Context-aware Data Driven Sensor Data Analysis: With Application to H2S Concentration Prediction in Urban Drainage Networks

open access articleThis paper presents a context-aware data-driven approach for the analysis of big data from sensors. Different from conventional methods, this approach incorporates exogenous variables or contextual information that influences the dynamic behaviour of the monitored system. In the context of water distribution systems, for example, key system variables including water demand variations and pressure are significantly affected by factors like time of day, the day of the week, unusual events, seasonal variations and weather conditions. This contextual information creates dynamic relationships between water demand and pressure, which are critical for understanding system behaviour. Specifically, the context-aware method will use present and past observed values from sensors (which are normally time-series data recording the system’s dynamic behaviour), in addition to also including contextual information regarding the spatial context (e.g., the correlation between the values of different sensors) and temporal context (e.g., correlation between observed values and days of the week and time of the day). The method is applied to the prediction of Hydrogen Sulphide (H2S) concentration in a real-world urban drainage network, based on the analysis of big real-time data sets from different sensors. Although the datasets are variables with non-uniform time intervals, uncertainties, and faulty data, the context-aware method identifies the correlations among different datasets to predict the concentration of H2S with high accuracy (R2 > 0.92; RMSE = 0.029). The method is also proven robust for a Deep Neural Networks approach

Entrepreneuring mothers’ identity work and motivation from the perspective of possible selves

The file attached to this record is the author's final peer reviewed version. The Publisher's final version can be found by following the DOI link.This study examines how mothers who engage in entrepreneurship negotiate their evolving identities, highlighting the role of multi-domain possible selves in shaping their experiences. We examine twenty-nine biographical narratives of entrepreneuring mothers, drawing on theories of gender and entrepreneurship and entrepreneurial identity and the concept of possible selves. Thematic categories of hoped, feared and ought selves are employed as an organising frame, to examine the inter-relationships between gendered social expectations, individual self-conceptions and entrepreneurial motivations that enable motherhood to serve as both a catalyst and constraint to entrepreneurial endeavour. We introduce the concept of Reflexive Accommodation to explain how mothers reconcile identity dissonance, leveraging entrepreneurship as a flexible, values-aligned pathway that integrates professional aspirations with maternal responsibilities. We highlight the role of narrative in making sense of these transitions and illustrate how conflicting ought selves can amplify feared selves, exerting powerful motivational influence. Our study extends current understanding of the recursive relationship between identity multiplicity, identity dissonance and entrepreneurial activities, challenging dominant assumptions about entrepreneurial orientation. By situating EI within whole-life identity projects in a liquid-modern context, we contribute to scholarship on gender and entrepreneurship by offering insights into the social situatedness of identity while emphasizing individual agency in entrepreneurial decision-making

How Do Institutional Pressures Reshape the Association Between Corporate Sustainability Disclosure and Firm Value in Emerging Economies? The Moderating Role of the Audit Committee Function

open access articleThis study examines the influence of normative (e.g., voluntary sustainability reporting guidelines) and coercive (e.g., mandatory corporate governance [CG] requirements) pressures on the relationship between corporate sustainability disclosure (CSD) and financial performance (FP), focusing on the moderating role of audit committee characteristics. Using 1863 firm-year observations from 207 companies listed and unlisted on the Amman Stock Exchange (2014–2022), the study employs panel quantile regression and two-stage PQR to address endogeneity issues. Results show that CSD adoption increased after the 2018 sustainability guidelines, positively affecting FP. Audit committee size and independence strengthen the CSD–FP link, particularly after the 2017 CG reforms, indicating coercive pressures' role in enhancing governance. However, frequent audit committee meetings and technical expertise may weaken the CSD–FP relationship. The study emphasizes governance frameworks shaped by normative and coercive pressures as key to maximizing the financial benefits of sustainability disclosures for firms

SPAC: Sampling-based Progressive Attribute Compression for Dense Point Clouds

The file attached to this record is the author's final peer reviewed version. The Publisher's final version can be found by following the DOI link.We propose an end-to-end attribute compression method for dense point clouds. The proposed method combines a frequency sampling module, an adaptive scale feature extraction module with geometry assistance, and a global hyperprior entropy model. The frequency sampling module uses a Hamming window and the Fast Fourier Transform to extract high-frequency components of the point cloud. The difference between the original point cloud and the sampled point cloud is divided into multiple sub-point clouds. These sub-point clouds are then partitioned using an octree, providing a structured input for feature extraction. The feature extraction module integrates adaptive convolutional layers and uses offset-attention to capture both local and global features. Then, a geometry-assisted attribute feature refinement module is used to refine the extracted attribute features. Finally, a global hyperprior model is introduced for entropy encoding. This model propagates hyperprior parameters from the deepest (base) layer to the other layers, further enhancing the encoding efficiency. At the decoder, a mirrored network is used to progressively restore features and reconstruct the color attribute through transposed convolutional layers. The proposed method encodes base layer information at a low bitrate and progressively adds enhancement layer information to improve reconstruction accuracy. Compared to the best anchor of the latest geometry-based point cloud compression (G-PCC) standard that was proposed by the Moving Picture Experts Group (MPEG), the proposed method can achieve an average Bjøntegaard delta bitrate of -24.58% for the Y component (resp. -21.23% for YUV components) on the MPEG Category Solid dataset and -22.48% for the Y component (resp. -17.19% for YUV components) on the MPEG Category Dense dataset. This is the first instance that a learning-based attribute codec outperforms the G-PCC standard on these datasets by following the common test conditions specified by MPEG. Our source code will be made publicly available on https://github.com/sduxlmao/SPAC

A novel graph model for resolving power-asymmetric conflicts: Application in hierarchical diagnosis and treatment systems

The file attached to this record is the author's final peer reviewed version. The Publisher's final version can be found by following the DOI link.Abstract: As Chinese society undergoes rapid aging and urbanization, the existing medical service system faces significant challenges, including unequal resource distribution, a shortage of high-quality resources, and inefficient allocation. To address these issues, the hierarchical diagnosis and treatment system (HDTS) has been introduced to optimize medical resource allocation and utilization. However, implementing HDTS encounters complex conflicts of interest among multiple decision-makers (DMs), compounded by ambiguity, uncertainty, and power asymmetry. This paper proposes the power-asymmetric additive graph model for conflict resolution (PAAGMCR), a versatile tool that integrates qualitative and quantitative methods to address stakeholder conflict in HDTS implementation in Shandong, China. The optimal solution s18 can be identified using PAAGMCR: the Shandong Provincial Government should standardize medical treatment processes in tertiary hospitals, invest in grassroots medical facilities, allocate funds for public awareness campaigns, and encourage patients to seek initial treatment at the grassroots level. Tertiary hospitals should collaborate with grassroots hospitals to utilize subsidies for equipment upgrades and workforce training. Patients and their families should adhere to HDTS principles and make informed healthcare decisions. Furthermore, this study outlines an evolutionary path from the initial to the optimal state, offering theoretical support for resolving real-world conflicts. Finally, strategic recommendations are provided according to the analysis result of the conflict to guide DMs in implementing HDTS effectively

Authenticating Basil (Ocimum spp.): An Integrated Quality Control Strategy

open access article Collaboration between: Biomolecular Technology Group, Leicester School of Allied Health Science, Faculty of Health and Life Sciences, De Montfort University, Leicester LE1 9BH, UK Plant Biology and Systematics, CSIR—Central Institute of Medicinal and Aromatic Plants, Research Centre, Bengaluru 560065, India Academy of Scientific and Innovative Research (AcSIR), Ghaziabad 201002, India Phytochemistry Division, CSIR—Central Institute of Medicinal and Aromatic Plants, Lucknow 226015, India Leicester School of Pharmacy, Faculty of Health and Life Sciences, De Montfort University, Leicester LE1 9BH, UKStandardisation is essential to ensure the quality, efficacy, and safety of basil oil products. Although Ocimum basilicum L. is the most widely traded species, other Ocimum species are often sold under the same name, increasing the risk of misidentification and adulteration. Intraspecific variation in morphology and chemical composition further complicates standardisation, highlighting the need for a comprehensive authentication strategy. This study evaluates genetic, chemical, and morphological methods for the authentication of commercial basil accessions to support accurate species identification and product standardisation. Samples were analysed using DNA barcoding (matK, trnH-psbA, rbcL, rpl16), GC-MS-based chemical profiling, and trichome characterisation via scanning electron microscopy. Phylogenetic analysis placed all commercial samples within a broad clade encompassing O. basilicum, its hybrids, and related species. Species-specific single nucleotide variations in matK and trnH-psbA supported the identification of distinct accessions. Notably, liquorice basil showed genetic similarities to non-basilicum species, suggesting the need to revisit its classification. Chemical profiling revealed substantial variation in essential oil composition, with some samples dominated by linalool and eugenol, and others by methyl chavicol, raising potential safety concerns. Morphological analysis further highlighted differences in trichome density, particularly in the blue spice variety. The findings underscore the limitations of using a single method for basil authentication and advocate for an integrated approach. DNA barcoding supports species identification, while chemical profiling is essential for chemotype differentiation. Developing reliable DNA markers and incorporating combined analyses into routine quality control can strengthen industry standards for natural product authentication

Capturing the past, shaping the future: A scope review of photogrammetry in cultural building heritage

open access articleHistoric buildings and urban streetscapes face increasing threats from climate change, development, and aging infrastructure, creating a pressing need for accurate and scalable documentation methods. This review assesses the combined use of photogrammetry and unmanned aerial vehicle (UAV) technologies in preserving built cultural heritage. We systematically analyze the end-to-end workflow, from the sophisticated processing of imagery into highly detailed and accurate 3D models in photogrammetry software via data acquisition using diverse UAV platforms and sensor payloads. Through case studies, including the mapping of ancient Maya sites in the Yucatán Peninsula and the conservation of the Notre Dame Cathedral, the review highlights the accuracy, efficiency, and accessibility offered by this technological synergy, underscoring its significance for heritage conservation, research, and the development of digital twins. Furthermore, it explores how these advancements foster public engagement and virtual accessibility, enabling immersive experiences and enriched educational opportunities. The paper also critically assesses the inherent technical, ethical, and legal challenges associated with this methodology, offering a balanced perspective on its application. By synthesizing the current knowledge, this review proposes future research trajectories and advocates for best practices, aiming to guide heritage professionals in leveraging photogrammetry and UAVs for the effective documentation and safeguarding of global cultural heritage

Changes in Health-Related Behaviours Among Adults Who Accessed Real-World Weight Management Support: 12-Month Outcomes

open access articleBackground Large weight losses are desirable, but their benefits are short-lived without sustained behaviour changes that can be maintained at the household level. This longitudinal study, conducted in a real-life setting, investigated changes in weight, dietary habits, activity levels, and physical and mental well-being of members of a community weight management programme (Slimming World), compared with a matched cross-sectional reference group from the general population. The wider influence on the dietary and activity habits of family members was also explored. Methods Longitudinal data were collected from members at 0-4 weeks (T1), 3 months (T2), and 12 months (T4) after joining. The reference group completed surveys at each time point. Diet quality scores (NDQS) were calculated using a validated tool, hours of moderate-intensity physical activity were recorded, and mental well-being was assessed using adapted items from the SF Health Survey. Changes in members’ behaviours and comparisons with the reference group were analysed using within- and between-group ANOVAs with p-adjusted post-hoc comparisons. Results Of the 1,884 members who provided baseline data, 174 (7.5% male) completed surveys at T1, T2, and T4. At baseline, mean BMI and age were 34.7 ± 7.0 kg/m² and 53.0 ± 12.0 years, respectively. Mean weight change at 12 months was -7.5%. Member NDQS increased from baseline to T1 (11.5 ± 3.2 vs 14.1 ± 2.4, p 0.05). At T2, 40.7% of members reported encouraging others in their household to become more active, and this proportion remained consistent at T4 (40.5%, p > 0.05). Conclusion Although the low response rate across all three surveys is a limitation, the findings suggest that Slimming World’s behaviour change programme is effective in supporting adults (mainly females) living with obesity to make health-related behaviour changes. Members achieved clinically significant weight loss and improvements in diet quality, physical activity, and mental well-being compared with the reference group. These changes were maintained at 12 months, with an additional positive influence reported on family members’ lifestyle habits

Trust driven group decision making: Research progress and prospects from the perspective of consensus

The file attached to this record is the author's final peer reviewed version. The Publisher's final version can be found by following the DOI link.Trust driven Group Decision Making (TGDM) is a new type of decision making process conducted through trust relationships and information exchange between individuals in the social network environment. By systematically organizing the research progress of TGDM and exploring its future research directions, the GDM research for consensus will be promoted. Firstly, this article combs the development status and research trends in recent years based on bibliometrics methods, and then summarizes and discusses the important literature related to TGDM. Secondly, it defines the scientific research category and basic framework of GDM and TGDM. Thirdly, the basic related concepts of TGDM problems are summarized, and then its characteristics and function are analyzed. Finally, it analyzes the problems and challenges faced by TGDM research and explores future research directions. It finds that many scholars have constructed multi-dimensional TGDM models from different perspectives, which have shown wonderful application performance in fields such as product design, failure mode and effects analysis, meta universe virtual communities, and Water–Energy–Food. In addition, it will be a very promising research direction to in-depth investigate TGDM driven by scene, behavior and decision maker’s personality characteristics