An efficient heuristic for scheduling batches of parts in a flexible flow system

Flexible manufacturing systems (FMSs) are a class of automated systems that can be used to improve productivity in batch manufacturing. Four stages of decision making have been defined for an FMS—the design, planning, scheduling, and control stages. This research focuses on the planning stage, and specifically in the area of scheduling batches of parts through the system.The literature to date on the FMS planning stage has mostly focused on the machine grouping, tool loading, and parttype selection problems. Our research carries the literature a step further by addressing the problem of scheduling batches of parts. Due to the use of serial-access material-handling systems in many FMSs, the batch-scheduling problem is modeled for a flexible flow system (FFS). This model explicitly accounts for setup times between batches that are dependent on their processing sequence.A heuristic procedure is developed for this batch-scheduling problem—the Maximum Savings (MS) heuristic. The MS heuristic is based upon the savings in time associated with a particular sequence and selecting the one with the maximum savings. It uses a two-phase method, with the savings being calculated in phase I, while a branch-and-bound procedure is employed to seek the best heuristic solution in phase II. Extensive computational results are provided for a wide variety of problems. The results show that the MS heuristic provides good-quality solutions.     

Reliability‐based estimation of the survival of Listeria monocytogenes in chorizos

Reliability analysis is especially important when critical decisions are to be made involving potentially severe adverse consequences such as foodborne illness. Owing to uncertainty associated with the parameters controlling survival of Listeria monocytogenes in chorizo (a Mexican‐style sausage), the time needed to reduce the count by a certain number (n) of logs (t_nD) is probabilistic. In this paper the first‐order second‐moment (FOSM) method based on Taylor series expansion is used to derive the expected value and standard deviation of t_nD as function of the operating conditions (random variables) affecting survival, namely initial water activity (a_w0) of the sausage batter, storage temperature (T) and airflow velocity (F), along with their uncertainties characterised by their means and coefficients of variation. For any given n the derived t_nD probability distribution enables one to determine an estimate of t_nD for any desired level of reliability or confidence level, such as 50% (median value), 95%, 99%, etc. Among the conclusions drawn were: (i) the variability associated with T and F has a minor effect on estimating uncertainty in t_nD, whereas the reliability of t_nD estimation is greatly influenced by the uncertainty in a_w0; and (ii) the uncertainty in a_w0 has the greatest impact when a_w0 of the sausage formulation exceeds 0.90. The approach used and discussed in this paper can be applied to any survival/inactivation study to incorporate the effect of uncertainty in the various extrinsic and intrinsic parameters on the survival kinetics of the pathogen in a food system under evaluation. Copyright © 2006 Society of Chemical Industry     

An improved algorithm for solving a multi-period facility location problem

An exact algorithm for the multi-period facility location problem is proposed that efficiently integrates mixed-integer and dynamic programming methods. Two simplification procedures are introduced to reduce the size of the general multi-period facility location problem substantially. Because the proposed algorithm utilizes dynamic programming to obtain the optimal sequence over the entire planning horizon, many near-optimal solutions also become available that are extremely useful for postoptimality analysis. The solution method is tested and compared with a well-known procedure on several problems with varying conditions. The comparisons appear very promising, and the required CPU times by the proposed method are substantially reduced.     

Synthesis and optimization of high surface area mesoporous date palm fiber-based nanostructured powder activated carbon for aluminum removal

Date palm fiber (DPF) derived from agrowaste was utilized as a new precursor for the optimized synthesis of a cost-effective, nanostructured, powder-activated carbon (nPAC) for aluminum (Al3+) removal from aqueous solutions using carbonization, KOH activation, response surface methodology (RSM) and central composite design (CCD). The optimum synthesis condition, activation temperature, time and impregna-tion ratio were found to be 650 ℃, 1.09 hour and 1:1, respectively. Furthermore, the optimum conditions for removal were 99.5％and 9.958 mg·g-1 in regard to uptake capacity. The optimum conditions of nPAC was analyzed and characterized using XRD, FTIR, FESEM, BET, TGA and Zeta potential. Moreover, the adsorption of the Al3+ conditions was optimized with an integrated RSM-CCD experimental design. Regression results revealed that the adsorption kinetics data was well fitted by the pseudo-second order model, whereas the adsorption isotherm data was best represented by the Freundlich isotherm model. Optimum activated carbon indicated that DPF can serve as a cost-effective precursor adsorbent for Al3+removal.     

Sulfamic acid functionalized slag for effective removal of organic dye and toxic metal from the aqueous samples

Surface functionalization of blast furnace slag with sulfamic acid(a zwitterion) was performed for the removal of Cr~(3+) and methylene blue dye(MB) from water samples. The slag functionalization process was optimized using Response Surface Methodology Design. Statistical analysis of the parameters that include the sulfamic acid amount(A), reaction time(B), and temperature(C) revealed that(A) increase had a negative effect on the adsorption of both pollutants by the zwitterion slag, whereas(B) and(C)increase presented a positive impact. At the optimum condition of 2 g sulfamic acid amount, 50 min reaction time and 37 °C temperature, the prepared slag showed a removal efficiency of more than 90% for both Cr~(3+) and MB. Surface characterization by SEM/EDS, FTIR, XPS and surface area analyser, showed an improvement in surface properties and the incorporation of zwitterionic NH_2~+ and S@O groups of sulfamic acid. Adsorption isotherm and kinetic studies conducted with the zwitterion slag showed the adsorption process was suited to Freundlich isotherm model and pseudo-second-order kinetic model.The thermodynamic study conducted revealed the spontaneity of the process based on the calculated negative DG(Gibb's free energy) values. The prepared zwitterion slag offered easy regeneration with dilute HCl solution and showed a considerable removal(Cr3+: 65% and MB: 80%) for both pollutants even after 3 cycles of usage.     

Anomaly Detection for Industrial Internet of Things Cyberattacks

The evolution of the Internet of Things (IoT) has empowered modern industries with the capability to implement large-scale IoT ecosystems, such as the Industrial Internet of Things (IIoT). The IIoT is vulnerable to a diverse range of cyberattacks that can be exploited by intruders and cause substantial reputational and financial harm to organizations. To preserve the confidentiality, integrity, and availability of IIoT networks, an anomaly-based intrusion detection system (IDS) can be used to provide secure, reliable, and efficient IIoT ecosystems. In this paper, we propose an anomaly-based IDS for IIoT networks as an effective security solution to efficiently and effectively overcome several IIoT cyberattacks. The proposed anomaly-based IDS is divided into three phases: pre-processing, feature selection, and classification. In the pre-processing phase, data cleaning and normalization are performed. In the feature selection phase, the candidates’ feature vectors are computed using two feature reduction techniques, minimum redundancy maximum relevance and neighborhood components analysis. For the final step, the modeling phase, the following classifiers are used to perform the classification: support vector machine, decision tree, k-nearest neighbors, and linear discriminant analysis. The proposed work uses a new data-driven IIoT data set called X-IIoTID. The experimental evaluation demonstrates our proposed model achieved a high accuracy rate of 99.58%, a sensitivity rate of 99.59%, a specificity rate of 99.58%, and a low false positive rate of 0.4%.     

Modelling of air damping effect on the performance of encapsulated MEMS resonators

Microsystem Technologies - The dynamic performance of a micro-resonator depends on its energy loss mechanism which is quantified by Q-factor (Quality factor). This paper presents numerical...     

Secured Data Storage Using Deduplication in Cloud Computing Based on Elliptic Curve Cryptography

The tremendous development of cloud computing with related technologies is an unexpected one. However, centralized cloud storage faces few challenges such as latency, storage, and packet drop in the network. Cloud storage gets more attention due to its huge data storage and ensures the security of secret information. Most of the developments in cloud storage have been positive except better cost model and effectiveness, but still data leakage in security are billion-dollar questions to consumers. Traditional data security techniques are usually based on cryptographic methods, but these approaches may not be able to withstand an attack from the cloud server's interior. So, we suggest a model called multi-layer storage (MLS) based on security using elliptical curve cryptography (ECC). The suggested model focuses on the significance of cloud storage along with data protection and removing duplicates at the initial level. Based on divide and combine methodologies, the data are divided into three parts. Here, the first two portions of data are stored in the local system and fog nodes to secure the data using the encoding and decoding technique. The other part of the encrypted data is saved in the cloud. The viability of our model has been tested by research in terms of safety measures and test evaluation, and it is truly a powerful complement to existing methods in cloud storage.     

Ubi-App: a ubiquitous application for universal access from handheld devices

Universal access from a handheld device (such as a PDA, cell phone) at any time or anywhere is now a reality. Ubicomp Assistant (UA) (Sharmin et al. in Proceedings of the 21st annual ACM symposium on applied computing (ACM SAC 2006), Dijon, France, pp 1013–1017, 2006) is an integral service of MARKS (Sharmin et al. in Proceedings of the third international conference on information technology: new generations (ITNG 2006), Las Vegas, Nevada, USA, pp 306–313, 2006). It is a middleware developed for handheld devices, and has been designed to accommodate different types of users (e.g., education, healthcare, marketing, or business). This customizable service employs the ubiquitous nature of current short range, low-power wireless connectivity and readily available, low-cost lightweight mobile devices. These devices can reach other neighbouring devices using a free short-range ad hoc network. To the best of the authors’ knowledge, the UA service is the only service designed for these devices. This paper presents the details of Ubi-App, a ubiquitous application for universal access from any handheld device, which uses UA as a service. The results of a usability test and performance evaluation of the prototype show that Ubi-App is useful, easy to use, easy to install, and does not degrade the performance of the device.