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The purpose of this study is to experimentally investigate the trends in shock wave Mach number that were observed when different diaphragm material combinations were used in the small-scale shock tube.

A small-scale shock tube was designed and fabricated having a maximum Mach number production capacity to be 1.5 (theoretically). Two microphones attached in the driven section were used to calculate the shock wave Mach number. Preliminary tests were conducted on several materials to obtain the respective bursting pressures to decide the final set of materials along with the layered combinations.

According to the results obtained, 95 GSM tracing paper was seen to be the strongest reinforcing material, followed by 75 GSM royal executive bond paper and regular 70 GSM paper for aluminium foil diaphragms. The quadrupled layered diaphragms revealed a variation in shock Mach number based on the position of the reinforcing material. In quintuple layered combinations, the accuracy of obtaining a specific Mach number was seen to be increasing. Optimization of the combinations based on the production of the shock wave Mach number was carried out.

The shock tube was designed taking maximum incident shock Mach number as 1.5, the experiments conducted were found to achieve a maximum Mach number of 1.437. Thus, an extension to further experiments was avoided considering the factor of safety.

The paper presents a detailed study on the effect of change in the material and its position in the layered diaphragm combinations, which could lead to variation in Mach numbers that are produced. This could be used to obtain a specific Mach number for a required study accurately, with a low-cost setup.

This paper aims to present a combination modeling method using multi-source information in the process to improve the accuracy of the dimension propagation relationship for assembly variation reduction.

Based on a variable weight combination prediction method, the combination model that takes the mechanism model and data-driven model based on inspection data into consideration is established. Furthermore, the combination model is applied to qualification rate prediction for process alarming based on the Monte Carlo simulation and also used in engineering tolerance confirmation in mass production stage.

The combination model of variable weights considers both the static theoretical mechanic variation propagation model and the dynamic variation relationships from the regression model based on data collections, and provides more accurate assembly deviation predictions for process alarming.

A combination modeling method could be used to provide more accurate variation predictions and new engineering tolerance design procedures for the assembly process.

The purpose of this paper is to explore the synergic effect of wild turmeric (Curcuma Aromatica Salisb.) and holy basil (Ocimum Tenuiflorum L.) combination herbal extracts treatment on the moisture management properties of cotton, lyocell and micro-denier single jersey knitted fabrics and the factors affecting it, which is intended for the development of healthcare apparel products.

The pre-treated single jersey knitted fabrics of cotton, lyocell and micro-denier polyester fabrics were given finishing treatment with the wild turmeric (Curcuma Aromatica Salisb.) and holy basil (Ocimum Tenuiflorum L.) combination herbal extract proportions of 100%:0%, 75%:25%,50%:50%; 25%:75% and 0%:100%. The D-optimal factorial design developed using Design Expert software was used for the study. The finishing treatments were carried out using the pad−dry−cure method. The aim of the work is to find out the influence of combination herbal extract proportion, textile material and their interaction effect on the moisture management properties.

The ANOVA results revealed that the overall moisture management properties of single jersey knitted fabrics are influenced by the material type, combination herbal extract proportion and the interaction between material type and the combination herbal extracts proportion. The overall moisture management properties of combination herbal extracts treated cotton single jersey fabrics are found to be better than that of lyocell and micro-denier polyester fabrics due to their excellent accumulative one-way transport capability after the finishing treatment. Among the combination herbal extract proportions, 50:50 per cent combination herbal extract proportion was found to be better than other proportions.

The study on the moisture management properties of combination herbal extracts of wild turmeric (Curcuma Aromatica Salisb.) and holy basil (Ocimum Tenuiflorum L.) is a novel attempt to explore the synergic effect of active constituents in both the herbs.

A firm's inventions provide technical information for product planning and technical support for new product development (NPD). In the knowledge-based theory, inventing is regarded as a process of knowledge combination. This paper aims to classify the firm's inventive capabilities based on the combinatorial view and investigate the effects of inventive capabilities on NPD performance.

Four types of inventive capabilities are identified concerned with the knowledge used to combine in the inventive activities. By utilizing a dataset of 572 firms from China's automotive manufacturing industry, the roles of different inventive activities in the generation of new inventions are compared. Then the effects of different inventive capabilities on NPD performance are empirically examined by using negative binomial regression analysis.

The time series for the number of patented inventions derived from different types of combinations generally exhibits a steady upward trend, and the number of patents derived from recombination is much higher. The empirical results demonstrate that the inventive capabilities associated with reused recombination and creative recombination exhibit positive effects on NPD performance, and the inventive capabilities associated with novel combination and original combination exhibit non-linear effects on NPD performance.

The findings contribute to NPD literature by investigating the effects of different inventive capabilities on NPD performance. This study also provides guidelines for manufacturing managers to improve NPD performance by building appropriate inventive capabilities.

The purpose of this paper is to explore the effects of product and process innovation on the relationships between knowledge combination capability and organizational performance.

The data were collected from small and medium‐sized enterprises (SMEs) belonging to the sector of Spanish technology‐based firms (TBFs). This study employs the structural equation model (SEM) to examine the relationships between product and process innovation, knowledge combination capability, and organizational performance.

The findings of this study suggest, first, that knowledge combination capability has a significant impact on product and process innovation. Second, product and process innovation mediate the relations between knowledge combination capability and organizational performance.

The sample includes only firms in the technology sector; future studies could analyze another kind of organization. Further studies might also use a wider sample that encompasses several countries.

This paper implies that, to achieve better performance in technology SMEs, the managers should focus more on knowledge combination capability to improve innovation, as this is the key to excellent business performance.

This study contributes to the literature on innovation and knowledge. It examines the mediating effects of product and process innovation, knowledge combination capability, and organizational performance in technology SMEs. The research demonstrates empirically that innovation strengthens the effect of knowledge on firm performance in the sector studied.

The purpose of this paper is to draw a parallel between color combinations in coats of arms of the twelfth century and color combinations in current brand logotypes of 400 companies from the Forbes Global 2000 list.

In this work, the frequency of color combinations displayed in medieval coats of arms and in the brand logotypes of the top 400 largest companies in the Forbes list were compared.

One of the main findings of this research is the stability of color usage in two visual identity systems – coats of arms and brand logotypes – although 800 years separate them. In these two identification systems, almost the same colors are preferred or rejected. Yet, even though it is regularly argued that color will submerge the consumption world, this research shows that, in the twenty-first century, visual identities of brands are rather less colorful than medieval coats of arms: nowadays, at a global level, half of the logotypes are formed with white combined with red and/or blue.

By drawing a parallel between two visual identification systems that are coats of arms and logotypes, the results from this study highlight the stability in color usage and color combinations along the centuries. Thus, it seems that modern analysis of color combination practices could greatly benefit from the history and historical evolution of coats of arms. Far from being out of date, the study of coats of arms can provide marketers with interesting insights about the rules and implementation of color combinations when designing logotypes.

Given that the use of Google Trends data is helpful to improve forecasting performance, this study aims to investigate whether the precision of forecast combination can benefit from the use of Google Trends Web search index along with the encompassing set.

Grey prediction models generate single-model forecasts, while Google Trends index serves as an explanatory variable for multivariate models. Then, three combination sets, including sets of univariate models (CUGM), all constituents (CAGM) and constituents that survive the forecast encompassing tests (CSET), are generated. Finally, commonly used combination methods combine the individual forecasts for each combination set.

The tourism volumes of four frequently searched-for cities in Taiwan are used to evaluate the accuracy of three combination sets. The encompassing tests show that multivariate grey models play a role to be reckoned with in forecast combinations. Furthermore, the empirical results indicate the usefulness of Google Trends index and encompassing tests for linear combination methods because linear combination methods coupled with CSET outperformed that coupled with CAGM and CUGM.

With Google Trends Web search index, the tourism sector may benefit from the use of linear combinations of constituents that survive encompassing tests to formulate business strategies for tourist destinations. A good forecasting practice by estimating ex ante forecasts post-COVID-19 can be further provided by scenario forecasting.

To improve the accuracy of combination forecasting, this research verifies the correlation between Google Trends index and combined forecasts in tourism along with encompassing tests.

過去的研究顯示 Google 搜尋趨勢資料有助於改善旅遊需求預測的準確度，本研究就此進一步探討 Google 搜尋趨勢網頁搜尋指標與涵蓋性檢定的使用對於組合預測準確度所造成的影響。

本研究以 Google 搜尋趨勢指標做為多變量灰色預測模式的解釋變數，並以單變量與多變量灰色模式產生各別預測值。在分別產生由所有的單變量模式 (CUGM)所有的模式 (CAGM), 以及經過涵蓋性檢定所留存下來之模式 (CSET) 所組成之集合後，就各別的組合集以常用的組合方法產生預測值。

以台灣的四個熱搜旅遊城市的旅遊人數進行三個組合集的預測準確度分析。涵蓋性檢定顯示多變量灰色模式在組合預測中扮演重要的角色，而結果亦呈現線性組合方法在 CSET優於在 CUGM 與 CAGM 的準確度，突顯搜尋趨勢指標與涵蓋性檢定對於線性組合方法的有用性。

藉由 Google 搜尋趨勢網頁搜尋指標與涵蓋性檢定，旅遊部門應可透過線性組合方法的預測規劃旅遊目的地的經營策略。新冠疫情下於各季的事前預測亦可結合情境預測具體呈現。

為提升組合預測在旅遊需求的預測準確度，本研究結合涵蓋性檢定以分析 Google 搜尋趨勢指標與組合預測準確度之間的關聯性。

旅遊需求,涵蓋性檢定,Google 搜尋趨勢,灰色預測,組合預測

研究型论文

Dado que el uso de los datos de Google Trends es útil para mejorar la precisión de las predicciones, este estudio examina si el uso del índice de búsqueda web de Google Trends combinado con la agregación de relevancia puede mejorar la precisión del predictor.

El modelo predictivo gris genera predicciones bajo un único modelo, mientras que el modelomultivariado utiliza el indicador Google Trends como variable explicativa. Se generaron tresensamblajes generales, incluido el Modelo armónico único (CUGM), los ensamblajes de todos loscomponentes (CAGM) y la prueba de presencia de componentes con predicción (CSET). Laspredicciones individuales encada grupo luego se combinan utilizando métodos de correlación deuso común.

Utilizando el número de turistas en las cuatro ciudades más investigadas de Taiwán, los tresgrupos combinados se clasificaron según su precisión. Las pruebas incluidas muestran que losmodelos multivariados en escala de grises son importantes para la predicción. Además, losresultados de las pruebas muestran que el índice de Google Trends y las pruebas que incluyenmétodos de suma lineal son útiles porque los métodos combinados con CSET funcionan majorque los métodos combinados con CSET. CAGM y VCUG.

La industria de viajes puede usar el índice de búsqueda web de Google Trends para desarrollarestrategias comerciales para atracciones basadas en un conjunto lineal de componentes.

Con el objetivo de mejorar la precisión de los pronósticos agregados, este estudio investiga larelación entre el índice de tendencias de Google y las expectativas generales de viaje junto con laevidencia global.

Demanda de viajes, Experiencia global, Tendencias de Google, Predicción gris

Trabajo de investigación

We study the sparse grid combination technique as an efficient method for the solution of fluid dynamics problems. The combination technique needs only O(h^–1_n(log(h^–1_n))^d–1) grid points for d‐dimensional problems, instead of O(h^–d_n) grid points used by the full grid method. Here, h_n = 2^–n denotes the mesh width of the grids. Furthermore, provided that the solution is sufficiently smooth, the accuracy (with respect to the L₂‐ and the L_∞‐norm) of the sparse grid combination solution is O(h^α_n(log(h^–1_n))^d–1), which is only slightly worse than O(h^α_n) obtained by the full grid solution. Here, α includes the order of the underlying discretization scheme, as well as the influence of singularities. Thus, the combination technique is very economic on both storage requirements and computing time, but achieves almost the same accuracy as the usual full grid solution. Another advantage of the combination technique is that only simple data structures are necessary. Where other sparse grid methods need hierarchical data structures and thus specially designed solvers, the combination method handles merely d‐dimensional arrays. Thus, the implementation of the combination technique can be based on any “black box solver”. However, for reasons of efficiency, an appropriate multigrid solver should be used. Often, fluid dynamics problems have to be solved on rather complex domains. A common approach is to divide the domain into blocks, in order to facilitate the handling of the problem. We show that the combination technique works on such blockstructured grids as well. When dealing with complicated domains, it is often desirable to grade a grid around a singularity. Graded grids are also supported by the combination technique. Finally, we present the first results of numerical experiments for the application of the combination method to CFD problems. There, we consider two‐dimensional laminar flow problems with moderate Reynolds numbers, and discuss the advantages of the combination method.

Mergers and acquisitions occur frequently in organizations, but rarely achieve their desired financial and strategic objectives. Personal experience gained in more than 50 corporate combinations shows that many factors account for the dismal track record of mergers and acquisitions, including underestimating the multitude of integration issues and problems that arise as organizations come together and the pervasiveness and depth of human and cultural issues triggered in a combination. These dynamics have been found in combinations involving organizations of all sizes, in all industry sectors and across international boundaries. Reviews the human, organizational and cultural dynamics affecting mergers and acquisitions, and reports recent trends influencing interventions to enhance merger and acquisition success. Describes consulting approaches and methods required to minimize employee stress, management crisis and culture clash and to enhance the desired financial and strategic results of mergers and acquisitions.

Order picking is one of the most costly logistics processes in warehouses. As a result, the optimization of order picking processes has received an increased attention in recent years. One potential source for improving order picking is the reduction of picker blocking. The purpose of this paper is to investigate picker blocking under different storage assignment and order picker-route combinations and evaluate its effects on the performance of manual order picking processes.

This study develops an agent-based simulation model (ABS) for order picking in a rectangular warehouse. By employing an ABS, we are able to study the behaviour of individual order pickers and their interactions with the environment.

The simulation model determines shortest mean throughput times when the same routing policy is assigned to all order pickers. In addition, it evaluates the efficiency of alternative routing policies–storage assignment combinations.

The paper implies that ABS is well-suited for further investigations in the field of picker blocking, for example, with respect to the individual behaviour of agents.

Based on the results of this paper, warehouse managers can choose an appropriate routing policy that best matches their storage assignment policy and the number of order pickers employed.

This paper is the first to comprehensively study the effects of different combinations of order picker routing and storage assignment policies on the occurrence of picker blocking.