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This paper aims to provide feasible countermeasures for the application of lean manufacturing in automatic sand casting workshops to optimize production line balance.

A production line balance optimization model for sand casting workshops is proposed. The value stream mapping (VSM) approach is applied to diagnose the problems in the production process. An optimization scheme is established based on eliminate, combine, rearrange, simplify and increase theory and Kanban management method. Further, simulation is done to compare current VSM and future VSM.

After implementing the proposed model, findings indicated that the idle time of equipment was effectively reduced, the line of balance of the production line was increased by 44.7%, the production lead time was shortened by 60.3% and the production capacity was increased by 50.0%.

Application of the optimization model in this study is limited to sand casting workshops that have realized automatic or semi-automatic production.

This paper provides an optimization model for the implementation of lean manufacturing in sand casting workshops and provides a reference case that reflects the actual application of lean manufacturing tools in a real situation.

The purpose of this paper is to discuss an adaptive SLA mechanism for service sharing in virtual environment, which can organize and govern QoS items in terms of service execution time, reliability, and availability, and provides a common understanding about services, responsibilities, priorities, guarantees and warranties-related virtual cooperative issues.

The management framework for SLA is introduced, based on which the whole process including SLA contract, adaptive SLA negotiation strategy, SLA deployment and SLA assessment are discussed, and the prototype is implemented in the cloud manufacturing platform.

A proposed SLA framework for service sharing in virtual environments is given; electronic contracts are designed in the framework for encapsulating measurable aspects of service level agreements so as to provide common understanding about the service; and an improved SLA negotiation strategy with three phases is presented for the dynamicity of the virtual services.

The paper presents a very useful adaptive SLA mechanism for service sharing in virtual environments that can be utilized in concurrent or future advanced manufacturing modes.

To study the application of three‐dimensional differential geometric (DG) guidance commands to a realistic missile defense engagement, and the application of the Newton's iterative algorithm to DG guidance problems.

The classical differential geometry theory is introduced firstly to transform all the variables in DG guidance commands from an arc length system to the time domain. Then, an algorithm for the angle‐of‐attack and the sideslip angle is developed by assuming the guidance curvature command and guidance torsion command equal to its corresponding value of current trajectory. Furthermore, Newton's iteration is utilized to develop iterative solution of the stated algorithm and the two‐dimensional DG guidance system so as to facilitate easy computation of the angle‐of‐attack and the sideslip angle, which are formulated to satisfy the DG guidance law.

DG guidance law is viable and effective in the realistic missile defense engagement, and it is shown to be a generalization of gain‐varying proportional navigation (PN) guidance law and performs better than the classical PN guidance law in the case of intercepting a maneuvering target. Moreover, Newton's iterative algorithm has sufficient accuracy for DG guidance problem.

Provides further study on DG guidance problem associated with its iterative solution.

Rolling bearings based on rotating machinery are one of the most widely used in industrial applications because of their low cost, high performance and robustness. The purpose of this paper is to describe how to identify degradation condition of rolling bearing and predict its fault time in big data environment in order to achieve zero downtime performance and preventive maintenance for the rolling bearing.

The degradation characteristic parameters of rolling bearings including intrinsic mode energy and failure frequency were, respectively, extracted from the pre-processed original vibration signals using EMD and Hilbert transform. Then, Spearman’s rank correlation coefficient and PCA were used to obtain the health index of the rolling bearing so as to detect the appearance of degradations. Furthermore, the degradation condition of the rolling bearings might be identified through implementing the monotonicity analysis, robustness analysis and degradation analysis of the health index.

The effectiveness of the proposed method is verified by a case study. The result shows that the proposed method can be applied to monitor the degradation condition of the rolling bearings in industrial application.

Further experiment remains to be done so as to validate the effectiveness of the proposed method using Apache Hadoop when massive sensor data are available.

The paper proposes a methodology for rolling bearing condition monitoring representing the steps that need to be followed. Real-time sensor data are utilized to find the degradation characteristics.

The result of the work presented in this paper form the basis for the software development and implementation of condition monitoring system for rolling bearings based on Hadoop.

Ant colony algorithm is widely used in recent years as a heuristic algorithm. It provides a new way to solve complicated combinatorial optimization problems. Having been enlightened by the behavior of ant colony's searching for food, positive feedback construction and distributed computing combined with certain heuristics are adopted in the algorithm, which makes it easier to find better solution. This paper introduces a series of ant colony algorithm and its improved algorithm of the basic principle, and discusses the ant colony algorithm application situation. Finally, several problems existing in the research and the development prospect of ACO are reviewed.

This study aims to evaluate and summarize the effectiveness of cognitive behavioral therapy (CBT) and internet-based CBT (ICBT) interventions on relapse prevention and severity of symptoms among individuals with major depressive disorder (MDD). CBT is one of the most used and suggested interventions to manage MDD, whereas ICBT is a novel effective proposed approach.

The review was conducted following the preferred reporting items for systematic review and meta-analysis protocol. A comprehensive and extensive search was performed to identify and evaluate the relevant studies about the effectiveness of CBT and ICBT on relapse prevention and severity of symptoms among patients with MDD.

A total of eight research studies met the inclusion criteria and were included in this systematic review. RCT studies were conducted to assess and evaluate the effectiveness of CBT and ICBT on relapse prevention and severity of symptoms among patients with MDD. It has been found that CBT is a well-supported and evidently based effective psychotherapy for managing depressive symptoms and reducing the relapse and readmission rate among patients diagnosed with MDD. The ICBT demonstrated greater improvements in depressive symptoms during major depressive episodes among patients with MDDS. The ICBT program had good acceptability and satisfaction among participants in different countries.

Despite the significant findings from this systematic review, certain limitations should be acknowledged. First, it is important to note that all the studies included in this review were exclusively conducted in the English language, potentially limiting the generalizability of the findings to non-English speaking populations. Second, the number of research studies incorporated in this systematic review was relatively limited, which may have resulted in a narrower scope of analysis. Finally, a few studies within the selected research had small sample sizes, which could potentially impact the precision and reliability of the overall conclusions drawn from this review. The authors recommend that nurses working in psychiatric units should use CBT interventions with patients with MDD.

This paper, a review of the literature gives an overview of CBT and ICBT interventions to reduce the severity of depressive symptoms and prevent patients’ relapse and rehospitalization and shows that CBT interventions are effective on relapse prevention among patients with MDD. In addition, there is still no standardized protocol to apply the CBT intervention in the scope of reducing the severity of depressive symptoms and preventing depression relapse among patients with major depressive disorder. Further research is needed to confirm the findings of this review. Future research is also needed to find out the most effective form and contents of CBT and ICBT interventions for MDD.

CBT is a psychological intervention that has been recommended by the literature for the treatment of major depressive disorder (MDD). It is a widely recognized and accepted approach that combines cognitive and behavioral techniques to assist individuals overcome their depressive symptoms and improve their overall mental well-being. This would speculate that effectiveness associated with several aspects and combinations of different approaches in CBT interventions and the impact of different delivery models are essential for clinical practice and appropriate selection of the interventional combinations.

This systematic review focuses on the various studies that explore the effectiveness of face-to-face CBT and ICBT in reducing depressive symptoms among patients with major depressive disorder. These studies were conducted in different countries such as Iran, Australia, Pennsylvania and the USA.