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The purpose of this paper is to present, for the first time, a mathematical model for a piston skirt in mixed lubrication with respect to applying a smart fluid in lubrication. In this way, the smart fluid, as a lubricant with controlled variable viscosity, is proposed and applied to minimize the power loss in the interaction between liner and skirt.

Based on signal processing, the relationships between viscosity of lubricant and the friction loss, the hydrodynamic and contact friction force consequently are found, as part of an effective approach to acquire the function of variable viscosity.

It is shown that hydrodynamics and contact friction forces can be controlled and minimized by using the variable viscosity signal with the optimized viscosity signal technique.

In this paper, a mathematical model for a piston skirt in mixed lubrication with respect to applying a smart fluid in lubrication is presented for the first time.

This paper aims to study the specific factors causing suburban-dwelling elderly American women to patronize different soup kitchens in Pennsylvania and investigate how non-profit and government agencies could better meet the needs of this food-insecure population, to fill a significant gap in the growing literature on food insecurity in high-income countries.

The authors conducted observational visits and structured interviews with over 200 patrons and staff of soup kitchens in two counties in the US state of Pennsylvania over visits spanning two years.

Elderly American women have very distinct soup kitchen needs and usage patters that differ from other patrons in eight key ways the authors identify. From these, the authors identify four central themes that food assistance reform in the US must address to improve the wellbeing of this subpopulation.

The authors find that significant structural changes of the US food relief system must be made to better meet the needs of food-insecure elderly American women.

Soup kitchens should begin to offer information about federal programs, group transportation and other resources tailored to elderly women at soup kitchens to significantly improve their wellbeing, and help relieve the burden born by US non-profit food assistance organizations.

No studies to date have focused on the soup kitchen use of this specific population, whose needs, living circumstances, attitudes toward charity and socioeconomic realities differ significantly from other subpopulations of soup kitchen patrons.

In pursuit of socio-economic growth, scholars and policymakers in emerging economies continues to show interest in understanding technology-based start-up (i.e. tech start-up) emergence, to help mitigate persistent failure experienced during commercialization. Howbeit, some scholars lamented that extant studies that investigated tech start-up emergence are mostly fragmented, because they focus on specific event/sub-process in tech start-up gestation. Thus, this study aims to conduct a systematic literature review to discover, harmonize and develop a framework that describes the interaction among varying dimensions of events/sub-processes that characterizes tech start-up emergence in an emerging economy.

To conduct this study, the authors engaged a concept-centric systematic literature review. Having developed a search protocol, the authors searched through information systems database, and other relevant discipline databases, to select relevant articles for review.

The systematic review revealed various dimensions of events (i.e. opportunity discovery and selection, team formation and domain consensus, bootstrapping and the development of minimum viable product and market experimentation feedback) that are critical to tech start-up emergence. Most prior studies are isolated, as they focus their investigation on specific event. Thus, from this review, the authors developed a framework harmonizing various dimensions of events characterizing emergence of a viable tech start-up.

The researchers conducted this study in response to lingering call for harmonized study that provides in-depth description of how different dimensions of events interact and characterize tech start-up emergence. Consequently, the study resulted in a descriptive framework. Furthermore, the findings highlight some practical implications and proposes new study directions as future research agenda for scholars interested in tech start-up emergence.

The purpose of this paper is to review the current application areas of shape memory alloy (SMA) actuators in intelligent robotic systems and devices.

This paper analyses how actuation and sensing functions of the SMA actuator have been exploited and incorporated in micro and macro robotic devices, developed for medical and non‐medical applications. The speed of response of SMA actuator mostly depends upon its shape and size, addition and removal of heat and the bias force applied. All these factors have impact on the overall size of the robotic device and the degree of freedom (dof) obtained and hence, a comprehensive survey is made highlighting these aspects. Also described are the mechatronic aspects like the software and hardware used in an industrial environment for the control of such nonlinear actuator and the type of sensory feedback devices incorporated for obtaining better control, positioning accuracy and fast response.

SMA actuators find wide applications in various facets of robotic equipments. Selecting a suitable shape, fast heating and cooling method and better intelligent control technique with or without feedback devices could optimize its performance.

The frequency of SMA actuation purely depends on the rate of heat energy added to and removed from the actuator, which in turn depends upon interrelated nonlinear parameters.

For increasing the dof of robots, number of actuators also have to be increased that leads to complex control problems.

Explains the suitability of SMA as actuators in smart robotic systems, possibility of miniaturisation. It also highlights the difficulties faced by the SMA research community.

The purpose of this paper is to study various combination forms of the three basic sharing elements (i.e. orders sharing, manufacturers capacity sharing and suppliers capacity sharing) in the cluster supply chain (CSC), formulate a distributed model to protect enterprises’ decision privacy and seek to develop an effective method for solving the distributed complex model.

A distributed assembly cluster supply chain configuration (ACSCC) model is formulated. An improved augmented Lagrangian coordination (ALC) is proposed and used to solve the ACSCC model. A series of experiments are conducted to validate the improved ALC and the model.

Two major findings are obtained. First, the market order’s quantity change and the sales price of the product have a great impact on both the optimal results of the ACSCC and the cooperative strategy, especially, when the market order increases sharply, enterprises have to adopt multiple cooperative strategies to complete the order; meanwhile, the lower sales price of the product helps independent suppliers to get more orders. Second, the efficiency and computational accuracy of the improved ALC method are validated as compared to the centralized ALC and Lingo11.

This paper formulated the single-period ACSCC model under certain assumptions, yet a multi-period ACSCC model is to be developed, a more comprehensive investigation of the relationships among combination forms is to be extended further and a rigid proof of the improved ALC is necessary.

Enterprises in the industrial cluster should adopt different cooperative strategies in terms of the market order’s quantity change and the sales price of the product.

The proposed various combination forms of sharing elements and the formulated ACSCC model provide guidance to managers in the industrial cluster to choose the proper policy.

This research studies various combination forms of the three basic sharing elements in the CSC. A distributed ACSCC model has been established considering simultaneously multiple sharing elements. An improved ALC is presented and applied to the ACSCC problem.

Managing project completion within the stipulated time is significant to all firms' sustainability. Especially for software start-up firms, it is of utmost importance. For any schedule variation, these firms must spend 25 to 40 percent of the development cost reworking quality defects. Significantly, the existing literature does not support defect rework opportunities under quality aspects among Indian IT start-ups. The present study aims to fill this niche by proposing a unique mathematical model of the defect rework aligned with the Six Sigma quality approach.

An optimization model was formulated, comprising the two objectives: rework “time” and rework “cost.” A case study was developed in relevance, and for the model solution, we used MATLAB and an elitist, Nondominated Sorting Genetic Algorithm (NSGA-II).

The output of the proposed approach reduced the “time” by 31 percent at a minimum “cost”. The derived “Pareto Optimal” front can be used to estimate the “cost” for a pre-determined rework “time” and vice versa, thus adding value to the existing literature.

This work has deployed a decision tree for defect prediction, but it is often criticized for overfitting. This is one of the limitations of this paper. Apart from this, comparing the predicted defect count with other prediction models hasn’t been attempted. NSGA-II has been applied to solve the optimization problem; however, the optimal results obtained have yet to be compared with other algorithms. Further study is envisaged.

The Pareto front provides an effective visual aid for managers to compare multiple strategies to decide the best possible rework “cost” and “time” for their projects. It is beneficial for cost-sensitive start-ups to estimate the rework “cost” and “time” to negotiate with their customers effectively.

This paper proposes a novel quality management framework under the Six Sigma approach, which integrates optimization of critical metrics. As part of this study, a unique mathematical model of the software defect rework process was developed (combined with the proposed framework) to obtain the optimal solution for the perennial problem of schedule slippage in the rework process of software development.

Friction stir processing (FSP) is overviewed with the process variables, along with the thermal aspect of different metals.

With its inbuilt advantages, FSP is used to reduce the failure in the structural integrity of the body panels of automobiles, airplanes and lashing rails. FSP has excellent process ability and surface treatability with good corrosion resistance and high strength at elevated temperatures. Process parameters such as rotation speed of the tool, traverse speed, tool tilt angle, groove design, volume fraction and increase in number of tool passes should be considered for generating a processed and defect-free surface of the workpiece.

FSP process is used for modifying the surface by reinforcement of composites to improve the mechanical properties and results in the ultrafine grain refinement of microstructure. FSP uses the frictional heat and mechanical deformation for achieving the maximum performance using the low-cost tool; the production time is also very less.

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Rolling element bearings (REBs) are commonly used in rotating machinery such as pumps, motors, fans and other machineries. The REBs deteriorate over life cycle time. To know the amount of deteriorate at any time, this paper aims to present a prognostics approach based on integrating optimize health indicator (OHI) and machine learning algorithm.

Proposed optimum prediction model would be used to evaluate the remaining useful life (RUL) of REBs. Initially, signal raw data are preprocessing through mother wavelet transform; after that, the primary fault features are extracted. Further, these features process to elevate the clarity of features using the random forest algorithm. Based on variable importance of features, the best representation of fault features is selected. Optimize the selected feature by adjusting weight vector using optimization techniques such as genetic algorithm (GA), sequential quadratic optimization (SQO) and multiobjective optimization (MOO). New OHIs are determined and apply to train the network. Finally, optimum predictive models are developed by integrating OHI and artificial neural network (ANN), K-mean clustering (KMC) (i.e. OHI–GA–ANN, OHI–SQO–ANN, OHI–MOO–ANN, OHI–GA–KMC, OHI–SQO–KMC and OHI–MOO–KMC).

Optimum prediction models performance are recorded and compared with the actual value. Finally, based on error term values best optimum prediction model is proposed for evaluation of RUL of REBs.

Proposed OHI–GA–KMC model is compared in terms of error values with previously published work. RUL predicted by OHI–GA–KMC model is smaller, giving the advantage of this method.

This tribological investigation aims to identify the effect of WS₂ deposition on the Al 6061 surface and optimize the dry sliding conditions to enhance the friction and abrasion wear behavior.

WS₂-deposited Al 6061-T6 surface was considered for this tribological investigation. The design of the experiment was based on the Box–Behnken design of the response surface methodology approach, which is used to evaluate the interaction effect of input parameters on friction coefficient (COF) and specific wear rate (SWR). The abrasive wear behavior of WS₂ deposition against SiC emery sheet was explored through pin-on-disc experimentation by varying applied load (L), sliding velocity (V) and distance (D). Using analysis of variance and regression model, COF and SWR were predicted.

Based on composite desirability criteria, multi-objective optimization was performed to minimize the COF and SWR. The obtained optimal sliding conditions are L = 10 N, V = 2 m/s and D = 949.49 m. The validation test results indicate that the experimental and predicted data are in good conformance. For optimized conditions, worn surface characterization was done using a scanning electron microscope with energy dispersive spectroscopy, and X-ray diffraction analysis was performed to ensure the formation of WS₂ phases on worn-out surfaces. Furthermore, a counter body surface with collected wear debris has been analyzed.

Almost the industries are now focused on a new surface modification technique, which improves the surface and tribological characteristics. This research work specifically relates the tribological effect of WS₂ deposition on an Al 6061-T6 surface through a novel electrical discharge deposition approach and optimizes the dry sliding conditions to improve the frictional and abrasive wear resistance.

The purpose of this study was to the synthesis of Fe₃O₄@SiO₂ nanocomposites and using it as an adsorbent for removal of diazinon from aqueous solutions. Structural characteristics of the synthesized magnetic nanocomposite were described by Fourier transform infrared spectroscopy and scanning electron microscopy.

The effects of different parameters including pH (2-10), contact time (1-180 min), adsorbent dosage (100-2000 mg L⁻¹) and initial diazinon concentration (0.5–20 mg L⁻¹) on the removal processes were studied. Finally, isotherm and kinetic and of adsorption process of diazinon onto Fe₃O₄@SiO₂ nanocomposites were investigated.

The maximum removal efficiency of diazinon (96%) was found at 180 min with 1000 mg L⁻¹ adsorbent dosage using 0.5 mg L⁻¹ diazinon concentration at pH = 7. The experimental results revealed that data were best fit with the pseudo-second-order kinetic model (R² = 0.971) and the adsorption capacity was 10.90 mg g⁻¹. The adsorption isotherm was accordant to Langmuir isotherm.

In the present study, the magnetic nanocomposites were synthesized and used as an absorbent for the removal of diazinon. The developed method had advantages such as the good ability of Fe₃O₄@SiO₂ nanocomposites to remove diazinon from aqueous solution and the magnetic separation of this absorbent that make it recoverable nanocomposite. The other advantages of these nanocomposites are rapidity, simplicity and relatively low cost.