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This study aims to establish a new system to predict the defect liability phase (DLP) cost using the Six Sigma methodology, which investigates sources of variations and reduces the error level to 3.4 per million through five phases: define, measure, analyze, design and verify.

After the initial handover of the construction project, the DLP follows the practical completion. During this stage, the contractor is responsible for the remedy of any defects that appeared in the project. Many researchers have studied defect reasons and their associated costs in different industries, while the construction industry remains a green field for this kind of research. The objective of this study was to develop a model to predict the DLP cost. The research methodology adopted the five stages of the Six Sigma cycle: defining objectives, measuring the data, analyzing performance, designing the model and verifying the results. Twenty factors were identified as potential factors affecting the DLP cost. Factors were categorized into two main clusters: project data and organization data. Interviews were conducted with 42 project management experts, who have 8–35 years of experience in construction project management, to rank the 20 factors based on their importance. Simo’s procedure was used to obtain the weight of each factor affecting the DLP cost based on the opinions of the experts. The Pareto principle was used to select the “Vital Few” factors affecting the DLP cost, and six factors were selected. The design of experiments (DOE) was used to establish a dynamic model to predict the DLP cost using a sample of 41 construction projects obtained from the above-mentioned 42 project management experts. The model accuracy was verified using data obtained from a different sample of five construction projects, which were not used to establish the model.

The results showed that among the 20 factors, only six were found to have a cumulative impact of 50% over the cost of the DLP: type of project, project contract value, nationality of the employer, project manager experience, DLP duration and sector of the employer. A model was established through the DOE to predict the DLP cost using the values of the aforementioned factors.

As a natural limitation of using DOE, the newly developed model can be applied to predict the DLP cost based on data within the range of data used during the model development, which means that model is confined within the specific measured values of factors. Furthermore, it will be beneficial for future studies to study the impact of other factors related to the types of materials or equipment used in building the project because it was not considered during this study because of the huge diversities in these factors and difficulties in determining its impact on the DLP cost.

The unique results of using DOE through Minitab software facilitated obtaining of a dynamic model, which means that researchers can modify any value of the six factors and monitor instantly the expected change in the DLP cost, which will allow a better understanding of the impact of each factor on the DLP cost. Moreover, the new model will help contractors to predict the expected DLP cost to be added for their project budget, which will mitigate the risk of cost overrun resulted from the cost of defect rectification.

A dynamic model was established to predict the DLP cost using the DOE. The new model was validated, and the prediction error ranged from −18% to +21%.

Studies on academic entrepreneurship (AE) agree on the significant impact that Universities can have on entrepreneurial development. AE deploys through fundamental activities, like the start-up of new companies and the connection of the University with Enterprises. The purpose of this paper is to analyse the role of digital learning platforms (DLP) to connect Universities and Enterprises effectively. Although the literature has extensively investigated DLP, there is a lack of understanding of the role of DLP in supporting digital AE. This paper focuses, in particular, on the functional requirements that have to distinguish the development of DLPs supporting education-based activities of knowledge transfer between academia and enterprise.

The research is carried out, adopting a case study methodology. A single and holistic case regarding a DLP developed for the strategic and exclusive deployment of AE activities is proposed to describe and discuss the functional requirements of such Platform.

The DLP is a virtual learning space in which Enterprises and Universities can interact. The definition of design requirements is crucial for the efficacy of DLPs and needs to be carefully supported. Various criteria are proposed, respect to the various stakeholders engaged in DAE learning platform (Universities, Enterprises, students, employees), and according to the short- and long-term objectives of Universities and Entrepreneurship connection.

The paper explores an original case of DLP established in AE, to connect Universities and Enterprises. The research also sheds light on the under focussed typology of AE activities regarding education-based knowledge exchange. They are currently unaddressed by the literature on AE.

In some three-dimensional (3D) printing application scenarios, e.g., model manufacture, it is necessary to print large-sized objects. However, it is impossible to implement large-size 3D printing using a single projector in digital light processing (DLP)-based mask projection 3D printing because of the limitations of the digital micromirror device chips.

A multi-projector DLP with energy homogenization (EHMP-DLP) scheme is proposed for large-size 3D printing. First, a large-area printing plane is established by tiling multiple projectors. Second, the projector set’s tiling pattern is obtained automatically, and the maximum printable plane is determined. Third, the energy is homogenized across the entire printable plane by adjusting gray levels of the images input into the projectors. Finally, slices are automatically segmented based on the tiling pattern of the projector set, and the gray levels of these slices are reassigned based on the images of the corresponding projectors.

Large-area high-intensity projection for mask projection 3D printing can be performed by tiling multiple DLP projectors. The tiled projector output energies can be homogenized by adjusting the images of the projectors. Uniform ultraviolet energy is important for high-quality printing.

A prototype device is constructed using two projectors. The printable area becomes 140 × 210 mm from the original 140 × 110 mm.

The proposed EHMP-DLP scheme enables 3D printing of large-size objects with linearly increasing printing times and high printing precision. A device was established using two projectors to practice the scheme and can easily be extended to larger sizes by using more projectors.

The purpose of this study is to explore the synthesis of novel conductive photo-resins to produce flexible conducting composites for use in additive manufacturing. By using direct ink writing (DIW) additive manufacturing, this study aims to explore the fabrication of multimaterial devices with conductive and insulating components. Using digital light processing (DLP) additive manufacturing, this study aims to fabricate detailed objects with higher resolution than material extrusion 3D printing systems.

In this paper, several photocurable conducting resins were prepared for DIW and DLP additive manufacturing. These resins were then cured using 405 nm near UV light to create intrinsically conductive polymer (ICP) composites. The electrochemical properties of these composites were analysed, and the effect of co-monomer choice and crosslinking density was determined. These results determined a suitable resin for subsequent additive manufacture using DIW and DLP. These 3D printing techniques were used to develop flexible conducting devices of submillimetre resolution that were fabricated with unmodified, commercially available 3D printers.

Cyclic voltammetry and volume conductivity analysis of the conducting resins determined the most conductive resin formula for 3D printing. Conductive devices were fabricated using the two 3D printing techniques. A multimaterial soft conducting device was fabricated using DIW, and each conducting component was insulated from its neighbours. DLP was used to fabricate a soft conducting device with good XY resolution with a minimum feature size of 0.2 mm. All devices were prepared in unmodified commercially available 3D printers.

These findings have value in the development of soft robotics, artificial muscles and wearable sensors. In addition, this work highlights techniques for DIW and DLP additive manufacturing.

Several original conducting resin formulae were developed for use in two 3D printing systems. The resulting 3D-printed composites are soft and flexible while maintaining their conductive properties. These findings are of value to both polymer chemists and to the field of additive manufacturing.

This paper aims to propose a medium-term forecast model for the daily passenger volume of High Speed Railway (HSR) systems to predict the daily the Origin-Destination (OD) daily volume for multiple consecutive days (e.g. 120 days).

By analyzing the characteristics of the historical data on daily passenger volume of HSR systems, the date and holiday labels were designed with determined value ranges. In accordance to the autoregressive characteristics of the daily passenger volume of HSR, the Double Layer Parallel Wavelet Neural Network (DLP-WNN) model suitable for the medium-term (about 120 d) forecast of the daily passenger volume of HSR was established. The DLP-WNN model obtains the daily forecast result by weighed summation of the daily output values of the two subnets. Subnet 1 reflects the overall trend of daily passenger volumes in the recent period, and subnet 2 the daily fluctuation of the daily passenger volume to ensure the accuracy of medium-term forecast.

According to the example application, in which the DLP-WNN model was used for the medium-term forecast of the daily passenger volumes for 120 days for typical O-D pairs at 4 different distances, the average absolute percentage error is 7%-12%, obviously lower than the results measured by the Back Propagation (BP) neural network, the ELM (extreme learning machine), the ELMAN neural network, the GRNN (generalized regression neural network) and the VMD-GA-BP. The DLP-WNN model was verified to be suitable for the medium-term forecast of the daily passenger volume of HSR.

This study proposed a Double Layer Parallel structure forecast model for medium-term daily passenger volume (about 120 days) of HSR systems by using the date and holiday labels and Wavelet Neural Network. The predict results are important input data for supporting the line planning, scheduling and other decisions in operation and management in HSR systems.

The purpose of this study is to develop a additive manufacturing (AM) process for the fabrication of ionic polymer–metal composite (IPMC) devices with complex designs that would be time-consuming to replicate using conventional manual methods. These IPMC devices have considerable potential in electroactive polymers (EAPs) and soft actuators.

This paper presents a novel three–dimensional (3D) AM technique to develop IPMCs. Digital light processing (DLP) fabrication of soft EAPs was undertaken using a vat-based AM method, followed by deposition of cost-effective outer silver electrodes.

DLP-fabricated devices were compared to conventional Nafion™-117 devices. DLP layer-by-layer fabrication of these devices allowed for good resolution for a range of printed objects. Electrical actuation of the DLP-produced IPMCs showed tip displacements of up to 3 mm, and greater actuation was seen in the presence of lithium rather than magnesium cations. The IMPCs showed good ion exchange capacities, while electrochemical analysis showed the reversible formation and removal of AgCl layers in addition to ion movement.

The AM of these devices allows for rapid prototyping as well as potential use in the development of multiple degrees of freedom actuators and devices.

An original resin formulation was developed for DLP 3D printing. This formula is chemically distinct from the conventional Nafion™-117 membranes that can be purchased. Additionally, this method allows for the manufacture of complex objects that would be difficult to machine by hand. These findings are of value to both the fields of polymer chemistry and AM.

The purpose of this paper is to introduce a novel technique for printing with multiple materials using the DLP method. Digital-light-processing (DLP) printing uses a digital projector to selectively cure a full layer of resin using a mask image. One of the challenges with DLP printing is the difficulty of incorporating multiple materials within the same part. As the part is cured within a liquid basin, resin switching introduces issues of cross-contamination and significantly increased print time.

The material handling challenges are investigated and addressed by taking inspiration from automated storage and retrieval systems and using an active cleaning solution. The material tower is a compact design to facilitate the storage and retrieval of different materials during the printing process. A spray mechanism is used for actively cleaning excess resin from the part between material changes.

Challenges encountered within the multi-material DLP technology are addressed and the experimental prototype validates the proposed solution. The system has a cleaning effectiveness of over 90 per cent in 15 s with the build area of 72 inches, in contrast to the previous work of 50 per cent cleaning effectiveness in 2 min with only 6 inches build area. The method can also hold more materials than the previous work.

The techniques from automated storage and retrieval system is applied to develop a storage system so that the time complexity of swapping is reduced from linear to constant. The whole system is sustainable and scalable by using a spraying mechanism. The design of the printer is modular and highly customizable, and the material waste for build materials and cleaning solution is minimized.

This paper aims to focus on applications of stochastic linear programming (SLP) to managerial accounting issues by providing a theoretical foundation and practical examples. SLP models may have more implications – and broader ones – in industry practice than deterministic linear programming (DLP) models do.

This paper introduces both DLP and SLP methods. In addition, continuous and discrete SLP models are explained. Applications are demonstrated using practical examples and simulations.

This research work extends the current knowledge of SLP, especially concerning managerial accounting issues. Through numerical examples, SLP demonstrates its great ability of hedging against all scenarios.

This study serves as an addition to building a cumulative tradition of research on SLP in managerial accounting. Only a few SLP studies in managerial accounting have focused on the development of such an instrument. Thus, the measurement scales in this research can be used as the starting point for further refining the instrument of optimization in managerial accounting.

Since 1995, the Library of Virginia’s Digital Library Program (DLP) has created digital images of more than 700,000 original document pages, 1,100 maps, 36,000 photographs, and 1.6 million catalog card images, and has created 32 bibliographic databases with more than 330,000 MARC records, 50 electronic card indexes, and numerous electronic finding aids. The bulk of the DLP’s funding comes from the Library Services and Technology Act (LSTA) federal program, but in 1997 the Library received a grant from the Andrew W. Mellon Foundation to catalog and digitize the Virginia Historical Inventory Project (VHI). After an introduction to the DLP and VHI, this article will discuss the costs and benefits of creating the online version and will compare the one‐time development cost and subsequent delivery of the digital resource to the long‐term costs and benefits of providing access to these materials via traditional means.

Additive Manufacturing (AM) conventionally necessitates an intermediary slicing procedure using the standard tessellation language (STL) data, which can be computationally burdensome, especially for intricate microcellular architectures. This study aims to propose a direct slicing method tailored for digital light processing-type AM processes for the efficient generation of slicing data for microcellular structures.

The authors proposed a direct slicing method designed for microcellular structures, encompassing micro-lattice and triply periodic minimal surface (TPMS) structures. The sliced data of these structures were represented mathematically and then convert into 2D monochromatic images, bypassing the time-consuming slicing procedures required by 3D STL data. The efficiency of the proposed method was validated through data preparations for lattice-based nasopharyngeal swabs and TPMS-based ellipsoid components. Furthermore, its adaptability was highlighted by incorporating 2D images of additional features, eliminating the requirement for complex 3D Boolean operations.

The direct slicing method offered significant benefits upon implementation for microcellular structures. For lattice-based nasopharyngeal swabs, it reduced data size by a factor of 1/300 and data preparation time by a factor of 1/8. Similarly, for TPMS-based ellipsoid components, it reduced data size by a factor of 1/60 and preparation time by a factor of 1/16.

The direct slicing method allows for bypasses the computational burdens associated with traditional indirect slicing from 3D STL data, by directly translating complex cellular structures into 2D sliced images. This method not only reduces data volume and processing time significantly but also demonstrates the versatility of sliced data preparation by integrating supplementary features using 2D operations.