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Navigating organic influence: a scientific evaluation of online review mechanisms
No full textOnline reviews have emerged as a dominant force in shaping consumer perceptions and reducing the need for traditional advertising in the hospitality and tourism industry. This study conducts a scientific evaluation of online review research using a novel systematic review method, combining co-citation analysis with text mining, to uncover the intellectual and thematic foundations of this evolving field. Our analysis reveals four key domains: (i) emotional and experiential drivers of review behaviour; (ii) moderating effects of customer profiles, digital platforms, and social influence; (iii) business and consumer outcomes of review engagement; and (iv) the growing impact of smart technologies on review generation and perception. By synthesizing fragmented literature through this dual-method approach, we offer new theoretical insights into how online reviews function as a form of earned advertising, with persuasive power comparable to or exceeding that of paid media
Guest editorial: Big data technologies and applications in Web 3.0 – trends and challenges
No full textFrom prediction to sustainability: AI for smart energy management in wastewater treatment plants
Get PDFIn wastewater treatment plants (WWTPs), accurate energy forecasting is crucial for optimizing operations, promoting self- sufficiency, and ensuring sustainability. We compare and evaluate the performance of Machine Learning (ML) techniques for energy self-consumption (i.e., long-term memory (LSTM), support vector machines (SVM), recurring neural networks (RNN), gated recurrent units (GRU), and XGBoost), to forecast energy generation (EG) and energy consumption (EC) in WWTPs. The performance of models is evaluated using metrics (i.e., mean absolute error (MAE), mean absolute percentage error (MAPE), and root mean square error (RMSE)), based on a solid dataset of daily operating records. The findings show that GRU achieves the highest performance, with an RMSE of 0.102, MAE of 0.085, and R² of 0.978, followed by LSTM, GRU, and RNN. The models demonstrate temporal prediction capabilities, as well as driving energy efficiency and reducing operational costs in WWTPs. This paper offers insights into implementing ML for sustainable energy management in WWTPs to energy forecasting, enhancing energy self-consumption, and boosting operational efficiency and environmental sustainability
APR-FL: Defending Against Hidden Backdoor Attacks With Adaptive Parameter Replacement
No full textFederated learning (FL) has become a key technology for achieving efficient and reliable edge AI decision-making in consumer electronics devices. However, its application in open network environments exposes the reliability of model decisions to serious threats from hidden backdoor attacks. Although existing defense methods (such as Weak DP and F2L) are able to resist these attacks to a certain extent, they rely on adding noise or dropping information, which directly leads to a decline in model main task performance. This article proposes a novel FL with Adaptive Parameter Replacement (APR-FL), which optimally ensures the aggregation of legitimate updates without introducing additional noise. Specifically, we design an Adaptive Parameter Replacement (APR) strategy that identifies key parameters (i.e., old parameters) exhibiting minimal changes during the local model update process and retrains the key parameters (i.e., new parameters) with a small, clean dataset. When the APR strategy replaces the old parameters with the new ones, we design a multi-task loss (MTL) function that separates the local loss into the loss of the main task and the loss of the defense backdoor task. Thereafter, the parameters generate legitimate updates and upload them to the server for aggregation. No legitimate updates are missed during the aggregation process. Therefore, APR-FL can balance both backdoor defense and model performance. Experiments on the MNIST, CIFAR-10, and Tiny-ImageNet datasets demonstrate that APR-FL consistently outperforms other methods regarding Main task Accuracy (MA) and Backdoor Accuracy (BA) by achieving an MA of 99.12% on the MNIST dataset and BA of 31.51% and 32.56% on CIFAR-10 (iid) and Tiny-ImageNet datasets, respectively
Efficiency of Multi-layer Greywater and Rainwater Treatment for Sustainable Water Management Through Water Reuse in Irrigation
No full textThe integrated management of greywater and collected rainwater for water reuse is a sustainable approach to reducing stress on freshwater sources and addressing water scarcity. This study proposes using treated greywater and rainwater for agricultural and landscape irrigation and evaluates the compliance of reclaimed water with European Union standards. Accordingly, an aerobic membrane bioreactor (MBR) was used to treat greywater, while a cross-flow flat sheet ultrafiltration (UF) system was employed for the treatment of collected rainwater. Treated greywater and treated rainwater were mixed in a 25:75 volumetric ratio and subjected to ultraviolet (UV) disinfection to produce reclaimed water. In greywater treatment, the MBR achieved removal efficiencies of 65.5±4.3% for chemical oxygen demand (COD), 62.5±2.9% for soluble COD, 47.8±1.9% for total nitrogen, and 47.2±6.1% for total phosphorus. Turbidity was reduced from 36.6±8.0 NTU to 0.77±0.10 NTU. Similarly, the UF system effectively reduced the turbidity of collected rainwater from 6.33±0.64 NTU to 0.39±0.09 NTU. Both treatment systems produced coliform-free effluents, with no detection of fecal coliform or Escherichia coli. Despite this outcome, the UV disinfection system played a critical role in ensuring the reclaimed water was free of pathogenic microorganisms. Thus, the effluents from the MBR and UF systems were treated with UV disinfection as a final quality assurance step. The results demonstrated that the proposed treatment configuration is applicable at the tested mixing ratio. The study also highlights the need to investigate different mixing ratios and alternative greywater and rainwater sources, as these factors influence influent characteristics and reclaimed water quality
Temperature and Rainfall Shape the Breeding Ecology of Barn Swallows Hirundo rustica in an Arid Region
Get PDFLocal weather conditions play a critical role in shaping avian reproduction, yet our understanding of these patterns in arid environments, where climate change is expected to increase drought frequency, is limited. This study investigated the effects of temperature, rainfall, and wind speed on the breeding ecology of Barn Swallows (Hirundo rustica) in an arid region of Algeria during 2023 and 2024. Using generalized linear mixed models (GLMMs), we assessed these effects for several life-history traits and breeding parameters including clutch size, incubation duration, hatching success, nestling period duration, and reproductive success. Our results indicate that clutch size decreased seasonally, while incubation duration increased with rainfall and was shorter for second clutches. Hatching success correlated positively with the amount of precipitation and was higher in second clutches. Nestling period duration increased both seasonally and with prolonged incubation. Reproductive success benefitted from greater rainfall but declined as the season progressed and when incubation was extended. Additionally, higher temperatures reduced renesting probability, suggesting that heat may limit Barn Swallows' reproductive efforts. Our results highlight the importance of rainfall in shaping reproductive success and reveal the negative impact of high temperatures on the breeding performance of Barn Swallows in this region. In a context of climate change and increasing drought frequency, these findings underscore the challenges that Barn Swallow populations might face in arid environments in the near future
A Game of Snakes and Ladders: Armed forces Families with Children Requiring Additional Support with their Education
No full textThis study represents the first comprehensive investigation into the experiences of children from armed forces families with Additional Support Needs (ASN) who are either residing in, or transferring to, Scotland. The impetus for this research stemmed from a recognition that, despite the significant number of service children with ASN in Scotland, there was a marked absence of empirical research exploring their educational experiences, the challenges encountered in seeking support, and their perspectives on the support received.The study was funded by the Armed Forces Covenant Fund Trust and was conducted over a two-year period, from April 2023 to March 2025. The primary aim of the project was to establish a clear and effective pathway that supports both serving and veteran parents, and their children with ASN, during transitions into and out of the Scottish education system. A core objective was to equip policymakers with evidence-based recommendations by identifying the key enablers and barriers affecting the child’s educational experience, overall wellbeing, and family life—ultimately driving systemic change.To achieve the aim the research was divided into five phases:1)A review of the academic literature;2)Review of national policies and advice services;3)Gathering and analysis of data on the target population;4)Interviews with education staff, armed forces children and their families; 5) The project in synthesis: development of a pathway.An advisory board, composed of a diverse group of stakeholders was established to provide strategic guidance throughout the project. The research team adopted the perspective of families navigating the education and support systems for children with ASN, ensuring that the lived experiences of these families remained central.This study offers novel insights and makes a significant contribution to a relatively underexplored area, building upon existing academic work while addressing notable gaps in the literature
Exploring mothers' perspectives on the early mother-infant relationship to inform midwifery practice: A qualitative study
Get PDFBackgroundThe mother-infant relationship plays a crucial role in individual and community health. The nature of the mother-infant relationship can influence the social and emotional development of the infant, impacting their lifelong health. Midwives are key in providing health promotion for women and babies and are ideally placed to support women during the early development of the mother-infant relationship.AimsThis study aimed to explore new mothers' perspectives on developing the early mother-infant relationship to inform midwifery practice.MethodsThis study is phase one of a mixed-method, exploratory sequential design project. In this phase, 14 women were interviewed within six weeks of birth to explore experiences that influenced the developing relationship with their baby. To support reflection women were provided with a visual prompt activity. These interviews informed the co-design workshops to develop an intervention to support midwives to promote the early mother infant relationship. A research protocol was published prior to undertaking the study, however as this research did not seek to modify or measure any heath related outcomes it was not registered as a clinical trial.ResultsKey themes from the interviews included: making moments that matter; the role of the village; feeling like I'm winning; supportive health professionals, and forming a new family. These themes underpinned strategies to support the mother-infant relationship.ConclusionThe findings from this study offer ways for midwives to support the emerging mother-infant relationship during the early postnatal period. Facilitating mothers to engage with their baby as well as integrating key people is important. Midwives also play a key role in building maternal confidence and competence. Further research is needed to embed and evaluate strategies in midwifery practice
CAST: Efficient Traffic Scenario Inpainting in Cellular Vehicle-to-Everything Systems
Get PDFAs a promising vehicular communication technology, Cellular Vehicle-to-Everything (C-V2X) is expected to ensure the safety and convenience of Intelligent Transportation Systems (ITS) by providing global road information. However, it is difficult to obtain global road information in practical scenarios since there will still be many vehicles on the road without onboard units (OBUs) in the near future. Specifically, although C-V2X vehicles have sensors that can perceive their surroundings and broadcast their perceived information to the C-V2X system, their line-of-sight (LoS) is limited and obscured by the environment, such as other vehicles and terrain. Besides, vehicles without OBUs cannot share their perceived information. These two problems cause extensive areas with unperceived information in the C-V2X system, and whether vehicles are in these areas is unknown. Thus, extending the perceivable range of the limited scenario for C-V2X applications that require global road information is necessary. To this end, this paper pioneers investigating the scenario inpainting task problem in C-V2X. To solve this challenging problem, we propose an effiCient trAffic Scenario inpainTing (CAST) solution consisting of a generative architecture and knowledge distillation, simultaneously considering the inpainting precision and computation efficiency. Extensive experiments have been conducted to demonstrate the effectiveness of CAST in terms of Precise Inpaint Rate (PIR), Rough Inpaint Rate (RIR), Lane-Level Inpaint Rate (LLIR), and Inpaint Confidence Error (ICE), paving the way for novel solutions for the inpainting problem in more complex road scenarios
Assessing Self-Reported Prolonged Grief Disorder with ‘Clinical Checks’: A Proof of Principle Study
Get PDFPsychological assessment is commonly conducted using either self-report measures or clinical interviews; the former are quick and easy to administer, and the later are more time consuming and require training. Self-report measures have been criticised for producing higher estimates of symptom and disorder presence relative to clinical interviews, with the assumption being that self-report measures are prone to Type 1 errors. Here, we introduce the use of ‘clinical checks’ within an existing self-report measure. These are brief supplementary questions intended to clarify and confirm initial responses, similar to what occurs in a clinical interview. Clinical checks were developed for the items of the International Grief Questionnaire (IGQ), a self-report measure of ICD-11 prolonged grief disorder (PGD). Data were collected as part of a community survey of mental health in Ukraine. Individual symptom endorsements for the IGQ significantly decreased with the use of clinical checks, and the percentage of the sample that met the ICD-11 diagnostic requirements for PGD fell from 13.6% to 10.2%, representing a 24.8% reduction of cases. The value and potential broader application of clinical checks are discussed
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