Aspects Of Design Research Articles

Library design, amenities, and services for enhancing the reading experience

This study explored user perspectives on library design elements, amenities, and services that can potentially enhance the reading experience. Data were collected through a self-administered questionnaire with 537 students. The majority of respondents (86.8%) reported enjoying reading in library environments. Regarding design aspects, having an integrated layout with shelves, chairs and tables cohesively arranged in the reading room was most appealing. Users also favored the presence of other readers and collaborative table designs. Group-study rooms were viewed positively, with 51.2% strongly liking the option to use them and perceiving benefits for learning. While opinions were divided on introducing food services, most supported adding new technologies like 3D printers, art galleries, and museum exhibits. Chi-square analysis revealed significant associations between enjoying library reading and preferences for shared reading spaces with other users (p = 0.024) and group-study rooms. In conclusion, fostering a sense of community, scholarly exchange, and integrated space conducive to focused reading emerged as key factors for enhancing the reading experience in libraries.

(Invited) Enhancing Stability for Elevated Temperature CO2 Electrolysis: Insights from Catalyst Development and Impact of Carbon Protection

While most lab-scale CO2 reduction experiments are conducted at ambient temperatures (20-25°C), maintaining such low temperatures at an industrial scale will become challenging due to joule heating. In our study, we pushed the boundaries further than the already existing systems, opting for a higher temperature of 85°C for CO2 reduction. This deliberate choice was made to accentuate the deviations from ambient conditions and explore their effects on CO2 electrolyzers.Our current emphasis at elevated temperatures focuses on catalyst development, specifically addressing stability issues induced by the increased temperature. Nevertheless, it was soon discovered that several modifications were required to the system, both in terms of cell materials and operational conditions (e.g. gaskets, pressure arrangements, GDE type, etc.) to allow stable operation at 85°C for extended duration and especially to avoid flooding. Prior research provided valuable insights into the multifaceted impacts of elevated temperatures on the electrochemical reduction of CO2, and proof they go beyond catalyst kinetics. With this in mind, we have synthesized a series of bismuth-related nano catalysts, including oxidized, metallic and carbon containing nanoparticles, and have quantitatively evaluated their activity, selectivity towards formate, and stability at 85°C. Preliminary observations suggest that catalysts without carbon exhibit high initial selectivity, but demonstrate diminished stability over a 24-hour timeframe compared to catalysts with a protective carbon layer. Specifically, we observed an initially high faradaic efficiency toward formate of 85% for commercial bismuth oxide nanoparticles, in contrast to 75% for bismuth nanoparticles with carbon. However, over a 24-hour timeframe, the carbon-free catalyst showed a 20% decrease in FE, while for the carbon-containing catalyst this drop was halved in the same timeframe.By evaluating the role of carbon additives (e.g. their oxidation state, pore size, surface area, hydrophobicity, etc.) our results yield valuable insights into the impact of carbon additives on stability and overall performance, especially at elevated temperature. These findings uncovered critical aspects for catalyst design, providing essential knowledge for future larger-scale applications. Moreover, we shed light on the required advancements and remaining challenges for elevated temperature CO2 reduction electrolysis, which we believe will become the predominant mode of operation for CO2 electrolysis in the future. Figure 1

Solar Thermal Integrated Alkaline Water Electrolyser for Fast Ramping Up

There is significant recent interest in clean hydrogen production from renewable water electrolysis. In general, alkaline water electrolysis (AWE) operates with a voltage of ca. 2 V due to ohmic and overpotential-derived loss (0.7~0.8 V) [1]. In addition, it requires 0.5~1 hours for the ramping-up, which hinders its wider implementation. Our intended solution is to introduce a heat-tolerant electrolyser integrated with a chemically resistant solar thermal collector, providing a heated electrolyte to increase the reactivity [2] and reduce the ramp-up time. In this work, we discuss key dimensional design aspects of the thermal collector for alkaline electrolyser, considering the control feasibility of operating temperature and fluidic parameters. Furthermore, we also demonstrate our proof-of-concept experimental results using our alkaline electrolyser integrated with the solar-thermal collector at an elevated temperature. The time-dependent current output of the solar-thermal integrated AWE over the first 1 h yielded ca. 70% higher compared to the conventional AWE system. E. Amores, J. Rodríguez, J. Oviedo, A. De Lucas-Consuegra, Open Eng 2017, 7,141–152.B. Zhang, Q. Daniel, M. Cheng, L. Fan, L. Sun, Faraday Discuss 2017, 198, 169–179 Figure 1

Awe Inducing Elements in Virtual Reality Applications: A Prospective Study of Hospitalized Children and Caregivers.

Background: Hospitalized pediatric patients and their caregivers often experience anxiety and fear, resulting in withdrawal and aggression. Despite virtual reality (VR) being a safe and effective anxiolytic, it is unknown what software design aspects contribute to its effectiveness. This prospective observational study evaluated which VR application elements increased awe, which is correlated with improved behavior and satisfaction. Methods: Patients aged 6 to 25 years and their caregivers at an academic pediatric hospital interacted with a custom VR application that compared design aspects, including environment, graphics fidelity, and presence of a motivational character. Outcomes investigated self-reported awe, vastness, accommodation, and engagement. Data were analyzed using repeated measure ANOVA tests and correlation analyses. Results: A total of 202 participants were enrolled, and 179 (88 pediatric patients, 91 adult caregivers) were included in the final analysis. A fictional environment was more effective at increasing awe in pediatric patients (P = 0.030) compared with a realistic environment. However, increased graphics fidelity was more effective at increasing awe in caregiver adults (P = 0.023) compared with low resolution graphics. Presence of a motivational character did not influence awe in either patients or caregivers (P = 0.432, P = 0.904, respectively). All measures of awe were positively correlated with application engagement (P < 0.005). Conclusion: In conclusion, when software developers design VR software for pediatric patients and their caregivers, fictional settings and increased graphic fidelity should be considered for pediatric patients and adults, respectively. Future studies will explore other VR elements in gameplay settings.

Bibliometric analysis of research trends in biogranulation technology for wastewater treatment.

Inadequate management and treatment of wastewater pose significant threats, including environmental pollution, degradation of water quality, depletion of global water resources, and detrimental effects on human well-being. Biogranulation technology has gained increasing traction for treating both domestic and industrial wastewater, garnering interest from researchers and industrial stakeholders alike. However, the literature lacks comprehensive bibliometric analyses that examine and illuminate research hotspots and trends in this field. This study aims to elucidate the global research trajectory of scientific output in biogranulation technology from 1992 to 2022. Utilizing data from the Scopus database, we conducted an extensive analysis, employing VOSviewer and the R-studio package to visualize and map connections and collaborations among authors, countries, and keywords. Our analysis revealed a total of 1703 journal articles published in English. Notably, China emerged as the leading country, Jin Rencun as the foremost author, Bioresource Technology as the dominant journal, and Environmental Science as the prominent subject area, with the Harbin Institute of Technology leading in institutional contributions. The most prominent author keyword identified through VOSviewer analysis was "aerobic granular sludge," with "sequencing batch reactor" emerging as the dominant research term. Furthermore, our examination using R Studio highlighted "wastewater treatment" and "sewage" as notable research terms within the field. These findings underscore a diverse research landscape encompassing fundamental aspects of granule formation, reactor design, and practical applications. This study offers valuable insights into biogranulation potential for efficient wastewater treatment and environmental remediation, contributing to a sustainable and cleaner future.

Integrating knowledge graphs into machine learning models for survival prediction and biomarker discovery in patients with non–small-cell lung cancer

Accurate survival prediction for Non-Small Cell Lung Cancer (NSCLC) patients remains a significant challenge for the scientific and clinical community despite decades of advanced analytics. Addressing this challenge not only helps inform the critical aspects of clinical study design and biomarker discovery but also ensures that the ‘right patient’ receives the ‘right treatment’. However, survival prediction is a highly complex task, given the large number of ‘omics; and clinical features, as well as the high degree of freedom that drive patient survival. Prior knowledge could play a critical role in uncovering the complexity of a disease and understanding the driving factors affecting a patient’s survival. We introduce a methodology for incorporating prior knowledge into machine learning–based models for prediction of patient survival through Knowledge Graphs, demonstrating the advantage of such an approach for NSCLC patients. Using data from patients treated with immuno-oncologic therapies in the POPLAR (NCT01903993) and OAK (NCT02008227) clinical trials, we found that the use of knowledge graphs yielded significantly improved hazard ratios, including in the POPLAR cohort, for models based on biomarker tumor mutation burden compared with those based on knowledge graphs. Use of a model-defined mutational 10-gene signature led to significant overall survival differentiation for both trials. We provide parameterized code for incorporating knowledge graphs into survival analyses for use by the wider scientific community.

Experimental and numerical based model formulation for estimation of subgrade resilient modulus using the repeated load CBR test considering in situ state of stress

Characterizing subgrade in terms of resilient modulus is a crucial aspect of flexible pavement design. This paper proposes a methodology and predictive model to estimate the resilient modulus with better consideration of subgrade soils’ in situ stress state using a simple Repeated Load CBR (RLCBR) test. RLCBR tests were conducted on eight subgrade soils at three moisture contents. Numerical studies were conducted by simulating the CBR test in the commercial package LS-DYNA® to understand the stress state under plunger loading concerning field conditions. A new model was proposed for the characterization of subgrade soils based on laboratory RLCBR tests and the FEM, considering the stress state experienced by subgrade soils in the field. The proposed model was validated using data from four other soils and showed good agreement. The study model showed a better predictive capacity for the low plastic subgrade soils than previously developed models. Practicing engineers can use the developed model for estimating the subgrade resilient modulus at the recommended stress state for mechanistic pavement design while understanding the soil’s load-deformation behavior.

Machine Learning-Driven Prediction of DLD Chip Throughput

Abstract The microfluidic chip technology, capable of manipulating fluids at the micrometer-scale, is increasingly being applied in the fields of cell biology, molecular biology, chemistry, and life sciences. The densely integrated microfluidic chip devices enable high-throughput parallel experiments and integration of various operational units. However, the development of densely integrated microfluidic chips also comes with high demands on driving equipment. Due to manufacturing processes and inherent design limitations, the driving capability of the equipment is restricted. To address potential challenges faced by microfluidic chips in the development towards integrated biological microsystems and to maximize their high-throughput performance, improvements are required not only in selecting appropriate driving equipment but also in design aspects. This study focuses on the DLD chip and delves into the complexity of microfluidic chip design. By combining Bézier curves to characterize arbitrarily shaped micropillars and conducting finite element analysis to compute the pressure field of DLD chips, we explore methods utilizing random forest, XGBoost, LightGBM, and ANN machine learning algorithms to predict the impedance of DLD chips. Our objective is to guide engineers in designing chips with smaller impedance (lower pressure drop) and larger throughput more quickly and efficiently during the design phase. Ultimately, through evaluating the predictive capabilities of the four models on new data, we select the ANN algorithm model to predict the pressure drop under different designs of DLD chips. This offers possibilities for enhancing the efficiency and integration of microfluidic technology in biomedical applications.

Проблемы правоприменения нового основания освобождения от уголовной ответственности (ст. 78.1 УК РФ)

The article provides a conceptual and legal-technical analysis of the grounds for exemption from criminal liability provided for in Article 781 of the Criminal Code of the Russian Federation. The shortcomings of the legal technique and the substantive aspect of the legislative novel are emphasized. The purpose of the work is to search for an algorithm for applying a new basis for exemption from criminal liability, since there are questions from the point of view of the flawlessness of the introduced legislative novel, namely: compliance with the general logic of the institution of exemption from criminal liability; legal and technical design and content aspect.

Exploration of alive-and-ventilator free days as an outcome measure for clinical trials of Resuscitative interventions.

Outcome selection is a critically important aspect of clinical trial design. Alive-and-ventilator free days is an outcome measure commonly used in critical care clinical trials, but has not been fully explored in resuscitation science. A simulation study was performed to explore approaches to the definition and analysis of alive-and-ventilator free days in cardiac arrest populations. Data from an in-hospital cardiac arrest observational cohort and from the Pragmatic Airway Resuscitation Trial were used to inform and conduct the simulations and validate approaches to alive-and-ventilator free days measurement and analysis. Alive-and-ventilator-free days is a flexible outcome measure in cardiac arrest populations. An approach to alive-and-ventilator free days that assigns -1 days when return of spontaneous circulation is not achieved provides a wider distribution of the outcome and improves statistical power. The optimal approach to the analysis of alive-and-ventilator free days varies based on the expected impact of the intervention under study on rates of return of spontaneous circulation, survival, and ventilator-free survival. Alive-and-ventilator free days adds to the armamentarium of clinical trialists in the field of resuscitation science.

Engineering Systems with Standards and Digital Models: Development of a 15288-SysML Grid

The paradigm shift that has spurred the fourth industrial revolution, in what is termed Industry 4.0, has ushered in the need to adopt digital technologies throughout the worldwide industrial base to support system design efforts. The adoption of digital technologies with a digital enterprise and the creation of cyber–physical systems are central tenets of Industry 4.0 and directly support profitable business models, improvements in efficiency, and ensure durable quality for the modern industrial base. However, the techniques for engineering systems require new, improved, digital life cycle process models if Industry 4.0—and the goals for its integrated systems—are to be realized. The development of a technique that improves the life cycles for systems within the digital enterprise is required. The 15288-SysML Grid described herein supports the Industry 4.0 paradigm and its associated digital enterprise. This is accomplished through (1) the application of a modern life cycle process model (i.e., the adapted diamond); (2) the utilization of international standards for systems; and (3) the adoption of the four fundamental aspects of system design supported by model-based systems engineering (MBSE) and the systems modeling language (SysML).

Planning framework for establishment of hydrogen hubs incorporating the path towards net-zero-driven policies

Hydrogen hubs are emerging as strategic solutions to meet governmental emission reduction targets. A critical aspect of hydrogen hub design involves diversifying end uses, encompassing mobility, backup/storage, heating and cooling provision, onsite electricity generation, and grid energy supply. This paper introduces a comprehensive hydrogen hub model catering to these varied applications. Additionally, a novel multi-stage planning framework is proposed to assist hub operators in making informed decisions regarding the integration of diverse renewable energy sources and infrastructure. This framework addresses varying demands efficiently and economically. Moreover, to align with governmental emission reduction policies leading to net-zero targets, a proactive strategy is suggested. Through zoning for commercial, residential, educational, and healthcare sectors, the Sydney hydrogen hub is envisioned under two scenarios: business-as-usual and towards-net-zero design. Notably, CO2 emissions are projected to decrease from 18.3 ktonnes in the former to 4.7 ktonnes in the latter scenario.

Bayesian-Edge system for classification and segmentation of skin lesions in Internet of Medical Things.

Skin diseases are severe diseases. Identification of these severe diseases depends upon the abstraction of atypical skin regions. The segmentation of these skin diseases is essential to rheumatologists in risk impost and for valuable and vital decision-making. Skin lesion segmentation from images is a crucial step toward achieving this goal-timely exposure of malignancy in psoriasis expressively intensifies the persistence ratio. Defies occur when people presume skin diseases they have without accurately and precisely incepted. However, analyzing malignancy at runtime is a big challenge due to the truncated distinction of the visual similarity between malignance and non-malignance lesions. However, images' different shapes, contrast, and vibrations make skin lesion segmentation challenging. Recently, various researchers have explored the applicability of deep learning models to skin lesion segmentation. This paper introduces a skin lesions segmentation model that integrates two intelligent methodologies: Bayesian inference and edge intelligence. In the segmentation model, we deal with edge intelligence to utilize the texture features for the segmentation of skin lesions. In contrast, Bayesian inference enhances skin lesion segmentation's accuracy and efficiency. We analyze our work along several dimensions, including input data (datasets, preprocessing, and synthetic data generation), model design (architecture, modules), and evaluation aspects (data annotation requirements and segmentation performance). We discuss these dimensions from seminal works and a systematic viewpoint and examine how these dimensions have influenced current trends. We summarize our work with previously used techniques in a comprehensive table to facilitate comparisons. Our experimental results show that Bayesian-Edge networks can boost the diagnostic performance of skin lesions by up to 87.80% without incurring additional parameters of heavy computation.

Evaluation of Sustainable Development for Low-Volume Roads in Sarawak

The national socio-economic development programme prioritises rural development, and the state of Sarawak requires many rural roads to connect existing villages to the main roads. This programme can be achieved by upgrading existing non-standard access roads or constructing new access roads. The current challenges and issues related to the sustainable development of low-volume roads (LVRs) in the Malaysian state of Sarawak were investigated in this study. The key goals of the study were to understand the challenges of sustainable development in Low Volume Roads (LVRs) in Sarawak using expert interviews as well as surveys on experienced engineers. It aimed at clarifying what LVRs are, highlighting challenges to do with designing and planning them, and also discussing pavement design aspects. The data for the research was collected through interviews with experts in state Public Work Department (PWD) and consultants with more than 15 years’ experience in road planning and construction. An online questionnaire was distributed and it targeted government agencies, consultants, contractors, with 52 respondents out of 200 possible. This helped to identify important problems associated with LVR development; it stressed that a proper knowledge of LVR design, planning difficulties and those specific concerning pavement designs is needed among professionals within Sarawak road building industry. Based on the findings, immediate need for actions was required to be taken by road agencies to investigate the development and appropriate design of LVRs also to record and gather historical data on current LVRs for further improvement.

Quantum key distribution in optical fibers: a comprehensive design view of the overall quantum layer beyond transmission

This paper reports the network operator point of view about the introduction of quantum key distribution (QKD) in optical networks to secure the data plane and/or specific applications, focusing on the design aspects that go beyond pure transmission. The functional architecture of a quantum key distribution network is depicted focusing on the integration in the existing telecommunications infrastructure. Some use cases of the utilization of the QKD layer, presenting results from in-field demonstrations, are reported together with a technology agnostic numerical model about resource sharing in a metropolitan area network environment.

Analysis of Corporate Brand Image Building Method Based on Visual Merchandising

As an important marketing strategy, visual marketing plays a pivotal role in shaping the brand image of enterprises. This paper will focus on the analysis of corporate brand image building methods based on visual merchandising, and discuss how to enhance corporate brand image through visual marketing strategies from the aspects of product design and packaging, advertising creativity, offline terminal layout and product display.

Design thinking mindset: a user-centred approach toward innovation in the Welsh creative industries

ABSTRACT The Design Thinking (DT) mindset, a fundamental aspect of User-Centred Design (UCD), is often presented as a powerful and accessible trigger for innovation. There is much discussion in the extant literature on the communication of the DT concept to non-design practitioners to encourage broader use of design-led innovation. The present study expands this discussion by exploring which DT mindset attributes are considered meaningful for non-design professionals in small and medium-sized enterprises (SMEs). It presents a UCD intervention that embeds the DT mindset attributes to introduce design-led innovation to SMEs of the Welsh creative industries. Semi-structured interviews, non-participatory observation, and reflective open-ended questioning are utilised to analyse the impact of the intervention in supporting creative industry practitioners’ understanding of the DT mindset and design-led innovation approaches. Our findings demonstrate that the intervention has prompted participants to (i) challenge the idea of ‘ideas,’ (ii) frame failure as an option, (iii) learn to trust the process, (iv) acquire innovation self-efficacy, (v) view collaboration = learning, and (vi) change their perception of innovation. We discuss how these identified ‘mindset shifts’ are valuable toward building individual innovation capacities in non-design industries.

A comprehensive study on effective triple‐phase boundary density and its correlation with active anode thickness in solid oxide fuel cells

AbstractSolid oxide fuel cells (SOFCs) are highly promising devices for efficient and low‐emission energy conversion. The effective triple‐phase boundary (TPB) density refers to the fraction of percolated TPB density that effectively contributes to the current production during cell operation. This is one of the most fundamental and least understood aspects of the cell design and performance assessment. This study methodically investigates the effective TPB density, using a computational fluid dynamics model based on the TPB‐based kinetics and its correlation with the active anode thickness. Experimental data from previously published studies with varying thicknesses of anode functional layer and operating regimes are utilized to validate the model. The results of this study reaffirm that a significant fraction of the percolated TPB density in SOFCs remains unused during cell operation. This finding emphasizes the need to consider the effective TPB density for theoretical and experimental investigations focusing on optimizing cell performance. Furthermore, an inverse relationship is observed between the effective TPB density and the active anode thickness; a lower active anode thickness corresponds to a higher effective TPB density and vice versa. These findings contribute to advancing sustainable energy systems by guiding the development of more efficient SOFC designs and operational strategies that effectively utilize TPB sites.

Investigation of seismic resistant structures with various moment-resisting frame systems and pushover analysis

Earthquakes are a serious threat in the construction of multi-storey buildings in Indonesia, which are divided into several seismic design categories. The design of seismic-resistant buildings requires the management of plastic hinges to reduce seismic loads. The aspects of seismic-resistant structural design in Indonesia are regulated by SNI 1726:2019, SNI 1727:2020, and SNI 2847:2019. Intermediate Moment Resisting Frame System (IMRFS) and Special Moment Resisting Frame System (SMRFS) are used based on seismic category and earthquake intensity. Pushover analysis is used to analyze the structures behavior when exposed to seismic loads. This research designs seismic resistant structures with IMRFS and SMRFS at different locations with the aim of assessing structural performance and gaining reinforcement to the concrete ratio, which is relevant for the design and construction of multi-storey buildings in Indonesia. The results of this research are the structural performance levels, reinforcement volume, concrete volume, and the reinforcement to concrete volume ratio. Both IMRFS and SMRFS reached Immediate Occupancy to Life Safe performance levels after the earthquake because their monitored displacement was not significantly different. The structural failure modes of both systems meet the Strong Column–Weak Beam requirements. The distribution of plastic hinges also remains in the Immediate Occupancy category.

High-quality implementation for a continuous-in-time financial API in C#

In recent years, there has been a rising interest in potentially complex software and financial industries with applications in many engineering fields. With this rise comes a host of developing a usable and consistent Application Programming Interface (API). Prioritize designing and building the software ensures to enrich the platform and emphasize inventorying APIs. In this paper, we proposed a high-quality API to implement the continuous-in-time financial model. The existing discrete framework cannot be evaluated at any time period, involving drawbacks in operating the data structures. Then, the continuous framework is implemented based on the measure theory paradigm. Our proposal uses mathematical modeling, which consists of some objects as measures and fields. It is suitable to develop this API in C# to provide the requirement quality in programming language professionally. This also integrates demands, codes, and verification in the system development life cycle. The advantages are aimed at increasing the structuring and readability. The presented work provides an overview of the design, implementation, testing, and delivery aspects of the API, highlighting the importance of architecture, testing, and numerical choices. The article gives an overview of the API by describing the implementation concerning the data structures and algorithms. These algorithms are based on using the Task Parallel Library (TPL) that makes the API easier and more fruitful for data parallel to benefit from the advantages provided by the .NET Framework.