Suppression of superconductivity in nanowires by bulk superconductors

Transport measurements were made on a system consisting of a zinc nanowire array sandwiched between two bulk superconducting electrodes (Sn or In). It was found that the superconductivity of Zn nanowires of 40 nm diameter is suppressed either completely or partially by the superconducting electrodes. When the electrodes are driven into their normal state by a magnetic field, the nanowires switch back to their superconducting state. This phenomenon is significantly weakened when one of the two superconducting electrodes is replaced by a normal metal. The phenomenon is not seen in wires with diameters equal to and thicker than 70 nm.Comment: 4 pages, 5 figure

OPTIMIZING MAINTENANCE STRATEGIES FOR ELECTRICAL AND MECHANICAL EQUIPMENT IN PVC MANUFACTURING: A MACHINE LEARNING AND SIMULATION FRAMEWORK

Objectives: This paper examines real-world data from a PVC manufacturing plant in Saudi Arabia to develop predictive statistical models using machine learning techniques.   Theoretical Framework: A framework to optimize a maintenance strategy in a PVC production line begins with obtaining historical data. This data provides insights into the behavior of each station, highlighting performance patterns, failure rates, and maintenance histories. Analyzing this data helps identify critical areas prone to downtime or inefficiencies.   Method: The method begins with collecting real-world historical data from the PVC manufacturing line for four years, spanning from 2021 to 2024. This data includes key performance metrics, maintenance records, and failure incidents. The collected data was then analyzed using statistical methods to identify trends and patterns in station behavior and downtime. Following this, machine learning techniques were employed, explicitly utilizing a Random-Forest-classifier to classify failure risks and predict future maintenance needs. The model’s performance was validated by comparing it with actual maintenance outcomes to ensure accuracy. Finally, the data analyzed was used to simulate the manufacturing line, enabling the selection of the optimal maintenance strategy to minimize downtime and operational costs for the PVC production process.   Results and Discussion: The results of the simulation study suggest an opportunistic maintenance strategy, as a hybrid approach, is the most effective for the PVC production line. This strategy combines preventive and corrective maintenance elements, offering flexibility in responding to various failure scenarios. The proposed approach can serve as a test environment, allowing for the evaluation of different maintenance strategies under real-world conditions to optimize performance and minimize downtime.   Implications of the Research: This study promotes the adoption of data analytics to drive continuous improvements, contributing to the transition towards Industry 4.0, where the Internet of Things (IoT) plays a central role. By leveraging real-time data, organizations can optimize their processes daily. Additionally, the research proposes using simulation techniques to test maintenance strategies before their actual implementation. This risk-free approach allows for the evaluation of methods in a controlled environment, ultimately leading to significant reductions in both time and costs.   Originality/Value: This study's results are derived from a real-world PVC manufacturing plant in Saudi Arabia, with data collected through site visits and original plant logs. The value of this research lies in its practical application: the approach can be adapted and deployed across various production lines, serving as a benchmark for simulating new maintenance techniques or implementing changes in existing systems. This provides a robust framework for improving operational efficiency and optimizing maintenance strategies in diverse industrial settings. The main objective is to identify common failures and forecast their occurrence based on past incidents. The study employs the Random-Forest-Classifier algorithm to process the dataset and improve prediction accuracy. The results are then integrated into simulation modeling, offering valuable insights into proactive measures and opportunistic maintenance strategies within PVC manufacturing. The research aims to minimize unexpected breakdowns and provide practical recommendations to optimize maintenance practices, thereby improving operational efficiency. The paper concludes with a simulation model that demonstrates how opportunistic actions can enhance Overall Equipment Efficiency (OEE) by leveraging insights from the predictive model

Characterization of healing following atherosclerotic carotid plaque rupture in acutely symptomatic patients: an exploratory study using in vivo cardiovascular magnetic resonance.

BACKGROUND: Carotid plaque rupture, characterized by ruptured fibrous cap (FC), is associated with subsequent cerebrovascular events. However, ruptured FC may heal following stroke and convey decreased risk of future events. This study aims to characterize the healing process of ruptured FC by assessing the lumen conditions, quantified by the lumen curvature and roughness, using in vivo carotid cardiovascular magnetic resonance (CMR). METHODS: Patients suffering from transient ischemic attack underwent high resolution carotid MR imaging within 72 hours of the acute cerebrovascular ischemic event. CMR imaging was repeated at 3 and 12 months in 26 patients, in whom FC rupture/erosion was observed on baseline images and subsequent cerebrovascular events were recorded during the follow-up period. Lumen curvature and roughness were quantified from carotid CMR images and changes in these values were monitored on follow-up imaging. RESULTS: Healing of ruptured plaque was observed in patients (23 out of 26) without any ischemic symptom recurrence as shown by the lumen surface becoming smoother during the follow-up period, characterized by decreasing maximum lumen curvature (p < 0.05), increasing minimum lumen curvature (p < 0.05) and decreasing lumen roughness (p < 0.05) during the one year follow-up period. CONCLUSIONS: Carotid plaque healing can be assessed by quantification of the lumen curvature and roughness and the incidence of recurrent cerebrovascular events may be high in plaques that do not heal with time. The assessment of plaque healing may facilitate risk stratification of recent stroke patients on the basis of CMR results.RIGHTS : This article is licensed under the BioMed Central licence at http://www.biomedcentral.com/about/license which is similar to the 'Creative Commons Attribution Licence'. In brief you may : copy, distribute, and display the work; make derivative works; or make commercial use of the work - under the following conditions: the original author must be given credit; for any reuse or distribution, it must be made clear to others what the license terms of this work are

Process Completing Sequences for Resource Allocation Systems with Synchronization

This paper considers the problem of establishing live resource allocation in workflows with synchronization stages. Establishing live resource allocation in this class of systems is challenging since deciding whether a given level of resource capacities is sufficient to complete a single process is NP-complete. In this paper, we develop two necessary conditions and one sufficient condition that provide quickly computable tests for the existence of process completing sequences. The necessary conditions are based on the sequence of completions of � subprocesses that merge together at a synchronization. Although the worst case complexity is O(2�), we expect the number of subprocesses combined at any synchronization will be sufficiently small so that total computation time remains manageable. The sufficient condition uses a reduction scheme that computes a sufficient capacity level of each resource type to complete and merge all � subprocesses. The worst case complexity is O(�⋅�), where � is the number of synchronizations. Finally, the paper develops capacity bounds and polynomial methods for generating feasible resource allocation sequences for merging systems with single unit allocation. This method is based on single step look-ahead for deadly marked siphons and is O(2�). Throughout the paper, we use a class of Petri nets called Generalized Augmented Marked Graphs to represent our resource allocation systems

A SImulation-Based Approach for Dock Allocation in a Food Distribution Center

This research endeavor focused on the warehouse receiving process at a large food distribution center, which comprises of trucks with goods reaching the destination warehouse, unloading and finally putting away the contents to the specific aisles. Discrete event simulation was used to model the current system’s functioning and to identify operational inefficiencies which were quantified through a detailed value stream mapping exercise. Inspired by ‘lean’ philosophy, a dock allocation algorithm was designed to take into account the relationship between the dock location and the destination aisle to ‘optimally’ assign the trucks to the docks. After validating the baseline, new scenarios incorporating the allocation algorithm were tested. Two of the scenarios showed an average reduction of 30% in daily travel distance for the ‘put-away’ personnel. The simulation model also helped visualize the benefits that would accrue through the use of lean principles to reduce the non-value added time in warehouse operations

Modeling of Biological Intelligence for SCM System Optimization

This article summarizes some methods from biological intelligence for modeling and optimization of supply chain management (SCM) systems, including genetic algorithms, evolutionary programming, differential evolution, swarm intelligence, artificial immune, and other biological intelligence related methods. An SCM system is adaptive, dynamic, open self-organizing, which is maintained by flows of information, materials, goods, funds, and energy. Traditional methods for modeling and optimizing complex SCM systems require huge amounts of computing resources, and biological intelligence-based solutions can often provide valuable alternatives for efficiently solving problems. The paper summarizes the recent related methods for the design and optimization of SCM systems, which covers the most widely used genetic algorithms and other evolutionary algorithms

Nosocomial infection control in healthcare settings: Protection against emerging infectious diseases

The Middle East respiratory syndrome (MERS) outbreak in Korea in 2015 may be attributable to poor nosocomial infection control procedures implemented. Strict infection control measures were taken in the hospital where an imported case with MERS was treated in southern China and 53 health care workers were confirmed to be MERS-CoV negative. Infection control in healthcare settings, in which patients with emerging infectious diseases such as MERS, Ebola virus disease, and the severe acute respiratory syndrome (SARS) are diagnosed and treated, are often imperfect. When it comes to emerging or unknown infectious diseases, before the imported case was finally identified or community transmission was reported, cases have often occurred in clusters in healthcare settings. Nosocomial infection control measures should be further strengthened among the workers and inpatients in designated healthcare settings that accommodate suspected cases suffering from emerging or unknown infectious diseases. ELECTRONIC SUPPLEMENTARY MATERIAL: The online version of this article (doi:10.1186/s40249-016-0118-9) contains supplementary material, which is available to authorized users