Large-scale Adoption Research Articles

Photovoltaic Integrated Shading Devices in the Retrofitting of Existing Buildings on Chinese Campuses Within a Regional Context

Retrofitting existing buildings to be more energy-efficient is a tremendous contribution to the sustainability of society. The application of photovoltaic integrated shading devices (PVSDs) accords with this ambition by blocking out unwanted radiant heat gain and generating clean electricity. The deployment of PVSDs needs sensible design strategies to optimize the production of renewable energy while retaining the aesthetic quality of the built-up environment, especially in historic campuses. The concept was tested in a case study of buildings in South China University of Technology (SCUT) using Ladybug 1.4.0 and PVsyst 7.2, utilizing the existing “Xia’s shading” design method in historical environments and optimizing the design from the perspective of photovoltaic performance. Firstly, the photovoltaic (PV) panels were integrated as architectural components, and the parameters were incorporated into a mathematical equation based on “Xia’s shading” design method. This was followed by the assessment of the solar energy harvesting potential based on simulated annual solar irradiation values. Lastly, the PV panels’ solar irradiation potential under these different parameters was shown in figures to identify the optimum parameters combination for PVSD applications. The proposed methodology could evolve as a design tool and thus further assist in promoting the large-scale adoption of PVSDs in retrofit projects.

Wearable sensors for cardiological monitoring in the SUS: regulatory challenges and impacts on Brazilian Public Health

Cardiovascular diseases are the leading cause of death in Brazil, with approximately 400,000 deaths annually, which corresponds to almost 30% of all deaths in the country. In addition to the impact on mortality, these conditions generate an enormous economic burden for the health system, with costs associated with the treatment of diseases such as heart attack and heart failure exceeding R$50 billion per year. In this context, the use of wearable devices for continuous monitoring of cardiovascular parameters emerges as an opportunity to improve the management and prevention of these diseases. This study conducts a scoping review of the regulatory challenges involved in the incorporation of wearables into the Unified Health System (SUS), with a focus on cardiological monitoring. Articles published between 2019 and 2024 were analyzed, discussing issues such as data accuracy, interoperability between systems, protection of sensitive data, and training of health professionals. In addition, the study examines the economic and social implications of the adoption of these technologies in a context of profound socioeconomic inequalities. The results indicate that, although wearables have the potential to transform cardiovascular care in Brazil, regulatory and technical barriers still limit their large-scale adoption in the SUS. Adequate regulation, aligned with the General Data Protection Law (LGPD), and investments in infrastructure and training are essential for the safe and effective integration of these technologies into public health.

Enhancing English education with Natural Language Processing: Research and development of automated grammar checking, scoring systems, and dialogue systems

Abstract. In this paper, we investigate the scope for transformational change that these technologies offer to English education. We examine how NLP can support English education through automated grammar checking having students text checked for grammatical errors in real time, automated scoring systems which evaluate written and verbal English against predefined criteria, and dialogue systems which interacts with learners in English to help develop their speaking and listening skills in an engaging and non-judgmental environment. We document the current status of these NLP applications, examine their educational benefits, highlight some of the challenges faced by the technologies and finally discuss the way forward for large-scale adoption of such technologies, bringing the research to a logical conclusion. We hope that this study will provide a comprehensive understanding of how NLP can be employed to transform education in English and also highlight some of the associated challenges and ethical dilemmas.

Hydrogen in Burners: Economic and Environmental Implications

For centuries, fossil fuels have been the primary energy source, but their unchecked use has led to significant environmental and economic challenges that now shape the global energy landscape. The combustion of these fuels releases greenhouse gases, which are critical contributors to the acceleration of climate change, resulting in severe consequences for both the environment and human health. Therefore, this article examines the potential of hydrogen as a sustainable alternative energy source capable of mitigating these climate impacts. It explores the properties of hydrogen, with particular emphasis on its application in industrial burners and furnaces, underscoring its clean combustion and high energy density in comparison to fossil fuels, and also examines hydrogen production through thermochemical and electrochemical methods, covering green, gray, blue, and turquoise pathways. It discusses storage and transportation challenges, highlighting methods like compression, liquefaction, chemical carriers (e.g., ammonia), and transport via pipelines and vehicles. Hydrogen combustion mechanisms and optimized burner and furnace designs are explored, along with the environmental benefits of lower emissions, contrasted with economic concerns like production and infrastructure costs. Additionally, industrial and energy applications, safety concerns, and the challenges of large-scale adoption are addressed, presenting hydrogen as a promising yet complex alternative to fossil fuels.

Just another tool in their repertoire: uncovering insights into public and patient perspectives on clinicians' use of machine learning in perioperative care.

Successful implementation of machine learning-augmented clinical decision support systems (ML-CDSS) in perioperative care requires the prioritization of patient-centric approaches to ensure alignment with societal expectations. We assessed general public and surgical patient attitudes and perspectives on ML-CDSS use in perioperative care. A sequential explanatory study was conducted. Stage 1 collected public opinions through a survey. Stage 2 ascertained surgical patients' experiences and attitudes via focus groups and interviews. For Stage 1, a total of 281 respondents' (140 males [49.8%]) data were considered. Among participants without ML awareness, males were almost three times more likely than females to report more acceptance (OR = 2.97; 95% CI, 1.36-6.49) and embrace (OR = 2.74; 95% CI, 1.23-6.09) of ML-CDSS use by perioperative teams. Males were almost twice as likely as females to report more acceptance across all perioperative phases with ORs ranging from 1.71 to 2.07. In Stage 2, insights from 10 surgical patients revealed unanimous agreement that ML-CDSS should primarily serve a supportive function. The pre- and post-operative phases were identified explicitly as forums where ML-CDSS can enhance care delivery. Patients requested for education on ML-CDSS's role in their care to be disseminated by surgeons across multiple platforms. The general public and surgical patients are receptive to ML-CDSS use throughout their perioperative care provided its role is auxiliary to perioperative teams. However, the integration of ML-CDSS into perioperative workflows presents unique challenges for healthcare settings. Insights from this study can inform strategies to support large-scale implementation and adoption of ML-CDSS by patients in all perioperative phases. Key strategies to promote the feasibility and acceptability of ML-CDSS include clinician-led discussions about ML-CDSS's role in perioperative care, established metrics to evaluate the clinical utility of ML-CDSS, and patient education.

Assessment of local mechanical properties of laser powder bed fused aluminium alloy by non-destructive testing based on FIMEC indentation

Laser powder bed fusion process is a versatile metal additive manufacturing process. Although significant progress has been made so far, there is still limited large-scale adoption of this technique by the industry. The main problems are repeatability and lack of proper knowledge. In this work, an innovative and non-destructive testing methodology, based on flat-top cylinder indentation, was used to define the mechanical properties of laser powder bed fused aluminium alloy to highlight any variations induced by the combination of process parameters, for global characterization, and by the building direction, for local characterization. Results show similar or improved global mechanical properties of the laser powder bed fused specimens when compared to traditional die-casted ones. Indentation tests highlight a local dependence of properties along the building direction in favor of the upper part of the samples.

Designing a Bidirectional Power Flow Control Mechanism for Integrated EVs in PV-Based Grid Systems Supporting Onboard AC Charging

This paper investigates the potential use of Electric Vehicles (EVs) to enhance power grid stability through their energy storage and grid-support capabilities. By providing auxiliary services such as spinning reserves and voltage control, EVs can significantly impact power quality metrics. The increasing energy consumption and the global imperative to address climate change have positioned EVs as a viable solution for sustainable transportation. Despite the challenges posed by their variable energy demands and rising numbers, the integration of a smart grid environment with smart charging and discharging protocols presents a promising avenue. Such an environment could seamlessly integrate a large fleet of EVs into the national grid, thereby optimizing load profiles, balancing supply and demand, regulating voltage, and reducing energy generation costs. This study examines the large-scale adoption of EVs and its implications for the power grid, with a focus on State of Charge (SOC) estimation, charging times, station availability, and various charging methods. Through simulations of integrated EV–PV charging profiles, the paper presents a lookup-table-based data estimation approach to assess the impact on power demand and voltage profiles. The findings include multiple charging scenarios and the development of an optimal control unit designed to mitigate the potential adverse effects of widespread EV adoption.

A Novel Instructional Design Model for Developmental Researchers and Instructional Design Practitioners in Pattern Construction Open Education

Advances in fashion pattern construction technologies continually introduce novel approaches that address the limitations of existing practices. To keep pace, these emerging technologies must be widely disseminated to update practitioners’ skills and advance current practices. The literature identifies two primary dissemination methods: the traditional instructor-led method and the learning management systems-based open educational resources (LMS-based OER) method. The former, heavily reliant on apprenticeships for technical skill development, is inefficient, ineffective, and unsustainable. In contrast, the OER-based approach offers a more efficient, effective, and sustainable alternative, positioning it as the superior method for disseminating emerging technologies in this field. However, the effective establishment and implementation of effective OERs require an OER-oriented instructional design model (IDM) tailored to the specific learning needs and large-scale adoption theories pertinent to pattern construction. Currently, such a model is absent, hindering the field’s transition to a theory-based dissemination approach customised to its unique practices and impeding the efficient, widespread adoption of novel pattern construction technologies. Using a developmental approach, this study addresses this gap by introducing a novel OER-oriented IDM termed OER-PattEdu. This study contributes to both theoretical and methodological literature and holds practical implications for transforming lifelong learning and professional development in the field into a more efficient and sustainable paradigm.

SUSTAINABLE URBAN TRANSFORMATION:

The integration of electric mobility with Smart Grids represents a transformative strategy for the development of sustainable urban systems, characterized by a reduced carbon footprint and high energy efficiency. This article carries out a systematic review with a bibliometric approach on the intersection between these two concepts, exploring not only the operational and environmental advantages, but also the social and economic implications of this synergy in building smart cities. The results indicate that the combination of electric mobility and Smart Grids is a rapidly growing area of study, with a significant increase in publications in high-impact scientific journals. Authors from globally renowned institutions have contributed to the consolidation of this field of research, reinforcing its relevance in the context of sustainable urban policies. Furthermore, an analysis of the literature reveals that Smart Grids, by incorporating new communication and information technologies, not only enable the large-scale adoption of electric mobility, but also promote the integrated and efficient management of urban infrastructure. This interconnectivity between systems allows for better optimization of the use of resources, as well as supporting the transition to more sustainable and resilient urban mobility.

Evaluation of transition to 100% electric vehicles (EVs) by 2052 in the United States

With the rising need to transition from fossil fuel consumption to renewables, the transportation industry is foreseeing large-scale adoption of electric vehicles (EVs). According to various studies, the petroleum resources will last until or around 2052. Utilizing the US Federal Highway Administration (FHWA) published data on the number of registered vehicles by each state, the profile of vehicles by 2052 was forecasted using a time series. The vehicle profile comprised vehicles by each type classified as automobiles, buses, trucks, and motorcycles. This future profile became the basis of the quantification of total EVs by the end of 2052. Based on the available fuel economy data published by the US Department of Energy, the average energy consumption per mile (kWh/mile) of each vehicle type was factored into the energy demand calculation for 100% electrification by 2052. A roof-top solar photovoltaic system is easy to install on unoccupied roof space for US house owners. Obtaining the capacity of such a roof-top solar PV system acts as a good decision-making criterion for both house owners and developers. However, for city-dwellers living in multi-family residential buildings, the potential of utilization of roof-top solar PV becomes a subject of future work when effective sharing of resources for local power generation by renewables and development of community-shared EV charging infrastructure is concerned. How well EVs performed against internal combustion engine (ICE) vehicles in terms of fuel efficiency and reduced carbon emissions is a driving factor for this transition. For energy demand calculations, a base model was framed, and results were obtained by utilizing mathematical and statistical tools. After accounting for the effects of improved fuel efficiency or reduced energy consumption by EVs over time, the model was modified to obtain revised energy demand and, thus, effective energy generation required. Accordingly, the capacity of EV charging infrastructure required by each state determined the need for preparation by utility companies and local jurisdictions. With limitations on battery size and thus increased frequency for drivers to return for charging, additional fast chargers (level 3) at existing gasoline stations become an option that requires further assessment.

Harnessing explainable Artificial Intelligence (XAI) for enhanced geopolymer concrete mix optimization

Geopolymer concrete (GC) emerges as a sustainable alternative yet faces challenges in achieving optimal resource utilization for strength development. Balancing these aspects is crucial for its large-scale adoption as a sustainable material. The type and dosage of precursors, activator, curing, and mixing conditions influence compressive strength, setting time, and workability. Moreover, multiple experimental trials are required for a desirable geopolymer blend. Even the experimental parameters alone do not meet the design principles concerning sustainable construction. This paper presents a study on the mix design and interpretation of machine learning techniques (MLT) with XAI. To train the model, extensive experimental databases using the shapley additive explanations (SHAP) technique rank input factors that impact the strength aspect. The prediction models' performance was compared using coefficient of determination (R2) and root mean square error (RMSE). SHAP interpretations reveal that temperature, Na to Al ratio, and NaOH molarity are the main factors influencing the compressive strength of GC. Further, these parameters were crucial in developing the dense geopolymer matrix. By integrating XAI into the MLT approach, we have also opened new criteria for understanding the complex relationships between geopolymer concrete potential parameters and their compressive strength.

Codifying transformative learnings: Examining the codified boundary object characteristics that afford project-to-project learning between sustainability demonstrations

Sustainability demonstration projects, which bring emerging technologies closer to large-scale adoption through public-private collaborations, are critical for sustainable development and climate change response. These projects are motivated by knowledge development however immature practices are resulting in missed opportunities for project-to-project learning. This challenge is consistent with cross-domain findings that effective project learning is complex and rare, constrained by temporal, geographical and organisational barriers. A key approach to spanning scale-barriers and community-of-practice boundaries is the externalised sharing of codified learnings within boundary objects, where these objects require structural characteristics that afford project-to-project learning. To date, these characteristics have not been studied in sustainability demonstration contexts, forming a significant research gap. We conduct an integrative review of the cross-domain literature and, as a new contribution to knowledge, propose a reconceptualised 13-characteristic schema for codified boundary objects affording project-to-project learning between sustainability demonstrations. The study advances the theory of knowledge sharing from projects for sustainability by putting boundary object characteristics into context providing insight into enabling and restricting potential along with identifying key research gaps for future research.

Green Hydrogen Production From Non-Traditional Water Sources: A Sustainable Energy Solution With Hydrogen Storage and Distribution.

Green hydrogen development plays an essential role in creating a sustainable and environmentally conscious society while reducing reliance on traditional fossil fuels. Proton Exchange Membrane Water Electrolysers (PEMWEs), are sensitive to water quality, with various impurities impacting their efficiency, the quality of the hydrogen produced, and the device's lifespan. High-purity water is required for PEM electrolyzers; Type II water, which is required for commercial electrolyzers, must have a resistivity greater than 1 MΩ cm, sodium, and chloride concentrations less than 5 μg/L, and total organic carbon (TOC) content less than 50 parts per billion. The majority of electrolyzers operate on freshwater, or total dissolved solids (TDS) <0.5 g/kg, whereas brackish, rainwater, wastewater, and seawater have TDSs of 1-35 g/kg, 0.01-0.15 g/kg, 0.5-2 g/kg, and 35-45 g/kg, respectively. This critical review offers, for the first time, a comprehensive overview of relevant impurities in operating electrolyzers and their impact. The findings of this study indicate that electrolysis-based H2 processes are promising options that contribute to the H2 production capacity but require improvements to produce larger competitive volumes. In addition, the main challenges and opportunities for generating, storing, transporting, and distributing hydrogen, as well as large-scale adoption are discussed.

Empowering E-mobility: Day-ahead dynamic time of use tariff for electric vehicle charging

The greenhouse gases and preservation of the environment have been the most prioritized concern in many countries and international organizations, leading to set aims and initiatives for lowering the dependency on fossil fuels and minimizing carbon emissions. As a part of many initiatives, great emphasis is given to promoting electric vehicles (EVs) over their traditional carbon fuel-based counterpart. To support large-scale EV adoption, a dynamic tariff scheme is required that addresses complex issues like user comfort, savings, and utility revenue. In this research work, a dynamic pricing scheme is modeled for the centralized EV charging stations (EVCS) on a residential feeder. This pricing scheme integrates the existing static time of use (ToU) pricing to an hourly dynamic ToU tariff that varies according to the demand level in the residential feeder. The proposed tariff plan provides a day-ahead dynamic structure with a probable price variation range to aid the EV owners and the day-ahead charging mechanism to schedule the EVs beforehand. The developed scheme calculates the returns and adjusts the tariff including the demand charge and the recovery component on a daily, monthly, and yearly basis for the unsatisfactory investment revenue. Also, user convenience is ensured by setting the minimum possible tariff as well as satisfying the minimum revenue target with the integration of particle swarm optimization (PSO) in designing the pricing structure. The simulation results demonstrate the effectiveness of the proposed dynamic pricing scheme maintaining a trade-off between the interests of all parties involved. Moreover, the simulation outcome of the test case indicates that EV owners can save more than 32 % on charging bills if they plan correctly. On the other hand, the utility can earn more than 2.4 times the existing rate during the peak time of the residential feeder.

Feasibility of utilizing recycled coarse aggregates in commercial concrete production

This study addresses a critical gap in circular construction practices by assessing the use of high-quality Recycled Coarse Aggregates (RCA) from end-of-life concrete on an industrial scale. Unlike previous studies, which predominantly relied on theoretical mix designs or laboratory-level experiments, this research focuses on real-world applicability, employing commercially produced RCA and conventional production methods in industrial settings to identify upscaling challenges. Advanced Dry Recovery technology is utilized to produce high-quality RCA for both ready-mix and prefab concrete production. To ensure practical relevance, the research examines three water-to-cement ratios for ready-mix concrete and three strength classes for prefab concrete, all prepared and cast in a commercial setting using standard industrial practices. The results show that by selecting the appropriate application for RCA, there is potential for concrete companies to produce mixes using 100% RCA that meet standard requirements in terms of fresh, mechanical, and durability properties without the need for extra treatments or specific mixing methods, particularly when the water absorption of RCA is less than 4%. Achieving optimal performance requires adjustments in the mix design, specifically by considering the effective water-to-cement ratio. Additionally, the study underscores the impact of the parent concrete's properties on the RCA quality. This research not only demonstrates the feasibility of employing RCA in industrial-scale concrete production along with its associated challenges but also highlights the potential for enhancing circularity in the construction industry through large-scale adoption of RCA, thereby contributing to sustainable and circular construction practices.

Role of Technology in Developing Indian Print Industry

Three-dimensional (3D) printing enables patient-specific anatomical level productions with high adjustability and resolution in microstructures. With cost-effective manufacturing for high productivity, 3D printing has become a leading healthcare and pharmaceutical manufacturing technology, which is suitable for variety of applications including tissue engineering models, anatomical models, pharmacological design and validation model, medical apparatus and instruments. Construction industry is very labor-intensive and one of the major sources of employment in the world. The industry is experiencing low productivity with minimum technological innovations for decades. In recent times, various automation technologies including 3D printing have received increasing interests in construction. 3D printing in construction is found to be very promising to automate the construction processes and have the potential of saving laborious work, material waste, construction time, risky operation for humans, etc. There has been a comprehensive body of research conducted to understand the recent advances, future prospects and challenges of large-scale adoption of 3D printing in construction projects.

System development for enhancing social media advertisement engagement through XLNet-based personality classification

This research focuses on addressing the challenge of implementing personalized advertisements in the retail industry, where existing methods often face complexities that hinder their swift and large-scale adoption. The primary objective of this study was to develop a scalable and efficient social media advertisement personalization system by employing advanced personality classification techniques. The system utilizes the myPersonality dataset, grounded in the Big 5 OCEAN traits theory, to accurately classify user personalities. By integrating the XLNet model, optimized for personality classification, the system achieves a classification accuracy of 97.47 %, with precision, recall, and F1-Score values of 0.95, 0.94, and 0.94, respectively. The findings demonstrate that personalized advertisements, driven by accurately classified personality traits, significantly enhance user interaction rates, showing a 24 % improvement over generalized advertisements. This improvement in engagement suggests that the system can effectively personalize advertisements to resonate more deeply with users, fostering stronger connections between users and the advertised content. The proposed system's high accuracy and improved interaction rates make it a valuable addition to current marketing strategies, enhancing both engagement and conversion rates. This innovative approach has the potential to transform personalized advertising, making it more effective and widely adoptable within the marketing sector

Greenhouse gas emissions in US beef production can be reduced by up to 30% with the adoption of selected mitigation measures

Greenhouse gas (GHG) emissions from beef production in the United States are unevenly distributed across the supply chain and production regions, complicating where and how to reduce emissions most effectively. Using spatially explicit life cycle assessment methods, we quantify the baseline GHG emissions and mitigation opportunities of 42 practices spanning the supply chain from crop and livestock production to processing. We find that the potential to reduce GHGs across the beef sector ranges up to 30% (20 million tonnes CO2e reduced and 58 million tonnes CO2 sequestered each year relative to the baseline) under ubiquitous adoption assumptions, largely driven by opportunities in the grazing stage. Opportunities to reduce GHGs in the feed, grazing and feedlot stages vary across regions, yet large-scale adoption across the entire beef supply chain is important. These findings reveal promising locations and practices to invest in to advance mitigation goals and an upper-end theoretical potential for mitigation in the beef industry.

A type of multifunctional cellulose nanocrystal composite silicone-based polymer coating for marine antibiofouling

Nanocomposite polymer coatings are being used as a new generation of marine antibiofouling coatings because of their toxin-free chemical composition and ease of large-scale adoption. Cellulose nanocrystal (CN) exhibits significant potential for composite reinforcement. Herein, CN was surface-modified via α,ω-bis(3-(2-hydroxyl-terminated polydimethylsiloxane (HTPDMS), resulting in dihydroxyl-terminated poly(dimethylsiloxane)-grafted CN (HP-g-CN). The amine-terminated PDMS as the foundational component was sequentially reacted with isophorone diisocyanate, isophthalaldehyde, and carbon disulfide to produce PDMS-based poly (urea-thiourea-imine) (PDMS-PUTI). Subsequently, a composite (PDMS-PUTI/HP-g-CN) was produced through physical blending. The intrinsic imine bonds and dynamic hydrogen-bonding network were responsible for the self-healing properties, which achieved a healing efficiency of up to 89.2 %. HP-g-CN was grafted with the non-leaching lubricant, HTPDMS, resulting in improved mechanical properties (1.38 MPa of ultimate strength) and adhesion strength (2.43 MPa), along with the self-cleaning and self-lubricating performance (0.700 coefficient) of the coating. Additionally, the fouling resistance to bovine serum albumin (BSA, 10.44 μg cm−2), bacteria (∼97.08 % and ∼ 98.05 % reduction for Pseudomonas sp. (P. sp.) and Shewanella sp. (S. sp.), respectively), and diatoms (∼27 cells mm−2) was further enhanced. Marine field tests conducted over 90 days revealed that the coatings were static fouling-resistant for an extended period. This study demonstrated a multifunctional, high-performance, and environmentally friendly nanocomposite polymer coating for preventing marine biofouling.

Implementation of the Acute InpatientMedicine-High Reliability, Learning Environment, and Workforce Development Initiative (AIM-HI) in rural Veterans Health Administration hospitals: A mixed methods evaluation protocol.

Few rural hospital medicine programs include workforce development training that provides social and professional support for interdisciplinary teams. Even fewer include training that creates supportive learning environments that result in higher staff satisfaction, lower burnout, and reduced turnover. The Acute Inpatient Medicine-High Reliability, Learning Environment, and Workforce Development Initiative (AIM-HI) aims to create supportive learning environments in Veterans Health Administration (VA) rural hospital medicine teams. AIM-HI is a type II hybrid implementation study utilizing a convergent mixed methods approach to evaluate the Relational Playbook, a workforce development intervention, and three implementation strategies: behavioral nudges, learning and leadership collaboratives, and leadership coaching. AIM-HI implementation will occur in waves, enrolling additional hospitals every 12 months. In the first wave, AIM-HI will be implemented at three tertiary VA hospitals that treat at least 1000 rural Veterans annually and have an active inpatient hospital medicine program. The primary outcomes in year 1 will be the acceptability, appropriateness, and feasibility of AIM-HI assessed through participant surveys and interviews. In subsequent years, trends in the learning environment, job satisfaction, burnout, and turnover scores will be assessed using a linear mixed-effect model. The anticipated impact of AIM-HI is to evaluate the utility of the implementation strategies and assess trends in Playbook intervention outcomes. The Playbook has strong face validity; however, before large-scale adoption across the VA enterprise, it is essential to establish the acceptability, appropriateness, and feasibility of the Playbook and implementation strategies, as well as to gather data on AIM-HI effectiveness.