Search Results (1,340)

In collaborative projects, traditional resource allocation methods often rely on company-assigned contribution rates, which can be subjective and lead to unfair outcomes. To address this, we propose a quadratic programming model that integrates participants’ self-reported rankings of their contributions across projects with company evaluations. The model aims to minimize deviations from company-assigned rates while ensuring consistency with participants’ perceived contribution rankings. Extensive simulations demonstrate that the proposed method reduces allocation errors by an average of 50.8% compared to the traditional approach and 21.4% against the method considering only individual estimation tendencies. Additionally, the average loss reduction in individual resource allocation ranges from 40% to 70% compared to the traditional method and 10% to 50% against the estimation-based method, with our approach outperforming both. Sensitivity analyses further reveal the model’s robustness and its particular value in flawed systems; the error is reduced by approximately 75% in scenarios where company evaluations are highly inaccurate. While its effectiveness is affected by factors such as team size variability and self-assessment errors, the approach consistently provides more equitable allocation of resources that better reflects actual individual contributions, offering valuable insights for improving fairness in team projects. Full article

Background: The importance of understanding dietary habits during working hours is becoming increasingly evident. As demonstrated, dietary habits have been shown to exert a considerable influence on the productivity of workers and the creation of a healthier workplace. Objective: The aim of this study is to assess the nutritional quality and self-perception of lunches consumed by workers in Piedmont (Italy). Methods: A questionnaire, supported by the EasyDietWeb 4.3.0 software, was used to assess the macronutrient composition of the meals and to evaluate adherence to the “Healthy Eating Plate” (HEP) guidelines. The basal metabolic rate and total daily energy expenditure of the subjects were calculated. Results: The survey results, which included 189 participants, revealed that a notable proportion of the respondents consumed homemade meals at their place of work. The majority of meals reported by the participants did not align with the HEP composition due to the absence of one or more components, especially vegetables. The participants’ perceptions of meal balance frequently diverged from the actual nutritional quality of the meals. Finally, overweight participants exhibited a higher risk of reporting diseases (OR = 4.4, 95% CI = 1.6–12.0). Conclusions: This study provides insight into the dietary habits of a specific group of workers regarding their lunch consumption. This underscores the significance of enhancing public awareness regarding dietary choices and nutritional intakes, as adhering to proper dietary routines is paramount for preserving a state of well-being and sustaining a healthy lifestyle. Full article

Intolerance of uncertainty (IU) and behavioral inhibition (BI) are personality traits associated with avoidance of the unfamiliar. Both are linked to anxiety and other disorders. However, most research on personality has relied on self-report, which may not correspond to actual behavior. An alternative is to observe behavior in computer-based tasks designed to assess personality. The current study sought to develop such a task, based on the conditioned place preference paradigm, which is sensitive to IU but not BI. Participants foraged for reward in a virtual environment consisting of multiple interconnected rooms. In the training phase, the rich room was paired with a higher number of wins than losses. The poor room was the opposite. In the test phase, participants could freely search any of the rooms, including a completely new room. Although most showed a strong initial preference for the new room, those with higher self-reported IU left this room faster, foraging there significantly less than those with lower IU. This preference also depended on information provided about the new room. There was a strong positive correlation between IU and BI; however, the latter was unrelated to behavior. Thus, the task captures a unique component of IU. Full article

The safety of lithium-ion batteries during transportation is critically important. However, current standards exhibit limitations, as their environmental testing parameter thresholds fail to fully encompass actual transportation conditions. To enhance both safety and standard applicability, in this study, we focused on four representative environmental conditions: temperature, vibration, shock, and low atmospheric pressure. Field measurements were conducted across road, rail, and air transport modes using a self-developed data acquisition system based on the NearLink communication technology. The measured data were then compared with the threshold values defined in current international and national standards. The results reveal that certain measured values exceeded the upper limits prescribed by existing standards, indicating limitations in their applicability under extreme transport conditions. Based on these findings, we propose revised testing parameters that better reflect actual transport risks, including a temperature cycling range of 72 ± 2 °C (high) and −40 ± 2 °C (low), a shock acceleration limit of 50 gn, adjusted peak frequencies in the vibration PSD profile, and a minimum pressure threshold of 11.6 kPa. These results provide a scientific basis for optimising safety standards and improving the safety of lithium-ion battery transportation. Full article

Background/Objectives: Physical activity is a cornerstone of diabetes mellitus (DM) management and is strongly recommended in the American Diabetes Association (ADA)’s guidelines. This study aims to investigate the self-reported physical activity levels of individuals with DM in Germany, as well as the barriers and facilitators they encounter. Methods: Individuals with type 1 DM (T1DM) and type 2 DM (T2DM) were asked to fill out an online questionnaire that was partly based on the International Physical Activity Questionnaire (IPAQ). Results: The questionnaire was completed by 338 persons with either T1DM (57.1%) or T2DM (42.9%) (females: 56.2%, males: 42.0%, gender diverse persons: 1.8%) of all age groups (at least 18 years). In total, 80.5% of respondents were aware of the current physical activity recommendations. Among the respondents, 58% reported meeting the recommendations for endurance-type physical activity, while only 30.5% reported meeting those for strength training. The three most frequently cited barriers to physical activity were lack of time, lack of motivation and current state of health. Supporting factors included coverage of costs, availability of exercise programs in close proximity to the patient’s home and target group specific exercise programs. Conclusions: The results imply that many individuals with DM in Germany do not meet ADA’s physical activity recommendations, especially considering that self-reports often overestimate actual behavior. In particular, the actual number of individuals who regularly engage in strength training may be too low. There is a clear need to better communicate the benefits of different forms of physical training and to provide physical activity programs aligned with patients’ individual needs. Full article

In this paper, the accuracy of basic and advanced spiral inductor models for gallium nitride (GaN) integrated inductors is evaluated. Specifically, the experimental measurements of geometrically scaled circular spiral inductors, fabricated in a radio frequency (RF) GaN-on silicon technology, are exploited to estimate the errors of two lumped geometrically scalable models, i.e., a simple π-model with seven components and an advanced model with thirteen components. The comparison is performed by using either the standard performance parameters, such as inductance (L), quality factor (Q-factor), and self-resonance frequency (SRF), or the two-port scattering parameters (S-parameters). The comparison reveals that despite a higher complexity, the developed advanced model achieves a significant reduction in SRF percentage errors in a wide range of geometrical parameters, while enabling an accurate estimation of two-port S-parameters. Indeed, the correct evaluation of both SRF and two-port S-parameters is crucial to exploit the model in an actual circuit design environment by properly setting the inductor geometrical parameters to optimize RF performance. Full article

Background/Objectives: Accurate dietary assessment is crucial for nutritional epidemiology, but tools like 24 h recalls (24HRs) face challenges with missing or implausible data. The Automated Self-Administered 24 h Dietary Assessment Tool (ASA24) facilitates large-scale data collection, but its lack of interviewer input may lead to implausible dietary recalls (IDRs), affecting data integrity. Multiple imputation (MI) is commonly used to handle missing data, but its effectiveness in high-variability dietary data is uncertain. This study aims to assess MI’s accuracy in estimating nutrient intake under varying levels of missing data. Methods: Data from 24HRs completed by 743 adolescents (ages 13–18) in Ontario, Canada, were used. Implausible recalls were excluded based on nutrient thresholds, creating a cleaned reference dataset. Missing data were simulated at 10%, 20%, and 40% deletion rates. MI via chained equations was applied, incorporating demographic and psychosocial variables as predictors. Imputed values were compared to actual values using Spearman’s correlation and accuracy within ±10% of true values. Results: Spearman’s rho values between the imputed and actual nutrient intakes were weak (mean ρ ≈ 0.24). Accuracy within ±10% was low for most nutrients (typically < 25%), with no clear trend by missingness level. Diet quality scores showed slightly higher accuracy, but values were still under 30%. Conclusions: MI performed poorly in estimating individual nutrient intake in this adolescent sample. While MI may preserve sample characteristics, it is unreliable for accurate nutrient estimates and should be used cautiously. Future studies should focus on improving data quality and exploring better imputation methods. Full article

Background/Objectives: Falls are recognized as a leading cause of injury, with approximately one in ten incidents resulting in physical injury. Although virtual reality (VR)-based interventions have been explored for fall prevention, systematic reviews and meta-analyses remain limited. This study aimed to assess research trends and evaluate the effectiveness of VR-based fall prevention through a systematic review and meta-analysis. Methods: This review was conducted following the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines. A comprehensive literature search was carried out in PubMed, EBMASE, Cumulative Index to Nursing and Allied Health Literature (CINAHL), Cochrane Library, and Korean databases from their inception through 31 December 2024. A total of 49 studies met the inclusion criteria, and a meta-analysis was conducted on 37 studies with available data using “R” 4.4.1 software. Effect sizes (ESs) and 95% confidence intervals (CIs) were calculated for key outcomes. Results: The VR-based interventions showed a statistically significant positive effect on falls self-efficacy, as measured by the Falls Efficacy Scale (FES) (ES = 0.28, 95% CI: 0.17–0.39, p < 0.001). However, no significant reduction was observed in the number of falls (ES = −0.31, 95% CI: −0.80–0.17, p = 0.20). Subgroup analysis by participant medical condition for the FES revealed the largest effects in the Parkinson’s disease (PD) group (ES = 0.61), followed by the multiple sclerosis (MS) (ES = 0.34), the “other” group (ES = 0.25), and “healthy” participants (ES = 0.24). A statistically significant reduction in the number of falls was observed only in the MS group (ES = −0.56). Conclusions: VR-based interventions are effective in improving falls self-efficacy, particularly among individuals with neurological conditions, such as Parkinson’s disease and multiple sclerosis. However, evidence for a reduction in actual fall incidence remains limited. Further large-scale, long-term studies are needed to evaluate the sustained impact of VR interventions on fall prevention outcomes. Full article

Asthma is the leading chronic condition in children and adolescents, requiring continuous monitoring to effectively prevent and manage symptoms. Symptom monitoring can guide timely and effective self-management actions by children and their parents and inform treatment decisions by healthcare providers. This paper examines two conventional monitoring methods, including symptom-based and peak expiratory flow (PEF) monitoring, reviews early efforts to quantify respiratory symptoms, and introduces an emerging sensor-based mHealth approach. Although symptom-based monitoring is commonly used in clinical practice, its adequacy is a concern due to its subjective nature, as it primarily relies on individual perception. PEF monitoring, while objective, has shown weak correlations with actual asthma activity or lung function and suffers from suboptimal adherence among youth. To enhance objectivity in symptom monitoring, earlier efforts focused on quantifying respiratory symptoms by harnessing mechanical equipment. However, the practicality of these methods for daily use is limited due to the equipment’s bulkiness and the time- and labor-intensive nature of data processing and interpretation. As an innovative alternative, sensor-based mHealth devices have emerged to provide automatic, objective, and continuous monitoring of respiratory symptoms. These wearable technologies offer promising potential to overcome the issues of perceptual inaccuracy and poor adherence associated with conventional methods. However, many of these devices are still in developmental or testing phases, with limited data on their clinical efficacy, usability, and long-term impact on self-management behaviors. Future research and robust clinical trials are warranted to establish their role in asthma monitoring and management and improving asthma outcomes in children and adolescents. Full article

This study investigates the encroachment risk in the Makause informal settlement by analysing resident survey data to identify key contributing factors and build predictive models. Encroachment threatens the water infrastructure through damage, contamination, and service disruptions, highlighting the need for informed, community-based planning. The data was collected from 105 residents, with responses (“Yes,” “No,” “Unsure”) analysed using descriptive statistics and a one-way ANOVA to identify significant differences across categories. The ReliefF algorithm was used to rank the importance of features predicting the encroachment risk. These inputs were then used to train, validate, and test an Artificial Neural Network (ANN) model. The Artificial Neural Network demonstrated a high predictive accuracy, achieving correlation coefficients above 95% and low mean squared errors. The ANOVA identified statistically significant mean differences for selected variables, while ReliefF helped determine the most influential predictors. A high agreement level (p > 0.900) between predicted and actual responses confirmed the model’s validity. This research introduces an innovative, data-driven framework that integrates machine learning and a statistical analysis to support municipalities and utility providers in engaging informal communities to protect infrastructure. While this study is limited to Makause and may be affected by a self-reported bias, it demonstrates the potential of Artificial Neural Networks and ReliefF in enhancing the risk analysis and infrastructure management in informal settlements. Full article

This study explores the potential of immersive virtual reality (IVR) to enhance knowledge transfer in vocational education, particularly in bridging the gap between academic learning and practical workplace application. The focus lies on relevant predictors for actual learning transfer, namely knowledge acquisition and the transfer-related self-efficacy. Additionally, the Cognitive Affective Model of Immersive Learning (CAMIL) is used to investigate potential predictors in IVR learning. This approach allows for empirical testing of the CAMIL and validation of its assumptions using empirical data. To address the research questions, a quasi-experimental field study was conducted with 141 retail trainees at a German vocational school. Participants were assigned to either an IVR group or a control group receiving traditional instruction. The intervention spanned four teaching sessions of 90 min each, focusing on the design of a retail sales area based on sales-promoting principles. To assess subject-related learning outcomes, a domain-specific knowledge test was developed. In addition, transfer-related self-efficacy and other relevant constructs were measured using Likert-scale questionnaires. The results show that IVR-based instruction significantly improves knowledge acquisition and transfer-related self-efficacy compared to traditional teaching methods. In terms of the CAMIL-based mechanisms, significant correlations were found between transfer-related self-efficacy and factors such as interest, motivation, academic self-efficacy, embodiment, and self-regulation. Additionally, correlations were found between knowledge acquisition and relevant predictors such as interest, motivation, and self-regulation. These findings underscore IVR’s potential to facilitate knowledge transfer in vocational school, highlighting the need for further research on its long-term effects and the actual application of learned skills in real-world settings. Full article

Car interior design should evoke emotions, offer comfort, convey safety and at the same time project the brand identity of the car manufacturer. Lighting is used to address these functions. Modules required for automotive interior lighting often feature injection-moulded (IM) light guides, whereas woven fabrics with polymer optical fibres (POFs) offer certain technological advantages and show first-series applications in cars. In the future, car interior illumination will become even more important in the wake of megatrends such as autonomous driving. Since the increase in deployment of these technologies facilitates a need for an economical comparison, this paper aims to deliver a cost-driven approach to fulfil the aforementioned objective. Therefore, the cost structures of the supply chains for an IM-based and a POF-based illumination module are analysed. The employed research methodologies include an activity-based costing approach for which the data is collected via document analysis and guideline-based expert interviews. To account for data uncertainty, Monte Carlo simulations are conducted. POF-based lighting modules have lower initial costs due to continuous fibre production and weaving processes, but are associated with higher unit costs. This is caused by the discontinuous assembly of the rolled woven fabric which allows postponement strategies. The development costs of the mould generate high initial costs for IM light guides, which makes them beneficial only for high quantities of produced light guides. For the selected scenario, the POF-based module’s self-costs are 11.05 EUR/unit whereas the IM module’s self-costs are 14,19 EUR/unit. While the cost structures are relatively independent from the selected scenario, the actual self-costs are highly dependent on boundary conditions such as production volume. Full article

Accurate segmentation of densely stacked and weakly textured objects remains a core challenge in robotic depalletizing for industrial applications. To address this, we propose MaskNet, an instance segmentation network tailored for RGB-D input, designed to enhance recognition performance under occlusion and low-texture conditions. Built upon a Vision Transformer backbone, MaskNet adopts a dual-branch architecture for RGB and depth modalities and integrates multi-modal features using an attention-based fusion module. Further, spatial and channel attention mechanisms are employed to refine feature representation and improve instance-level discrimination. The segmentation outputs are used in conjunction with regional depth to optimize the grasping sequence. Experimental evaluations on camshaft depalletizing tasks demonstrate that MaskNet achieves a precision of 0.980, a recall of 0.971, and an F1-score of 0.975, outperforming a YOLO11-based baseline. In an actual scenario, with a self-designed flexible magnetic gripper, the system maintains a maximum grasping error of 9.85 mm and a 98% task success rate across multiple camshaft types. These results validate the effectiveness of MaskNet in enabling fine-grained perception for robotic manipulation in cluttered, real-world scenarios. Full article

The principles of fractal geometry have revolutionized the characterization of complex geometric objects since Benoit Mandelbrot’s groundbreaking work. Richardson’s method for determining the fractal dimension of boundaries laid the groundwork for Mandelbrot’s later developments in fractal theory. Despite extensive research, challenges remain in accurately calculating fractal dimensions, particularly when dealing with digital images and their inherent limitations. This study examines the numerical artifacts introduced by Richardson’s method when applied to the Von Koch Island, a classic fractal curve, and proposes a novel approach for computing fractal dimensions in image analysis. The Koch snowflake serves as a key example in this analysis; it serves to assess the algorithm of fractal dimension calculation as his theoretical one is known. However, there is a fundamental difference between the theoretical calculation of fractal dimension and the actual calculation of the fractal dimension from digital images with a given resolution undergoing discretization. We propose eight different calculation methods based on Richardson’s area–perimeter relationship: the Self-Convolution Patterns Research (SCPR) method accurately estimates the fractal dimension, as the 95% confidence interval includes the theoretical dimension. Full article

The impacts of renovation actions on pre- and post-retrofitting building performances are complex to analyse, particularly small and potentially self-actuated actions, such as adding insulation layers to a cold roof slab or changing doors. These interventions are widespread in small residential houses and cases where the owners are the residents. However, a large research gap currently remains regarding the impact of sustainable solutions on building performance. This study aims to address this issue by proposing a methodology based on commercial cloud monitoring solutions and middleware development that analyses and reports on the impact of such solutions to end users, allowing for an analysis of real variations in air temperature levels. The methodology is applied to two single/double-family residential houses, acting as demo cases for verification, across a multi-year time horizon. In both cases, measurements were conducted before and after typical limited renovation actions. Alongside the proposed methodology, descriptions of the smart solutions’ requirements are provided. The results mainly focus on temperature variations. Finally, the impact of the solutions on energy consumption was analysed for one of the buildings, and feedback was briefly provided by the users. Full article