Search Results (98)

Background: A childhood burn presents new and unfamiliar challenges to patients and their parents during recovery. These injuries can negatively impact activities such as independence in self-care, participation in physical activity, and social interaction. As such, pediatric burn patients are at risk of poorer quality of life (QoL) outcomes after their burn. In this longitudinal, observational cohort study, we examined the social, demographic, and clinical factors that were associated with a poor QoL at 12 months postburn for pediatric patients aged > 2 years with non-severe burns in Western Australia. Methods: Inpatients were recruited from the pediatric burn unit at Perth Children’s Hospital in Western Australia between February 2021 and September 2022. Demographic and family information (age, sex, postcode, parental education, languages spoken at home) and clinical data (burn cause, TBSA%, location, surgical interventions, length of stay) were collected at baseline. At 6 and 12 months, caregivers completed the Brisbane Burn Scar Impact Profile (BBSIP). Results: A total of 37 caregivers completed the Brisbane Burn Scar Impact Profile (BBSIP). For the child’s QoL, 57% of caregivers reported that some impact remained for overall QoL, 32% for sensory intensity, 46% for sensitivity, 22% for daily living (22%), and 19% for emotional reactions. Parent worry was impacted in 46% of caregivers. Being female was associated with greater long-term impacts, particularly in overall functioning and parental worry. The burn location also influenced outcomes, with injuries to the upper limbs linked to higher sensory intensity and emotional impact. Children from culturally and linguistically diverse (CaLD) backgrounds, indicated by those speaking a language other than English at home (LOTE), demonstrated significantly greater effects across several domains, including overall impact, daily living, appearance, and parent worry. Conclusions: A substantial proportion of children continued to experience impacts from non-severe burns across multiple domains, indicating that even small-area burns can have lasting effects. The factors associated with worse scores were the child being female, the families being linguistically diverse, and upper body burns. Full article

Failure Mode and Effects Analysis (FMEA) is a systematic risk assessment tool that effectively evaluates the safety and reliability of products prior to their deployment. However, traditional FMEA fails to consider and leverage inherent system-specific information during risk assessment, while also neglecting the weights of risk factors (RFs) when processing data related to the Risk Priority Number (RPN). This leads to significant subjectivity in the final risk ranking of failure modes. To overcome these drawbacks, this study proposes an improved FMEA risk assessment method based on load sharing, aiming to develop an improved FMEA method that addresses the critical limitations of traditional approaches by integrating load sharing principles and systematic weight determination, thereby enhancing risk assessment objectivity and accuracy in complex multi-component systems. First, probabilistic linguistic terms are adopted to quantify experts’ risk assessment information, and the geometric mean method is then used to aggregate assessments from multiple experts. Second, the Fuzzy Best–Worst Method (FBWM) is employed to determine the relative weights of the three RPN factors (Occurrence, Severity, and Detection). Additionally, partial system structural data are obtained through load sharing, and these data—combined with the calculated factor weights—are integrated into the Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) to generate the final risk ranking of failure modes. Finally, a case study of a magnetic crane is conducted to verify the feasibility and effectiveness of the proposed method, supplemented by comparative experiments to demonstrate its superiority. Full article

Prematurity and autism spectrum disorder (ASD) are risk factors for alterations in language development. Their coexistence, frequent in twin pregnancies, may result in atypical communicative profiles that require specific interventions. This case report analyzed the linguistic, cognitive, and socioemotional development of two premature twins with ASD, relating the results to the therapeutic strategies applied. Standardized tests were applied to measure cognitive, linguistic, adaptive, and socioemotional development. The intervention combined the TEACCH, ABA, DIR/Floortime, and Hanen—More Than Words models. Both children showed significant impairments in communication, executive functions, and autonomy, with differentiated clinical profiles. Individualized interventions favored advances in functional language, emotional regulation, and routines, although challenges in language generalization and pragmatics persisted. The combination of prematurity and ASD creates complex challenges that require individualized therapeutic approaches. Early and intensive intervention, based on structured and relational approaches, is useful to promote functional and communicative development. Full article

One of the sources of risk inherent to construction projects is the quality of spatial data. Damage to buried pipes and cables often causes accidents, delays, or stoppages of construction works. Fuzzy logic is a method for studying the risk. It is employed to describe complex or poorly defined phenomena that can hardly be characterised with probabilistic methods. The article proposes a method for assessing the risk of damaging underground utilities based on a fuzzy inference engine. The author first defined linguistic variables and assigned them values based on risk factors. The membership functions for the linguistic variables were modelled using expert judgement. Then, the author determined qualitative fuzzy sets with the rule base. Finally, the values were converted into crisp values. The defuzzification technique employed was the centre of gravity. The proposed method can assess the risk of damage to underground utilities for spatial data exhibiting diverse quality classes. It will be employed to generate large-scale risk maps. The proposed fuzzy logic solution is an effective and appropriate tool for assessing the risk of damage to underground utilities arising from the quality of subsurface data. It should not be regarded as a universal substitute for PRA (Probabilistic Risk Assessment) but as a complementary methodology that is particularly well-suited to risk assessment in data-poor environments characterised by epistemic uncertainty and reliance on qualitative expert judgement. Full article

An intelligent safety risk assessment model is proposed by integrating the λ-fuzzy measure, Choquet integral, and triangular fuzzy numbers. It addresses the limitations of conventional methods like AHP that neglect nonlinear interactions among risk factors. The framework quantifies expert linguistic judgments to capture synergistic and substitutive relationships. Validation using two Sinopec seismic projects shows a 23.3% reduction in assessment time and an 18.1% accuracy improvement. Computed λ values (HB: −0.999997; SC: −0.999821) confirm strong substitutive interactions. Sensitivity analysis demonstrates robustness, with ±10% fuzzy measure variation causing <±3% output change. The model provides a computationally efficient, reliable tool for seismic acquisition and other complex industrial systems. Full article

Fuzzy logic has been applied to a wide range of problems, including process control, object recognition, image and signal processing, prediction, classification, decision-making, optimization, and time series analysis. These apply to solar energy systems. Though experts in renewable energy prefer fuzzy logic techniques, their contribution to the decision-making process of solar energy systems lies in the possibility of illustrating risk factors and introducing the concepts of linguistic variables of data from solar energy applications. In solar energy systems, the primary beneficiaries and audience of the fuzzy logic techniques are solar energy policy makers, as it concerns decision-making models, ranking of criteria or weights, and assessment of the potential location of the installation of solar energy plants, depending on the case. In a real-world scenario, fuzzy logic allows easy and efficient controller configuration in a non-linear control system, such as a solar panel. This study attempts to review the role and contribution of fuzzy logic in solar energy based on its applications. The findings from the review revealed that the fuzzy logic application identifies and detects faults in solar energy systems as well as in the optimization of energy output and the location of solar energy plants. In addition, fuzzy model (predicting), hybrid model (simulating performance), and multi-criteria decision-making (MCDM) are components of fuzzy logic techniques. As the review indicated, these are useful as a solution to the challenges of solar energy systems. Importantly, the integration and incorporation of fuzzy logic and neural networks should be recommended for the efficient and effective performance of solar energy systems. Full article

Boat collisions pose severe threats to maritime safety, economic activity, and environmental sustainability. Conventional risk assessment methods—such as Failure Mode and Effects Analysis, and Fault Tree Analysis—are widely applied but remain inadequate for addressing the uncertainty, subjectivity, and interdependency of risk factors in complex maritime environments. This study proposes a fuzzy Multi-Criteria Decision-Making framework for the risk assessment of boat collisions. The model integrates fuzzy logic with Analytic Hierarchy Process for criterion weighting and the Technique for Order Preference by Similarity to the Ideal Solution for risk ranking. Fuzzy logic is employed to capture linguistic expert judgments and to manage vague or incomplete data, which are common challenges in marine operations. Key collision risk factors—human error, boat engine system failure, environmental conditions, and intentional threats—are identified through literature review, incident data analysis, and expert consultation. A comparative analysis with a baseline non-fuzzy model demonstrates the added value of the fuzzy-integrated framework, showing improved capacity to handle imprecision and uncertainty. The model outputs not only prioritise risk rankings but also support the identification of critical control actions and effective safety measures. A case study of Nigerian waters illustrates the practicality of the framework in guiding risk mitigation strategies and informing policy decisions under uncertainty. Full article

Background: Adverse early childhood developmental outcomes across physical, cognitive, language, communication, and socioemotional domains are major global health concerns. This systematic review aimed to synthesise perinatal and childhood risk factors using a socioecological model. Methods: We searched six databases for cohort, case–control, and cross-sectional studies published between January 2000 and January 2024. Studies reporting risk factors for adverse developmental outcomes were included. Findings were organised across individual, interpersonal, community, and societal levels using a socioecological model. The protocol was registered in PROSPERO (CRD42023447352). Results: A total of 175 studies were included. Individual-level risk factors, including preterm birth, low birth weight, male sex, chronic illness, undernutrition, and excessive screen use, were associated with adverse developmental outcomes, while exclusive breastfeeding, reading books, and storytelling were protective factors. Interpersonal risks included maternal age, education, mental health, and pregnancy complications. Community and societal risks include environmental pollution, access to education, conflict, and healthcare access. Conclusions: Improving early childhood developmental outcomes may require intervention at multiple levels. Future studies may need to focus on the influence of culturally and linguistically diverse backgrounds and environmental exposures on early childhood developmental outcomes. Full article

Objectives: The objective of this manuscript is to translate, adapt, and validate an Arabic version of the Diabetes Eating Problem Survey—Revised (DEPS-R) questionnaire to assess disordered eating behaviors (DEBs) in adolescents with T1D in Saudi Arabia. Additionally, the study sought to estimate the prevalence of DEBs and analyze its associations with glycemic control and diabetes-related complications. Methods: A cross-cultural validation study was conducted following the COSMIN guidelines. The DEPS-R questionnaire was translated into Arabic through forward and backward translation involving expert panels, including psychiatrists, diabetologists, and linguists. A sample of 409 people with type 1 diabetes (PwT1D) (58.4% females) aged 12–20 years was recruited from outpatient diabetes clinics in the five main regions of Saudi Arabia. Participants completed the Arabic DEPS-R and the validated Arabic version of the SCOFF questionnaire. Sociodemographic, anthropometric, and biochemical data were collected, and statistical analyses, including confirmatory factor analysis (CFA) and internal consistency tests, were conducted. Results: The Arabic DEPS-R exhibits strong internal consistency (Cronbach’s alpha = 0.829) and high test–retest reliability (ICC = 0.861), with a CFA supporting a three-factor structure, namely body weight perception, disordered eating behaviors (DEBs), and bulimic tendencies. Notably, higher DEPS-R scores are significantly linked to elevated HbA1c levels, increased BMI, and more frequent insulin use. Alarmingly, 52.8% of participants show high-risk DEB, which is directly associated with poor glycemic control (HbA1c ≥ 8.1%) and a heightened risk of diabetic ketoacidosis (DKA). Conclusions: The Arabic DEPS-R is a valid and reliable tool for screening DEBs among Saudi adolescents with T1D. Findings underscore the necessity for early identification and intervention to mitigate the impact of EDs on diabetes management and overall health outcomes. Full article

Background: Human papillomavirus (HPV) is the most common sexually transmitted infection in the United States (U.S.) and a major contributor to several cancers, including cervical, anal, penile, and oropharyngeal cancers. Although a safe and effective vaccine is available, HPV vaccination rates remain suboptimal, particularly among racial, ethnic, and immigrant minority groups. This study explored multiple factors, such as cultural, social, and structural influences, influencing HPV vaccine decision-making among Cape Verdean immigrant parents in the U.S., a population currently underrepresented in HPV research. Methods: Qualitative study using individual, in-depth interviews with Cape Verdean immigrant parents of children aged 11 to 17 years living in the U.S. Interviews were transcribed verbatim and analyzed thematically using the social ecological model (SEM) to identify barriers and facilitators at the intrapersonal, interpersonal, organizational, community, and policy levels. Results: Forty-five Cape Verdean parents (27 mothers, 18 fathers) participated. Fathers were significantly older than mothers (50.0 vs. 41.1 years, p = 0.05). Most were married or partnered (60%), had at least a high school education (84.4%), and reported annual household incomes of US$50,000 or more (66.7%), with no significant gender differences. Nearly all spoke Creole at home (95.6%). Fathers had lower acculturation than mothers (p = 0.05), reflecting less adaptation to U.S. norms and language use. Most parents had limited knowledge of HPV and the vaccine, with gendered beliefs and misconceptions about risk. Only seven mothers (25.9%) reported receiving a provider recommendation; all indicated that their children had initiated vaccination (1 dose or more). Mothers were the primary decision-makers, though joint decision-making was common. Trust in providers was high, but poor communication and the lack of culturally and linguistically appropriate materials limited informed decision-making. Stigma, misinformation, and cultural taboos restricted open dialogue. Trusted sources of information included schools, churches, and Cape Verdean organizations. While parents valued the U.S. healthcare system, they noted gaps in public health messaging and provider engagement. Conclusions: Findings revealed that HPV vaccine uptake and hesitancy among Cape Verdean immigrant parents in the U.S. were influenced by individual beliefs, family dynamics, healthcare provider interactions, cultural norms, and structural barriers. These findings highlight the need for multilevel strategies such as culturally tailored education, community engagement, and improved provider communication to support informed vaccination decisions in this population. Full article

Pedestrian behaviour and safety are essential components of urban sustainability. They are influenced by a complex interplay between various factors from different perspectives, particularly in culturally and linguistically diverse (CALD) communities. This study presents a comprehensive overview of the factors influencing pedestrian behaviour and safety with a focus on CALD communities. By synthesizing the existing literature, the study identifies six key groups of influencing factors: social–psychological, cultural, risk perceptions, environmental, technological distractions, and demographic differences. It discovers that well-designed interventions, such as tailored education campaigns and programs, may effectively influence pedestrian behaviour. These interventions emphasize the importance of targeted messaging to address specific risks (e.g., using mobile phones while crossing the road) and engage vulnerable groups, including children, seniors, and CALD communities. The study reveals that CALD communities face higher risks of pedestrian injuries and fatalities due to language barriers, unfamiliarity with local road rules, and different practices and approaches to road safety due to cultural differences. This study underlines the importance of developing and promoting tailored road safety education programs to address the unique challenges faced by CALD communities to help promote safer pedestrian environments for all. Full article

To address potential risks during the construction process, improve construction quality and engineering safety, this paper constructs a construction safety risk analysis model for concrete gravity dams in cold regions based on fuzzy VIKOR-LEC. Firstly, an expert team employs linguistic variables to evaluate the likelihood of accidents (L), the frequency of personnel exposure to hazardous environments (E), and the consequences of accidents (C) for various risk factors in the LEC model. Secondly, the fuzzy analytic hierarchy process (FAHP) and maximum deviation method were used to construct a risk factor weight analysis matrix and find subjective and objective weights, respectively, to obtain the comprehensive weights of risk factors. Thirdly, VlseKriterijumska Optimizacija Kompromisno Resenje (VIKOR) is introduced to improve the traditional LEC model and is used to calculate the risk priority number. Finally, in order to further verify the validity of the model, this paper selects the example of Linhai Reservoir dam in Heilongjiang Province to analyze the management of the construction safety risk. The research results may provide a scientific basis for the safety management of gravity dam construction projects in cold areas, and help to improve the level of project management and reduce construction risks. Full article

The construction industry plays a key role in economic development but continues to face challenges in promoting employee well-being, particularly mental health and social sustainability. While existing decision-making tools emphasize environmental and economic factors, the social dimension remains largely overlooked, creating a significant gap in both research and practice. To address this, the study develops a decision support tool (DST) to help construction organizations prioritize strategic investments that enhance employee social sustainability. The tool is based on a hybrid multi-criteria decision-making framework, combining the Analytical Hierarchy Process (AHP) with Fuzzy MOORA to integrate both quantitative and qualitative assessments. A literature review, along with findings from a previous empirical study, identified 27 validated criteria, grouped into seven core sustainability alternatives. Additionally, five decision criteria (cost, risk, compatibility, return on investment, and difficulty) were refined through expert interviews. The DST was implemented as a modular Excel-based tool allowing users to input data, conduct pairwise comparisons, evaluate alternatives using linguistic scales, and generate a final ranking through defuzzification. A case study in a private construction company showed Training and Development and Work Environment as top priorities. An online expert focus group confirmed the DST’s clarity, usability, and strategic relevance. By addressing the often-neglected social pillar of sustainability, this tool offers a practical and transparent framework to support decision-making, ultimately enhancing employee well-being and organizational performance in the construction sector. Full article

Cesarean sections (CSs) are essential in certain medical contexts but, when overused, can carry risks for both the mother and child. In the unique multilingual landscape of Luxembourg, this study explores whether non-medical factors—such as the language spoken—affect CS rates. Through a survey conducted with women in Luxembourg, we first applied statistical methods to investigate the influence of various social and linguistic parameters on CS. Additionally, we explored how these factors relate to the feelings of happiness and respect women experience during childbirth. Subsequently, we employed four machine learning models to predict CS based on the survey data. Our findings reveal that women who speak Spanish have a statistically higher likelihood of undergoing a CS than women that do not report speaking that language. Furthermore, those who had CS report feeling less happy and respected compared to those with vaginal births. With both limited and augmented data, our models achieve an average accuracy of approximately 81% in predicting CS. While this study serves as an initial exploration into the social aspects of childbirth, it underscores the need for larger-scale studies to deepen our understanding and to inform policy-makers and health practitioners that support women during their pregnancies and births. This preliminary research advocates for further investigation to address this complex social issue comprehensively. Full article

To address the challenges of assessing information security risks in smart energy systems, this study proposes a multi-attribute decision support method based on interval type-2 fuzzy numbers (IT2TrFN). First, expert questionnaires were designed to gather insights from eight specialists in the fields of smart energy and safety engineering. Linguistic terms associated with IT2TrFN were employed to evaluate indicators, converting expert judgments into fuzzy numerical values while ensuring data reliability through consistency measurements. Subsequently, a decision hierarchy structure and an expert weight allocation model were developed. By utilizing the score and accuracy functions of IT2TrFN, the study determined positive and negative ideal solutions to rank and prioritize the evaluation criteria. Key influencing factors identified include the rate of excessive initial investment, regulatory stringency, information security standards, environmental pollution pressure, and incident response timeliness. The overall risk index was calculated as 0.5839, indicating a moderate level of information security risk in the evaluated region. To validate the robustness of the model, sensitivity analyses were conducted by varying IT2FWA (Weighted aggregated operator) and IT2FGA (Weighted geometric operator) operator selections and adjusting weight coefficients. The results reveal that key indicators exhibit high risk under different scenarios. This method provides an innovative tool for the scientific evaluation of information security risks in smart energy systems, laying a solid theoretical foundation for broader regional applications and the expansion of assessment criteria. Full article