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This study aims to develop and evaluate a modular smart garment design framework that simplifies the technical content associated with smart garment design.

Smart garment design challenges were first identified through literature review and interviews. Then, a modular framework and toolkit was created to address these challenges. Finally, workshops were held to evaluate the modular toolkit.

Interviews highlighted the need for easier attachment of hard devices to soft textile materials, simpler electrical connection creation and straightforward device selection. A modular framework was proposed and divided into four elements: (1) the Central Computation Module, (2) Peripheral Electrical Modules, (3) Securely Attaching Modules with Substrates and (4) Managing Intra-garment Connections. Workshops showed the modular framework had statistically significant improvements in function and certain ease ratings when compared to non-modular components.

This research identified specific technical challenges faced by smart garment designers and alleviated them through a modular smart garment framework that in workshops outperformed non-modular components in key function and ease ratings.

Modular capacity units enable rapid reconfiguration, providing tactical flexibility to efficiently meet customer demand during disruptions and ensuring sustainability. Moreover, the Physical Internet (PI) enhances the potential of modular capacity in addressing efficiency, sustainability, and resilience challenges. To evaluate the sustainability and resilience advantages of the PI-enabled reconfigurable modular system (PI-M system), this paper studies a PI-enabled sustainable and resilient production-routing problem with modular capacity.

We develop a multi-objective optimization model to assess the sustainability and resilience benefits of combining PI and modular capacity in a chemical industry case study. A hybrid solution approach, combining the augmented e-constraint method, construction heuristic, and hybrid adaptive large neighborhood search, is developed.

The experimental results reveal that the proposed solution approach is capable of obtaining better solutions than the Gurobi and the existing heuristic in a shorter running time. Moreover, compared with the traditional system, the PI only and traditional with modular capacity systems, PI-M system has significant advantages in both sustainability and resilience.

To the best of our knowledge, this study is the first to integrate the PI and modular capacity and investigate sustainability and resilience in the production-routing problem.

In product modular design tasks, especially in the high-tech manufacturing industry, buyers and supplies play distinct roles, which may have different impacts on product architectural and modular innovation. Prior research has tended to view product innovation as a holistic concept, overlooking the importance of this differentiated influence. This study, from a modular design perspective, aims to clarify the impact of black-box supplier involvement on product architectural and modular innovation, as well as the influence of product modularity on these relationships.

Based on the theory of product modular design, this study decomposes product innovation into architectural and modular innovation from the perspective of the product internal structure to conduct in-depth theoretical analysis and model construction. A total of 276 valid questionnaires are collected from typical Chinese high-tech manufacturing firms and used to empirically test the constructed theoretical model using multiple hierarchical regression analysis.

The results show that black-box supplier involvement positively affects modular innovation and takes an inverted U-shape, as moderated by product modularity. However, the impact of black-box supplier involvement on architectural innovation shows contradictory differences at different modularity levels. Under a low level of product modular design, black-box supplier involvement has a negative impact on architectural innovation, but under a moderate level of modular design, it has a positive impact. After the degree of modular design exceeds a certain threshold, the impact gradually weakens.

The results provide valuable insights for managers, highlighting the need to avoid oversimplifying the innovation impact of black-box suppliers solely based on overall product innovation. Instead, a more accurate assessment of the innovation contributions of both the buyer and supplier should be based on the degree of architectural and modular innovation. Additionally, the findings suggest that managers should consider the alignment between their company's product modular design features and innovation priorities (i.e. modular innovation or architectural innovation) when determining an appropriate supplier collaborative development strategy.

This study not only reveals the different impacts of black-box supplier involvement on architectural and modular innovation, but also proves the significant synergistic innovation effect of the relationship between black-box supplier involvement and product modularization. It constitutes an enriched and deepened exploration in the existing research on supplier involvement in product innovation.

This paper aims to introduce a custom-designed integrated nucleic acid detection polymerase chain reaction (PCR) instrument for clinical detection applications.

The PCR instrument can make rapid, sensitive, low-cost and quantitative molecular diagnosis compared with the current routine test flow from the pipette, series reagent to RT-PCR by manual manipulation. By integrating the multichannel automatic pipetting module, heat amplification module and real-time fluorescence detection module for the first time, the custom-designed integrated nucleic acid detection PCR instrument can achieve sample collection, subpackage, mixing, extracting, measuring and result presentation.

The multichannel automatic pipetting module was assembled with an accuracy of 0.4% (2 microliters) for accuracy measurement. Besides, the accuracy and sensitivity of nucleic acid using integrated low-cost nucleic acid detection PCR instruments were checked with COV-2019 virus (staining method) and African swine fever virus (probe method) under different concentrations.

Because of its high cost, complex system and bulky laboratory settings, including sample subpackage, mixing, extracting, measuring and finally result in presentation, the current nucleic acid detection system is not suitable for field operation and disease diagnosis in remote areas. The group independently designed and assembled an integrated low-cost multichannel nucleic acid detection PCR instrument, including a multichannel automatic pipetting module, a heat amplification module and a real-time fluorescence detection module.

The above equipment showed better reliability compared with commercial qPCR. These results can lay the foundation for functional, fast and low-cost PCR equipment for trace measurements.

This paper presents the determination of the maximum power point of a bifacial photovoltaic (PV) system using two different cell models. The optimal power point is determined by using genetic algorithm (GA), as an optimisation tool. The purpose of this paper is to find which of the two analysed models gives better results in the determination of the maximum power point of a bifacial PV system for different solar irradiations. The quality of the results gained from both models is analysed based on the value of the objective function.

In this research work, the maximum power point of bifacial PV modules is determined by using two different PV cell models, such as the simplified and two-diode models of PV cells. Based on the input electrical data for the analysed bifacial PV module as well as the mathematical model of the two PV cell presentations, the values for the current and the voltage at the maximum power point for a given solar irradiation and working temperature are determined by the algorithm for each solution in the population and generation.

From the presented results and the performed analysis, it can be concluded that GA is quite appropriate for this purpose and gives adequate results for both models and for all working conditions. The two-diode model was found to be more suitable compared with the simplified model due to its complexity. Therefore, although the power difference for each of the scenarios for the two compared models does not differ significantly among the two models, it is in favour of the two-diode model. Which implicates that the for fast and simple calculation the simplified model can also do the job.

This approach can be very successfully applied in the design process of a PV plant to forecast the output characteristics of the PV system if there is enough information about the weather conditions for a given location. This procedure can be very helpful in the process of selection of right PV module and inverter for a given location.

An optimisation technique using GA as an optimisation tool has been developed and successfully applied in the determination of the maximum power point for a bifacial PV module using to different models of solar cell. The results are compared with the analytically determined values as well as with the values given from the producer and they show good agreement.

This paper aims to investigate the moisture diffusion behavior in a system-in-package module systematically by moisture-thermalmechanical-coupled finite element modeling with different structure parameters under increasingly harsh environment.

A finite element model for a system-in-package module was built with moisture-thermal-mechanical-coupled effects to study the subsequences of hygrothermal conditions.

It was found in this paper that the moisture diffusion path was mainly dominated by hygrothermal conditions, though structure parameters can affect the moisture distribution. At lower temperatures (30°C~85°C), the direction of moisture diffusion was from the periphery to the center of the module, which was commonly found in simulations and literatures. However, at relatively higher temperatures (125°C~220°C), the diffusion was from printed circuit board (PCB) to EMC due to the concentration gradient from PCB to EMC across the EMC/PCB interface. It was also found that there exists a critical thickness for EMC and PCB during the moisture diffusion. When the thickness of EMC or PCB increased to a certain value, the diffusion of moisture reached a stable state, and the concentration on the die surface in the packaging module hardly changed. A quantified correlation between the moisture diffusion coefficient and the critical thickness was then proposed for structure parameter optimization in the design of system-in-package module.

The different moisture diffusion behaviors at low and high temperatures have seldom been reported before. This work can facilitate the understanding of moisture diffusion within a package and offer some methods about minimizing its effect by design optimization.

The aggressive competition in the real estate market forces real estate developers to tackle the challenge of selecting the best project construction phasing alternative. The real estate industry is characterized by high costs, high profit and high risks. The schedules of real estate projects are also characterized by having large number of repetitive activities that are executed over a long duration. The repetitiveness, long duration of execution, the high amounts of money involved and the high risk made it desirable to leverage the impact of changes in phasing plans on net present value of amounts incurred and received over the long execution and selling duration. This also changes the project progress, and delivery time as well as their respective impact on customer degree of satisfaction. This research addresses the problem of selecting the best phasing alternative for real estate development projects while maximizing customer satisfaction and project profit.

The research proposes a model that generates all construction phasing alternatives and performs decision-making to rank all possible phasing alternatives. The proposed model consists of five modules: (1) Phasing Sequencing module, (2) Customer Satisfaction module, (3) Cash-In calculation module, (4) Cost Estimation module and (5) Decision-making module. A case study was presented to demonstrate the practicality of the model.

The proposed model satisfies the real estate market's need for proper construction phasing plans evaluation and selection against the project's main success criteria, customer satisfaction and project profit. The proposed model generates all construction phasing alternatives and performs multi-criteria decision making to rank all possible phasing alternatives. It quantifies the score of the two previously mentioned criteria and ranks all solutions according to their overall score.

The research proposes a model that assist real estate market's need for proper construction phasing plans evaluation and selection against the project's main success criteria, customer satisfaction and project profit. The proposed model can be used to conclude general guidelines and common successful practices to be used by real estate developers when deciding the construction phasing plan. In this study the model is based on business models where all the project units are sold, rental cases are not considered. Also, the budget limitations that might exist when phasing is not considered in the model computations.

The model can be used as a complete platform that can hold all real estate project data, process revenues and cost information for estimating profit, plotting cash flow profiles, quantifying the degree of customer satisfaction attributable to each phasing alternative and providing recommendation showing the best one. The model can be used to conclude general guidelines and common successful practices to be used by real estate developers when tackling the challenge of selecting construction phasing plans.

Recent trends have shown the integration of Chinese word segmentation (CWS) and part-of-speech (POS) tagging to enhance syntactic and semantic parsing. However, the potential utility of hierarchical and structural information in these tasks remains underexplored. This study aims to leverage multiple external knowledge sources (e.g. syntactic and semantic features, lexicons) through various modules for the joint task.

We introduce a novel learning framework for the joint CWS and POS tagging task, utilizing graph convolutional networks (GCNs) to encode syntactic structure and semantic features. The framework also incorporates a pre-defined lexicon through a lexicon attention module. We evaluate our model on a range of public corpora, including CTB5, PKU and UD, the novel ZX dataset and the comprehensive CTB9 dataset.

Experimental results on these benchmark corpora demonstrate the effectiveness of our model in improving the performance of the joint task. Notably, we find that syntax information significantly enhances performance, while lexicon information helps mitigate the issue of out-of-vocabulary (OOV) words.

This study introduces a comprehensive approach to the joint CWS and POS tagging task by combining multiple features. Moreover, the proposed framework offers potential adaptability to other sequence labeling tasks, such as named entity recognition (NER).

In 2022, the Baltimore professional development school (PDS) partner schools, John Ruhruh Elementary/Middle School (JREMS) and Notre Dame of Maryland University (NDMU) received funds through a Maryland Educational Emergency Revitalization (MEER) grant to determine (a) to what extent additional resources and professional development would increase JREMS teachers’ efficacy in technology integration and (b) to what extent NDMU professional development in the form of workshops and self-paced computer science modules would result in greater use of technology in the JREMS K-8 classrooms. Results indicated a statistically significant improvement in both teacher comfort with technology and integrated use of technology in instruction.

Survey data were collected on teacher-stated comfort with technology before and after grant implementation. Teachers’ use of technology was also measured by unannounced classroom visits by administration before and after the grant implementation and through artifacts teachers submitted during NDMU professional development modules.

Results showing significant increases in self-efficacy with technology along with teacher integration of technology exemplify the benefits of a PDS partnership.

This initiative was original in its approach to teacher development by replacing required teacher professional development with an invitation to participate and an incentive for participation (a personal MacBook) that met the stated needs of teachers. Teacher motivation was strong because teammates in a strong PDS partnership provided the necessary supports to induce changes in teacher self-efficacy.

The need to enhance student support is evident in higher education (HE) curricula. In addition to the complications created by the COVID-19 pandemic, the current strategies used in academia are criticised for their lack of appropriate student support in HE. The study focused on the themes under Section 4 of the National Student Survey (NSS): availability to contact tutors, receiving good advice and guidance and availability of good advice. The study aimed to provide recommendations for enhancing academic support by developing drivers that need implementation during course delivery.

A documental analysis and a qualitative survey were adopted for this study. A documental analysis of 334 mid-module reviews (MMRs) from levels three to six students in the built environment (BE) discipline. Critical themes identified from the MMRs were fed forward in developing a questionnaire for academics. A sample of 23 academics, including a Head of school, a Principal lecturer, Subject leads and Lecturers, participated in the questionnaire survey. Content analysis is adopted through questionnaire data to develop drivers to enhance academic support in BE. These drivers are then modelled by interpretive structural modelling (ISM) to identify their correlation to NSS Section 4 themes. A level partition analysis establishes how influential they are in enhancing academic support.

The study identified nine drivers, where two drivers were categorised as fundamental, two as significant, four as important, and one insignificant in enhancing academic support in HE. Module leaders’/tutors’ improving awareness and detailing how academic support is provided were identified as fundamental. Differentiating roles in giving advice and the importance of one-to-one meetings were identified as significant. A level partitioning diagram was developed from the nine drivers to illustrate how these drivers need to be implemented to promote the best practices in academic support in HE.

The identified drivers and their categories can be used to set prioritised guidelines for academics and other educational institutions to improve students’ overall satisfaction.

Novelty from the study will be the developed drivers and the level partitioning diagram to assist academics and academic institutions in successfully integrating academic support into HE curricula.