Single-atom nanozymes for electrochemical sensing oxidative stress biomarkers.

Developing reliable and low-cost sensors for oxidative stress biomarkers has gained significant interest for understanding and managing chronic diseases. Recently, single-atom nanozymes (SANs) with atomically dispersed metal sites and unique metal–nitrogen–carbon (M–N–C) structures have emerged as promising redox enzyme mimetics, offering superior catalytic efficiency and specificity, and with exceptional sensing capabilities for oxidative stress biomarkers such as hydrogen peroxide (H2O2), nitric oxide (NO), glutathione (GSH), and uric acid (UA). This review discusses key progress, challenges, and future directions in SANs for electrochemical determination of oxidative stress biomarkers, aiming to guide future research toward practical and impactful solutions in healthcare and disease management

Assessing the feasibility of carbon dioxide sequestration in abandoned oil and gas wells: a case study of the UK continental shelf: accelerating the transition to a better energy future.

This study examines the feasibility of repurposing abandoned offshore wells for geological CO2 storage, focusing on three candidate fields in the UK Continental Shelf (UKCS). The fields were evaluated based on storage capacity, leakage risk, development cost, and distance to shore. A structured ranking approach was used to compare site suitability under multiple weighting scenarios. Field B consistently ranked highest, offering a favourable balance of subsurface integrity, reservoir performance, and accessibility. Existing wells in Field B were screened using the REX-CO2 tool, applying parameters from prior risk-based studies. The resulting moderate risk scores indicate that the wells could be reused following further inspection and minor remediation. Reservoir simulations were performed to model CO2 behaviour over a 200-year period. Under structural trapping, CO2 migrated vertically and accumulated below the caprock, but pressure build-up over time raised containment concerns. In contrast, residual trapping led to greater long-term stability, with more than three-quarter of the injected CO2 staying immobilised after two centuries. The findings demonstrate that Field B meets both geological and operational requirements for CO2 storage. This work provides a practical framework for screening and evaluating mature offshore assets for integration into carbon storage strategies, with broader relevance to similar settings across the North Sea

Transforming legacy oil and gas well infrastructure: assessing CO2 sequestration in the UK continental shelf.

An important decarbonisation pathway in the reduction of greenhouse gas emissions is the at scale storage of CO2 in geological formations. This research considers the technical feasibility and geospatial potential for repurposing abandoned wells within the United Kingdom Continental Shelf region for underground CO2 storage - offering a low-impact pathway to accelerate deployment. Using a comprehensive multi-step strategy which integrates a well integrity screening methodology and TOPSIS-based multi-criteria approach, candidate fields were ranked based on storage capacity, risk analysis, and economic viability. Comparison of three fields within the Northern, Central, Southern and North Sea basins revealed Field B (Central North Sea) as the most suitable candidate. Additionally, reservoir models were developed to simulate the CO2 plume migration and long-term containment mechanisms for a 200-year post-injection period with results indicating that residual trapping was the dominant CO2 retention mechanism, ensuring long-term storage security. This research study demonstrates the potential to repurpose abandoned wells for carbon storage projects, reducing associated environmental footprint and supporting the achievement of Net Zero targets. The evidence-based framework offers a scalable and transferable methodology which can be replicated in other mature offshore basins globally, with practical insights on integrating circular economy principles within carbon management strategies

A multi-objective optimization strategy of microgrid energy management toward coordinated charging for electric vehicles and economic costing.

The rapid proliferation of electric vehicles (EVs) presents significant grid challenges to the construction and stable operation of urban electricity microgrids. To mitigate operational instability risks induced by uncoordinated large-scale EV charging integration in microgrids in urban and built-up areas, this study proposes a hierarchical two-layer optimization framework for power management in high-penetration EV-grid scenarios. The upper-layer of the hierarchical two-layer optimization framework model focuses on EV-related objectives, incorporating constraints, battery depreciation costs, and user satisfaction into the objective function. The lower-layer model includes microgrid components: photovoltaic units, wind turbines, micro wind turbines, and diesel engines. The aim is to minimize operational costs and load variance within the microgrid, thereby enhancing the power grid stability. To solve this model effectively, a hybrid optimization algorithm combining an improved sparrow search algorithm with a neural network is proposed, and the algorithm is benchmarked and validated against three meta-heuristic optimization algorithms for its efficacy. The study evaluates the scheduling outcomes under three distinct EV strategies, and simulation results demonstrate the effectiveness of the proposed algorithm in solving the model. In the coordinated charge-discharge mode, moving charging loads from peak tariff periods to off-peak tariff periods lowers daily costs by 2.9%. Adding distributed generation lowers daily costs by an additional 6.1%, which helps the microgrid system stay stable

Transforming manufacturing quality management with cognitive twins: a data-driven, predictive approach to real-time optimization of quality.

In the ever-changing world of modern manufacturing, maintaining product quality is of great importance, yet extremely difficult due to complexities and the dynamic production paradigm. Currently, quality is rather reactively measured through periodic inspections and manual assessments. Traditional quality management systems (QMS), through these reactive measures, are often inefficient because of their higher operational cost and delayed defect detection and mitigation. The paper introduces a novel cognitive twin (CT) framework, which is the next evolved version of digital twin (DT). It is designed to advance the current quality management in flexible manufacturing systems (FMSs) through real-time, data-driven, and predictive optimization. This proposed framework uses four data types, namely feedstock quality (Qf), machine degradation (Qm), product processing quality (Qp), and quality inspection (Qi). By utilizing the power of machine learning algorithms, the cognitive twin constantly monitors and then analyzes real-time data. The cognitive twin optimizes the above quality components. This enables a very proactive decision making through an augmented reality (AR) interface by providing real-time visual insights and alerts to the operators. Thorough experimentation was conducted on the aforementioned FMS. Through the experiments, it was revealed that the proposed cognitive twin outperforms conventional QMSs by a great margin. The cognitive twin achieved a 2% improvement in the total quality scores. A 60% decrease in defects per unit (DPU) is observed as well as a sharp 40% decrease in scrap rate. Furthermore, the overall equipment efficiency (OEE) increased to 93–96%. The overall equipment efficiency increased by 11.8%, on average, from 82% to 93%, and the scrap rate decreased by 33.3% from 60% to 40%. The excellent results showcase the effectiveness of cognitive twin quality management via minimum wastage, continuous quality improvement, and enhancement in operational efficiency in the paradigm of smart manufacturing. This research study contributes to the field of industry 4.0 by providing a comprehensive, scalable, and adaptive quality management solution, thus leading the way for further advancements in intelligent manufacturing systems

FusDreamer: label-efficient remote sensing world model for multimodal data classification.

World models significantly enhance hierarchical understanding, improving data integration and learning efficiency. To explore the potential of the world model in the remote sensing (RS) field, this paper proposes a label-efficient remote sensing world model for multimodal data fusion (FusDreamer). The FusDreamer uses the world model as a unified representation container to abstract common and high-level knowledge, promoting interactions across different types of data, i.e., hyperspectral (HSI), light detection and ranging (LiDAR), and text data. Initially, a new latent diffusion fusion and multimodal generation paradigm (LaMG) is utilized for its exceptional information integration and detail retention capabilities. Subsequently, an open-world knowledge-guided consistency projection (OK-CP) module incorporates prompt representations for visually described objects and aligns language-visual features through contrastive learning. In this way, the domain gap can be bridged by fine-tuning the pre-trained world models with limited samples. Finally, an end-to-end multitask combinatorial optimization (MuCO) strategy can capture slight feature bias and constrain the diffusion process in a collaboratively learnable direction. Experiments conducted on four typical datasets indicate the effectiveness and advantages of the proposed FusDreamer

Using a disclosure index instrument to quantify attributes of corporate disclosure. [Case study]

Corporate disclosure is a theoretical concept that cannot be measured directly. However, the literature provides two approaches to measure it. The first approach investigates actual information disclosure and tries to operationalize the concept of disclosure into its main attributes such as quantity and quality. The second approach relies on the fact that corporate disclosure is an unobservable variable and uses some observable variables to proxy for it such as firm size. Each approach has its advantages and disadvantages. Also, the choice of research philosophy affects how a researcher approaches and measures corporate disclosure. As a positivist, I approach corporate disclosure as an objective and measurable phenomenon that has identifiable causes and consequences. I measured attributes of corporate disclosure using the first approach, mainly a disclosure index method, whether self-constructed or developed by a third party. I have made extensive use of the disclosure index method in my doctorate project and several publications. In this Case Study, I explain what a disclosure index is, the different variations of a disclosure index, how to develop a disclosure index for your study using examples from my research, and how to test its reliability and validity. The purpose is to help readers develop their own disclosure indices for their research

Efficient water management in building: an approach to promote sustainable building construction in India.

The growing concerns surrounding water scarcity have spiked an interest in embodied water (EW) studies globally. The EW of a building is the sum of the amount of water needed to manufacture all the building materials throughout their supply chain (indirect embodied water (IEW)), plus the amount of water needed for the building construction (direct embodied water (DEW)). For a building with an operational period of fifty years, this EW can constitute almost 35% of the total building water footprint. India is currently under an urban boom that has resulted in an increased demand for residential building construction projects. The growing water scarcity within the country and the huge contribution of EW in the total building water footprint, suggests the need for EW management in the country. This study thus aims at developing a framework for EW management to promote sustainable building construction in India. To achieve the research aim, this study adopts a sequential explanatory multiphase mixed method research design, and uses case studies, archival search, online questionnaires and semi-structured interviews as its research strategy. Reinforced concrete (RC) frame buildings with clay masonry walls are the most common type of residential building construction in India, constituting 45% of the residential building stock, and are selected as case study buildings for analysis. Two case study buildings were analysed to determine the commonly used construction materials and construction activities undertaken for their construction. These materials and activities were further analysed to determine their embodied water coefficient (EWC) which is the amount of water needed for their manufacturing and execution respectively. These EWC values were used to calculate the EW of the case study buildings to be in the range of 0.32-0.35kL/m2. Moreover, an analysis of the IEW and embodied carbon (EC) of these case study buildings revealed that both the EW and EC need to be considered when selecting construction materials to aid in the selection of materials with the lowest environmental impact. An online questionnaire conducted with construction professionals working in India revealed that 45.7% construction sites in India meter their water consumption to monitor their usage as they have to pay to purchase this water. Furthermore, semi-structured interviews with construction professionals and construction material manufacturers revealed that there is a lack of government regulations for EW management, and - where they exist - the company faces many challenges for its implementation. The proposed framework created for EW management thus focuses on minimising the challenges for the implementation of government regulations, creation of benchmarks for optimum water consumption and creation of awareness among people regarding water management. The findings of this research have many contributions in practice and theory. The development of optimum water consuming benchmarks and the proposed framework can aid in water management on construction sites and material manufacturing plants during their planning and operation stages. Moreover, this research contributes to theory by developing a methodology for EW measurement that has previously been adopted globally but not in India

Financial challenges of students and early-career professionals working in the healthcare sector: a scoping review.

The global healthcare workforce is facing a substantial shortage and an uneven distribution of qualified professionals, which restricts access to essential healthcare services. This shortage could be mitigated through more effective support of healthcare workers in training. Therefore, an overview of existing economic barriers for this demographic is necessary. To review the existing literature on financial challenges of students and early-career professionals in the healthcare sector. Following the PRISMA-ScR-guidelines, publications published between January 2008 and February 2024 were identified using PubMed and Scopus. 17,268 articles were screened by reviewing their titles and abstracts followed by a detailed review of full texts with cross-validation. Themes were identified, clustered, and analyzed. This scoping review included 167 articles focusing on the themes debt (36.5%, n=61) and loans (10.2 %, n=17) and their influence on career pathways, the role of employment for career satisfaction, summarizing findings concerning salary (29.9%, n=50), finances (25.1%, n=42), funding (10.8%, n=18), and savings (10.2%, n=17), and obstacles toward a sustainable lifestyle, which included results considering career choice (34.1%, n=57), migration (7.2%, n=12), gender disparity (6.0%, n=10) and working conditions (2.4 %, n=4). Efforts to close the healthcare workforce gap require greater investment in training, compensation, and support of junior healthcare workers. Students and early-career professionals need particular attention to build a sustainable, resilient, and reliable healthcare workforce

Enhancing disease clustering through symptom-based analysis and large language model interpretations.

Humans face various diseases that are mainly caused by environmental conditions and living habits. These diseases exhibit several symptoms and can share a relationship based on their symptoms. The identification and interpretation of these groups of symptom-based diseases can aid in developing treatment plans for a new outbreak of disease. This research explores the intersection of machine learning and healthcare, specifically focusing on the enhancement of disease classification through symptom-based cluster analysis. By leveraging unsupervised machine learning algorithms, patterns and relationships within diverse symptom datasets were identified, revealing novel associations and subtypes in disease manifestation. The integration of a Large Language Model (LLM), specifically OpenAI's Generative Pretrained Transformer(GPT), played a pivotal role in interpreting and communicating the complex outputs of the machine learning process. The results indicated a significant improvement in defining distinct clusters based on the relationship between diseases and symptoms, with GPT-4o providing simplified explanations that bridge the gap between machine-generated insights and healthcare professional's understanding. The study's findings offer a more profound understanding of the distinctive features characterising the different clusters of diseases generated by the machine learning models