Search Results (117)

In response to global sustainability challenges and digital transformation, this conceptual paper explores the intersection of Artificial Intelligence (AI), Human Resource Management (HRM), and Circular Economy (CE). Drawing on Resource-Based View, Stakeholder Theory, Institutional Theory, and the Socio-Technical Systems perspective, we propose an integrated framework in which AI and HRM function as complementary enablers of sustainable, circular transformation. The framework identifies enablers (e.g., green HRM, digital infrastructure), barriers (e.g., ethical concerns, skill gaps), and contextual mediators. This study contributes to sustainability and digital innovation literature and suggests practical pathways for ethically aligning workforce and AI capabilities in CE adoption. Full article

Walking and cycling, as active modes of transportation, play a vital role in advancing sustainable urban mobility by reducing emissions and improving public health. However, widespread adoption faces challenges such as inadequate infrastructure, safety concerns, socio-cultural barriers, and policy limitations. This study systematically reviewed 56 peer-reviewed articles from 2004 to 2024, across 30 countries across five continents, employing the Capability, Opportunity and Motivation-Behaviour (COM-B) framework to identify the main drivers of walking and cycling behaviours. Findings highlight that the lack of dedicated infrastructure, inadequate enforcement of road safety measures, personal and traffic safety concerns, and social stigmas collectively hinder active mobility. Strategic interventions such as developing integrated cycling networks, financial incentives, urban planning initiatives, and behavioural change programs have promoted increased engagement in walking and cycling. Enhancing urban mobility further requires investment in pedestrian and cycling infrastructure, improved integration with public transportation, the implementation of traffic-calming measures, and public education campaigns. Post-pandemic initiatives to establish new pedestrian and cycling spaces offer a unique opportunity to establish enduring changes that support active transportation. The study suggests expanding protected cycling lanes and integrating pedestrian pathways with public transit systems to strengthen safety and accessibility. Additionally, leveraging digital tools can enhance mobility planning and coordination. Future research is needed to explore the potential of artificial intelligence in enhancing mobility analysis, supporting the development of climate-resilient infrastructure, and informing transport policies that integrate gender perspectives to better understand long-term behavioural changes. Coordinated policy efforts and targeted investments can lead to more equitable transportation access, support sustainability goals, and alleviate urban traffic congestion. Full article

This study presents a novel predictive framework for estimating electric vehicle (EV) charging demand in smart cities, contributing to the advancement of data-driven infrastructure planning through behavioural and spatial data analysis. Motivated by the accelerating regional demand accompanying EV adoption, this work introduces HCB-Net: a hybrid deep learning model that combines Convolutional Neural Networks (CNNs) for spatial feature extraction with Extreme Gradient Boosting (XGBoost) for robust regression. The framework is trained on user-level survey data from two demographically distinct UK regions, the West Midlands and the North East, incorporating user demographics, commute distance, charging frequency, and home/public charging preferences. HCB-Net achieved superior predictive performance, with a Root Mean Squared Error (RMSE) of 0.1490 and an $R^2$ score of 0.3996. Compared to the best-performing traditional model (Linear Regression, $R^2=0.3520$), HCB-Net improved predictive accuracy by 13.5% in terms of $R^2$, and outperformed other deep learning models such as LSTM ($R^2=-0.3756$) and GRU ($R^2=-0.6276$), which failed to capture spatial patterns effectively. The hybrid model also reduced RMSE by approximately 23% compared to the standalone CNN (RMSE = 0.1666). While the moderate $R^2$ indicates scope for further refinement, these results demonstrate that meaningful and interpretable demand forecasts can be generated from survey-based behavioural data, even in the absence of high-resolution temporal inputs. The model contributes a lightweight and scalable forecasting tool suitable for early-stage smart city planning in contexts where telemetry data are limited, thereby advancing the practical capabilities of EV infrastructure forecasting. Full article

Ionic liquids (ILs) have attained considerable attention as cellulose solvents. Nevertheless, the detailed mechanism of cellulose dissolution in ILs is not clearly defined. It is crucial to recognize the role of the individual components of the ILs to fully understand this mechanism. During this study, the effect of alkyl chain length in imidazolium cation was examined using synthesized ILs which are composed of common acetate anion and imidazolium cations with different alkyl substituents. This study also aimed to investigate the odd–even effect of alkyl chain carbons. Furthermore, whereas most published investigations on cellulose dissolution in ILs used microcrystalline cellulose (MCC), which has a far lower degree of polymerization, in this study, cotton cellulose was used. During the dissolution experiments, cotton cellulose (5% w/w) was added to each IL, and the progress of the dissolution was monitored using polarized light microscopy (PLM). The regeneration of cellulose was performed by using water as the anti-solvent, and the regenerated cellulose was characterized by Fourier-transform infrared (FTIR) spectroscopy and scanning electron microscopy (SEM). During these experiments, it was noted that ILs with odd C3 and C5 carbon chains were less effective at dissolving cellulose than those with even C2 and C4 alkyl chains. Additionally, after regeneration, biomaterials for a variety of applications could be produced. Full article

Evidence appears to be building that direct exposure to natural landscapes characterized by significant green cover, such as forests, can help to reduce chronic health conditions such as obesity, stress, hypertension, chronic cardiovascular conditions, depression, anxiety, cancer, and diabetes. One way to encourage greater exposure to nature may be through the use of nature prescriptions, whereby clinicians formally recommend (or prescribe) time in nature to their patients. Based on self-reported data, we describe the implementation and lessons learned from a pilot field experiment examining the clinical use of nature-based versus conventional exercise recommendations in rural Oregon. We discuss the potential benefits of such recommendations, as well as identify several challenges and opportunities associated with field experiments seeking to evaluate whether nature prescriptions, offered as one part of patients’ overall treatment plans, meaningfully improve human health outcomes in clinical settings. We conclude with several recommendations for practitioners and researchers interested in implementing and evaluating nature-based exercise programs to improve public health. Full article

Oligosaccharides and glycoconjugates play essential roles in various biological processes such as cellular recognition and signaling, and thus have attracted tremendous attention in the synthetic and biological communities over the past few decades. Contributing to this field, we have achieved the synthesis of the aminoxyglycoside pentasaccharide subunit of Pseudomonas aeruginosa polysaccharide synthesis locus (Psl) exopolysaccharide through an efficient 23 step process. This pentasaccharide was designed with an aminooxy derivative at the reducing end, which was used in a 2-step oxime-based bioconjugation to the protein carrier CRM197, with an epitope ratio of 1:4. The conjugate vaccine could generate anti-Psl antibodies that could recognize P. aeruginosa PAO1 bacteria and initiate opsonophagocytic killing of the bacteria. In addition, the aminoxyglycoside could be conveniently conjugated to a bifunctional aldehyde-biotin reagent, which can be used for quantifying antibody titers in vaccination studies. Full article

The annual inflation rate in Sri Lanka accelerated to record levels in recent years, especially after the COVID-19 pandemic. Though the inflation rate had declined to pre-pandemic levels by mid-2024, it is of great importance to identify the factors that caused hyperinflation during the COVID-19 pandemic. The objective of this study is to investigate the drivers of inflation in Sri Lanka using a structural vector autoregressive model and a multiple regression model. The study assesses both the global drivers and the domestic drivers of inflation. The study uses monthly data on the inflation rate, global oil price, exchange rate, policy rate, the global supply chain pressure index, and unemployment rate, covering the period from January 2020 to August 2024, focusing on the period of rapid increase in the inflation rate in Sri Lanka. The empirical results of the study provide evidence to conclude that the inflation rate in Sri Lanka during the 2020–2024 period was mainly driven by the growth rates in money supply, exchange rates, and global supply chain disruptions. The results also show that the volatility of the Sri Lanka inflation rate is mostly explained by the money supply and exchange rate movements in the long run. Full article

The integration of Industry 4.0 technologies, Circular Economy (CE) principles, and Green Human Resource Management (GHRM) offers transformative potential to address global sustainability challenges. Industry 4.0, characterized by advanced digital technologies like IoT, Additive Manufacturing (AM), and Big Data Analytics (BDAA), enhances operational efficiency, resource optimization, and waste minimization. Concurrently, CE redefines economic models through resource conservation, lifecycle extension, and reduced environmental impact, supported by frameworks like ReSOLVE. GHRM aligns human resource practices with sustainability objectives, fostering Green behaviors and embedding environmental considerations into organizational culture. Despite the individual benefits of these frameworks, their combined application remains underexplored, with limited research on their systemic integration. This study addresses this gap by examining the synergies between Industry 4.0 technologies, CE principles, and GHRM strategies, identifying opportunities and challenges in their implementation. A theoretical model is proposed, emphasizing systemic innovation, resource efficiency, and collaborative value chains as key enablers of sustainable development. The model highlights the necessity of aligning technological advancements with human-centric approaches to overcome behavioral, organizational, and infrastructural barriers in transitioning toward sustainability. The findings offer practical insights for policymakers and industry leaders, outlining strategies for integrating Industry 4.0 with CE and GHRM to drive sustainability transitions. By synthesizing technological, environmental, and human resource dimensions, this research contributes both theoretically and practically, positioning organizations to enhance sustainability while maintaining competitiveness in evolving economic landscapes. Full article

Connection systems are a critical component of buildings constructed with engineered wood products (EWPs), influencing structural integrity, durability, and construction efficiency. This systematic review categorises connection types into mechanical, adhesive, and interlocking systems and evaluates their structural performance, adaptability in prefabrication, applicable design standards, and modelling approaches. The review synthesises recent trends in EWP connection research, highlighting key developments in digital fabrication, reversible joints, and sustainable construction. Findings emphasise the need for standardisation, performance validation, and hybrid systems to support the wider adoption of prefabricated timber structures in environmentally responsible building practices. Full article

This study explores the impact of road transport on the environment, focusing on noise pollution. Using high-resolution, one-minute data from a low-cost environmental sensor, this research examines traffic flow dynamics, meteorological influences, and their relationship to noise along a major transport corridor. The methodology combines cluster analysis and descriptive statistics to evaluate the effects of deploying a Smart Motorway Variable Speed Limit (SMVSL) system over a six-month monitoring period. Results indicate that SMVSL systems not only smooth traffic flow but also significantly reduce noise variability, particularly during peak hours, thus mitigating noise peaks associated with adverse health outcomes. LAeq values were found to differ modestly between day and night, with clustering revealing a reduction in extreme noise events (LAmax > 70 dB(A)) in SMVSL scenarios dominated by heavy goods vehicles. This study further identifies associations between unmanaged speed regimes and elevated noise levels, enriching our understanding of the environmental impacts of unregulated traffic conditions. These findings inform sustainable planning and policy strategies aimed at improving urban environmental quality and enhancing public health outcomes. Full article

Access to high-quality dental healthcare remains a challenge in many countries due to limited resources, lack of trained professionals, and time-consuming report generation tasks. An intelligent clinical decision support system (ICDSS), which can make informed decisions based on past data, is an innovative solution to address these shortcomings while improving continuous patient support in dental healthcare. This study proposes a viable solution with the aid of multimodal artificial intelligence (AI) and large language models (LLMs), focusing on their application for generating orthopantomography radiology reports and answering questions in the dental domain. This work also discusses efficient adaptation methods of LLMs for specific language and application domains. The proposed system primarily consists of a Blip-2-based caption generator tuned on DPT images followed by a Llama 3 8B based LLM for radiology report generation. The performance of the entire system is evaluated in two ways. The diagnostic performance of the system achieved an overall accuracy of 81.3%, with specific detection rates of 87.9% for dental caries, 89.7% for impacted teeth, 88% for bone loss, and 81.8% for periapical lesions. Subjective evaluation of AI-generated radiology reports by certified dental professionals demonstrates an overall accuracy score of 7.5 out of 10. In addition, the proposed solution includes a question-answering platform in the native Sinhala language, alongside the English language, designed to function as a chatbot for dental-related queries. We hope that this platform will eventually bridge the gap between dental services and patients, created due to a lack of human resources. Overall, our proposed solution creates new opportunities for LLMs in healthcare by introducing a robust end-to-end system for the automated generation of dental radiology reports and enhancing patient interaction and awareness. Full article

Among urban public transport systems, light rail, mass transit, and tram systems offer sustainable travel options. However, many of these systems, particularly in developed countries, fail to meet user needs and the expectations of transport authorities. Increasing the demand for urban rail systems as an alternative to private cars is essential for achieving net zero targets and Sustainable Development Goals. This study investigates the factors influencing urban rail demand using qualitative data analysis, with a focus on thematic analysis. A systematic review of 53 studies from the UK, Europe, and worldwide, including journal articles and transport research reports, was conducted and coded using NVivo Version 15 software. Six main categories emerged: land use and accessibility, service quality, user benefits, governance, sustainability aspects, and user-focused elements. These categories, along with their themes and sub-themes, were analysed using cross-tabulations to compare attributes across domains. The key findings indicate that accessibility and intermodal connectivity are crucial for encouraging urban rail use, while ticketing, station facilities, walkability, travel costs, ventilation, and security also moderately influence user preferences. This study provides essential guidelines for policymakers and transport providers to improve urban rail systems and informed the development of a questionnaire to explore the interrelationships of these factors, discussed in a forthcoming paper. Full article

This study presents a bibliometric analysis of spatial data infrastructure (SDI) research and its application in city development. The fast urbanization and growing complexity of urban management recognize the importance of SDI in supporting sustainable urban planning and innovative city development. This study systematically reviews trends in the publications, key contributors, keywords, and thematic areas of SDI and urban settings. The study uses bibliometric tools such as VOSviewer and Biblioshiny, as well as data from 2003 to 2023. The results show that the number of publications has expanded, and the growth rate in publications has accelerated since 2013, increasing significantly due to geospatial technologies and broadening interest in the concept of smart cities. It identifies the key authors, countries, and collaborative networks that have recognized initiation in the research area. It puts forward the core contributions of Germany, Italy, and Croatia in this field. This research uses keyword co-occurrence and thematic mapping to illustrate dynamic areas of emphasis, including incorporating 3D city models with smart mapping and the application domains of Geographical Information Systems (GISs) and SDI in urban planning. This study further elaborates on other significant developing trends, such as implementing participatory sensing in environmental monitoring and securing SDI within smart city applications. It also highlights enhanced international collaborations toward strengthening the global knowledge base of the challenges in sustainable city development. Hence, this bibliometric analysis is supposed to be used for future research and policy decisions within SDI and city development. Overall, this study will support research by providing a direction for the literature on SDI and city studies and arranging bases for future studies that recommend developing urban resilience and sustainability using the effective practice of geospatial data. Full article

This review explores recent advancements in electric vehicles (EVs), focusing on the transformative role of artificial intelligence (AI) in battery management systems (BMSs) and system control technologies. While EVs are integral to sustainable transportation, challenges remain in optimising battery longevity, energy efficiency, and safety. AI-driven techniques—such as machine learning (ML), neural networks (NNs), and reinforcement learning (RL)—enhance battery state of health (SOH) and state of charge (SOC) predictions, as well as temperature regulation, offering superior accuracy over traditional methods. Additionally, AI-powered control frameworks optimise energy distribution, regenerative braking, and power allocation under varying driving conditions. Deep RL enables adaptive, self-learning capabilities that improve energy efficiency and extend battery life, even in dynamic environments. This review also examines the integration of the Internet of Things (IoT) and big data analytics in EV systems, enabling predictive maintenance and fleet-level optimisation. By analysing these advancements, this paper highlights AI’s pivotal role in shaping next-generation, energy-efficient EVs. Full article