Search Results (357)

Modern parenting styles cover a wide spectrum, from strict tiger parenting to supportive lighthouse parenting. Among these, helicopter parenting remains particularly controversial: while some consider it a protective approach, others believe that excessive control can hinder the development of a child’s autonomy and independence. Our research is significant because it approaches the topic from a rarely examined pedagogical perspective, thereby contributing to the psychology-dominated literature. We conducted our analysis using the EBSCO Discovery Service search engine and followed the steps of the PRISMA protocol. The aim of the study is to explore the relationship between helicopter parenting and academic achievement, as well as to review the factors—such as career-related decisions and adaptation—that may play a mediating role in this relationship. It reviews 33 studies published between 2012 and 2024, most of which use quantitative methods. Most of these studies were conducted in the United States and focus primarily on the college-age demographic. Our findings show that recognising and addressing helicopter parenting behaviours is essential to supporting student success. Furthermore, educational institutions should more actively make use of the potential resources and opportunities offered by such parenting behaviours. Overall, our findings confirm that helicopter parenting remains an under-researched topic at both the national and European levels, particularly in the context of education and public education systems, where further research is clearly needed. Full article

Background/Objectives: Treating critically ill patients is complex and often subjective. This study investigates adherence to clinical guidelines for sepsis among different providers. Considering the strengths of the recommendations, we hypothesize that heterogeneity in the decision-making process will be low and independent of provider background and psychological makeup. Methods: This cohort study used two clinical vignettes of sepsis. Providers were given standardized treatment plans for 7 days, and their responses were recorded. Demographical, professional, and psychological (ambiguity tolerance, defensiveness, anxiety due to uncertainty, risk-taking behavior, decision styles, and optimism) variables were acquired. Results: Crystalloids were commonly used in both vignettes. Pressor engagement, especially norepinephrine, increased significantly after the third day. Providers recommended antibiotics and no provider stopped antibiotic therapy. Cluster analysis revealed no differences in therapy implementation among provider types, but some differences existed between the two vignettes. Cluster #1 was characterized by the implementation of early light bundle therapy combined with the use of pressors and a notable enhancement in therapies by the fifth day (Early Cluster). Cluster #2 (Minimalists) involved consistent engagement only in light bundle therapy throughout the treatment period. Cluster #3 (Escalation) comprised providers who rapidly escalated treatment using multiple different modalities. Cluster #3 stood out as most providers were female, non-MD, with significant ICU duties, and enhanced rational thinking. Conclusions: Providers differ in implementation styles of the sepsis treatment standard based on types of therapies selected not studied psychological variables. Full article

Personality traits, decision-making styles, and socio-demographic information help understand the characteristics of women requesting voluntary termination of pregnancy (VTP) and monitor the timing and quality of this service However, our current data do not allow for an in-depth exploration of additional, less evident factors that might influence the decision. The aim was to characterise a sample of women requesting VTP in terms of socio-demographic, personality, and decision-making style variables, and to examine the associations among these factors. We conducted a cross-sectional study administering the General Decision-Making Style (GDMS) test, the Big Five Questionnaire (BFQ) and the Personality Inventory for DSM-5 (PID-5) to women requesting VTP certification. BFQ scores were generally high, especially for “Conscientiousness” and “Openness”. “Agreeableness” and “Openness” scores were lower in women with low vs. high education (diff = −8.2 [−13.9, −2.4] and diff = −7.5 [−15.0, −0.1]); “Extraversion” was higher in employed women. PID-5 scores for “Detachment” and “Psychoticism” were higher in medium vs. high education (diff = 1.6 [0.05, 3.2] and diff = 1.9 [0.1, 3.8]), but not at pathological level; “Negative affect” was lower in women with children (diff = −1.6 [−3.1, −0.2]). GDMS scores were not associated with socio-demographic factors. “Avoidant” and “Spontaneous” styles were negatively associated with “Conscientiousness” and “Emotional Stability”; “Rational” style was positively associated with “Conscientiousness” and “Disinhibition”. High BFQ and low PID-5 scores suggest no personality dysfunction in women undergoing VTP. No predominant decision-making style emerged, but associations between personality traits, decision-making, and socio-demographic factors such as educational level, employment and parity, were observed. Full article

We develop a convergence framework for Grünwald–Letnikov (GL) fractional and classical integer difference operators acting on sequences in fuzzy-paranormed (fp) spaces, motivated by data that are imprecise and contain sporadic outliers. Fuzzy paranorms provide a resolution-dependent notion of proximity, while statistical and lacunary statistical convergence downweight sparse deviations by natural density; together, they yield robust criteria for difference-filtered signals. Within this setting, we establish uniqueness of fp–${\mathsf{\Delta }}^m$ statistical limits; an equivalence between fp-statistical convergence of ${\mathsf{\Delta }}^m$ (and its GL extension ${\mathsf{\Delta }}^{\alpha }$) and fp-strong p-Cesàro summability; an equivalence between lacunary fp-${\mathsf{\Delta }}^m$ statistical convergence and blockwise strong p-Cesàro summability; and a density-based decomposition into a classically convergent part plus an fp-null remainder. We also show that GL binomial weights act as an ${\ell }^1$ convolution, ensuring continuity of ${\mathsf{\Delta }}^{\alpha }$ in the fp topology, and that nabla/delta forms are transferred by the discrete Q–operator. The usefulness of the criteria is illustrated on simple engineering-style examples (e.g., relaxation with memory, damped oscillations with bursts), where the fp-Cesàro decay of difference residuals serves as a practical diagnostic for Cesàro compliance. Beyond illustrative mathematics, we report engineering-style diagnostics where the fuzzy Cesàro residual index correlates with measurable quantities (e.g., vibration amplitude and energy surrogates) under impulsive disturbances and missing data. We also calibrate a global decision threshold ${\tau }_{\mathrm{glob}}$ via sensitivity analysis across $(\alpha ,p,m)$, where $m\in \mathbb{N}$ is the integer difference order, $\alpha >0$ is the fractional order, and $p\ge 1$ is the Cesàro exponent, and provide quantitative baselines (median/M-estimators, ${\ell }_1$ trend filtering, Gaussian Kalman filtering, and an $\alpha$-stable filtering structure) to show complementary gains under bursty regimes. The results are stated for integer m and lifted to fractional orders $\alpha >0$ through the same binomial structure and duality. Full article

Background/Objectives: Autism is characterized by atypical sensory processing, which affects spatial and temporal perception. Here, we explore sensory processing in children with autism, focusing on visuospatial and temporal tasks across visual and auditory modalities. Methods: Ninety-two children aged 4 to 6 participated, divided into three groups: autism (n = 32), neurotypical chronological age-matched controls (n = 28), and neurotypical developmental age-matched controls (n = 32). The autism group consisted of high-functioning children (26 boys). The participants completed computer-based tasks requiring spatial and temporal processing. Response accuracy and reaction times were recorded. Results: The autism group demonstrated higher accuracy in temporal tasks (visual and auditory modalities) and comparable accuracy in visuospatial modality, but slower response times in all tasks compared to both neurotypical controls. These results suggest a strategy that prioritizes accuracy over speed, while preserving spatial and temporal processing in autism. Conclusions: These findings suggest that temporal processing, rather than the sensory modality, drives decision-making strategies in children with autism. Our findings highlight the need for interventions aligned with autistic children’s slower but accurate processing style to support social interaction and reduce stress. In a fast-paced digitalized world, autistic children might benefit from slower, balanced, and inclusive, evidence-based approaches that align with their cognitive rhythm and reduce overstimulation. By incorporating these unique strategies, targeted programs can enhance the quality of life and adaptive skills of children with autism, thereby fostering better integration into social and sensory-rich environments. Full article

Background: Shared Decision-Making (SDM) is important for patient-centred care, especially in areas such as gynaecological oncology where treatment decisions are often multifaceted. This study aimed to implement and measure SDM in a gynaecological oncology outpatient clinic, specifically assessing the impact of the BRAN (Benefits, Risks, Alternatives, Nothing) tool on patient and physician perceptions. Methods: A two-phased prospective observational and survey mixed-methodology study was conducted at the tertiary Northern Gynaecological Oncology Centre (NGOC) outpatient clinic in Gateshead, United Kingdom, from October 2023 to November 2024. SDM champions provided staff training. Patient and physician perspectives were measured using the nine-item Shared Decision Making Questionnaire (SDM-Q-9). Phase one was a baseline assessment; phase two involved the implementation of BRAN posters and leaflets. Statistical analyses included the Mann–Whitney U Test and Fisher’s Exact Test. A post-implementation online staff survey was conducted. Results: A total of 207 patients and 13 physicians participated: 107 patients and 13 physicians in phase one and 100 patients and 12 physicians in phase two. Whilst no statistically significant difference in patients’ perceptions of SDM was found between phases (p = 0.73), physicians’ perceptions showed a statistically significant improvement after BRAN tool implementation (p < 0.01). The staff survey results indicated that 84% observed increased patient involvement, and 92% agreed that SDM helped achieve consultation goals. Conclusions: The implementation of SDM at the NGOC led to a statistically significant improvement in the subjective use of SDM by physicians’, despite no significant change in patients’ perceptions, possibly due to high baseline levels. Staff reported increased patient engagement and improved consultation styles. These findings support implementing SDM in gynaecological oncology outpatient settings Full article

The quality of posters at pharmacy conferences can vary. We created a mixed-approach rubric (MAR) for poster quality. Evidence from multiple sources (systematic review, further analysis of rater scores, verbal feedback from raters) showed the need to slightly modify that MAR, which we accomplished. Our objectives here were to re-evaluate scoring using this revised MAR (rMAR) and to further examine the attributes of lower-quality versus higher-quality posters. Two faculty raters independently scored each poster using the rMAR for recent posters presented at a pharmacy education conference. The Rasch Measurement Model provided psychometric evidence and poster-quality measures. These measures were then linear-regressed with attributes of logical sequencing, QR-code presence/use, submission abstract presence, and wordiness. Moreover, Traditional vs. Contemporary poster formats were compared. Raters scored 642 posters (267 from 2023, 375 from 2024). The Rasch Measurement Model showed a distinct separation of posters into lower quality versus higher quality. The rMAR’s rating scale continued to function well (like the original MAR had) among multiple raters. Poster-quality measures were significantly positive when linearly regressed with logical sequencing, QR-code presence/use, absence of submission abstract, and decreased wordiness. Moreover, Contemporary poster formats (either Persky-style or Billboard-style) were higher quality on average than Traditional poster formats. This evidence-based rMAR showed a helpful validation of poster-quality scores. Regression confirmed findings from the initial MAR (before revision), and choice of poster format proved a notable decision affecting poster quality. Full article

Automated Vehicles (AVs) are being developed with the aim to reduce the occurrence and severity of Road Traffic Crashes (RTCs). Studies suggest AVs may improve the safety of Vulnerable Road Users (VRUs), particularly on road crossings. However, exposure to novel technology over time may lead to behavioural adaptation. Thus, understanding VRUs’ behavioural intentions towards AVs is crucial for their safe integration into traffic. We investigate four external factors pedestrians consider when crossing a road in front of an AV. An online questionnaire with 281 participants assessed crossing intentions, focusing on road gradient, weather, pedestrian–AV distance, and AV type. Personality traits and self-reported behaviour were measured. Anderson’s experimental protocol revealed all factors significantly influenced crossing decisions. Using hierarchical clustering followed by K-means clustering, the participants were classified into three different profiles: risk-averse, resolute, and indecisive pedestrians. We provide evidence of a strong link between crossing decisions, reported behaviours and psychological facets while interacting with an AV at crossings. Pedestrian profiling allows targeting preventative measures for groups based on unique characteristics, maximising efficiency thereof. Furthermore, pedestrian profiling can inform AV’s driving style to support safer road interactions. This is salient for resolute pedestrians, who take more risks, which may lead to severe RTCs. Full article

Urban historic districts play a vital role in shaping the cultural identity and heritage of cities. However, many of these areas face challenges such as aging buildings and deteriorating infrastructure. At the same time, the increasing frequency of extreme rainfall has led to a rise in flood events, placing these vulnerable districts at greater risk. Therefore, it is essential to carry out a comprehensive and objective assessment of their resilience to flood disasters. This study establishes a G-IC model for evaluating the resilience of urban historic districts to flood disasters based on the game combination empowerment-improved cloud model method. The proposed method has been demonstrated in the Soviet-style building complex of the Daye Steel Plant in Huangshi and reveals that the driving force layer exhibits weak resilience; the pressure and state layers show general resilience; the impact and response layers demonstrate weak resilience; and the overall resilience of the district is categorized as weak. The consistency of the results was verified by calculating the cloud similarity, which shows that the constructed new model has certain rationality and feasibility, and the evaluation results are relatively accurate. The findings offer valuable insights for policy-making and support for decision-makers in local government departments. Full article

Entrepreneurship has proven to be a dynamic force for societal change in urgent global transformation toward sustainability. While governments and established institutions often move slowly, a new wave of entrepreneurs—guided by passion, purpose, and a holistic worldview—are actively shaping alternative paths to sustainable development. This study investigates how entrepreneurs integrate personal purpose, well-being, and a holistic approach into their businesses and lifestyles. Drawing on observations and interviews with selected entrepreneurs, the research employs thematic analysis to explore their motivations, decision-making processes, and the role of environments in shaping their ventures. The findings highlight a distinctive approach of conscious entrepreneurs: a shift from profit maximisation to values-based operations, educational marketing to foster authentic client engagement, and a regenerative leadership style that prioritises balance, relational intelligence, and co-creation. Unlike digital nomads or traditional influencers, these entrepreneurs represent a grounded, intentional lifestyle rooted in inner development and collective transformations. The results of this study contribute to a growing body of interdisciplinary literature on sustainability and entrepreneurship by offering an expanded understanding of purpose and passion in sustainable/conscious entrepreneurship and proposing an updated business model canvas for such entrepreneurs. Full article

Fine-grained modeling of driving styles is critical for decision making in autonomous driving. However, existing methods are constrained by the high cost of manual labeling and a lack of interpretability. This study proposes an unsupervised disentanglement framework based on a variational autoencoder (VAE), which, for the first time, enables the automatic extraction of interpretable driving style representations from car-following trajectories. The key innovations of this work are threefold: (1) a dual-decoder VAE architecture is designed, leveraging driver identity as a proxy label to guide the learning of the latent space; (2) self-dynamics and interaction dynamics features are decoupled, with an attention mechanism employed to quantify the influence of the lead vehicle; (3) a bidirectional interpretability verification framework is established between latent variables and trajectory behaviors. Evaluated on a car-following dataset comprising 25 drivers, the model achieves a Driver Identification accuracy of 98.88%. Mutual information analysis reveals the physical semantics encoded in major latent dimensions. For instance, latent dimension z₂₂ is strongly correlated with the minimum following distance and car-following efficiency. One-dimensional latent traversal further confirms that individual dimensions modulate specific behavioral traits: increasing z₂₂ improves safety margins by 18% but reduces efficiency by 23%, demonstrating that it encodes a trade-off between safety and efficiency. This work provides a controllable representation framework for driving style transfer in autonomous systems and offers a more granular approach for analyzing driver behavior in car-following scenarios, with potential for extension to broader driving contexts. Full article

Despite China’s success in its new energy vehicle (NEV) transition, significant regional imbalances persist, raising the question of why provincial policy effectiveness is so context-dependent. To investigate this, this study develops a novel framework to measure policy “quality” and “style”, systematically quantifying 2455 provincial policy documents from 2013 to 2023. Our empirical analysis reveals that policy quality—encompassing its authoritativeness, instrument strength, and resource commitment—is a far more decisive determinant of effectiveness than sheer policy quantity. We identify three primary policy styles with distinct impacts: substantive-driving policies are crucial for stimulating market demand, whereas coordinative-programmatic policies are more effective in guiding industrial supply, revealing a significant goal-mismatch. Conversely, high-level authoritative policies can unexpectedly inhibit infrastructure development. Crucially, the study finds that provincial policies act more as “catalysts” than “creators”, their effectiveness being highly contingent on local economic, fiscal, and industrial fundamentals. The findings of this research offer direct implications for policymaking: decision-makers should shift their focus from pursuing policy quantity to enhancing policy quality and design targeted, “precision-irrigation” policy instrument portfolios tailored to the specific contexts and development objectives (e.g., promoting sales or guiding production) of different regions. Full article

Virtual reality (VR) has become increasingly popular and has started entering homes, schools, and clinics, yet evidence on how children interact during free-form, unguided play remains limited. Understanding how interaction dynamics relate to player performance is essential for designing more accessible and engaging VR experiences, especially in educational contexts. For this reason, we developed VRBloons, a child-friendly VR game about popping balloons. The game logs real-time gameplay telemetry such as total hand movement, accuracy, throw rate, and other performance related gameplay data. By analyzing several feature-engineered metrics using unsupervised clustering and non-parametric statistical validation, we aim to identify distinct behavioral patterns. The analysis revealed several associations between input preferences, movement patterns, and performance outcomes, forming clearly distinct clusters. From the performed analysis, input preference emerged as an independent dimension of play style, supporting the inclusion of redundant input mappings to accommodate diverse motor capabilities. Additionally, the results highlight the opportunities for performance-sensitive assistance systems that adapt the difficulty of the game in real time. Overall, this study demonstrates how telemetry-based profiling can shape the design decisions in VR experiences, offering a methodological framework for assessing varied interaction styles and a diverse player population. Full article

For most, if not all, AI-accelerated hardware, communication with the agent is expensive and heavily bottlenecks the hardware performance. This omnipresent hardware restriction is also found in neuromorphic computing: a novel style of computing that involves deploying spiking neural networks to specialized hardware to achieve low size, weight, and power (SWaP) compute. In neuromorphic computing, spike trains, times, and values are used to communicate information to, from, and within the spiking neural network. Input data, in order to be presented to a spiking neural network, must first be encoded as spikes. After processing the data, spikes are communicated by the network that represent some classification or decision that must be processed by decoder logic. In this paper, we first present principles for interconverting between spike trains, times, and values using custom-designed spiking subnetworks. Specifically, we present seven networks that encompass the 15 conversion scenarios between these encodings. We then perform three case studies where we either custom design a novel network or augment existing neural networks with these conversion subnetworks to vastly improve their communication performance with the outside world. We employ a classic space vs. time tradeoff by pushing spike data encoding and decoding techniques into the network mesh (increasing space) in order to minimize intra- and extranetwork communication time. This results in a classification inference speedup of 23× and a control inference speedup of 4.3× on field-programmable gate array hardware. Full article

Wine quality is closely linked to sensory attributes such as aroma, taste, and mouthfeel, all of which are influenced by grape variety, “terroir”, and vinification practices. Among these, aroma is particularly important for consumer preference, and it results from a complex interplay of numerous volatile compounds. Conventional sensory methods, such as descriptive analysis (DA) performed by trained panels, offer valuable insights but are often time-consuming, resource-intensive, and subject to individual variability. Recent advances in sensor technologies—including electronic nose (E-nose) and electronic tongue (E-tongue)—combined with chemometric techniques and machine learning algorithms, offer more efficient, objective, and predictive approaches to wine aroma profiling. These tools integrate analytical and sensory data to predict aromatic characteristics and quality traits across diverse wine styles. Complementary techniques, including gas chromatography (GC), near-infrared (NIR) spectroscopy, and quantitative structure–odor relationship (QSOR) modeling, when integrated with multivariate statistical methods such as partial least squares regression (PLSR) and neural networks, have shown high predictive accuracy in assessing wine aroma and quality. Such approaches facilitate real-time monitoring, strengthen quality control, and support informed decision-making in enology. However, aligning instrumental outputs with human sensory perception remains a challenge, highlighting the need for further refinement of hybrid models. This review highlights the emerging role of predictive modeling and sensor-based technologies in advancing wine aroma evaluation and quality management. Full article