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Recent advancements in information and communication technologies have led to the rapid growth of electronic commerce market. In the United States, e-commerce retail sales for 2002 reached $45.6 billion, indicating an increase of 26.9% from 2001 while total retail sales increased 3.1% during the same period. Although e-commerce sales account for only 1.4% of total sales in this country, forecasts show that the magnitude of digital economy will continue to expand. The logistical requirements of e-commerce goods that extend to each customer's address stimulate greater complexity in traditional supply chain management, potentially causing higher costs for freight supply chain participants. To harness the economic potential of e-commerce, it is important to encourage the development of a freight transportation system that will support its steady growth, while avoiding the possible negative effects from the changes in freight transportation. Due to the intrinsic nature of e-commerce goods, advances in home delivery have the potential to promote the growth of e-commerce as well as to create sustainable urban freight transportation systems. Based on the case study of the United States, this paper presents an in-depth discussion of the key challenges arising in home delivery operations, and proposes potential solution strategies that will lead to more efficient and reliable home delivery systems.

The purpose of this paper is to develop and test the thermal interface materials (TIM) for application in assembly of semiconductor chips to package. Good adhesion properties (>5 MPa shear strength) and low thermal interface resistance (better than for SAC solders) are the goal of this research.

Mechanical and thermal properties of TIM joints between gold plated contacts of chip and substrate were investigated. Sintering technique based on Ag pastes was applied for purpose of this study. Performance properties were assessed by shear force tests and thermal measurements. Scanning electron microscopy was used for microstructural observations of cross-section of formed joints.

It was concluded that the best properties are achieved for pastes containing spherical Ag particles of dozens of micrometer size with flake shaped Ag particles of few micrometers size. Sintering temperature at 230°C and application of 1 MPa force on the chip during sintering gave the higher adhesion and the lowest thermal interface resistance.

The new material based on Ag paste containing mixtures of Ag particles of different size (form nanometer to dozens of microns) and shape (spherical, flake) suspended in resin was proposed. Joints prepared using sintering technique and Ag pastes at 230°C with applied pressure shows better mechanical and thermal than other TIM materials such as thermal grease, thermal gel or thermally conductive adhesive. Those material could enable electronic device operation at temperatures above 200°C, currently unavailable for Si-based power electronics.

This paper aims to investigate voiding phenomena in solder joints under thermal pads of light-emitting diodes (LEDs) assembled in mass production environment by reflow soldering by using seven low-voiding lead-free solder pastes.

The solder pastes investigated are of SAC305 type, Innolot type or they are especially formulated by the manufacturers on the base of (SnAgCu) alloys with addition of some alloying elements such as Bi, In, Sb and Ti to provide low-void contents. The SnPb solder paste – OM5100 – was used as a benchmark. The solder paste coverage of LED solder pads was chosen as a measure of void contents in solder joints because of common usage of this parameter in industry practice.

It was found that the highest coverage and, related to it, the least void contents are in solder joints formed with the pastes LMPA-Q and REL61, which are characterized by the coverage of mean value 93.13% [standard deviation (SD) = 2.72%] and 92.93% (SD = 2.77%), respectively. The void diameters reach the mean value equal to 0.061 mm (SD = 0.044 mm) for LMPA-Q and 0.074 mm (SD = 0.052 mm) for REL61. The results are presented in the form of histograms, plot boxes and X-ray images. Some selected solder joints were observed with 3D computer tomography.

The statistical analyses are carried out on the basis of 2D X-ray images with using Origin software. They enable to compare features of various solder pastes recommended by manufacturers as low voiding. The results might be useful for solder paste manufacturers or electronic manufacturing services.

This paper aims to perform thermal and mechanical characterization for silver-based sintered thermal joints. Layer quality affects thermal and mechanical performance, and it is important to achieve information about how materials and process parameters influence them.

Thermal investigation of the thermal joints analysis method was focused on determination of thermal resistance, where temperature measurements were performed using infrared camera. They were performed in two modes: steady-state analysis and dynamic analysis. Mechanical analysis based on measurements of mechanical shear force. Additional characterizations based on X-ray image analysis (image thresholding), optical microscope of polished cross-section and scanning electron microscope image analysis were proposed.

Sample surface modification affects thermal resistance. Silver metallization exhibits the lowest thermal resistance and the highest mechanical strength compared to the pure Si surface. The type of dynamic analysis affects the results of the thermal resistance.

Investigation of the layer quality influence on mechanical and thermal performance provided information about different joint types.

This paper aims to describe an interdisciplinary and innovative research conducted in Switzerland, at the Geneva School of Business Administration HES-SO and supported by the State Archives of Neuchâtel (Office des archives de l'État de Neuchâtel, OAEN). The problem to be addressed is one of the most classical ones: how to extract and discriminate relevant data in a huge amount of diversified and complex data record formats and contents. The goal of this study is to provide a framework and a proof of concept for a software that helps taking defensible decisions on the retention and disposal of records and data proposed to the OAEN. For this purpose, the authors designed two axes: the archival axis, to propose archival metrics for the appraisal of structured and unstructured data, and the data mining axis to propose algorithmic methods as complementary or/and additional metrics for the appraisal process.

Based on two axes, this exploratory study designs and tests the feasibility of archival metrics that are paired to data mining metrics, to advance, as much as possible, the digital appraisal process in a systematic or even automatic way. Under Axis 1, the authors have initiated three steps: first, the design of a conceptual framework to records data appraisal with a detailed three-dimensional approach (trustworthiness, exploitability, representativeness). In addition, the authors defined the main principles and postulates to guide the operationalization of the conceptual dimensions. Second, the operationalization proposed metrics expressed in terms of variables supported by a quantitative method for their measurement and scoring. Third, the authors shared this conceptual framework proposing the dimensions and operationalized variables (metrics) with experienced professionals to validate them. The expert’s feedback finally gave the authors an idea on: the relevance and the feasibility of these metrics. Those two aspects may demonstrate the acceptability of such method in a real-life archival practice. In parallel, Axis 2 proposes functionalities to cover not only macro analysis for data but also the algorithmic methods to enable the computation of digital archival and data mining metrics. Based on that, three use cases were proposed to imagine plausible and illustrative scenarios for the application of such a solution.

The main results demonstrate the feasibility of measuring the value of data and records with a reproducible method. More specifically, for Axis 1, the authors applied the metrics in a flexible and modular way. The authors defined also the main principles needed to enable computational scoring method. The results obtained through the expert’s consultation on the relevance of 42 metrics indicate an acceptance rate above 80%. In addition, the results show that 60% of all metrics can be automated. Regarding Axis 2, 33 functionalities were developed and proposed under six main types: macro analysis, microanalysis, statistics, retrieval, administration and, finally, the decision modeling and machine learning. The relevance of metrics and functionalities is based on the theoretical validity and computational character of their method. These results are largely satisfactory and promising.

This study offers a valuable aid to improve the validity and performance of archival appraisal processes and decision-making. Transferability and applicability of these archival and data mining metrics could be considered for other types of data. An adaptation of this method and its metrics could be tested on research data, medical data or banking data.

The purpose of this paper is to analyze the individual steps during the printing of capacitor structures. The method of substrate preparation, the obtained roughness of conductive and dielectric layers are examined. Moreover, the capacitances of the obtained capacitors were examined.

Surface roughness and microscopic analysis were used to assess the quality of printed conductive structures. Two criteria were used to assess the quality of printed dielectric structures: the necessary lack of discontinuity of layers and minimal roughness. To determine the importance of printing parameters, a draft experimental method was proposed.

The optimal way to clean the substrate has been determined. The most important parameters for the dielectric layer (i.e. drop-space, table temperature, curing time and temperature) were found.

If dielectric layers are printed correctly, most problems with printing complex electronic structures (transistors, capacitors) will be eliminated.

The tests performed identified the most important factors for dielectric layers. Using them, capacitors of repeatable capacity were printed.

In the literature on this subject, no factors were found which were responsible for obtaining homogeneous dielectric layers.

This paper aims to develop thermal analysis method of thermal joints characterization. The impact on convection on thermal resistance analysis with use thermography for silver-based thermal joints were investigated for non-metallized and metalized semiconductor surfaces. Heat transfer efficiency depends on thermal conductivity; radiation was used to perform thermographic analysis; the convection is energy loss, so its removing might improve measurements accuracy.

Investigation of thermal joints analysis method was focused on determination of convection impact on thermal resistance thermographic analysis method. Measuring samples placed in vacuum chamber with lowered pressure requires transparent window for infrared radiation that is used for thermographic analysis. Impact of infrared window and convection on temperature measurements and thermal resistance were referred.

The results showed that the silicon window allowed to perform thermal analysis through, and the convection was heat transfer mode which create 15% energy loss.

It is possible to measure thermal resistance for silver-based thermal joints with convection eliminated to improve measurements accuracy.

This paper explores the convergence of Education 4.0 and Industry 4.0 and presents a Twin Peaks model for their seamless integration.

A high-level literature review is conducted to identify and discuss the important challenges and opportunities offered by both Education 4.0 and Industry 4.0. A novel Twin Peaks model is devised for the convergence of these domains and to cope with the challenges effectively.

The proposed Twin Peak model for the convergence of Education 4.0 and Industry 4.0 suggests that the development of these two domains is interdependent. It emphasizes ethical considerations, inclusivity and understanding the concerns of stakeholders from both education and industry. We have also explained how continuous incremental adaptation within the proposed Twin Peaks model might assist in addressing concerns of one sector with the opportunities of the other.

First, Education 4.0 and Industry 4.0 are reviewed in terms of opportunities and challenges they present. Second, a novel Twin Peaks model for the convergence of Education 4.0 and Industry 4.0 is presented. The proposed discovers that the convergence is adaptive, iterative and must be ethically sound while considering the broader societal implications of the digital transformation. Third, this study also acts as a torch-bearer for the necessity for more research of this kind to guarantee that our educational ecosystem is adaptable and capable of producing the skills required for success in the era of IR4.0.

The study aims to identify the status quo of artificial intelligence in entrepreneurship education with a view to identifying potential research gaps, especially in the adoption of certain intelligent technologies and pedagogical designs applied in this domain.

A scoping review was conducted using six inclusive and exclusive criteria agreed upon by the author team. The collected studies, which focused on the adoption of AI in entrepreneurship education, were analysed by the team with regards to various aspects including the definition of intelligent technology, research question, educational purpose, research method, sample size, research quality and publication. The results of this analysis were presented in tables and figures.

Educators introduced big data and algorithms of machine learning in entrepreneurship education. Big data analytics use multimodal data to improve the effectiveness of entrepreneurship education and spot entrepreneurial opportunities. Entrepreneurial analytics analysis entrepreneurial projects with low costs and high effectiveness. Machine learning releases educators’ burdens and improves the accuracy of the assessment. However, AI in entrepreneurship education needs more sophisticated pedagogical designs in diagnosis, prediction, intervention, prevention and recommendation, combined with specific entrepreneurial learning content and entrepreneurial procedure, obeying entrepreneurial pedagogy.

This study holds significant implications as it can shift the focus of entrepreneurs and educators towards the educational potential of artificial intelligence, prompting them to consider the ways in which it can be used effectively. By providing valuable insights, the study can stimulate further research and exploration, potentially opening up new avenues for the application of artificial intelligence in entrepreneurship education.

This study aims to introduce a deep neural network (DNN) to estimate the effective thermal conductivity of the flat heat pipe with spreading thermal resistance.

A total of 2,160 computational fluid dynamics simulation cases over up to 2,000 W/mK are conducted to regress big data and predict a wider range of effective thermal conductivity up to 10,000 W/mK. The deep neural networking is trained with reinforcement learning from 10–12 steps minimizing errors in each step. Another 8,640 CFD cases are used to validate.

Experimental, simulational and theoretical approaches are used to validate the DNN estimation for the same independent variables. The results from the two approaches show a good agreement with each other. In addition, the DNN method required less time when compared to the CFD.

The DNN method opens a new way to secure data while predicting in a wide range without experiments or simulations. If these technologies can be applied to thermal and materials engineering, they will be the key to solve thermal obstacles that many longing to overcome.