Search Results (151)

The “That Girl” self-optimization trend on TikTok, promoting beauty and productivity, had over 17.4 billion views by August 2024. “That Girl” video clips showcase perfectly organized daily routines, fitness activities, and healthy eating—allegedly to inspire other users to aspire to similar flawlessness. Based on social comparison theory, the “That Girl” archetype serves as an upward comparison target. We expected detrimental effects of viewing “That Girl” content on young women in terms of positive and negative affect and body satisfaction. Expanding other research in this area, possible effects on self-discipline and dieting intentions were explored. Focusing on immediate intraindividual changes, a preregistered two-group online experiment using a pre–post measurement design was conducted. Female participants (N = 76) watched four minutes of either 16 video clips showing “That Girl” content or nature videos (control condition). Mixed ANOVAs provided evidence of a significant adverse influence of watching “That Girl” videos on female recipients regarding all dependent variables with medium or large effect sizes. Post-hoc analyses revealed that these effects were driven by participants who reported upward comparisons to “That Girls”. Based on these results, the positive impact on self-improvement—as proclaimed by contributors of the “That Girl” trend—is critically questioned. Full article

Emerging digital technologies are transforming how consumers participate in financial markets, yet their benefits depend critically on the speed, reliability, and transparency of the underlying platforms. Online stock trading platforms must maintain high efficiency underload to ensure a good user experience. This paper presents performance analysis under various load conditions based on the containerized stock exchange system. A comprehensive data logging pipeline was implemented, capturing metrics such as API response times, database query times, and resource utilization. We analyze the collected data to identify performance patterns, using both statistical analysis and machine learning techniques. Preliminary analysis reveals correlations between application processing time and database load, as well as the impact of user behavior on system performance. Association rule mining is applied to uncover relationships among performance metrics, and multiple classification algorithms are evaluated for their ability to predict user activity class patterns from system metrics. The insights from this work can guide optimizations in similar distributed web applications to improve scalability and reliability under a heavy load. By framing performance not merely as a technical property but as a determinant of financial decision-making and well-being, the study contributes actionable insights for designers of consumer-facing fintech services seeking to meet sustainable development goals through trustworthy, resilient digital infrastructure. Full article

Objective: The objective of this study was to develop and assess the clinical feasibility of auto-segmentation and auto-planning methodologies for automated radiotherapy in prostate cancer. Methods: A total of 166 patients were used to train a 3D Unet model for segmentation of the gross tumor volume (GTV), clinical tumor volume (CTV), nodal CTV (CTVnd), and organs at risk (OARs). Performance was assessed by the Dice similarity coefficient (DSC), the Recall, Precision, Volume Ratio (VR), the 95% Hausdorff distance (HD95%), and the volumetric revision degree (VRD). An auto-planning network based on a 3D Unet was trained on 77 treatment plans derived from the 166 patients. Dosimetric differences and clinical acceptability of the auto-plans were studied. The effect of OAR editing on dosimetry was also evaluated. Results: On an independent set of 50 cases, the auto-segmentation process took 1 min 20 s per case. The DSCs for GTV, CTV, and CTVnd were 0.87, 0.88, and 0.82, respectively, with VRDs ranging from 0.09 to 0.14. The segmentation of OARs demonstrated high accuracy (DSC ≥ 0.83, Recall/Precision ≈ 1.0). The auto-planning process required 1–3 optimization iterations for 50%, 40%, and 10% of cases, respectively, and exhibited significant better conformity (p ≤ 0.01) and OAR sparing (p ≤ 0.03) while maintaining comparable target coverage. Only 6.7% of auto-plans were deemed unacceptable compared to 20% of manual plans, with 75% of auto-plans considered superior. Notably, the editing of OARs had no significant impact on doses. Conclusions: The accuracy of auto-segmentation is comparable to that of manual segmentation, and the auto-planning offers equivalent or better OAR protection, meeting the requirements of online automated radiotherapy and facilitating its clinical application. Full article

Near-infrared (NIR) spectroscopy, combined with multivariate calibration techniques such as stepwise decorrelation of variables (SELECT) and ordinary least squares (OLS) regression, was used to develop robust, reduced-spectrum regression models for quantifying key phenolic compound markers in various olive oils. These oils included nine extra virgin olive oil (EVOO) varieties, refined olive oil (ROO) blended with virgin olive oil (VOO) or EVOO, and pomace olive oil, both with and without hydroxytyrosol (HTyr) supplementation. Olive oils were analyzed before and after deep frying. The results show that HTyr ranged from 7.28 mg/kg in Manzanilla (lowest) to 21.43 mg/kg in Royuela (highest). Tyrosol (Tyr) varied from 5.87 mg/kg in Royuela (lowest) to 14.86 mg/kg in Hojiblanca (highest). Similar trends were observed in all phenolic fractions across olive oil cultivars before and after deep-frying. HTyr supplementation significantly increased both HTyr and Tyr levels in non-fried and fried supplemented oils, with HTyr rising from single digits in some controls (around 0 mg/kg) to over 300 mg/kg in most of the supplemented samples. SELECT efficiently reduced redundancy by selecting the most vital wavelengths and thus significantly improved the regression models for key phenolic compounds, including HTyr, Tyr, caffeic acid, decarboxymethyl ligstroside aglycone in dialdehyde form (oleocanthal), decarboxymethyl oleuropein aglycone in dialdehyde form (oleacein), homovanillic acid, pinoresinol, oleuropein aglycone in oxidized aldehyde and hydroxylic form (OAOAH), ligstroside aglycone in oxidized aldehyde and hydroxylic form (LAOAH), and total phenolic content (TPC), achieving correlation coefficients (R) of 0.91–0.98. The SELECT-OLS method generated highly predictive models with minimal complexity, using at most 30 wavelengths out of 700. The number of decorrelated predictors varied, at 12, 14, 15, 30, 30, 21, 30, 30, 30, and 18 for HTyr, Tyr, caffeic acid, oleocanthal, oleacein, homovanillic acid, pinoresinol, OAOAH, LAOAH, and TPC, respectively, demonstrating the adaptability of the SELECT-OLS approach to different spectral patterns. These reliable calibration models enabled online and routine quantification of phenolic compounds in EVOO, VOO, ROO, including both non-fried and fried as well as supplemented and non-supplemented samples. They performed well across eight deep-frying conditions (3–6 h at 170–210 °C). Implementing an NIR instrument with optimized variable selection would simplify spectral analysis and reduce costs. The developed models all demonstrated strong predictive performance, with low leave-one-out mean prediction errors (LOOMPEs) with values of 15.69, 8.47, 3.64, 9.18, 16.71, 3.26, 8.57, 13.56, 56.36, and 82.38 mg/kg for HTyr, Tyr, caffeic acid, oleocanthal, oleacein, homovanillic acid, pinoresinol, OAOAH, LAOAH, and TPC, respectively. These results confirm that NIR spectroscopy combined with SELECT-OLS is a feasible, rapid, non-destructive, and eco-friendly tool for the reliable evaluation and quantification of phenolic content in edible oils. Full article

Background/Objectives: Tension-type headache and migraine are common among children and adolescents, often causing significant distress and persisting into adulthood. While outpatient pain therapy is essential, it is not always sufficient. To enhance initial therapy consultations, we evaluated a new educational website in a pediatric outpatient pain clinic. Methods: Ninety-three children with headache (M_age = 12.66, SD_age = 2.86) visiting a specialized tertiary care center were randomly assigned to either an intervention or control group. The intervention group received immediate access to the website, while the control group was given access after the final assessment. Three online follow-up assessments occurred at four-week intervals after baseline. Recruitment occurred between April 2021 and October 2022. Results: Headache-related disability, headache days, and days with headache medication use significantly decreased over time (main effect; disability: β = −0.23, 95%-CI = [−0.36; −0.09], p = 0.001; days: β = −0.18, 95%-CI = [−0.32; 0.03], p = 0.018, medication: β = −0.16, 95%-CI = [−0.31; −0.02], p = 0.026). No statistically significant changes were observed for average headache intensity, passive pain coping, positive self-instructions, seeking social support, pain self-efficacy, and headache-related knowledge. Groups did not differ in their improvement over time (interaction effect). Per-protocol analysis yielded a similar trend: headache-related disability improved significantly with no interaction effects. Despite the limited impact on headache management, children rated the website as relevant and easy to understand. Conclusions: While well-received, the website’s effectiveness may have been limited by participants’ prior knowledge, concurrent therapies, and low engagement. Future research should focus on better integrating the tool into treatment plans, optimizing usage, and tailoring content to varying knowledge levels. Nevertheless, it shows potential as a long-term self-management tool. Full article

The rapid expansion of online grocery shopping has intensified the need for strategically located distribution hubs that ensure efficient and sustainable operations. Traditional location models emphasize economic and logistical factors but often neglect energy efficiency and environmental sustainability. This paper proposes a hybrid decision-making model that integrates the analytic hierarchy process (AHP) and the spherical fuzzy technique for order of preference by similarity to ideal solution (SFTOPSIS) to address the complexities of delivery hub location selection. The AHP is used to determine the relative importance of key decision-making criteria, including cost, accessibility, infrastructure, competition, and sustainability, while SFTOPSIS ranks the candidate locations based on their proximity to the ideal solution. Spherical fuzzy sets allow for a more nuanced treatment of uncertainty, improving decision-making accuracy in dynamic environments. The results demonstrate that this hybrid approach effectively manages incomplete and uncertain data, delivering a robust ranking of candidate locations. By incorporating sustainability as a key factor, this study provides a structured and adaptive framework for businesses to optimize logistics operations in the post-pandemic landscape. The proposed methodology not only enhances decision-making in location selection but contributes to the development of more resilient and sustainable supply chain strategies. Full article

Background: The effectiveness of treatment depends on recognizing the needs and limitations of patients. Hope is a personal resource that facilitates the treatment and recovery process. Dispositional hope encompasses goal-directed action, whereas perceived hope lacks reference to specific content or behavior. This study examined which construct is more strongly related to psychological, physical, and emotional health. Perceived hope requires a new tool for measurement. Adaptation to the Polish cultural context is the second goal of this research. Methods: Data were collected in the international online study Barometer of Hope (n = 1608). Adult participants completed the PSH, ADHS, and a battery of self-report questionnaires assessing several key well-being outcomes. Results: Perceived hope appears to be a more salient construct related to psychological health than dispositional hope, although both aspects of hope demonstrate similar associations with physical health. Confirmatory factor analysis (CFA) supported the hypothesized one-factor structure of the Polish version of the Perceived Hope Scale (PHS-PL), indicating high internal consistency as well as strong convergent and discriminant validity. The PHS-PL showed positive correlations with optimism, life satisfaction, happiness, positive affect, and dispositional hope, and negative correlations with depression/anxiety, loneliness, and negative affect. Additionally, perceived hope was negatively associated with the likelihood of a crisis scenario and positively associated with the likelihood of a flourishing scenario. Conclusions: Our findings confirm that hope is a health-enhancing resource. The PHS is a simple, short, culturally universal method that directly measures hope and can also be successfully used by non-psychologists, such as nurses, physicians, and caregivers. Full article

The exponential growth of multi-modal behavioral data in campus networks poses significant challenges for clustering analysis, including high dimensionality, redundancy, and attribute heterogeneity, which lead to degraded accuracy in existing methods. To address these issues, this study proposes a graph-theoretic subspace deep clustering framework that synergizes a deep sparse auto-encoder (DSAE) with a method of graph partitioning based on normalized cut. First, a four-layer DSAE is designed to extract discriminative features while enforcing sparsity constraints, effectively reducing data dimensionality and mitigating noise. Second, the refined subspace representations are transformed into a similarity graph, where normalized cut optimization partitions users into coherent behavioral clusters by balancing intra-cluster cohesion and inter-cluster separation. Experimental validation on three datasets—USER_DATA, MNIST, and COIL20—demonstrates the superiority of GTSDC. It achieves 91% accuracy on USER_DATA, outperforming traditional algorithms (e.g., CLIQUE, K-means) by 120% and advanced methods (e.g., deep subspace clustering) by 15%. The proposed framework not only enhances network resource allocation through behavior-aware analytics but also lays the groundwork for personalized educational services. This work bridges the gap between graph theory and deep learning, offering a scalable solution for high-dimensional behavioral pattern recognition. In simple terms, this new algorithm can more accurately analyze user behavior in campus networks. It helps universities better allocate network resources, such as ensuring smooth online classes, and can also provide personalized educational services to students according to their behavior patterns. Full article

As a knowledge-intensive activity, the Kansei engineering (KE) process encounters numerous challenges in the design knowledge flow, primarily due to issues related to information reliability, uncertainty, and subjectivity. Bridging this gap, this study introduces an advanced KE framework integrating a cloud model with Z-numbers (CMZ) and Bayesian elastic net regression (BENR). In stage-I of this KE, data mining techniques are employed to process online user reviews, coupled with a similarity analysis of affective word clusters to identify representative emotional descriptors. During stage-II, the CMZ algorithm refines K-means clustering outcomes for market-representative product forms, enabling precise feature characterization and experimental prototype development. Stage-III addresses linguistic uncertainties in affective modeling through CMZ-augmented semantic differential questionnaires, achieving a multi-granular representation of subjective evaluations. Subsequently, stage-IV employs BENR for automated hyperparameter optimization in design knowledge inference, eliminating manual intervention. The framework’s efficacy is empirically validated through a domestic cleaning robot case study, demonstrating superior performance in resolving multiple information processing challenges via comparative experiments. Results confirm that this KE framework significantly improves uncertainty management in design knowledge flow compared to conventional implementations. Furthermore, by leveraging the intrinsic symmetry of the normal cloud model with Z-numbers distributions and the balanced ℓ₁/ℓ₂ regularization of BENR, CMZ–BENR framework embodies the principle of structural harmony. Full article

The purpose of this study is to thoroughly examine the technical competencies of university websites in Northern Cyprus, as well as the contributions of Learning Management System (LMS) platforms integrated with these websites to user experience, with a focus on efficiency and sustainability in online education. This study adopts a “mixed-method” research model, which combines both quantitative and qualitative research methods for data collection and analysis, with the results being evaluated together. The participants of the research consist of the websites of universities in the Turkish Republic of Northern Cyprus (TRNC) and the IT department managers who will be consulted for their opinions on these websites. A total of 15 university websites were analyzed within the scope of the study, and interviews were conducted with the IT managers of these universities. During this process, analysis tools such as SimilarWeb, Alexa, Ahrefs, Moz, and SEMrush were utilized. For qualitative analysis, a semi-structured interview method was chosen to gather the views of IT managers on the websites. The findings emphasize the need for universities to optimize their digital strategies. Differences in website performance directly affect the strength of universities’ digital presence, while technological infrastructure, user experience, and the integration of educational tools emerge as key factors in improving this performance. Based on the results of this study, some universal solutions are proposed to optimize the digital strategies of universities in Cyprus. Full article

The accurate prediction of concrete temperature during arch dam construction is essential for crack prevention. The internal temperature of the poured blocks is influenced by dynamic factors such as material properties, age, heat dissipation conditions, and temperature control measures, which are highly time-varying. Conventional temperature prediction models, which rely on offline data training, struggle to capture these time-varying dynamics, resulting in insufficient prediction accuracy. To overcome these limitations, this study constructed a sparrow search algorithm–incremental support vector regression (SSA-ISVR) model for online concrete temperature prediction. First, the SSA was employed to optimize the penalty and kernel coefficients of the ISVR algorithm, minimizing errors between predicted and measured temperatures to establish a pretrained initial temperature prediction model. Second, untrained samples were dynamically monitored and incorporated using the Karush–Kuhn–Tucker (KKT) conditions to identify unlearned information, prompting model updates. Additionally, redundant samples were removed based on sample similarity and error-driven criteria to enhance training efficiency. Finally, the model’s accuracy and reliability were validated through actual case studies and compared to the LSTM, BP, and ISVR models. The results indicate that the SSA-ISVR model outperforms the aforementioned models, effectively capturing the temperature changes and accurately predicting the variations, with a mean absolute error of 0.14 °C. Full article

Efficient ship scheduling in inland waterways is critical for maritime transportation safety and economic viability. However, traditional scheduling methods, primarily based on First Come First Served (FCFS) principles, often produce suboptimal results due to their inability to account for complex spatial–temporal dependencies, directional asymmetries, and varying ship characteristics. This paper introduces SSRL (Ship Scheduling through Reinforcement Learning), a novel framework that addresses these limitations by integrating three complementary components: (1) a Q-learning framework that discovers optimal scheduling policies through environmental interaction rather than predefined rules; (2) a clustering mechanism that reduces the high-dimensional state space by grouping similar ship states; and (3) a sliding window approach that decomposes the scheduling problem into manageable subproblems, enabling real-time decision-making. We evaluated SSRL through extensive experiments using both simulated scenarios and real-world data from the Xiaziliang Restricted Waterway in China. Results demonstrate that SSRL reduces total ship waiting time by 90.6% compared with TSRS, 48.4% compared with FAHP-ES, and 32.6% compared with OSS-SW, with an average reduction of 57.2% across these baseline methods. SSRL maintains superior performance across varying traffic densities and uncertainty conditions, with the optimal information window length of 13–14 ships providing the best balance between solution quality and computational efficiency. Beyond performance improvements, SSRL offers significant practical advantages: it requires minimal computation for online implementation, adapts to dynamic maritime environments without manual reconfiguration, and can potentially be extended to other complex transportation scheduling domains. Full article

Bisphenol A diglycidyl ether (BADGE) and its related derivatives (BADGEs for short) are reactive epoxides condensed from bisphenol A (BPA) and epichlorohydrin. Nowadays, they are heavily used as additives in the production process of food and beverage contacting materials. However, BADGEs are considered as emerging organic pollutants due to their high toxicity including cytotoxicity, mutagenicity, and genotoxicity. In this work, an online analytical method integrated column-switching technique with supercritical fluid chromatography (SFC) was proposed for the simultaneous determination of bisphenol A diglycidyl ether and its derivatives. In this process, a homemade column was utilized in the first dimension of the column-switching SFC system to preconcentrate the analytes as well as eliminate interferences online. Under the optimal conditions, the obtained calibration curves for BADGEs showed good linearity ranging from 0.02 μg/mL to 10.00 μg/mL, while the values of LOD and LOQ were in the range of 0.0024–0.0035 μg/mL and 0.0080–0.0116 μg/mL, respectively. The optimized method exhibited a good recovery ranging from 85.6% to 105.5% with relative standard deviations less than 11.8%. The developed method provides an eco-friendly and effective way for the rapid and automated analysis of BADGEs at trace levels in canned beverages and can be applied to the high-throughput analysis of other similar matrices. Full article

The concept of blended education, which refers to the intensive integration of digital resources into the teaching process and its mixed online and on-site delivery, combining as much as possible the advantages of both methods in an optimal way, is becoming increasingly popular among teaching tools. There is no universal recipe for designing a successful blended course; the success of such courses is measured entirely through their degree of acceptance among students, defined by their emotional motivation to learn and the obtained practical results. Our study aimed to evaluate the motivational persistence degree (MPS) of medical students in connection with the students’ acceptance of different didactic tools involved in blended-learning approaches. Materials and Method: We investigated a sample comprising 523 students in Dental Medicine or General Medicine, belonging to all years of study, from four main Universities in Romania; we classified them according to their motivational persistence profile (using k-means data clustering) and we comparatively investigated the main relevant features of students in each cluster—gender, age group, opinions about the general usefulness of multimedia resources in the learning process, and their degree of acceptance of specific types of instructional materials involved in blended learning. Results: We found that the students who mostly enjoy the traditional learning style have average motivational persistence skills; they are perseverant and competitive, but they are not so good at planning their daily tasks. They enjoy external directions, set by teachers. The students who most enjoy PowerPoint presentations and those who enjoy instructional videos show similar behavior, both belonging to the cluster with the highest MPS score. They have the best motivational persistence skills amongst all categories; they are particularly excellent at planning and fulfilling daily tasks, as well as following their goals in the long term. The students who mostly enjoy online documentary sources belong also to a cluster with above average MPS score; they excel in fulfilling daily tasks, but exhibit weaker performance in recalling unachieved goals. These results are similar to those already reported in the literature; the strength of our study is in that it provides much more specific evaluations oriented to the motivational persistence degree, which is highly significant in the case of medical students, because it is a measure of their commitment in fulfilling certain tasks. Conclusions: Our results have the potential to highlight reasons for academic success or failure according to a student’ s profile, and will prove helpful in selecting the most appropriate didactic tools. Full article

Lattice materials are renowned for their exceptional mechanical performance and transformative potential for aerospace and structural engineering applications. However, current research primarily focuses on the effective elastic properties of lattice microstructures, whereas there are few studies on the prediction of their effective nonlinear properties, thus limiting the practical application of lattice materials. In addition, the characterization of complex micro structured lattice materials often requires the generation of many elements and performing nonlinear finite element analysis, which involves high computational costs. To address these challenges and enable the efficient prediction of the nonlinear effective properties of complex lattice microstructures in heterogeneous materials, the FEM-Cluster-based Analysis (FCA) approach is proposed. In the offline phase, a reduced-order model and offline database are established. In the online phase, the principle of the cluster minimum complementary energy incremental algorithm is used to rapidly predict the nonlinear effective properties of heterogeneous materials. This method is applied to conduct extensive comparisons with direct numerical simulation across two-dimensional and three-dimensional lattice materials to demonstrate that FCA can achieve similar accuracy while significantly enhancing computational efficiency, thereby offering promising potential for optimizing lattice material design in structural applications. Full article