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It is usually very difficult to find the causes of low yield performance in semiconductor manufacturing. In a typical wafer probing process, when the yield is low, engineers are required to examine both the equipment and the products to clarify if the low yield performance could be contributed by the testing conditions or the products. No matter which were the main cause, the time-consuming trouble-shooting process itself demanded resources thus the overall equipment effectiveness (OEE) is also damaged. This paper dealt with the trouble shooting data of wafer probing process. By examining the main factors that may affect the yield and equipment downtime in the wafer probing process, this study conducted various experiments to explore the relationship between yield and various wafer probing settings such as cleaning sheet size, probing overdrive, touch down time, and contact resistance. As a results, the optimal conditions of the main factors for improving yield and OEE are presented.

Paralysis of the frontalis muscle is extremely difficult to reverse. The best treatment for facial paralysis reanimation which preserves spontaneity and muscle specificity is end-to-end neurorrhaphy through cross-face nerve grafting. However, it is rarely possible. Muscle-nerve-muscle (MNM) neurotization consists of an interposition of a nerve graft connecting the normal muscle to the denervated muscle. The axons of the muscle with intact innervation grow inside a neural graft towards the paralyzed muscle resulting in neurotization. The purpose of this report is to present a case of frontalis muscle paralysis reanimated by MNM neurotization. A 65-year-old female patient presented complete facial paralysis after temporomandibular joint surgery. Five months afterwards, the patient spontaneously recovered facial muscle movements except the frontalis muscle. Definitive paralysis of the frontalis muscle was diagnosed after 11 months, and MNM neurotization was chosen and performed. Three strings of sural nerve were placed in separated tunnels in the subcutaneous plane, through small skin incisions to connect the two bellies of frontalis muscle bilaterally, and then sutured into the muscle pocket of each side. The patient presented voluntary and synchronic contraction of the bilateral frontalis muscle, 4 months after neurotization. Electroneuromyography confirmed muscle contraction by contralateral stimulation. Despite its efficacy still being researched, it is a very promising technique for the reanimation of small muscles in facial paralysis.

Defects on thin film transistor liquid crystal display (TFT-LCD) panel could be divided into either macro- or microdefects, depending on if they are easy to be detected by the naked eye or not. There have been abundant studies discussing the identification of macrodefects but very few on microones. This study proposed a multicategory classification model using a convolutional neural network model to work with automatic optical inspection (AOI) for identifying defective pixels on the TFT-LCD panel. Since the number of nondefective pixels outnumbered the defective ones, there exists a very serious class-imbalanced problem. To deal with that, this study designed a special training strategy that worked with data augmentation to increase the effectiveness of the proposed model. Actual panel images provided by a mobile manufacturer in Taiwan are used to demonstrate the efficiency and effectiveness of the proposed approach. After validation, the model constructed by this study had 98.9% total prediction accuracy and excellent specificity and sensitivity. The model could finish the detection and classification process automatically to replace the human inspection.

When a financial institute constructs a credit risk assessment model, qualitative, and quantitative data must be considered at the same time due to differences in the attributes of each form of dat...

Many consumers have adopted the food of dietary supplements. However, since the threshold of professional knowledge for dietary supplements is usually high, most of the consumers do not possess sufficient knowledge for making an appropriate purchase decisions as they concern buying the products of dietary supplements. As a result, many consumers rely on word-of-mouth as they need to make purchase decisions on buying dietary supplements. It causes health risk to consumers since the word-of-mouth may be wrong and thus to make consumers take wrong dietary supplements. To address this deficiency, this study explores how consumers’ knowledge for dietary supplements and risk perceptions influence their search behavior for word-of-mouth in the purchase of dietary supplements. With a two-stage cluster sampling strategy, 370 questionnaires were distributed to thecustomers of retail stores for selling Ganoderma dietary supplements, while 314 questionnaires were valid for statistical analysis. With the analysis of linear structural equation modeling, the causal structural relation among consumers’ product knowledge, risk perception and word-of-mouth search behavior was examined. According to the statistical results, consumers’ subjective product knowledge has greater overall impacts on the degree of word-of-mouth search behavior, compared with consumers’ objective product knowledge. Namely, if consumers have more subjective knowledge for products, they are less likely to rely on word-of-mouth for the purchase of dietary supplements. For promoting the concept of food safety to consumers, consumers should be prepared with more correct product knowledge, in order to develop trust in self product knowledge in the publicity of safe consumption of dietary supplements, and reduce their dependence on the reputation of the dietary supplements without clear sources.

The majority of the studies on credit risk assessment models for financial institutions during recent years focus on the improvement of imbalanced data or on the enhancement of classification accuracy with multistage modeling. Whilst multistage modeling and data pre-processing can boost accuracy somewhat, the heterogeneous nature of data may affects the classification accuracy of classifiers. This paper intends to use the classifier, eXtreme gradient boosting tree (XGBoost), to construct a credit risk assessment model for financial institutions. Cluster-based under-sampling is deployed to process imbalanced data. Finally, the area under the receiver operative curve and the accuracy of classifications are the assessment indicators, in the comparison with other frequently used single-stage classifiers such as logistic regression, self-organizing algorithms and support vector machine. The results indicate that the XGBoost classifier used by this paper achieve better results than the other three and can serve as a superior tool for the development of credit risk models for financial institutions.

Constructing a classification model for the multi-class data is a critical problem in many areas. In practical applications, data in multiple classes are often imbalanced which might result in a classification model with high overall accuracy rate but with low accuracy rate for the minority class. However, minority class is usually the more important one compared to other classes in practice. This study integrates dual response surface methodology, logistic regression analysis, and desirability function to develop an optimal re-sampling strategy for classifying multi-class imbalanced data to effectively improve the low classification accuracy rate of the minority class(es) while still maintain a certain accuracy rate for the majority class(es). Three data-sets drawn from KEEL Database were used in the numerical experiments. The results showed that the proposed method can effectively improve the low classification accuracy rate of the minority class in contrast to the previous work.

Glutamate can activate NMDA receptor (NMDAR) and subsequently induces excitotoxic neuron loss. However, roles of NMDARs in the blood-brain barrier (BBB) are little known. This study used a mouse cerebrovascular endothelial cell (MCEC) model to evaluate the effects of NMDAR activation on maintenance of the BBB and its possible mechanisms. Analysis of confocal microscopy revealed expressions of NMDAR subunits, GluN1 and GLUN2B, in MCECs. An immunoblot assay further showed the existence of GluN1 in plasma membranes of MCECs. In brain tissues, a confocal microscopic analysis demonstrated co-localization of GluN1 and factor VIII, a biomarker of MCECs. In addition, GluN1 mRNA was detected in MCECs and the brain. Functional assays showed that exposure of MCECs to NMDA increased calcium influx. Separately, NMDA suppressed transendothelial electrical resistance values, levels of occludin, and occludin tight junctions. As to the mechanism, NMDA stimulated sequential phosphorylations of extracellular signal-regulated kinase (ERK)1/2 and mitogen-activated ERK (MEK)1. Interestingly, amounts of matrix metalloproteinase (MMP)2 and MMP9 in MCECs were augmented by NMDA. The NMDA-induced alterations in ERK1/2 phosphorylation and occludin levels were reversed by pretreatment with PD98059, a MEK inhibitor, and MK-801, a NMDAR antagonist, respectively. Therefore, this study shows the functional presence of NMDARs in MCECs, and NMDAR activation can disrupt the MCEC-constructed tight junction barrier via activation of the MEK1/2-ERK1/2 signaling pathway and upregulation of MMP2/9 expressions.

Cloud computing business models include three service models: software as a service, platform as a service, and infrastructure as a service (IaaS). Cloud storage is a new storage model in which data are stored on host companies and people store data into it through the Internet, a derivative of IaaS. In the cloud storage, the maintenance and purchasing cost of memory space need to be considered to avoid the possibility of running out. There is a conflict between hosts and clients: clients expected to increase the amount of backup memory space to avoid shortage. On the other hand, hosts expected to decrease the cost of backup memory space to maximise profit. In order to resolve this issue, this paper proposed a theory of constraints (TOC)-based approach to solve the memory allocation of cloud storage, which uses market information to build a rolling forecast. Moreover, this paper uses practical and simulative data of cloud storage, provided by a cloud storage company, to verify the effectiveness an...

With the constantly changing patterns of war, more technologically exquisite weapons are designed, increasing in cost and complexity. Training maneuvers with live ammunition are expensive and are prone to accidental casualties. Thus, many countries are gradually adopting simulation training systems to replace some actual exercises to reduce casualties and still maintain maximum combat readiness. However, each simulation training system has a different background with regard to time, source, function goal, and quality of environment and staff. It is also more difficult to assess the benefit of simulation training systems. Moreover, traditional benefit assessments of military simulation training systems have merely considered their efficiency, not safety, causing biased conclusions. To solve these issues, this paper integrates the analytic hierarchy process (AHP), important-performance analysis (IPA), and the 2-tuple fuzzy linguistic representation model to determine the benefits of simulation training systems. To verify the proposed approach, a numerical example of the evaluation of a training simulator system’s benefit is adopted. Compared with the traditional AHP method, the proposed method does not lose any valuable information that is provided by experts and also considers training safety. Further, these data are presented in 2-dimensional graphs for managers to further guide the decision-making process.

Purpose – Semiconductor wafer fabrication (FAB) is recognized as one of the most complex manufacturing systems. A newly built FAB has to pass various audits from its customer before the customer's wafers are initially produced. 5S audit is one of them. In order to comply with customer (auditor) expectations toward 5S practice, this paper assists wafer fabrication managers in allocating the limited resources to places that are valued most by their customer. The paper aims to discuss these issues. Design/methodology/approach – This paper applied Yang's refined Kano model to Ho's 5S checklist to prioritise these checkpoints for a FAB. An empirical study based on Ho's 5S checklist from experienced internal auditors (respondents) to prioritise 5S activities was explored to justify its feasibility for a FAB and importance-satisfaction for customer. Findings – An empirical study in a case FAB demonstrated how the refined model prioritised 5S activities based on Ho's 5S checklist. The result of this study further showed that quality attributes possess different identities, which could offer management more framable scopes to implement 5S practice and sustain 5S scene management. Research limitations/implications – Since this empirical study was focused on a 300-mm wafer fabrication company, the results and findings may not generally explain other wafer-size fabrications. Practical implications – This study was applied to a real-world case of a newly built 300-mm semiconductor fabrication in Taiwan. It is a straightforward bridge to link a methodology in a practical manner to disseminate information to both researchers and practitioners. Originality/value – From the adoption of the refined Kano model, specific required check points for 5S practice are transformed from subjective, conceptual and linguistic practice to be identified, quantified and prioritised for semiconductor wafer fabrication under resources constraints to cater customer's 5S expectations and to generate more attention in building-up a much more robust scene management. This paper provided a systemic way to prioritise 5S activities for a semiconductor wafer fabrication.

Purpose – Semiconductor wafer fabrication (FAB) is recognized as one of the most complex manufacturing systems. A newly built FAB has to pass various audits from its customer before the customer's wafers are initially produced. 5S audit is among one of them. When building a state-of-the-art 300 mm/130 μm FAB, it is common to find hundreds of action items to complete in order to comply with the customer's (auditor) expectations toward 5S practice. The purpose of this paper is to assist wafer fabrication managers in allocating the limited resources on 5S practice to the places that are valued most by their customer. Design/methodology/approach – This study proposes an application of the Kano model with a modified customer satisfaction coefficient (CS coefficient) to effectively prioritize thousands of action items for a wafer fabrication. Findings – Some new discoveries associated with shop floor management that may be neglected by academicians and practitioners are presented here. Surprisingly, greatly substantial non-linear contributions of attractive to-scene management are revealed with little efforts on the 5S practice. These attributes prove to be impressive to customers and typical instructions beneficial to future 5S practice are embraced as well. Practical implications – A real case drawn from a 300 mm semiconductor wafer FAB in Taiwan is analyzed to demonstrate the effectiveness of this approach. Originality/value – From the adoption of this proposed methodology, an influential set of specific required checkpoints for 5S practice is transformed from subjective, conceptual and linguistic practice to be identified, quantified and prioritized for semiconductor wafer fabrication under resources constraints to cater the customer's 5S expectations and to generate more attentions in building-up a much more robust scene management.

Failure mode and effect analysis (FMEA) is one of the risk analysis techniques recommended by international quality certification systems, such as ISO 9000, ISO/TS 16949, CE, and QS9000. Most current FMEA methods use the risk priority number (RPN) value to evaluate the risk of failure. The RPN value is the mathematical product of the three parameters of a failure mode that is rated between 1 and 10 in terms of its severity (S), occurrence (O), and detection (D), respectively. However, the RPN method has been found with three main drawbacks: (1) high duplicate RPN values, (2) failure to consider the ordered weights of S, O, and D, and (3) failure to consider the direct and indirect relationships between the failure modes and causes of failure. Therefore, this paper integrates the technique for order preference by similarity to ideal solution (TOPSIS) and the decision-making trial and evaluation laboratory (DEMATEL) approach to rank the risk of failure. A case of an inlet plate ring that has been drawn from a professional mechanical factory is presented to further illustrate the proposed approach. After comparing the result that was obtained from the proposed method with the conventional RPN and DEMATEL methods, it was found that the proposed method can resolve the abovementioned RPN ranking issues and give a more appropriate risk assessment than other listed approaches to provide valuable information for the decision makers.

Information technology (IT) services are diverse and complex. Numerous service design methods have been developed for designing and developing products. However, owing to the limited availability of useful tools in IT service design, these methods are ineffective. This study proposes an innovative three-dimensional method for designing IT services. The proposed method considers user requirements, an organization's business requirements, service providers' management requirements as well as the decision-making criteria of management representatives to ensure a smooth implementation of a designed IT service. Using this method, a prototype system to improve campus wireless local area network services was developed and tested at a university in Taiwan. The prototype system reduced the need for repetitive authentication and the time required to solve service problems and address user complaints. In addition, the service design team observed an approximately 70% reduction in project cycle time. The proposed method provided a systematic means to organize the design and implementation of IT services.

This study proposed an enhanced simplified drum-buffer-rope (SDBR) model to be applied in a reentrant flow shop (RFS) in which job processing times are generated from a discrete uniform distribution and machine breakdowns are subject to an exponential distribution. In this enhanced SDBR model, the due-date assignment method, order release rule and dispatching rule were improved. The due dates and release dates of orders were determined by considering the total planned load of the capacity-constrained resource (CCR) in a random RFS. The deviation rate of buffer status is used as a dispatching rule to eliminate the influence of machine breakdowns. Simulations based on a real case company are used to evaluate the effective of the proposed model. The experimental results showed that our approach yields better performance than the other methods in terms of six due-date-related indexes when the product mix is with a large proportion of multi-reentrant orders and when the utilisation of CCR increases from 60 to 90%.

The characteristics of the semiconductor industry are short product life cycle, lumpy demand, long lead time, and so on. Theory of constraint suggests a simple replenishment policy to effectively manage supply chain inventory by a demand-pull approach combined with buffer management. However, a demand-pull approach sometimes causes out of stock when the products have lumpy demand. In order to effectively solve this issue, this article integrated market demand forecast information and demand-pull replenishment to improve the inventory management effectiveness of wafer fabrication. An integrated circuit design house provides actual demand per week and market forecast information to the wafer fab. Wafer fab uses inventory buffer to meet customer demand and adjust inventory quantity according to market forecast information and demand-pull replenishment policy. A case of a wafer product that has been drawn from a professional wafer foundry company located in Taiwan is presented to further illustrate the proposed approach. After comparing the result that was obtained from the proposed approach with the traditional demand-pull method, it was found that the proposed approach helps keep high service levels and avoid being out of stock.

5S practice follows structured 5S activities from structurize, systematize, sanitize, standardize, and self-discipline to deal with scene management in shop floor control, and it is regarded as the most troublesome aspect with respect to environmental safety and health for a semiconductor manufacturing fabrication. The improved action items for 5S activities can amount to thousands from messy paper filing to untightened chemical piping. However, there is no clear key performance indicator to evaluate how good (safe) the fab is and how to be good (safe) for 5S practice. Failure modes and effects analysis is an effective and efficient way to deal with risk assessment for 5S activities and to prioritize the action requests from the improved result of continuous improvement. However, when failure modes and effects analysis is applied to the risk assessment of 5S audit, the conventional risk priority number lacks of all comprehensive information and misleads to a bias for not considering weights of severity ( S), occurrence ( O), and detectability ( D). In order to improve the method of risk priority number evaluation, this article combining 2-tuple fuzzy linguistic representation model and weighted geometric averaging operators to quantify 5S audit findings is proposed to eliminate the bias from different 5S auditors. This is the first approach for the numerous 5S action items to be quantified and prioritized with resource constraints to sustain 5S practice robust. A case study in a fab was demonstrated to show how the model was implemented to approve its validity.

More and more enterprises have chosen to adopt a made-to-order business model in order to satisfy diverse and rapidly changing customer demand. In such a business model, enterprises are devoted to reducing inventory levels in order to upgrade the competitiveness of the products. However, reductions in inventory levels and short lead times force the operation between production and distribution to cooperate closely, thus increasing the practicability of integrating the production and distribution stages. The complexity of supply chain scheduling problems (integrated production and distribution scheduling) is known to be NP-hard. To address the issues above, an efficient algorithm to solve the supply chain scheduling problem is needed. This paper studies a supply chain scheduling problem in which the production stage is modelled by an identical parallel machine scheduling problem and the distribution stage is modelled by a capacitated vehicle routing problem. Given a set of customer orders (jobs), the probl...

Failure modes and effects analysis (FMEA) is used widely to improve product quality and system reliability, employing a risk priority number (RPN) to assess the influence of failures. The RPN is a product of three indicators—severity (S), occurrence (O), and detection (D)—on a numerical scale from 1 to 10. However, the traditional RPN method has been criticized for its four chief shortcomings: its (1) high duplication rate; (2) assumption of equal importance of S, O, and D; (3) not following the ordered weighted rule; and (4) failure to consider the direct and indirect relationships between failure modes (FMs) and causes of failure (CFs). To resolve these drawbacks, we propose a novel approach, integrating grey relational analysis (GRA) and the decision-making trial and evaluation laboratory (DEMATEL) method, to rank the risk of failure, wherein the GRA is used to modify RPN values to lower duplications and the ordered weighted rule is followed; then, the DEMATEL method is applied to examine the direct and indirect relationships between FMs and CFs, giving higher priority when a single CF causes FMs to occur multiple times. Finally, an actual case of the TFT-LCD cell process is presented to verify the effectiveness of our method compared with other methods in providing decision-makers more reasonable reference information.

Make-to-order or direct-order business models that require close interaction between production and distribution activities have been adopted by many enterprises in order to be competitive in demanding markets. This article considers an integrated production and distribution scheduling problem in which jobs are first processed by one of the unrelated parallel machines and then distributed to corresponding customers by capacitated vehicles without intermediate inventory. The objective is to find a joint production and distribution schedule so that the weighted sum of total weighted job delivery time and the total distribution cost is minimized. This article presents a mathematical model for describing the problem and designs an algorithm using ant colony optimization. Computational experiments illustrate that the algorithm developed is capable of generating near-optimal solutions. The computational results also demonstrate the value of integrating production and distribution in the model for the studied pr...

Failure modes and effects analysis (FMEA) has been used to identify the critical risk events and predict a system failure to avoid or reduce the potential failure modes and their effect on operations. The risk priority number (RPN) is the classical method to evaluate the risk of failure in conventional FMEA. RPN, which ranges from 1 to 1000, is a mathematical product of three parameters--severity ( $$S$$ ), occurrence ( $$O$$ ), and detection ( $$D$$ )--to rank and assess the risk of potential failure modes. However, there are some shortcomings of the conventional RPN method, such as: the RPN elements have many duplicate numbers; violate the assumption of measurement scales; and have not considered the weight of $$S, O$$ , and $$D$$ . In order to improve the aforementioned shortcomings of the conventional RPN calculation problem, this paper presents an easy yet effective method to enhance the risk evaluation capability of FMEA. The new method is named exponential risk priority number (ERPN), which uses a simple addition function to the exponential form of $$S, O$$ , and $$D$$ to substitute the conventional RPN method, which is a mathematical product of three parameters. Two practical cases are used to demonstrate that the ERPN method can not only resolve some problems of the conventional RPN method but also is able to provide a more accurate and reasonable risk assessment in FMEA.

This study presents the design, implementation and driving control of a switched-reluctance motor (SRM)-driven cooling fan. First, after exploring the motor structural features, a 70-W three-phase SRM with six of four teeth is designed, manufactured and estimated its key parameters. Secondly, a Miller converter and an asymmetric bridge converter are constructed with their switching and commutation shift schemes being properly designed. Thorough experimental evaluations are conducted for the SRM-driven fan powered by different converters. It shows that proper commutation shift can enhance the driving performance of SRM drive, particularly for the one powered using Miller converter. Further comparative performance assessment is also made between the developed SRM-driven fan and an available brushless DC motor fan with the same fan blade, the results confirm the application potential possessed by SRM. Finally, a front-end flyback switch-mode rectifier (SMR) is developed and used to establish well-regulated DC-link voltage for the SRM converter from mains with satisfactory line-drawn power quality.

We consider the security issues associated with bring your own device (BYOD) services on campus. More than 70% of the cyber users at the studied university access wireless campus networks through BYOD. Although wireless access is convenient for users, its network management remains a problem. We analyze existing BYOD solutions that are applicable only to particular wireless network architectures, that require users to register their devices to a management platform, or that install client software. Moreover, commercial software is often prohibitively expensive for educational institutes. We found that most users enter their usernames and passwords multiple times a day, resulting in inconvenient user authentications. Thus, we propose an EZ-Net BYOD service management system that imposes a fail-through authentication function to an existing wireless local area network mobility controller via multiple externally established authentication servers. This solution requires no client-side configuration and is not limited to any particular brand of wireless network equipment. The solution is evaluated and deployed on campus. The proposed EZ-Net BYOD system saves an average of 60 min of authentication time per month for each user and allows the studied university to save tens of thousands of US dollars on licensing fees for BYOD service management software.

The maximal entropy ordered weighted averaging (ME-OWA)-based decision making trial and evaluation laboratory (DEMATEL) method for reliability allocation has been examined. The assessment results show that most conventional reliability allocation methods have five fundamental problems. The first problem is the measurement scale; while the second problem is that the system allocation factors are not equally weighted to one another, the third problem is that most reliability allocations methods often neglect many important features, such as maintainability and risk issues. The fourth problem is that they do not consider indirect relations between subsystems or components, and the fifth problem is that they do not consider predicted failure rate in the apportionment process. This study evaluated reliability allocation using a fighter aircraft's digital flight control computer (DFLCC). The proposed method offers several benefits compared with current military and commercial approaches. The computational results clearly demonstrate the advantages of the proposed approach for solving the five fundamental problems.

Imbalanced data are common in many machine learning applications. In an imbalanced data set, the number of instances in at least one class is significantly higher or lower than that in other classes. Consequently, when classification models with imbalanced data are developed, most classifiers are subjected to an unequal number of instances in each class, thus failing to construct an effective model. Balancing sample sizes for various classes using a re-sampling strategy is a conventional means of enhancing the effectiveness of a classification model for imbalanced data. Despite numerous attempts to determine the appropriate re-sampling proportion in each class by using a trial-and-error method in order to construct a classification model with imbalanced data (Barandela, Vadovinos, Sanchez, & Ferri, 2004; He, Han, & Wang, 2005; Japkowicz, 2000; McCarthy, Zabar, & Weiss, 2005), the optimal strategy for each class may be infeasible when using such a method. Therefore, this work proposes a novel analytical procedure to determine the optimal re-sampling strategy based on design of experiments (DOE) and response surface methodologies (RSM). The proposed procedure, S-RSM, can be utilized by any classifier. Also, C4.5 algorithm is adopted for illustration. The classification results are evaluated by using the area under the receiver operating characteristic curve (AUC) as a performance measure. Among the several desirable features of the AUC index include independence of the decision threshold and invariance to a priori class probabilities. Furthermore, five real world data sets demonstrate that the higher AUC score of the classification model based on the training data obtained from the S-RSM is than that obtained using oversampling approach or undersampling approach.

Variation in the nature of multi-walled carbon nanotubes (MWCNTs) subjected to different degrees of oxidation was investigated. The microstructure was determined by high resolution transmission electron microscopy (HRTEM) and X-ray diffraction (XRD) methods, and the surface chemistry was evaluated in terms of the functional groups determined by X-ray photoelectron spectroscopy (XPS) and thermal analysis–mass spectroscopy (TA–MS). In addition, TGA was used to indicate the thermal stability of the nanotubes. Results demonstrate that the graphitic structure of nanotubes oxidized with a mild mixture of H2SO4/HNO3 was preserved. Decrease in the degree of crystallinity started with widening of the C(0 0 2) XRD diffraction peak, followed by this peak shifting towards lower angles. The oxygen content increased with increasing treatment time. A defect peak incorporated in deconvolution of XPS C1s spectra was helpful for detecting the generation of defect sites. The predominant surface functionalities of the nanotubes have been changed from basic to acidic groups after treatment for one day. The samples oxidized for two days had the most abundant surface –COOH and the highest oxidation resistance. The oxidation mechanism of MWCNTs in mild H2SO4/HNO3 mixture was proposed, which was a successive and iterative process, including the initial attack on active sites, and next the hexagon electrophilic attack generating new defects and introducing more oxygen, and then the tubes becoming thinner and shorter.

Stock management is a critical issue in supply chain management. Previous studies have shown that the demand-pull inventory replenishment strategy combined with a buffer management mechanism (DPBM) suggested by the theory of constraints (TOC) performs better than traditional replenishment strategies in supply chain stock management. In DPBM, buffer management is applied for controlling the target amount of stock to keep, which is adjusted by some predetermined rules that instruct when and how much to adjust the target level. Previous studies found that when applying DPBM to making replenishment decisions for products with a high-variation demand, long production lead-time, and short product life cycle, the conventional DPBM is not very effective. This article proposes a novel method for improving the conventional buffer management mechanism. Instead of the predetermined rules suggested by TOC, this study borrowed the concept of the exponentially weighted moving average approach that has been successfully applied in statistical quality control to design a new set of rules to assist in making buffer adjustment decisions based on the trends shown in demand. To evaluate the proposed method’s feasibility and validity, this article uses both real demand data provided by a wafer foundry factory in Taiwan and simulated data that reveal various demand patterns. As a result, the proposed method performs better than the conventional demand-driven replenishment strategies suggested by TOC, especially when demand has a large variance.

Reliability allocation is one of the most important factors to consider when determining the reliability and competitiveness of a product. The feasibility-of-objectives (FOO) technique has become the current standard for assessing reliability designs for military mechanical-electrical systems, whereas the average weighting allocation method is widely used for commercial applications. However, assessment results are biased because these methods share two fundamental problems. The first problem is the measurement scale, while the second problem is that the system allocation factors are not equally weighted to one another. Both problems represent serious flaws from a technical perspective. To address these issues, we propose the use of the maximal entropy ordered weighted averaging (ME-OWA) method, which efficiently resolves the shortcomings of the FOO technique and the average weighting allocation method. As a comparative case study between the ME-OWA method and the two standards used in the military and commercially, this study evaluates reliability allocation in the context of a fighter aircraft airborne radar system. The results from this comparison show that the proposed method is both accurate and flexible.