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The purpose of this paper is to establish the regression equation based upon a set of samples prepared through structured design of experiment and form a prediction model for prediction of the areal density gram per square meter (GSM) of the embroidered fabrics and study the influence of basic input parameters.

Embroidery samples are prepared taking input parameters as GSM of the base fabric, linear density of the embroidery thread and stitch density of the embroidery design. Three levels of values are identified for each of the input parameters. Taguchi and Box-Behnken experiment design principles are used to prepare two sets of samples. Linear multiple regression is used to determine the prediction equations based upon each of the two sets and the combined set as well. Prediction equations are statistically verified for the prediction accuracy. Also, surface curves are prepared to study the influence of embroidery parameters on the GSM.

It is found that all the three prediction models developed in this study can predict with a very satisfactory level of accuracy. However, the regression equation based upon the data set prepared according to Taguchi experiment design is emerged as the prediction model with highest level of prediction accuracy. Corresponding equation coefficients and several three-dimensional surface curves are used to study the influence of embroidery parameters and it is found that the stitch density is the most influential input parameter followed by stitch length and the GSM of base fabric.

This can be used to assess the GSM of embroidered fabrics before starting the actual embroidery process. So, this model can help the embroidery designers significantly to pre-estimate the GSM of the embroidered fabrics and select the design parameters accordingly. Also, this model can be a useful tool for estimation of thread consumption and thread cost in embroidery.

The input parameters used here are very basic parameters related to design and materials, which can be easily available. And also, a simple linear multiple regression is used to make the prediction equation simple and easy to use. So, this model can help the embroidery designers or garment designers to select/adjust the embroidery parameters and thread parameters accordingly in the planning and designing stage itself to ensure that the GSM of embroidered fabrics remains within desirable range. Also, this prediction model developed hereby may be a very useful tool for estimation of the consumption and cost of embroidery threads.

This paper presents a very fundamental study to reveal the effect of embroidery parameters on the GSM, through development of regression equations. It can help future researchers in optimizations of input parameters and forming a technical guideline for the embroidery designers for selection of the design parameters for a desired GSM of embroidered fabric.

A method for rapid prototyping based on electrophotographic powder deposition was investigated to study its potentials and to identify design and implementation challenges. This technique is referred to here as the electrophotographic rapid prototyping (ERP). In this technique, powder is printed layer‐by‐layer in the shape of the cross‐sections of the part using electrophotography a very widely used non‐impact printing method. Each layer of powder is consolidated by fusing before the next layer of powder is printed. A fully automated test bed was constructed that consists of a printing system, fusing/heating plate, build platform that has two‐degrees of freedom as well as software that drives the system.

A solid freeform fabrication (SFF) technique is described where powder is deposited layer‐by‐layer using electrophotographic printing. In the electrophotography process, powder is picked up and deposited using an electrostatically charged surface. A test bed was designed and constructed to study the application of electrophotography to SFF. It can precisely deposit powder in the desired shape on each layer. A polymer toner powder was used to build small components by thermally fusing each layer of printed powder using a hot compaction plate. The feasibility of 3D printing using this approach was also studied by printing a binder powder using electrophotography on to a part powder bed.

In the present work, a numerical method, based on the well established enthalpy technique, is developed to simulate the growth of binary alloy equiaxed dendrites in presence of melt convection. The paper aims to discuss these issues.

The principle of volume-averaging is used to formulate the governing equations (mass, momentum, energy and species conservation) which are solved using a coupled explicit-implicit method. The velocity and pressure fields are obtained using a fully implicit finite volume approach whereas the energy and species conservation equations are solved explicitly to obtain the enthalpy and solute concentration fields. As a model problem, simulation of the growth of a single crystal in a two-dimensional cavity filled with an undercooled melt is performed.

Comparison of the simulation results with available solutions obtained using level set method and the phase field method shows good agreement. The effects of melt flow on dendrite growth rate and solute distribution along the solid-liquid interface are studied. A faster growth rate of the upstream dendrite arm in case of binary alloys is observed, which can be attributed to the enhanced heat transfer due to convection as well as lower solute pile-up at the solid-liquid interface. Subsequently, the influence of thermal and solutal Peclet number and undercooling on the dendrite tip velocity is investigated.

As the present enthalpy based microscopic solidification model with melt convection is based on a framework similar to popularly used enthalpy models at the macroscopic scale, it lays the foundation to develop effective multiscale solidification.

The purpose of this study is to explore the relationship between the real time requirements of guests and efforts made by hoteliers to optimize revenue and guest service excellence.

Prior studies have demonstrated the impact of room rates on profitability. The majority of these studies assumed an algebraic relationship between room rates and room demand to obtain the optimal solution by applying calculus to the revenue or profit function. This study adopts an alternative approach by seeking to better understanding the ever changing needs of hotel guests and how their behavior patterns have evolved over time together with innovative approaches that revenue managers need to adopt to optimize revenue. In today’s environment, revenue management has evolved into a more holistic approach and the study assimilates information from senior professionals from a range of hotels in India based on semi-structured interviews.

Revenue management is no longer about forecasting supply and demand based on historical data. This paper yields insights on emerging areas of importance for revenue management and advocates a systematic approach that hoteliers can adopt and apply to every department to secure a bigger impact on revenue management.

The findings can be used by hoteliers to fine-tune the room rates determined by conventional methods to arrive at a realistic and definitive value for optimal room rates.

This study highlights the issues that arise from viewing revenue management in isolation and it also considers innovative, customer-focused ways to optimize revenues.

This study aims to investigate the conditions for the financial feasibility of an incentive-based model for self-drop or crowdsourced drop of the product to be returned at designated drop boxes (thereby ensuring a contactless process).

Constraint-based non-linear mathematical modeling was done for cost differential with and without crowdsourcing. This was analyzed against returns on investment for the installed infrastructure. Scenarios were looked into from the linear, iso-elastic and logarithmic demand functions to identify the optimal incentive policy. The results were further evaluated using “willingness to return” for customer willingness for product returns via drop boxes.

Crowdsourcing is viable when product returns are no more than 15%–20% of the overall products, with a logistics cost differential of 15%–25%. These were only viable when the product return incentive was within the range of 15%–20% of the product cost, as well as the penalty was in the range of 25 to 40% for wrong returns.

The findings are expected to aid the organizations in successfully designing product return policies while adhering to the post-COVID-19 norms, including contactless transactions and social distancing.

The study provides a look into the viability sensitivity of effective gains/profitability against the required level of service for returns, wrong returns, penalties and incentives for crowdsourcing in a developing country like India.

The Dominican trade deficit represents almost 16 per cent of its gross domestic product (GDP) and is insufficiently counteracted by tourism and remittances; not even a high devaluation closed the imbalance. Eighty per cent of the exports are from free trade zones. These facts reflect their low domestic entrepreneurial capacity. The purpose of this study is to critically evaluate the Dominican economic model.

The author motivates the discussion with descriptive statistics and then applies multiple time-series regressions at the macro level and at the industry level.

The attraction of foreign firms appears to substitute, and not complement, the building of local capacity. Regressions show that a GDP growth of 5 per cent does not decrease the high unemployment rate.

Using new Okun’s equations, it is concluded that sectors dominated by local producers and improvements in the trade balance better impact the unemployment. These findings challenge conventional wisdom that characterizes the Dominican economy as a “successful story”.