed medium carbon steel was susceptible to dynamic strain ageing due to interaction of mobile dislocations and solid atoms, such as carbon and/or nitrogen. The interaction between dislocations and solid atoms at 200–300 °C changes the work hardening rate and contributes to dynamic strain ageing. These interactions also increased abrasive wear resistance of the microalloyed medium carbon steel at 300 °C. Therefore, the inference can be drawn that dynamic strain ageing caused an improvement on abrasion resistance.     

A knowledge-based decision support system for the management of parts and tools in FMS

Flexible manufacturing systems (FMS) are very complex systems with large part, tool, and information flows. The aim of this work is to develop a knowledge-based decision support system (KBDSS) for short-term scheduling in FMS strongly influenced by the tool management concept to provide a significant operational control tool for a wide range of machining cells, where a high level of flexibility is demanded, with benefits of more efficient cell utilization, greater tool flow control, and a dependable way of rapidly adjusting short-term production requirements. Development of a knowledge-based system to support the decision making process is justified by the inability of decision makers to diagnose efficiently many of the malfunctions that arise at machine, cell, and entire system levels during manufacturing. In this context, this paper proposes three knowledge-based models to ease the decision making process: an expert production scheduling system, a knowledge-based tool management decision support systems, and a tool management fault diagnosis system. The entire system has been created in a hierarchical manner and comprises more than 400 rules. The expert system (ES) was implemented in a commercial expert system shell, Knowledge Engineering System (KES) Production System (PS).     

Hypergraph Models and Algorithms for Data-Pattern-Based Clustering

In traditional approaches for clustering market basket type data, relations among transactions are modeled according to the items occurring in these transactions. However, an individual item might induce different relations in different contexts. Since such contexts might be captured by interesting patterns in the overall data, we represent each transaction as a set of patterns through modifying the conventional pattern semantics. By clustering the patterns in the dataset, we infer a clustering of the transactions represented this way. For this, we propose a novel hypergraph model to represent the relations among the patterns. Instead of a local measure that depends only on common items among patterns, we propose a global measure that is based on the cooccurences of these patterns in the overall data. The success of existing hypergraph partitioning based algorithms in other domains depends on sparsity of the hypergraph and explicit objective metrics. For this, we propose a two-phase clustering approach for the above hypergraph, which is expected to be dense. In the first phase, the vertices of the hypergraph are merged in a multilevel algorithm to obtain large number of high quality clusters. Here, we propose new quality metrics for merging decisions in hypergraph clustering specifically for this domain. In order to enable the use of existing metrics in the second phase, we introduce a vertex-to-cluster affinity concept to devise a method for constructing a sparse hypergraph based on the obtained clustering. The experiments we have performed show the effectiveness of the proposed framework.     

Power Flattening in ARIES-RS Fusion Breeder Reactor Using Mixed Fuels

This study presents the possibility of the power flattening in the ARIES-RS breeder reactor using mixed (Th,U)C or (Th,U)N fuels. Two different types of mixing, namely, homogeneous mixing (HM) and linear mixing (LM) were used to investigate the uniformity of fission power distribution through the fuel zone. In HM, fraction of uranium content were kept constant in all rows of the fuel zone whereas, in LM the fraction of the uranium were linearly increased from the first to last fuel row in the fuel zone. Neutron transport calculations were performed with the aid of the SCALE4.3 system by solving the Boltzmann transport equation with the XSDRNPM code in 238 neutron groups and a S₈–P₃ approximation. Flat fission power distribution was maintained successfully for the blanket using linearly mixed fuels. However, the fission density profile was not uniform in the blanket with homogeneously mixed fuels. It decreased exponentially form the 1st to 10th fuel row.     

A minisum location problem with regional demand considering farthest Euclidean distances

We consider a continuous multi-facility location-allocation problem that aims to minimize the sum of weighted farthest Euclidean distances between (closed convex) polygonal and/or circular demand regions, and facilities they are assigned to. We show that the single facility version of the problem has a straightforward second-order cone programming formulation and can therefore be efficiently solved to optimality. To solve large size instances, we adapt a multi-dimensional direct search descent algorithm to our problem which is not guaranteed to find the optimal solution. In a special case with circular and rectangular demand regions, this algorithm, if converges, finds the optimal solution. We also apply a simple subgradient method to the problem. Furthermore, we review the algorithms proposed for the problem in the literature and compare all these algorithms in terms of both solution quality and time. Finally, we consider the multi-facility version of the problem and model it as a mixed integer second-order cone programming problem. As this formulation is weak, we use the alternate location-allocation heuristic to solve large size instances.     

Peripheral mRNA Expression and Prognostic Significance of Emotional Stress Biomarkers in Metastatic Breast Cancer Patients

Emotional stress is believed to be associated with increased tumor progression. Stress-induced epigenetic modifications can contribute to the severity of disease and poor prognosis in cancer patients. The current study aimed to investigate the expression profiles along with the prognostic significance of psychological stress-related genes in metastatic breast cancer patients, to rationalize the molecular link between emotional stress and cancer progression. We profiled the expression of selected stress-associated genes (5-HTT, NR3C1, OXTR, and FKBP5) in breast cancer including the stress evaluation of all participants using the Questionnaire on Distress in Cancer Patients–short form (QSC-R10). A survival database, the Kaplan–Meier Plotter, was used to explore the prognostic significance of these genes in breast cancer. Our results showed relatively low expressions of 5-HTT (p = 0.02) and OXTR (p = 0.0387) in metastatic breast cancer patients as compared to the non-metastatic group of patients. The expression of NR3C1 was low in tumor grade III as compared to grade II (p = 0.04). Additionally, the expression of NR3C1 was significantly higher in patients with positive estrogen receptor status. However, no significant difference was found regarding FKBP5 expression in breast cancer. The results suggest a potential implication of these genes in breast cancer pathology and prognosis.     

Quinolizidines as Novel SARS-CoV-2 Entry Inhibitors

COVID-19, caused by the highly transmissible severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2), has rapidly spread and become a pandemic since its outbreak in 2019. We have previously discovered that aloperine is a new privileged scaffold that can be modified to become a specific antiviral compound with markedly improved potency against different viruses, such as the influenza virus. In this study, we have identified a collection of aloperine derivatives that can inhibit the entry of SARS-CoV-2 into host cells. Compound 5 is the most potent tested aloperine derivative that inhibited the entry of SARS-CoV-2 (D614G variant) spike protein-pseudotyped virus with an IC₅₀ of 0.5 µM. The compound was also active against several other SARS-CoV-2 variants including Delta and Omicron. Results of a confocal microscopy study suggest that compound 5 inhibited the viral entry before fusion to the cell or endosomal membrane. The results are consistent with the notion that aloperine is a privileged scaffold that can be used to develop potent anti-SARS-CoV-2 entry inhibitors.