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The purpose of this paper is to develop a proper tool for structural analysis and designing in near‐fault regions where the level of earthquake hazard is more and its nature is different from far‐field regions.

The paper uses near‐fault records and pulse extraction using wavelet analysis and designs a spectra concept.

The paper developed a proper design spectra for near‐fault structural analysis and design.

This paper using pulse extraction for developing a 3D response spectrum with paying attention to earthquake magnitude is new and final design spectra proposed is valuable.

The purpose of this study is to develop a molecular imprinting electrochemical sensor for the specific detection of the anticancer drug amsacrine. The sensor used a composite of bacterial cellulose (BC) and silver nanoparticles (AgNPs) as a platform for the immobilization of a molecularly imprinted polymer (MIP) film. The main objective was to enhance the electrochemical properties of the sensor and achieve a high level of selectivity and sensitivity toward amsacrine molecules in complex biological samples.

The composite of BC-AgNPs was synthesized and characterized using FTIR, XRD and SEM techniques. The MIP film was molecularly imprinted to selectively bind amsacrine molecules. Electrochemical characterization, including cyclic voltammetry and electrochemical impedance spectroscopy, was performed to evaluate the modified electrode’s conductivity and electron transfer compared to the bare glassy carbon electrode (GCE). Differential pulse voltammetry was used for quantitative detection of amsacrine in the concentration range of 30–110 µM.

The developed molecular imprinting electrochemical sensor demonstrated significant improvements in conductivity and electron transfer compared to the bare GCE. The sensor exhibited a linear response to amsacrine concentrations between 30 and 110 µM, with a low limit of detection of 1.51 µM. The electrochemical response of the sensor showed remarkable changes before and after amsacrine binding, indicating the successful imprinting of amsacrine in the MIP film. The sensor displayed excellent selectivity for amsacrine in the presence of interfering substances, and it exhibited good stability and reproducibility.

This study presents a novel molecular imprinting electrochemical sensor design using a composite of BC and AgNPs as a platform for MIP film immobilization. The incorporation of BC-AgNPs improved the sensor’s electrochemical properties, leading to enhanced sensitivity and selectivity for amsacrine detection. The successful imprinting of amsacrine in the MIP film contributes to the sensor's specificity. The sensor's ability to detect amsacrine in a concentration range relevant to anticancer therapy and its excellent performance in complex sample matrices add significant value to the field of electrochemical sensing for pharmaceutical analysis.

The purpose of this study is to extend a sensitivity-based reliability technique for the processors deployed in industrial drive (ID).

The processor provides flexible operation, re-configurability, and adaptable compatibility in industrial motor drive system. A sensitivity-based model allows a robust tool for validating the system design. Sensitivity is the probability of a partial failure rate for a distributed variable; sensitivity and failure rates are also complementary. Conversely, traditional power electronic components reliability estimating standards have overlooked it, and it is essential to update them to account for the sensitivity parameter. A new sensitivity-based reliability prediction methodology for a typical 32-bit microprocessor operating at 30ºC deployed in ID is presented to fill this gap. The proposed techniques are compared with the estimated processor reliability values obtained from various reliability standards using the validated advanced logistics development tool. The main contribution of this work is to provide a sensitivity extended reliability method over the conventional method directing toward improving reliability, availability, and maintainability in the design of ID.

The analysis shows that the sensitivity of the processor’s circuit increases due to increases in complexity of the system by reducing the overall mean time between failure upon comparing among conventional reliability standards.

The significance of this paper lies in the overall, sensitivity-based reliability technique for processors in comparison to the traditional reliability complexity in IDs.

The purpose of this paper is to explore the role of four cognitive biases, namely, selective perception, exposure to limited alternatives, adjustment and anchoring, and illusion of control in anticipating and responding to Distributed-Denial-of-Service (DDoS) attacks.

The paper is based on exploratory case study research and secondary data on decision making in the Australian Bureau of Statistics (ABS) in regards to planning and managing DDoS attacks on Census day in 2016.

Cognitive biases limited the ABS’s awareness of the eCensus system’s vulnerabilities, preparation for and management of DDoS attacks. Cyberattacks are on the increase, and managers should expect and be prepared to deal with them.

Due to the sensitivity of the topic, it was not possible to interview relevant stakeholders. Analysis is based on high-quality secondary data that includes comprehensive government reports investigating the events on Census day.

Cyberattacks are inevitable and not an aberration. A checklist of actions is identified to help organisations avoid the failures revealed in the case study. Managers need to increase their awareness of cyberattacks, develop clear processes for dealing with them and increase the robustness of their decision-making processes relating to cybersecurity.

This the authors believe that it is the first major study of the DDoS attacks on the Australian census. DDoS is a security reality of the twenty-first century and this case study illustrates the significance of cognitive biases and their impact on developing effective decisions and conducting regular risk assessments in managing cyberattacks.

Food safety is among the most important topics in the world. According to WHO guidelines, aflatoxins are one of the most hazardous food toxins. Therefore, their detection in food products seems crucial due to health problems. The purpose of this paper is to discuss the different types of biosensors in aflatoxin determination.

Traditional detection methods are time consuming and expensive. As fast and accurate detection is important in monitoring food contaminants, alternative analytical methods would be essential. Biosensors are the intelligent design of sensitive sensors for precise detection of toxins in a short time. Various biosensors are being applied for aflatoxins detection in food products with many advantages over the traditional methods.

Biosensors are cost-effective, stable and have possessed high selectivity, specificity and accuracy in aflatoxins detection. Applying biosensors has been increased recently, so biosensing methods (optical, electrochemical, piezoelectrical, immunosensors, surface plasmon resonance and calorimetric) are discussed along with their advantages in this article.

More efforts should be occurred to detect and decrease the aflatoxins by biosensors, and some traits like accuracy and selectivity would be the purpose of future projects. The combination of various techniques would also help in toxin detection issue in food products, so high efforts in this regard are also required for the upcoming years.

This article also reviews different types of biosensors simultaneously and explains their specificity for aflatoxin determination in different food products and also the future trends and requirements.

This paper aims to study a generalized type of mixed-model assembly line with multi-manned workstations where multiple workers simultaneously perform different tasks on the same product. This special kind of assembly line is usually utilized to assemble different models of large products, such as buses and trucks, on the same production line.

To solve the mixed-model multi-manned assembly line balancing problem optimally, a new mixed-integer-programming (MIP) model is presented. The proposed MIP model is nondeterministic polynomial-time (NP)-hard, and as a result, a simulated annealing (SA) algorithm is developed to find the optimal or near-optimal solution in a small amount of computation time.

The performance of the proposed algorithm is examined for several test problems in terms of solution quality and running time. The experimental results show that the proposed algorithm has a satisfactory performance from computational time efficiency and solution accuracy.

This research is the very first study that minimizes the number of workers and workstations simultaneously, with a higher priority set for the number of workers, in a mixed-model multi-manned assembly line setting using a novel MIP model and an SA algorithm.