Seasonal limited product value perception research in a cross-cultural context

As a result of the Internet's homogeneity of information, customers in diverse regions have begun to consume products associated with foreign cultural features. Consumption of regional, local, and seasonal cultural elements has always been a powerful means of self-identity and self-expression for consumers. Utilizing seasonal features in product design can assist multinational brands in enhancing profitability, demonstrating brand innovation, and developing brand regional communities. Understanding how consumers perceive the value of this type of product is essential for formulating a subsequent strategy. In this study, we adopted and modified the luxury value perception model to evaluate which aspects would be the most influential factors, even the determining factors, when people make decisions in a cross-cultural context. We discovered that people's individual values have a significant impact on their decision-making, as well as their brand preference. Even if there are fewer restrictions because the users are in a cross-cultural environment, customers still consider the innovation and creativity of the design to be one of the most important factors in determining their satisfaction. Last but not least, we discuss the seasonal limited edition (SLE) strategy's potential as a sustainable path for brands

Comparing Hand Gestures and a Gamepad Interface for Locomotion in Virtual Environments

Hand gesture is a new and promising interface for locomotion in virtual environments. While several previous studies have proposed different hand gestures for virtual locomotion, little is known about their differences in terms of performance and user preference in virtual locomotion tasks. In the present paper, we presented three different hand gesture interfaces and their algorithms for locomotion, which are called the Finger Distance gesture, the Finger Number gesture and the Finger Tapping gesture. These gestures were inspired by previous studies of gesture-based locomotion interfaces and are typical gestures that people are familiar with in their daily lives. Implementing these hand gesture interfaces in the present study enabled us to systematically compare the differences between these gestures. In addition, to compare the usability of these gestures to locomotion interfaces using gamepads, we also designed and implemented a gamepad interface based on the Xbox One controller. We conducted empirical studies to compare these four interfaces through two virtual locomotion tasks. A desktop setup was used instead of sharing a head-mounted display among participants due to the concern of the Covid-19 situation. Through these tasks, we assessed the performance and user preference of these interfaces on speed control and waypoints navigation. Results showed that user preference and performance of the Finger Distance gesture were close to that of the gamepad interface. The Finger Number gesture also had close performance and user preference to that of the Finger Distance gesture. Our study demonstrates that the Finger Distance gesture and the Finger Number gesture are very promising interfaces for virtual locomotion. We also discuss that the Finger Tapping gesture needs further improvements before it can be used for virtual walking

Innovative Data Fusion Enabled Structural Health Monitoring Approach

Piezoceramic-based active sensing is a useful approach to structural health monitoring. This approach often involves a large number of distributed piezoceramic transducers. It may be confusing to incorporate each sensor data. It is desired to develop an automated health monitoring approach to obtain a comprehensive and accurate health monitoring result by simultaneously interpreting data from all sensors. In this paper, an innovative data fusion enabled structural health monitoring (SHM) approach based on the Dempster-Shafer (D-S) evidence theory is proposed to obtain comprehensive SHM results for a distributed sensor network in a civil infrastructure. Considering that evidence from multiple different information sources (sensor data) has different levels of significance, not all evidence is equivalently effective for the final decision. A weighted fusion damage index (WFDI) is proposed to perform damage identification based on the authors’ recently developed piezoceramic-based smart aggregates. Experimental data of a two-story concrete frame was used to study the effectiveness of the proposed weighted fusion damage index. Analyses show that the proposed weighted fusion damage index can reveal the damage status of different areas of the frame. The results are consistent with the visual inspection of the cracks on the concrete frame

Associations of 10 dietary habits with breast cancer: a Mendelian randomization study

IntroductionEpidemiological studies have revealed a link between dietary habits and the breast cancer risk. The causality of the association between food consumption and breast cancer requires further investigation.MethodsUsing Mendelian randomization, we assessed the causal effects of 10 dietary habits on the risks of breast cancer and its subtypes (estrogen receptor [ER]  +  and ER- breast cancer). We obtained dietary pattern data in 2018 (number of single-nucleotide polymorphisms [SNPs]  =  9,851,867) and breast cancer data in 2017 (number of SNPs  =  10,680,257) from IEU OpenGWAS. Rigorous sensitivity analyses were conducted to ensure that the study results were credible and robust.ResultsWe identified that genetic predisposition to higher dried fruit intake was linked to a reduced risk of overall breast cancer (inverse variance-weighted [IVW] odds ratio [OR] = 0.55; 95% confidence interval [CI]: 0.43–0.70; p = 1.75 × 10−6), ER+ breast cancer (IVW OR = 0.62; 95% CI: 0.47–0.82; p = 8.96 × 10−4) and ER− breast cancer (IVW OR = 0.48; 95% CI: 0.34–0.68; p = 3.18 × 10−5), whereas genetic predisposition to more oily fish intake was linked to a lower risk of ER+ breast cancer (IVW OR = 0.73; 95% CI: 0.53–0.99; p = 0.04).DiscussionOur findings suggest that a genetic predisposition for dried fruit and oily fish consumption may be protective against breast cancer; however, further investigation is required

Quantification of hypsarrhythmia in infantile spasmatic EEG:a large cohort study

Infantile spasms (IS) is a neurological disorder causing mental and/or developmental retardation in many infants. Hypsarrhythmia is a typical symptom in the electroencephalography (EEG) signals with IS. Long-Term EEG/video monitoring is most frequently employed in clinical practice for IS diagnosis, from which manual screening of hypsarrhythmia is time consuming and lack of sufficient reliability. This study aims to identify potential biomarkers for automatic IS diagnosis by quantitative analysis of the EEG signals. A large cohort of 101 IS patients and 155 healthy controls (HC) were involved. Typical hypsarrhythmia and non-hypsarrhythmia EEG signals were annotated, and normal EEG were randomly picked from the HC. Root mean square (RMS), teager energy (TE), mean frequency, sample entropy (SamEn), multi-channel SamEn, multi-scale SamEn, and nonlinear correlation coefficient were computed in each sub-band of the three EEG signals, and then compared using either a one-way ANOVA or a Kruskal-Wallis test (based on their distribution) and the receiver operating characteristic (ROC) curves. The effects of infant age on these features were also investigated. For most of the employed features, significant ({p} &lt; {0}.{05} ) differences were observed between hypsarrhythmia EEG and non-hypsarrhythmia EEG or HC, which seem to increase with increased infant age. RMS and TE produce the best classification in the delta and theta bands, while entropy features yields the best performance in the gamma band. Our study suggests RMS and TE (delta and theta bands) and entropy features (gamma band) to be promising biomarkers for automatic detection of hypsarrhythmia in long-Term EEG monitoring. The findings of our study indicate the feasibility of automated IS diagnosis using artificial intelligence.</p

Distinct resting-state effective connectivity of large-scale networks in first-episode and recurrent major depression disorder: evidence from the REST-meta-MDD consortium

IntroductionPrevious studies have shown disrupted effective connectivity in the large-scale brain networks of individuals with major depressive disorder (MDD). However, it is unclear whether these changes differ between first-episode drug-naive MDD (FEDN-MDD) and recurrent MDD (R-MDD).MethodsThis study utilized resting-state fMRI data from 17 sites in the Chinese REST-meta-MDD project, consisting of 839 patients with MDD and 788 normal controls (NCs). All data was preprocessed using a standardized protocol. Then, we performed a granger causality analysis to calculate the effectivity connectivity (EC) within and between brain networks for each participant, and compared the differences between the groups.ResultsOur findings revealed that R-MDD exhibited increased EC in the fronto-parietal network (FPN) and decreased EC in the cerebellum network, while FEDN-MDD demonstrated increased EC from the sensorimotor network (SMN) to the FPN compared with the NCs. Importantly, the two MDD subgroups displayed significant differences in EC within the FPN and between the SMN and visual network. Moreover, the EC from the cingulo-opercular network to the SMN showed a significant negative correlation with the Hamilton Rating Scale for Depression (HAMD) score in the FEDN-MDD group.ConclusionThese findings suggest that first-episode and recurrent MDD have distinct effects on the effective connectivity in large-scale brain networks, which could be potential neural mechanisms underlying their different clinical manifestations

Has “The Belt and Road” initiative promoted regional economic growth and economic innovation?

The article regards “The Belt and Road” initiative as a quasi-natural experiment. Based on the county panel data from 1999 to 2017, difference-in-differences model (DID) is used to examine the impact of the “The Belt and Road” initiative on regional economic growth and economic innovation. The study found that the “The Belt and Road” initiative can significantly increase the economic growth and innovation of the region. Through the placebo test and the robustness test, it shows good policy uniqueness characteristics. The article further analyzes the heterogeneity of the initiative. The study found that the initiative has more obvious economic growth and innovation in the central region

Efficient Protection Mechanism Based on Self-Adaptive Decision for Communication Networks of Autonomous Vehicles

The communication network of autonomous vehicles is composed of multiple sensors working together, and its dynamic topology makes it vulnerable to common attacks such as black hole attack, gray hole attack, rushing attack, and flooding attack, which pose a threat to the safety of passengers and vehicles; most of the existing safety detection mechanisms for a vehicle can only detect attacks but cannot intelligently defend against attacks. To this end, an efficient protection mechanism based on self-adaptive decision (SD-EPM) is proposed, which is divided into the offline phase and the online phase. The online phase consists of two parts: intrusion detection and efficient response. Attack detection and defense in the vehicular ad hoc networks (VANETs) are performed in terms of the attack credibility value (AC), the network performance attenuation value (NPA), and the list of self-adaptive decision. The simulation results show that the proposed mechanism can correctly identify the attack and respond effectively to different attack types. And, the negative impact on VANETs is small