Study on shop floor control system in semiconductor fabrication by self-organizing map-based intelligent multi-controller

To confirm semiconductor wafer fabrication (FAB) operating characteristics, the scheduling decisions of shop floor control systems (SFCS) must develop a multiple scheduling rules (MSRs) approach in FABs. However, if a classical machine learning approach is used, an SFCS in FABs knowledge base (KB) can be developed by using the appropriate MSR strategy (this method is called an intelligent multi-controller in this study) as obtained from training examples. A classical machine learning approach main disadvantage is that the classes (scheduling decision variables) to which training examples are assigned must be pre-defined. This process becomes an intolerably time-consuming task. In addition, although the best decision rule can be determined for each scheduling decision variable, the combination of all the decision rules may not simultaneously satisfy the global objective function. To address these issues, this study proposes an intelligent multi-controller that incorporates three main mechanisms: (1) a simulation-based training example generation mechanism, (2) a data preprocessing mechanism, and (3) a self-organizing map (SOM)-based MSRs selection mechanism. These mechanisms can overcome the long training time problem of the classical machine learning approach in the training examples generation phase. Under various production performance criteria over a long period, the proposed intelligent multi-controller approach yields better system performance than fixed decision scheduling rules for each of the decision variables at the start of each production interval.     

Recognition of control chart concurrent patterns using a neural network approach

Pattern recognition is an important issue in Statistical Process Control, as unnatural patterns exhibited by control charts can be associated with specific assignable causes adversely affecting the process. Neural network approaches to recognition of control chart patterns have been developed by several researchers in recent years, but to date these have been focused on recognition and analysis of single patterns such as sudden shifts, linear trends or cyclic patterns. This paper investigates the detection of concurrent patterns where more than one pattern exists simultaneously. The topology and training of a Back-Propagation Network (BPN) system is described. Extensive performance evaluation has been carried out using simulated data to develop a range of average run length-related performance indices, including new performance indices that are proposed to describe concurrent patterns recognition performance. Two evaluation scenarios were evaluated: in the first, unnatural patterns are already present; while in the second, patterns may appear progressively at any time. Numerical results are provided that indicate that the pattern recognizer can perform very well in the first scenario, while it performs effectively but not without deficiencies for some specific pattern combinations in the second evaluation approach. Limitations and potential improvements in the concurrent pattern recognition scheme are also discussed.     

The optimization of attribute selection in decision tree-based production control systems

This study develops a learning-based production control system (PCS) to support a manufacturing system to make on-line decisions that are robust in the face of various production requirements. Selecting essential system attributes (or features) based on various production requirements to construct PCS knowledge bases is a critical issue because of the existence of a large amount of shop floor information in a manufacturing system. However, a classical decision tree (DT) learning approach to construct dynamic dispatching knowledge bases does not consider the optimal subset of system attributes in the problem domain. To resolve this problem, this study develops a hybrid genetic algorithm/decision tree (GA/DT) approach for DT-based PCS. The hybrid GA/DT approach is used to simultaneously evolve an optimal subset of system attributes and determine learning parameters of the DT from a large set of candidate manufacturing system attributes according to various performance measures. For a given feature subset and learning parameters of a DT decoded by a GA, a DT was applied to evaluate the fitness in the GA process and to generate the PCS knowledge base. The results demonstrate that the proposed GA/DT-based PCS has, according to various performance criteria, a better long term system performance than those obtained with classical DT-based PCS and the heuristic individual dispatching rules, according to various performance criteria.     

Dihydroartemisinin–Bile Acid Hybridization as an Effective Approach to Enhance Dihydroartemisinin Anticancer Activity

A series of hybrid compounds based on natural products—bile acids and dihydroartemisinin—were prepared by different synthetic methodologies and investigated for their in vitro biological activity against HL-60 leukemia and HepG2 hepatocellular carcinoma cell lines. Most of these hybrids presented significantly improved antiproliferative activities with respect to dihydroartemisinin and the parent bile acid. The two most potent hybrids of the series exhibited a 10.5- and 15.4-fold increase in cytotoxic activity respect to dihydroartemisinin alone in HL-60 and HepG2 cells, respectively. Strong evidence that an ursodeoxycholic acid hybrid induced apoptosis was obtained by flow cytometric analysis and western blot analysis.     

Intravenous repletion of phosphorus deficiency in the chronic renal failure patients with severe hypophosphatemia

Severe hypophosphatemia is a potentially life-threatening medical condition and might lead to a fatal outcome in critically ill patients. The situation is further complicated by the co-morbid renal failure. We evaluated the efficacy and safety of the intravenous phosphate repletion in 15 renal failure patients with severe hypophosphatemia. Six patients with advanced renal failure and nine patients under maintenance hemodialysis, 7 males and 8 females, aged between 42 and 83 years old, were found to have serum phosphate level < 1.2 mg/dL from various medical conditions and were treated with intravenous phosphate infusion. The phosphate solution prepared from sodium dihydrogen phosphate (NaH2PO4), containing 13 mg/ml phosphate and 0.5 meq/ml sodium, in the dosage 2.5-3.0 mg phosphate/Kg body weight, was administered through the central venous lins every 6-8 hours. The infusion was discontinued once serum phosphate level reached 5.0-5.5 mg/dL. Serum ionized calcium, phosphate and intact parathyroid hormone levels were serially followed at different intervals, respectively. The hemodialyzed uremic patients received their dialysis treatment as scheduled. All patients survived the hypophosphatemic period and regained normal phosphate levels after repletion. The amount of phosphate administered to reach the target level ranged between 3438 and 9150 mg and the duration of treatment varied between six and seventeen days. Hypocalcemia (< 4.2 mg/dL) was noted at eight occasions during the whole treatment period but none was symptomatic. Eleven patients recovered from the offending illness. However, four patients expired due to reasons not directly consequent to and temporally remote from hypophosphatemia. We conclude that prompt repletion of severe hypophosphatemia and phosphate deficiency with relatively slower rate of NaH2PO4 solution intravenous infusion is a safe and effective mode of treatment for renal failure and uremic patients. The longer treatment period allowed the administered minerals full equilibration. The risk of hyperkalemia is avoided and the sodium/volume load can be eliminated by dialysis.     

Evaluation of transitive closure in distributed database systems

A special schema is constructed to represent the data of a binary relation so that the transitive closure can be evaluated efficiently in distributed database systems. The method is economical in communication cost and at the same time preserves the fast access paths for efficient parallel processing of transitive closure at local sites. Updating of data is also discussed     

Effects of high glucose culture on EGF effects and EGF receptors in the LLC-PK1 cells

OBJECTIVE: To study interns' perceptions of their learning during their rotation through a short stay unit (SSU). DESIGN: Case-based, qualitative research study. SETTING: A tertiary care pediatric hospital (The Children's Hospital, Boston, Mass). PARTICIPANTS: Ten interns who had worked in the SSU in the 3 months prior to June 1, 1995, and on a general medical team in the previous 12 months. INTERVENTION: None. MAIN OUTCOME MEASURES: In July 1995, the interns participated in focused, open-ended interviews lasting about 40 to 60 minutes to document their perceptions of their learning during their SSU rotation. The interviews were recorded on audiotape and transcribed prior to analysis. Data were analyzed to discern and categorize themes from the interns' responses. RESULTS: All interns responded favorably to their educational and learning experiences during their rotation through the SSU. Two major themes emerged: (1) the interns' learning, which was affected by the role of the attending physician, the organization and structure of the SSU, and the teaching strategies in the SSU; and (2) the interns' collaborative work with the nursing staff in the SSU, which affected patient care but did not facilitate the interns' learning. CONCLUSION: Clustering in the SSU of patients whose symptoms suggested straightforward diagnoses enhanced interns' educational experiences.     

Highly Conductive Ferroelectric Cellulose Composite Papers for Efficient Triboelectric Nanogenerators

Paper‐based electronics has attracted growing interest owing to many advantages of papers including low‐cost, abundance, flexibility, biocompatibility, and environmental friendliness. Despite recent progress in paper electronics, however, development of a high‐performance paper‐based triboelectric nanogenerator (TENG), which is a power‐generating device that converts mechanical energy into electric energy by coupling triboelectrification and electrostatic induction, remains a challenge mainly due to weak electron‐donating tendency of cellulose‐based papers. In this work, highly conductive ferroelectric cellulose composite papers containing silver nanowires and BaTiO₃ nanoparticles are fabricated, and their successful application for realizing a large‐area TENG with enhanced electrical output performance is demonstrated. It is found that triboelectric charge generation on the ferroelectric cellulose composite paper can be promoted by simple poling treatment, which significantly enhances TENG performance. The ferroelectric cellulose composite paper–based TENG exhibits an electrical output performance that surpasses those of aluminum‐based and pristine cellulose–based TENGs by more than two times, as well as outstanding output stability without a noticeable degradation in performance during 10 000 cycles of a repeated pushing test. The work demonstrates the great potential of multifunctional cellulose‐based papers for TENG and other self‐powered electronic applications.     

Learning-based multi-pass adaptive scheduling for a dynamic manufacturing cell environment

Because the essential attributes are uncertain in a dynamic manufacturing cell environment, to select a near-optimal subset of manufacturing attributes to enhance the generalization ability of knowledge bases remains a critical, unresolved issue for classical artificial neural network-based (ANN-based) multi-pass adaptive scheduling (MPAS). To resolve this problem, this study develops a hybrid genetic /artificial neural network (GA/ANN) approach for ANN-based MPAS systems. The hybrid GA/ANN approach is used to evolve an optimal subset of system attributes from a large set of candidate manufacturing system attributes and, simultaneously, to determine configuration and learning parameters of the ANN according to various performance measures. In the GA/ANN-based MPAS approach, for a given feature subset and the corresponding topology and learning parameters of an ANN decoded by a GA, an ANN was applied to evaluate the fitness in the GA process and to generate the MPAS knowledge base used for adaptive scheduling control mechanisms. The results demonstrate that the proposed GA/ANN-based MPAS approach has, according to various performance criteria, a better system performance over a long period of time than those obtained with classical machine learning-based MPAS approaches and the heuristic individual dispatching rules.     

Partition strategy for distributed query processing in fast localnetworks

A partition-and-replicate strategy for processing distributed queries referencing no fragmented relation is sketched. An algorithm is given to determine which relation and which copy of the relation is to be partitioned into fragments, how the relation is to be partitioned, and where the fragments are to be sent for processing. Simulation results show that the partition strategy is useful for processing queries in fast local network environments. The results also show that the number of partitions does not need to be large. The use of semijoins in the partition strategy is discussed. A necessary and sufficient condition for a semijoin to yield an improvement is provided