292 research outputs found
Tourism industry and employment generation in emerging seven economies: evidence from novel panel methods
To analyze E-7 economies, the authors employ panel data and relevant
panel data econometrics approaches for long-run relationships
Mean group, fully modified and dynamic OLS (MG, FMOLS,
DOLS) to monitor changes over time between variables, which is
important in actual studies. The modelsā primary findings are as follows:
The panel cointegration tests confirm log-run associations
among the targeted variables. International tourism has the largest
influence on creating direct jobs in the tourist industry. The control
variables like FDI and TI increase employment opportunities in the
targeted economies. Furthermore, the results confirm that total
natural resources reduce employment services in the E-7 economies.
Other factors that might affect the performance of the tourist
activity are not included in the model. Furthermore, given the availability
of official and consistent data, it only includes what has been
recorded up to 2020; our target was 2022, but due to data limitation,
it covers 2020. The objective is to assess both the significance
of tourist-related activities in creating jobs and the effect of tourism
on country-level factors where public policy may influence economic
activity
Study of L0-norm constraint normalized subband adaptive filtering algorithm
Limited by fixed step-size and sparsity penalty factor, the conventional
sparsity-aware normalized subband adaptive filtering (NSAF) type algorithms
suffer from trade-off requirements of high filtering accurateness and quicker
convergence behavior. To deal with this problem, this paper proposes variable
step-size L0-norm constraint NSAF algorithms (VSS-L0-NSAFs) for sparse system
identification. We first analyze mean-square-deviation (MSD) statistics
behavior of the L0-NSAF algorithm innovatively in according to a novel
recursion form and arrive at corresponding expressions for the cases that
background noise variance is available and unavailable, where correlation
degree of system input is indicated by scaling parameter r. Based on
derivations, we develop an effective variable step-size scheme through
minimizing the upper bounds of the MSD under some reasonable assumptions and
lemma. To realize performance improvement, an effective reset strategy is
incorporated into presented algorithms to tackle with non-stationary
situations. Finally, numerical simulations corroborate that the proposed
algorithms achieve better performance in terms of estimation accurateness and
tracking capability in comparison with existing related algorithms in sparse
system identification and adaptive echo cancellation circumstances.Comment: 15 pages,15 figure
Research on the growth of China's EV market and influencing factors of consumer behaviour
Electric vehicles with low energy consumption and that are environmentally friendly have become one of the automobile industry's development hotspots, as well as the primary research objects of automobile companies in many countries. It corresponds to people's desire for a greener lifestyle and aids in the realization of the beautiful vision of sustainable development. To encourage the quick expansion of the electric car sector, the Chinese government has implemented a number of preferential regulations.
This study constructs a framework of factors affecting the purchase behaviour of electric vehicles from three aspects of demographics, situation, and psychology, and discusses the consumer behaviour of Chinese consumers for electric vehicles, based on planned behaviour theory, innovation diffusion theory, and existing research results. Demographics, technical attributes, cost, brand, government policy, environmental consciousness, subjective norms, and product knowledge and experience were all found to be predictors of consumers' purchase behaviour of electric vehicles in this study. The primary data was acquired using an online questionnaire, and multiple regression analysis was used to assess the relevant factors.
The findings reveal that product knowledge and experience, technical characteristics, and government policies all have a significant positive impact on electric vehicle consumer behaviour, while cost has a significant negative impact on electric vehicle consumer behaviour
Hybrid polymer/ZnO solar cells sensitized by PbS quantum dots
Poly[2-methoxy-5-(2-ethylhexyloxy-p-phenylenevinylene)]/ZnO nanorod hybrid solar cells consisting of PbS quantum dots [QDs] prepared by a chemical bath deposition method were fabricated. An optimum coating of the QDs on the ZnO nanorods could strongly improve the performance of the solar cells. A maximum power conversion efficiency of 0.42% was achieved for the PbS QDs' sensitive solar cell coated by 4 cycles, which was increased almost five times compared with the solar cell without using PbS QDs. The improved efficiency is attributed to the cascade structure formed by the PbS QD coating, which results in enhanced open-circuit voltage and exciton dissociation efficiency
MicroTEE: Designing TEE OS Based on the Microkernel Architecture
ARM TrustZone technology is widely used to provide Trusted Execution
Environments (TEE) for mobile devices. However, most TEE OSes are implemented
as monolithic kernels. In such designs, device drivers, kernel services and
kernel modules all run in the kernel, which results in large size of the
kernel. It is difficult to guarantee that all components of the kernel have no
security vulnerabilities in the monolithic kernel architecture, such as the
integer overflow vulnerability in Qualcomm QSEE TrustZone and the TZDriver
vulnerability in HUAWEI Hisilicon TEE architecture. This paper presents
MicroTEE, a TEE OS based on the microkernel architecture. In MicroTEE, the
microkernel provides strong isolation for TEE OS's basic services, such as
crypto service and platform key management service. The kernel is only
responsible for providing core services such as address space management,
thread management, and inter-process communication. Other fundamental services,
such as crypto service and platform key management service are implemented as
applications at the user layer. Crypto Services and Key Management are used to
provide Trusted Applications (TAs) with sensitive information encryption, data
signing, and platform attestation functions. Our design avoids the compromise
of the whole TEE OS if only one kernel service is vulnerable. A monitor has
also been added to perform the switch between the secure world and the normal
world. Finally, we implemented a MicroTEE prototype on the Freescale i.MX6Q
Sabre Lite development board and tested its performance. Evaluation results
show that the performance of cryptographic operations in MicroTEE is better
than it in Linux when the size of data is small.Comment: 8 pages, 8 figure
Beam energy distribution influences on density modulation efficiency in seeded free-electron lasers
The beam energy spread at the entrance of undulator system is of paramount
importance for efficient density modulation in high-gain seeded free-electron
lasers (FELs). In this paper, the dependences of high harmonic micro-bunching
in the high-gain harmonic generation (HGHG), echo-enabled harmonic generation
(EEHG) and phase-merging enhanced harmonic generation (PEHG) schemes on the
electron energy spread distribution are studied. Theoretical investigations and
multi-dimensional numerical simulations are applied to the cases of uniform and
saddle beam energy distributions and compared to a traditional Gaussian
distribution. It shows that the uniform and saddle electron energy
distributions significantly enhance the performance of HGHG-FELs, while they
almost have no influence on EEHG and PEHG schemes. A numerical example
demonstrates that, with about 84keV RMS uniform and/or saddle slice energy
spread, the 30th harmonic radiation can be directly generated by a single-stage
seeding scheme for a soft x-ray FEL facility
A visualized model for identifying optimal candidates for aggressive locoregional surgical treatment in patients with bone metastases from breast cancer
BackgroundThe impact of surgical resection of primary (PTR) on the survival of breast cancer (BC) patients with bone metastasis (BM) has been preliminarily investigated, but it remains unclear which patients are suitable for this procedure. Finally, this study aims to develop a predictive model to screen BC patients with BM who would benefit from local surgery.MethodsBC patients with BM were identified using the Surveillance, Epidemiology, and End Results (SEER) database (2010 and 2015), and 39 patients were obtained for external validation from an Asian medical center. According to the status of local surgery, patients were divided into Surgery and Non-surgery groups. Propensity score matching (PSM) analysis was performed to reduce selection bias. Kaplan-Meier (K-M) survival and Cox regression analyses were conducted before and after PSM to study the survival difference between the two groups. The survival outcome and treatment modality were also investigated in patients with different metastatic patterns. The logistic regression analyses were utilized to determine significant surgery-benefit-related predictors, develop a screening nomogram and its online version, and quantify the beneficial probability of local surgery for BC patients with BM. Receiver operating characteristic (ROC) curves, the area under the curves (AUC), and calibration curves were plotted to evaluate the predictive performance and calibration of this model, whereas decision curve analysis (DCA) was used to assess its clinical usefulness.ResultsThis study included 5,625 eligible patients, of whom 2,133 (37.92%) received surgical resection of primary lesions. K-M survival analysis and Cox regression analysis demonstrated that local surgery was independently associated with better survival. Surgery provided significant survival benefits in most subgroups and metastatic patterns. After PSM, patients who received surgery had a longer survival time (OS: 46 months vs. 32 months, p < 0.001; CSS: 50 months vs. 34 months, p < 0.001). Logistic regression analysis determined six significant surgery-benefit-related variables: T stage, radiotherapy, race, liver metastasis, brain metastasis, and breast subtype. These factors were combined to establish the nomogram and a web probability calculator (https://sunshine1.shinyapps.io/DynNomapp/), with an AUC of 0.673 in the training cohort and an AUC of 0.640 in the validation cohort. The calibration curves exhibited excellent agreement. DCA indicated that the nomogram was clinically useful. Based on this model, surgery patients were assigned into two subsets: estimated sur-non-benefit and estimated sur-benefit. Patients in the estimated sur-benefit subset were associated with longer survival (median OS: 64 months vs. 33 months, P < 0.001). Besides, there was no difference in survival between the estimated sur-non-benefit subset and the non-surgery group.ConclusionOur study further confirmed the significance of local surgery in BC patients with BM and proposed a novel tool to identify optimal surgical candidates
Occlusion facial expression recognition based on feature fusion residual attention network
Recognizing occluded facial expressions in the wild poses a significant challenge. However, most previous approaches rely solely on either global or local feature-based methods, leading to the loss of relevant expression features. To address these issues, a feature fusion residual attention network (FFRA-Net) is proposed. FFRA-Net consists of a multi-scale module, a local attention module, and a feature fusion module. The multi-scale module divides the intermediate feature map into several sub-feature maps in an equal manner along the channel dimension. Then, a convolution operation is applied to each of these feature maps to obtain diverse global features. The local attention module divides the intermediate feature map into several sub-feature maps along the spatial dimension. Subsequently, a convolution operation is applied to each of these feature maps, resulting in the extraction of local key features through the attention mechanism. The feature fusion module plays a crucial role in integrating global and local expression features while also establishing residual links between inputs and outputs to compensate for the loss of fine-grained features. Last, two occlusion expression datasets (FM_RAF-DB and SG_RAF-DB) were constructed based on the RAF-DB dataset. Extensive experiments demonstrate that the proposed FFRA-Net achieves excellent results on four datasets: FM_RAF-DB, SG_RAF-DB, RAF-DB, and FERPLUS, with accuracies of 77.87%, 79.50%, 88.66%, and 88.97%, respectively. Thus, the approach presented in this paper demonstrates strong applicability in the context of occluded facial expression recognition (FER)
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