A new mathematical model for single machine batch scheduling problem for minimizing maximum lateness with deteriorating jobs

This paper presents a mathematical model for the problem of minimizing the maximum lateness on a single machine when the deteriorated jobs are delivered to each customer in various size batches. In reality, this issue may happen within a supply chain in which delivering goods to customers entails cost. Under such situation, keeping completed jobs to deliver in batches may result in reducing delivery costs. In literature review of batch scheduling, minimizing the maximum lateness is known as NP-Hard problem; therefore the present issue aiming at minimizing the costs of delivering, in addition to the aforementioned objective function, remains an NP-Hard problem. In order to solve the proposed model, a Simulation annealing meta-heuristic is used, where the parameters are calibrated by Taguchi approach and the results are compared to the global optimal values generated by Lingo 10 software. Furthermore, in order to check the efficiency of proposed method to solve larger scales of problem, a lower bound is generated. The results are also analyzed based on the effective factors of the problem. Computational study validates the efficiency and the accuracy of the presented model

Psoriasis Associated Hub Genes Revealed by Weighted Gene Co-Expression Network Analysis

Objective: Psoriasis, an immune-mediated disorder, is a multifactorial disease of unidentified cause. This study aims to discover the possible biomarkers of this papulosquamous skin disease.Materials and Methods: The gene chip GSE55201, resulted from an experimental study, including 44 psoriasis patients and 30 healthy controls was downloaded from GEO and weighted gene co-expression network analysis was utilized to identify the hub genes. The key modules were determined using the module eigenvalues. We used biological functions, cellular components, and molecular functions in the Gene Ontology analysis and Kyoto Encyclopedia of Genes and Genomes enrichment analysis in the gene metabolic pathway were used for enrichment analysis.Results: Adjacency matrix was built by using power adjacency function and the power to turn the correlation to adjacency matrix was 4 with a topology fit index of 0.92. Using the weighted gene co-expression network analysis, 11 modules were identified. The green-yellow module eigenvalues were significantly associated with psoriasis (Pearson correlation=0.53, p<0.001). Candidate hub genes were determined by their higher connectivity and relationship with module eigenvalue. The genes including SIGLEC8, IL5RA, CCR3, RNASE2, CPA3, GATA2, c-KIT, and PRSS33 were recorded as the hub genes.Conclusion: In summary we can conclude that SIGLEC8, IL5RA, CCR3, RNASE2, CPA3, GATA2, c-KIT, and PRSS33 have an important role in the immune response regulation and could be considered as a potential diagnostic biomarker and therapeutic target for Psoriasis

Instantaneous Frequency-Based Algorithm for Directional Relays

The looped structure of the transmission and subtransmission networks results in the bidirectional fault current passing through the protection relays. The detection of the current direction is relegated to the directional protection unit. The traditional directional overcurrent relays utilize the reference voltage phasor for the detection of the fault direction, which increases total cost due to the need for both current and voltage measurement units. In this article, a new current only directional algorithm based on the instantaneous frequency is introduced. The proposed method is robust to noise and changes in the grid parameters, such as a change in the power frequency and direction of normal power flow. To evaluate the algorithm, plenty of simulations and actual experiments are carried out in different fault conditions. Also, its performance is compared with some other directional algorithms. The results confirm that the proposed algorithm can detect fault direction with a high degree of accuracy

Fault direction identification utilizing new current-based index founded on rate of change of fault current

The proliferation of Distributed Generations (DGs) in distribution networks has provided power system operation improvement while raising some protection challenges. Turning to bi-directional, the protection schemes of the deregulated distribution networks should be able to deal with an out-of-zone fault. More specifically, DGs impose bi-directional fault current and directional relay should be employed to identify correct fault direction so that the protective relays are prevented from mal-operation of out-of-zone fault. This paper introduces a current-based directional algorithm that utilizes a pre-fault current signal as the reference. This algorithm is designed based on the pre-fault current and rate of change of the fault current. As it can be inferred from the mathematical basis of the proposed method, it has low sensitivity to decaying DC and noise components. Also, the proposed index has a certain and straightforward range of variation between (-1, 1) for backward and forward fault direction, respectively. The performance of the proposed algorithm is evaluated for different scenarios such as variation of fault resistance, sampling frequency, and types of faults in three simulated systems and a laboratory test bench. The simulation and experimental evaluation results show the accuracy and speed of the proposed algorithm in comparison with similar algorithms