bluuebunny/crossref_metadata_2025 huggingface dataset:
Characterization of cast rare earth WE43 magnesium alloy and parameter optimization for wire electrical discharge machining using TOPSIS & COPRAS
Authors: Solomon Jebarajan Manickam Aswin Immanuel , Gurusamy Selvakumar , Shanmugakani Senthil Kumar , Pandiarajan Narayanasamy , Pandiarajan Balasundar , Shunmugam Muthu Natarajan
Published: November 2024

The manuscript examines the fabrication of rare earth magnesium alloy WE43, and its performance under wire electrode discharge machining (WEDM) conditions. It focuses on WEDM parameter optimization using multi criteria decision making (MCDM) techniques. WE43 alloy manufactured using casting method were characterized using advanced microscopy and spectroscopy techniques to analyze the microstructure, phase structure, and mechanical properties. WEDM experiments explores the effect of pulse current, pulse-on time, pulse-off time, and voltage on the material removal rate (MRR) and surface roughness (SR). MCDM methods, like Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) and Complex Proportional Assessment (COPRAS), are used to identify optimal machining conditions, with both approaches agreeing on the same optimal parameter set: pulse current of 15 A, pulse-on time of 5 µs, pulse-off time of 15 µs, and voltage of 30 V. This combination produces the best balance between MRR and SR, confirmed by additional experiments. The findings highlight the potential of the WE43 alloy for applications requiring precision machining, offering valuable insights for industrial use and future research.
Retrenchment without effect? Exploring the link between pension reforms and public pension adequacy of new retirees in seven European countries (1993–2020)
Authors: Bridgen Paul , Meyer Traute , Davison Lisa
Published: November 2024

Since the 1980s population ageing and economic change has led to cost-containing pension reforms in many industrialized countries. Research has focused on the institutional side of such reforms, but the impact of cost containment policies for real retirees’ pensions remains under-researched. This study addresses this gap. We use the change in projected pension replacement rates for average earners established by the OECD (2002–2019) as an indicator of institutional retrenchment and compare this with the change in the ‘relative pension level’ of real pensioners covering the period 1993–2020 across seven European countries. With data from five waves of the Luxembourg Income Survey and linear regression models we examine whether the average pension income from public and mandatory systems, expressed in relation to average wages (‘relative pension level’), of individuals in the earliest wave, least affected by institutional retrenchment, were significantly different from those of the later waves. The analysis was repeated for subgroups of men and women. We find that institutional change is no reliable predictor of relative pension level change. In the five countries where institutional retrenchment happened, real pension levels fell in only two, in the two countries where they increased no significant change in pension levels occurred. Women’s relative pensions have been more consistent with the expectations of the institutional reform literature than men’s. Our results suggest other variables have mediated the relationship between institutions and pension outcomes, most probably employment change. We conclude that outcome-based analysis is an essential complement to institutional approaches. For policymakers the long-term impact of pension reform is hard to predict, but our results suggest that relative real pension income has been more robust than feared by many.
Revolutionizing packaging: Bioplastics for superior food and pharmaceutical solutions
Authors: Singh Priyanshu , Sharma Ameya , Rathee Ankit , Puri Vivek , Chopra Hitesh , Nagpal Manju , Kaur Malkiet , Malik Tabarak
Published: November 2024

The packaging industry is undergoing a significant transformation, driven by the pressing need for sustainable and eco-friendly solutions. Bioplastics have emerged as a promising alternative to traditional plastics, offering superior functionalities and environmental benefits for various applications, especially in food and pharmaceutical sector. This review explores the impact of bioplastics on packaging, focusing on their enhanced properties, sustainability and compatibility with stringent regulatory requirements. By extending the shelf life of food and pharmaceutical products, this improved barrier performance lowers waste and ensures product safety. Additionally, bioplastics have good biodegradability; in natural settings, they disintegrate into innocuous molecules, reducing their environmental impact. Reducing carbon emissions is another benefit of using bioplastics in packaging. Bioplastics are made with less greenhouse gas emissions than ordinary plastics, which help with the worldwide effort to fight climate change. Bioplastics may also be recycled and used again, which helps to conserve natural resources and cut down on waste. Moreover, bioplastics can be engineered to meet particular regulatory requirements, guaranteeing their appropriateness for a range of uses. Bioplastics have many benefits, however there are obstacles in the way of their broad use. These include the requirement for specialized recycling facilities, restricted supply, and increased production costs. However, it is anticipated that these issues will be resolved in the near future due to developments in bioplastic technology and rising demand for environmentally friendly alternatives. Bioplastics are expected to have a big impact on how people live in the future as efforts are made to lessen global warming and encourage sustainable habits.
Book review: Himanshu Roy & Jawaid Alam (Eds.), A History of Colonial India: 1757 to 1947
Authors: Kumar Vikash
Published: November 2024

Himanshu Roy & Jawaid Alam (Eds.), A History of Colonial India: 1757 to 1947 (New York: Routledge, 2022), 304 pp. ₹1,095 and £28.79 (E-book); £28.79 (Paperback).
Data level fusion of acoustic emission sensors using deep learning
Authors: Cheng Lu , Nokhbatolfoghahai Ali , Groves Roger M , Veljkovic Milan
Published: November 2024

The acoustic emission (AE) technique is commonly utilized for identifying source mechanisms and material damage. In applications requiring numerous sensors and limited detection areas, achieving significant cost savings, weight reduction, and miniaturization of AE sensors is crucial. This prevents excessive weight burdens on structures while minimizing interference with structural integrity. Thin Piezoelectric Wafer Active Sensors (PWAS), compared to conventional commercially available sensors, offer a miniature, lightweight, and affordable alternative. The low signal-to-noise ratio (SNR) of PWAS sensors and their limited effectiveness in monitoring thick structures result in the decreased reliability of a single classical PWAS sensor for damage detection. This research aims to enhance the functionality of PWAS in AE applications by employing multiple thin PWAS and performing a data-level fusion of their outputs. To achieve this, as a first step, the selection of the optimal PWAS is performed and a configuration is designed for multiple sensors. Pencil break lead (PBL) tests were performed to investigate the compatibility between selected PWAS and traditional WSα and R15α sensors. The responses of all sensors from different AE sources were compared in both the time and frequency domains. After that, convolutional neural networks (CNNs) combined with principal component analysis (PCA) are proposed for signal processing and data fusion. The signals generated by the PBL tests were used for network training and evaluation. This approach, developed by the authors, fuses the signals from multiple PWAS and reconstructs the signals obtained from conventional bulky AE sensors for damage detection. Three CNNs with different architectures were built and tested to optimize the network. It is found that the proposed methodology can effectively reconstruct and identify the PBL signals with high precision. The results demonstrate the feasibility of using a deep-learning-based method for AE monitoring using PWAS for real engineering structures.
The optimization of the actuator and sensor layout for piezoelectric smart blades
Authors: Jiang Jinhua , Gao Zhiyuan , Zhang Hesheng , Zhu Xiaojin
Published: November 2024

To reduce vibration-induced fatigue fractures of aero-engine blades, an active vibration control scheme for aero-engine blade has been developed proposed. In this paper, the Macro Fiber Composite (MFC) are used as actuators and sensors to suppress the vibration of the blade, but the position of the actuators and sensors has a great influence on the effectiveness of the blade vibration suppression. To achieve optimize suppress effect, the objective criterion for position optimization was proposed to be the absolute value normalized superposition of the strain of the first few natural frequency modes of the blade. First, a finite element analysis was conducted on the blade model. Then, the positions of the actuators and sensors were determined by using the differential evolution algorithm for optimization. The blade model with three layout strategies was constructed in ADAMS, and the ADAMS-Simulink joint simulation platform was constructed. Finally, the comparison of actuator and sensor effects with three position layout strategies is completed, the layout optimization strategy proposed in this paper achieves more significant vibration suppression with less control force was verified though the feedforward control experiments, and the effective of the proposed strategies are verified on the experimental platform for blade active vibration control.
Patients’ Trajectory With Lung Cancer From Treatment Initiation to End-Of-Life: A Retrospective Cohort Study Using Claims Data in Japan
Authors: Ogura Yuri , Iwasaku Masahiro , Ishida Mami , Katayama Yuki , Nishioka Naoya , Morimoto Kenji , Yamamoto Chie , Tokuda Shinsaku , Kaneko Yoshiko , Yamada Tadaaki , Yamazaki Hideya , Inoue Masayoshi , Ikai Hiroshi , Takayama Koichi
Published: November 2024

Introduction

This study aimed to describe the course of patients with lung cancer from treatment initiation to end-of-life.

Methods

This retrospective cohort study used Claims Data from the National Health Insurance and Advanced Elderly Medical Service System. We analyzed data from patients newly diagnosed with lung cancer between April 2013 and March 2021 who had been hospitalized at our University Hospital in urban area. We evaluated (1) treatment courses of these patients, and (2) end-of-life care and clinical factors related to end-of-life care.

Results

A total of 818 patients who were diagnosed with lung cancer and received lung cancer treatment were included, of whom 200 were assessed for end-of-life. During the study period, 464 patients underwent surgery, while 308 received chemotherapy without undergoing surgery. The patients generally received lung cancer treatment within 2 years. The median time from initial treatment to death was 13 months for deceased patients. Patients in the palliative care unit (PCU) constituted 9% at 30 days before death, 25% at 7 days before death, and 40.5% on the day of death, whereas only 15% died at home. The prevalence of end-of-life care in the home/PCU was elevated among patients receiving molecular targeted drugs and in the female group.

Conclusion

This study highlighted the patterns of end-of-life care following lung cancer treatment by using Claims Data. PCU utilization was concentrated in the period shortly before death, with fewer patients passing away at home. End-of-life care may be influenced by clinical factors, including the type of lung cancer treatment received, which may change the place of care.
Improving Oral Nutrition Intake Through One-on-One Meal Support for Patients With Eating Disorders
Authors: Dzelambong Terence Yuyun
Published: November 2024

INTRODUCTION: Treatment for eating disorders (EDs) after hospitalization requires structured environments such as residential care programs where treatment options such as meal support therapy can help manage the physical and psychological effects of their illness. The residential center for this project follows a multidisciplinary approach that uses cognitive behavior therapy (CBT) as a treatment modality for ED. Structured meal support is an essential part of implementing the center’s CBT program. AIM(S): The project aimed to determine if one-on-one mealtime support training for staff can help increase oral nutrition intake in adolescents with ED. METHODS: Meal consumption data for five residents of the ED center was used for this project. First, 2 weeks of meal consumption data were collected (210 meals). Then residential center staff were trained on using meal support strategies adapted from the Auckland Eating Disorder Manual. After training the staff and having them implement the strategies with ED clients for a week, another 2 weeks of meal consumption data were collected (210 meals). The preimplementation and postimplementation data were compared to determine if the intervention had an impact on the overall meal intake by residents of the ED center. RESULTS: Before implementing the project, patients completed 122 meals in 2 weeks, representing 58.1% of their meals and after the intervention, patients completed 141 meals representing 67.14% of their meals. CONCLUSION: Project findings show that the use of mealtime support strategies while providing one-on-one support for patients with EDs led to an increase in the overall oral intake.
Emotion Recognition Accuracy Among Individuals With ADHD: A Systematic Review
Authors: Shepard Emily C. , Ruben Mollie , Weyandt Lisa L.
Published: November 2024

Objective:

The aim of the present systematic review was to consolidate findings related to emotion recognition accuracy among individuals with attention deficit hyperactivity disorder (ADHD). The review also examined emotion recognition accuracy assessment methods as well as the contribution of gender to emotional recognition accuracy.

Method:

A total of 43 articles were identified based on inclusion criteria using PRISMA guidelines for article retrieval.

Results:

58% of retrieved articles reported significantly decreased emotion recognition accuracy among individuals with ADHD relative to neurotypical peers, with either global or emotion-specific differences identified. Notably, studies finding a significant difference in emotion recognition accuracy were of significantly lower quality according to a quality review checklist than the articles finding no difference ( p < .001). For example, when use of control conditions and multiple measures of emotion recognition accuracy were employed, no study found a significant difference in emotion recognition accuracy between individuals with and without ADHD. Regarding potential gender differences, not a single study included gender diversity beyond sex assigned at birth. The majority (86%) of studies included a predominantly male sample, with 15% including only male participants. While differences in emotion recognition accuracy were found between male and female participants in 7% of cases, future research is needed to identify the contribution of gender diversity.
The Prioritization of Prospection
Authors: Caruso Eugene M. , Maglio Sam J. , Van Boven Leaf
Published: November 2024

Academic Abstract

Humans frequently engage in mental time travel, reflecting on the past and anticipating the future. Although these processes may seem similar, research documents systematic differences between retrospection and prospection. We propose a conceptual framework to organize and explain these differences based on three axiomatic temporal asymmetries: The past occurs before the future; the past is more certain than the future; and the past is less controllable than the future. People’s experience with these axiomatic differences is internalized and overgeneralized to shape mental representations of the past and future. Our review shows that people generally prioritize prospection over retrospection, attending more to the future than the past and reacting more intensely to future events than to past events. We consider potential moderators of and constraints on the generality of prioritizing prospection. We explore the implications of these temporal asymmetries, emphasizing their theoretical and practical significance.

Public Abstract

While daily life centers on the present, people often reflect on the past and anticipate the future. But which direction of mental time travel—backward or forward—has more influence? We identify three key differences that shape how people engage with the past and future: time flows from past to future, the future is more uncertain, and people have more control over the future. These differences affect the frequency, intensity, and nature of thoughts and feelings, leading to predictable biases in how we mentally represent and emotionally engage with events over time. Because focusing on the future often provides greater benefits, people tend to prioritize prospection over retrospection in everyday life.
Transmucosal glucagon rapidly increases blood glucose concentration in healthy cats
Authors: Cohen Emily A , Porter Lauren , Crews Chiquitha D , Mott Jocelyn , Tardo Antonio M , Gilor Chen
Published: November 2024

Objectives

To evaluate the effect of transmucosal glucagon powder (Baqsimi; Amphastar Pharmaceuticals) on blood glucose (BG) concentrations in healthy cats and describe adverse reactions to its administration.

Methods

A randomized, controlled, crossover study was conducted on six healthy cats with a 7-day washout period between treatments. Transmucosal glucagon powder was administered intranasally and rectally and compared with intranasal placebo. Blood was collected at −15 and −1 mins before glucagon administration and 5, 15, 25, 35, 45 and 60 mins after to evaluate BG, plasma glucagon concentrations (pGlucagon) and plasma potassium concentrations (K+). Stress scores and adverse effects were recorded at all time points.

Results

Median pGlucagon in the nasal and rectal groups increased from baseline (nasal: 12.2 pmol/l, range 3.5–44.1; rectal: 6.9 pmol/l, range 2.9–21.1) to 218.5 pmol/l (range 7.9–349.8; P = 0.02) and 349.8 pmol/l (range 67.4–349.8; P = 0.01), respectively, 15 mins after administration. Median BG increased from baseline (101 mg/dl, range 91–110) 15 mins after nasal (137.5 mg/dl, range 104–251; P = 0.006) and rectal (229 mg/dl, range 99–285; P = 0.002) administration. Median K+ decreased from baseline (nasal: 3.8 mmol/l, range 3.6–4.1; rectal: 3.7 mmol/l, range 3.5–3.9) to 3.4 mmol/l (range 3.1–3.6; P = 0.04) at 15 mins with nasal administration, and to 3.2 mmol/l (range 3.1–3.6; P = 0.04) at 15 mins and 3.1 mmol/l (range 2.9–3.4; P = 0.01) at 25 mins with rectal administration. No significant changes were detected in the placebo group. No serious adverse effects were noted.

Conclusions and relevance

Transmucosal glucagon administration is effective in raising BG with minimal side effects in healthy cats. Future studies are needed to quantify the efficacy and safety of transmucosal glucagon in diabetic cats, especially during hypoglycemic crises.
Prediction of Distant Metastasis of Lymph-Node-Negative Primary Breast Cancer From Gene Expression Profiling Using Cox-Boost Regression Model
Authors: Hamidi Omid , Amini Payam , Tapak Leili , Zohrab Beigi Yasaman , Afshar Saeid , Dinu Irina
Published: November 2024

Backgrounds:

Distant metastasis in breast cancer patients contributes to increased breast cancer mortality, highlighting the urgent need for effective predictive strategies. Understanding metastasis mechanisms and identifying relevant biomarkers are crucial for improving patient outcomes and informing targeted therapies. This study employed a high-dimensional regression model to identify biomarkers linked to distant metastasis-free survival in breast cancer patients, with the goal of enhancing prognostic accuracy and guiding clinical decisions.

Methods:

We utilized the publicly available breast cancer dataset (GSE2034), which includes gene expression profiles for 22 283 genes across 286 samples. To identify relevant genes, we applied Cox-Boost regression and a random forest (RF) model. We then explored the association between the selected genes and metastasis-free survival outcomes using quantile regression, chosen for its ability to assess the impact of these genes across different survival quantiles ( P < .05). This approach complements the Cox-Boost model by providing a more detailed understanding of gene-survival relationships at various points in the survival distribution, thereby strengthening the robustness of our findings.

Results:

We identified 222 significant transcripts using univariate Cox regression models. By applying Cox-Boost, both with and without adjustment for ER+/− status, we identified 7 genes associated with time-to-relapse/metastasis in breast cancer patients: SNU13, CLINT1, ACBD3, NEK2, COL2A1, WFDC1, and RACGAP1. A similar approach was used for ER-positive patients. Patients were classified as high or low risk for metastasis based on the median prognostic index calculated from the identified genes ( P < .001). The top-ranked genes associated with high/low risk groups using RF were RACGAP1, NEK2, CCNA2, DTL, ACBD3, ARL6IP5, WFDC1, and PDCD4.

Conclusions:

We identified eleven key genes, including SNU13, CLINT1, ACBD3, NEK2, COL2A1, WFDC1, and RACGAP1, as well as CCNA2, DTL, ARL6IP5, and PDCD4, that are related to the risk of distant metastasis and may be used as biomarkers to predict distant metastasis of breast cancer.
Assessment of Human Health Risks from Aflatoxin M1 in Raw Milk: A Study from North Shewa Zone, Oromia Region, Ethiopia
Authors: Geleta Girma Selale , Nugussa Argachew , Faye Gezahegn , Ragassa Girma
Published: November 2024

This research aimed to assess the levels of AFM1 and evaluate the associated health risks from raw milk samples collected randomly from four selected towns. Ninety (n = 90) raw milk samples were randomly collected from smallholder dairy farmers in the North Shewa Zone, with 23 samples from Fiche, 23 from G/Gurracha, 22 from Dagem, and 22 from Sululta. The concentrations of AFM1 in the raw milk samples were determined using high-performance liquid chromatography with fluorescence detection (HPLC-FLD) after purification via an immunoaffinity column (IAC). AFM1 was detected in 76 (84.4%) raw milk samples, with 53 (58.9%) exceeding the maximum permissible limit established by the European Commission, 0.0500 µg/L. The highest AFM1 content was 2.00 µg/L and the lowest was 0.0100 µg/L. Additionally, risk assessment was performed using the margin of exposure (MOE), estimated daily intake (EDI), hazard index (HI), and cancer risk (CR). The results indicated that based on the average contamination levels of the milk during the study period and typical consumption rates, the average EDI of the adult population to AFM1 ranged from 0.374 to 0.852 ng/kg body weight (bw) per day. The calculated MOE values were less than 10,000, indicating potential health concerns. The mean HI value determined in this study was 2.70, which also suggests adverse health effects. Furthermore, the estimated risk of developing hepatocellular carcinoma (HCC) due to AFM1 exposure from milk consumption among adults was calculated to be 0.00170 cases per 100 000 individuals yearly. This finding indicates a significant risk of HCC, which justifies its continuous monitoring of dairy products throughout the entire supply chain, from production to consumption. Furthermore, our research highlights the need for further investigation into the risks posed by AFM1 in children, given their higher levels of milk consumption relative to adults.
Exposure to Environmental Pollutants and Risk of Diarrhea: A Systematic Review
Authors: Nwanaforo Eudora , Obasi Cecilia N , Frazzoli Chiara , Bede-Ojimadu Onyinyechi , Orisakwe Orish E
Published: November 2024

This systematic review investigates the association between environmental pollutants and the risk of diarrhea, a critical public health issue, particularly in low- and middle-income countries. The review synthesizes findings from various studies that highlight the impact of contaminants such as pesticides, heavy metals, polycyclic aromatic hydrocarbons (PAHs), microplastics, and parabens on gastrointestinal health. Following PRISMA guidelines, a comprehensive literature search across databases including PubMed, Scopus, and Google Scholar yielded 496 articles, of which 11 met the inclusion criteria for detailed analysis. The results indicate a significant correlation between exposure to specific pollutants—particularly pesticides like dichlorodiphenyltrichloroethane (DDT), PAHs, arsenic, cadmium, and microplastics—and increased incidences of diarrhea. Notably, studies revealed that prenatal exposure to DDT is linked to higher diarrhea rates among boys in urban settings, while pesticide exposure in childhood correlates with inflammatory bowel disease in adulthood. Mechanistically, these pollutants may disrupt gastrointestinal function through cholinergic effects and endocrine disruption, leading to altered gut motility and microbiome imbalances. Moreover, the review emphasizes the immunosuppressive effects of heavy metals such as mercury and cadmium, which compromise the immune response and increase susceptibility to gastrointestinal infections. Despite the identified associations, there is a notable gap in research regarding geographic distribution and pollutant impacts on health outcomes. The review underscores the necessity for public health interventions aimed at reducing exposure to these environmental pollutants to mitigate their adverse health effects. In conclusion, this systematic review highlights the urgent need for further epidemiological studies in underrepresented areas to enhance our understanding of how environmental pollutants influence public health globally. Recommendations include rigorous monitoring of pollutant levels, public health initiatives to reduce exposure, and policies that restrict emissions of harmful substances. Addressing environmental pollution is crucial for mitigating diarrheal diseases and protecting vulnerable populations from its detrimental effects.
Antipsychotic Medication Use Among Newly Admitted Long-term Care Residents During the COVID-19 Pandemic in Canada
Authors: Turcotte Luke A , Heckman George A , McArthur Caitlin , Saari Margaret , Sinn Chi-Ling Joanna , Mathias Krista , Khowaja Asif , Stall Nathan M , Hirdes John P
Published: November 2024

Objective:

Examination of the impact of the COVID-19 pandemic on rates of antipsychotic medication use, initiation and discontinuation, among newly admitted long-term care residents.

Design:

Repeated cross-sectional study.

Settings and Participants:

Long-term care home residents in Alberta, British Columbia and Ontario, Canada assessed with the Minimum Data Set (MDS) 2.0 assessment. The cohort was segmented according to admission during the pandemic (March 2020/2021) and 2 pre-pandemic (March 2018/2019 and March 2019/2020) periods.

Methods:

Multivariable logistic regression models were fit to characterize the association between long-term care admission during the COVID-19 pandemic and the use of antipsychotic medications. A second set of logistic regression models were fit among residents with follow-up assessments to characterize the association between long-term care admission and antipsychotic initiation/discontinuation at follow-up. All models were adjusted for resident characteristics including sex, age group, Aggressive Behaviour Scale score, Cognitive Performance Scale score, and diagnoses of Alzheimer’s disease and related dementias, anxiety disorder, depression, and bipolar disorder.

Results:

21 612 residents admitted during the pandemic and over 30 000 in each pre-pandemic period were included. Antipsychotic use increased during the pandemic period among newly admitted residents from both community (adjusted odds ratio [aOR] 1.20-1.29) and hospital settings (aOR 1.21-1.23). Residents admitted during the pandemic period were more likely to have antipsychotic medications initiated (aOR 1.25-1.26) and less likely to have had them discontinued (aOR 0.74-0.76) at the time of follow-up assessment.

Conclusion and Implications:

Multiple factors contributed to the observed increase in antipsychotic medication use among newly admitted long-term care home residents during the COVID-19 pandemic: increased medication use at the time of admission, increased medication initiation at follow-up, and decreased medication discontinuation at follow-up. A whole-systems approach that extends beyond long-term care into hospital and community settings is necessary to address this prevalent issue.
Enrichment and acceleration of highly motivated children’s instrumental music learning: A case study of a pilot development project
Authors: Juntunen Marja-Leena , López-Íñiguez Guadalupe
Published: November 2024

This case study examined a pilot development project to intensify the learning of instrumental music and enrich the development of highly motivated music school children in Finland. The project was built on four core components: (a) increased tuition, (b) integration of peer learning alongside one-on-one instruction, (c) parallel teaching, and (d) an emotionally nurturing environment. The school-year-long project was implemented in three music schools in collaboration with a higher education institution. The research findings suggest that all the core components intensified the children’s instrumental music learning and that group lessons and activities increased their motivation. Parallel teaching served as continuing education for the teachers and diversified the children’s learning; however, it also occasionally caused confusion among the children. The nurturing learning environment, coupled with collaboration between teachers and families, as well as the commitment and support of caregivers, sustained the children’s socio-emotional development and ability to cope with the concise schedule. Regarding the further development of such practices, based on this study, it can be suggested to better prepare the collaboration and parallel teaching of different teachers and to offer a clearer vision, negotiated with the participants, of what the child is expected to do at home and what the parents’ role is. The project was perceived as very time-consuming and intense. Therefore, finding the right balance in an extended instrumental education that integrates several elements is a challenge that needs further reflection. Overall, the project raised questions about how to reconcile the provision of equal opportunities for all children with the legitimation of special treatment in music education. These issues should remain the subject of careful, critical, and open consideration in music education policy and research.
Adapting ripple effects mapping for evaluating public health initiatives in complex systems: Reflections and recommendations from seven case studies
Authors: Creaser Amy , Dowling Lisa , Helme Zoe , Crowther Jamie , Casana Lily , Williams Rachel , Young Emma , Nobles James , Hall Jennifer
Published: November 2024

Ripple effects mapping is a qualitative and participatory method, developed to capture the dynamic nature and interacting elements of an initiative, and its impacts. We present our experiences of using ripple effects mapping to evaluate complex public health initiatives across seven case studies in Bradford, UK. Seven researchers engaged in qualitative reflective practice to capture their individual experiences of using ripple effects mapping within their research and practice; outlining how the method has been adapted and highlighting key reflections and recommendations for implementing ripple effects mapping in the future. We developed ten recommendations, with corresponding strategies, for implementing ripple effects mapping sessions. The recommendations outline how ripple effects mapping can be implemented to improve engagement and anticipate and overcome potential barriers. In doing so, we outline how ripple effects mapping can be used, and adapted, to evaluate various public health initiatives, in research and practice.
The definitional creep: Payment processing and the moral ordering of sexual content
Authors: Webber Val , Franco Rébecca Suzanne
Published: November 2024

Discussions of online content moderation often focus on the platform, however credit card networks and payment processors determine what content can be monetized and therefore placed on adult platforms. Through fieldwork and interviews among adult industry stakeholders and a survey of adult content creators, this paper demonstrates how these financial actors impose a moral ordering of sexuality that prioritizes credit card brand reputation and optics over the autonomy and integrity of sexual subjects. Visa and Mastercard, via payment processors, suppresses kink content in the name of ensuring consent and safety. This process inappropriately broadens the definition of ‘harmful’ sexual content—what we call ‘definitional creep’—such that private financial entities can effectively create de facto global obscenity law that suits corporate rather than collective interests.
Digital relatedness: A longitudinal study on social resources and the use of smart technology
Authors: Latikka Rita , Koivula Aki , Bergdahl Jenna , Oksanen Atte
Published: November 2024

The digital world is a vital place to connect with others. This study investigates individual differences in experiencing relatedness through new technologies, or “digital relatedness.” The study is grounded in a novel framework that combines social and digital capital and self-determination theory perspectives. We used a three-wave survey conducted from 2021 to 2023 involving 1226 Finnish adults and applied random effects within-between models for data analysis. The results show positive within- and between-person effects of a preference for interacting with artificially intelligent systems (over humans) and the use of smart technology on digital relatedness as well as positive between-person effects of a sense of local community belonging. The positive effect of using smart technology was particularly evident for individuals with a lower or medium level of local community belonging. The results suggest that frequent technology use can enhance digital relatedness, especially for those less connected to their local community.
Resilience agenda for gastronomy-driven tourism destinations
Authors: Rašan Dora , Mikinac Krešimir , Laškarin Ažić Marina
Published: November 2024

Purpose: This research aims to gather insights from industry experts in gastronomy tourism to identify and propose potential strategies necessary for enhancing the resilience of destinations in the post-COVID era and preparing them for future disruptions. Approach: To this end, the authors applied the digital whiteboard platform Mural to conduct a brainstorming session with seven industry experts. Findings: The outcomes of this research study unveiled a practical framework delineating a resilience agenda tailored for self-sustainable gastronomy-driven tourism destinations. This framework highlights four key strategies: (1) strengthening supply chains with local producers, (2) adopting sustainable waste management practices, (3) developing robust risk management plans, and (4) fostering community engagement and collaboration. Originality/value: This paper is the first to propose potential strategies from an industry expert’s perspective that can pull gastronomy tourism forward while enhancing destinations’ resilience. Incorporating these strategies into the planning and management of gastronomy-driven tourism destinations will be critical to ensure their long-term viability and success to adapt and thrive effectively in the face of future uncertainties.
Perception of leadership challenges within the Norwegian child welfare services: A Gordian knot
Authors: Vetås Esther , Hollekim Ragnhild , Iversen Anette Christine , Olaniyan Oyeniyi Samuel
Published: November 2024

The Norwegian child welfare service is under pressure both nationally and internationally after several unfavourable verdicts in the European Court of Human Rights (EMD) and national inspections that uncovered serious problems in the system. While the challenging situation for child welfare workers has been well documented in research, there is limited knowledge on how child welfare leaders (CWLs) experience their work situation. This article explores how CWLs experience their everyday work by applying the theoretical framework of the job demands-resources (JDR) model. Data were obtained through qualitative interviews with 12 CWLs and analysed thematically. The study shows that CWLs experience a relatively high imbalance between work demands and available resources in the workplace. Additionally, participants also experience staff challenges, seemingly impossible priorities, and a sense of great personal responsibility. Many leaders experience a work situation that can best be described as a Gordian knot.
Doing “ethics work” in practice: An analysis of care managers’ collegial discussions concerning reluctant clients
Authors: Taghizadeh Larsson Annika , Olaison Anna , Österholm Johannes
Published: November 2024

Few studies have explored the moral and ethical dimensions of how, in practice, social workers talk about and handle cases as part of their daily work. At the same time, identifying and examining the ethical dimensions of actual social work practice can be challenging because ethics is often hidden within the complex particularities of daily work. This article applies and develops Sarah Banks’ conceptualisation of “ethics work” to examine how care managers in Sweden are “doing ethics”, while seeking and providing collegial support and guidance on how to deal with the wishes of adult (potential) service users who are reluctant to accept support – or whose wishes in the matter are difficult to determine. The study is based on an analysis of 14 audio-recorded case conferences. With the help of Banks’ conceptualisation, the analysis makes visible how the case discussions in question encompass a complex “doing of ethics”, by means of which the welfare of individuals is balanced against care managers’ legal duty to adhere, without exception, to the principle of self-determination. The findings suggest that further research that applies and develops Banks’ conceptualisation of ethics work by examining “actual” social work practice in situ, while recognising the particularities of the social and societal context in which this work is taking place, could provide new knowledge and understanding about both the specific and common challenges that social workers face in different parts of the world. It could also make the ethical dimensions of social work practice more widely recognised and valued.
Challenges and Tensions in Enacting Culturally Responsive Pedagogy – A Case Study in Teaching International Baccalaureate Diploma Visual Arts at an Islamic School in Australia
Authors: Yusoff Noor Hasbi
Published: November 2024

Inclusive education in Australia incorporates education to support a wide range of students’ physical abilities, social conditions and culture-religious constructs. The case study described here focuses on religious inclusivity in the application of pedagogical strategy within the International Baccalaureate Diploma Programme Visual Arts curriculum. Specifically, it aims to analyse the challenges that may arise from enacting culturally-sensitive pedagogy in teaching Visual Arts at an Islamic secondary school in Melbourne, Victoria, Australia. The research includes analysis of available literature that discusses education in Islam, culturally-sensitive pedagogy and inclusive education. It also includes critical practice-based reflection on the pedagogical strategies used, classroom sessions and students’ responses.

The case study covered a time period of one week out of a 20-week unit of work of a Year 11 International Baccalaureate Diploma Visual Arts programme delivered to a class of Muslim students of diverse ethnic backgrounds. The two main challenges that arose are firstly, the need for the teacher to address the Islamic notion of adab (etiquette) in preventing wrong-doing while delivering content deemed prohibited by Islam; and secondly, the accessibility of information from the internet that may contradict the Islamic ethos of the school. The case study suggests that with a responsive attitude towards pedagogical strategy and a respectful approach to the cultural needs of the student demographic, it is possible to mitigate the tensions of delivering content that may be deemed culturally inappropriate in an Islamic school setting without compromising the demands of a rigorous curriculum such as the International Baccalaureate Diploma Visual Arts programme.
Building information modeling supported bridge structural health diagnosis and prognosis
Authors: Huang Dongqi , Lu Ye , Hua Linxin , Lin Solomon TK , Deng Jianghua , Chen Shuke
Published: November 2024

This study introduced a framework that facilitates bridge diagnostic and prognostic evaluations and related visualizations, leveraging building information modeling (BIM) techniques and data-driven structural health monitoring. Diagnostic and prognostic algorithms are proposed and able to provide intuitive and accessible structural insights via three-dimensional visualizations supported by the BIM techniques. Through the data communication channel established, virtual model information was successfully embedded into the bridge health evaluations, emphasizing the contribution of BIM techniques for data analysis. The proposed algorithms were applied to a case study of a reinforced concrete bridge. Strain distribution along the bridge deck was mapped in the 3D virtual model, which was able to precisely identify the critical location for potential damage. The residual fatigue life of different parts of the bridge deck was estimated and visualized by considering the main effects of traffic loading, exhibiting good alignments with the diagnostic results. The proposed framework is able to provide understandable and actionable structural information for stakeholders, facilitating the timely identification of structural issues and long-term planning of maintenance schedules.
Resilient fast convolutional sparse filtering with convergence directivity for the bearing fault diagnosis
Authors: Ma Hao , Han Baokun , Wang Jinrui , Zhang Zongzhen , Bao Huaiqian
Published: November 2024

The fast nonlinear convolutional sparse filtering (FNCSF), which detects fault features 42 h earlier than traditional methods in the intelligent maintenance system dataset and takes only 0.12 s, is widely considered a powerful tool for early fault diagnosis. However, random shocks caused by the structure or external interference pose challenges to the extraction accuracy of FNCSF. In addition, the extraction reliability of FNCSF is affected by computational instability and complex parameter settings. To address the above defects, resilient fast convolutional sparse filtering (RFCSF) is proposed in this study. First, the collected vibration signal is normalized by Z-score to eliminate the scale variability of the nonlinear transformation. Then, the frequency components of the signal are evenly divided by the initialized filters to indicate the convergence direction of fault and improve random shock resistance and extraction stability. Next, the nonlinear [Formula: see text] norm, which can effectively distinguish fault features from irrelevant disturbances, is regarded as the target of blind deconvolution. In the process of deconvolution, the filter converging to random shocks and noise is adaptively eliminated to improve the extraction efficiency and intelligence. Finally, the filtered signal is envelope demodulated to obtain fault information. In the simulation analysis, features under noise with −16 dB and random shocks with 50 m/s2 are accurately extracted by RFCSF with parameters robustness. Several experimental cases demonstrate that RFCSF with these advantages is a promising feature extraction tool.
Online intelligent perception, detection, and application of safety monitoring data: taking the right bank slope of PB Dam as an example
Authors: Li Yuanpeng , Shen Dingbin , Pei Liang , Lu Xiang , Li Yanling , Huang Huibao
Published: November 2024

Monitoring data is essential information that directly reflects the deformation and stability of slopes. However, existing research on dam slope safety monitoring mainly focuses on individual aspects, lacking overall consideration and comprehensive application, and the importance of monitoring data and its role in structural safety early warning have not been fully reflected. This study integrates data perception, detection, and security risk assessment techniques. By utilizing an online intelligent application platform, intelligent perception, detection, and application of monitoring data have been achieved. This platform adopts advanced perception technology, which can intelligently select the best measurement timing, consider meteorological and atmospheric refraction errors, obtain high-precision deformation monitoring data, identify mutation data through an improved cloud model that replaces the original data with the daily change rate of measured values, construct deformation statistical regression models and slope finite element models to achieve real-time and rapid prediction and warning of slope deformation and safety stability. Taking PB monitoring data as an example, the practical effectiveness of the platform was verified. The results show that the application platform fully meets the needs of dam slope prediction and early warning, with high data accuracy and prediction accuracy, providing necessary Technical support for intelligent monitoring and evaluation of slopes.
Damage mode classification in CFRP laminates using convolutional autoencoder and convolutional neural network on acoustic emission waveforms
Authors: Sai Krishna Yelamarthi , Raju Gangadharan , Desarkar Maunendra Sankar
Published: November 2024

The acoustic emission (AE) technique is a widely used nondestructive method for in-situ health monitoring of composite structures. Unlike metals, failure mechanisms in composite structures are complex, involving multiple damage modes, and each damage mode has a distinct AE signature. This work uses deep learning algorithms called convolutional autoencoder (CAE) and convolutional neural network (CNN) to classify damage modes in carbon fiber-reinforced polymer laminates using AE waveforms. Tensile experiments are carried out on laminates of various stacking sequences, and the acquired raw AE waveforms are transformed into time-frequency planes called spectrograms using short-time Fourier transform. CAE is used for retrieving deep features associated with damage modes in the latent space from these spectrograms. Subsequently, k-means is used to cluster the deep features in the latent space. Each cluster is labeled with a damage mode by inspecting their damage signatures using the scalograms. This labeled data is then used to train the CNN. The CNN, once trained is used on the AE data of pristine and notched quasi-isotropic specimens, and its ability to classify and identify the damage modes is investigated. The trained CNN achieves satisfactory classification accuracy of 96.9% on pristine quasi-isotropic specimen data and 96.4% on notched quasi-isotropic specimen data. When compared to the prediction accuracy of pure damage modes, the prediction accuracy of mixed-mode damage is slightly lower, at 92.5% for pristine and 91.3% for notched quasi-isotropic specimens. This reduction in accuracy is due to the spectrograms of mixed-mode damage containing energy distributed across multiple frequency bands. By classifying the AE waveforms of both pristine and notched quasi-isotropic specimens, the progression of different damage modes is analyzed through the cumulative count of AE waveforms associated with each damage type. These findings enhance the understanding of damage mode evolution in composite structures and contribute to structural health monitoring studies.