Miracle-dz/UniFAR-FTUs huggingface dataset:
An efficient wide area imaging scheme based on spiral scanning

There are three common scanning schemes for large area imaging for airborne camera—multi lens splicing, circumferential scanning or reciprocating scanning. However, the lens splicing form requires multiple lenses, and the system is cumbersome. The circumferential scanning imaging obtains a wide area image through azimuth continuous scanning and the field of view in the pitching direction of this imaging method is a fixed value. And the imaging efficiency of reciprocating scanning form is low due to the need for multiple mechanism turns. In this paper, a new spiral scanning scheme method over large area imaging is proposed, which carries the area array camera on the two-dimensional platform with external azimuth gimbal and internal pitch gimbal. Scanning in azimuth combined with pitch motion, the spiral scanning trail is composed for wide-area imaging, and this method requires only one area array camera and only one rotation in pitch direction in one imaging process, which has high imagery efficiency. At the same time, the spiral dynamic scanning algorithm which can isolate attitude disturbance and compensate flight movement to achieve the scanning imaging for large area of the specified area is deduced. Then the implementation form of the scanning method is also derived. Finally, we take some simulation to test the method. The results show that the spiral scanning algorithm can compensate the attitude disturbance and aircraft attitude disturbance, and can obtain the staring imaging of 254km2 (mapping radius of 9km) in 3.8 seconds, when the field of view angle of area array camera is 7.5 °× 11.25 °, the flight speed is 360km/h, the azimuth attitude disturbance is 15 °, the pitch attitude disturbance is 15 °, and the roll attitude disturbance is 8 °, at the flight altitude of 7500m. Therefore, the spiral scanning algorithm scheme is suitable for wide area imaging of moving carrier and has certain significance for the research of wide area scanning imaging system.
AI-driven Family Interaction Over Melded Space and Time

Computer-mediated interaction services connect people over a distance. However, we address that those people are often “locked in a frame”—which includes an interaction mode, a point in time, or a context of either person. We observe that such lock-ins make it difficult to shape the interaction to be mutually symmetric. In this article, we propose a semantic-equivalent melding of space and time to provide a new form of empathetic interaction. We present HomeMeld and MomentMeld, which aim to meld space and time by applying AI, respectively. HomeMeld provides a sense of living together to a family living apart with AI-driven autonomous robotic avatars. MomentMeld utilizes an ensemble of visual AI to create interaction topics by matching semantic-equivalent photos. In-the-wild experiments reveal that HomeMeld and MomentMeld open new possibilities for empathetic interaction. Finally, we introduce a new interaction service leveraging technical synergy of HomeMeld and MomentMeld.
SOLUTION OF THE EXTENDED CIR TERM STRUCTURE AND BOND OPTION VALUATION

The extended Cox-Ingersoll-Ross (ECIR) models of interest rates allow for time-dependent parameters in the CIR square-root model. This article presents closed-form pathwise unique solutions of these unsolved stochastic differential equations (s.d.e.s) in terms of functionals of their driving Brownian motion and parameters. It is shown that quadratics in solution of linear s.d. e.s solve the ECIR model if and only if the "dimension" of the model is a positive integer and that this solution can be achieved by construction of a pathwise unique "generalized" Ornstein-Uhlenbeck process from the ECIR Brownian motion. For real valued dimensions an extension of the time-change theorem of Dubins and Schwarz (1965) is presented and applied to show that a lognormal process solves the model through a stochastic time change. Pathwise equivalence to a rescaled time-changed Bessel square process is also established. These novel results are applied to characterize zero-hitting time and to produce transition density and zero-hitting conditions for the ECIR spot rate. the CIR term structure is then extended to ECIR under no arbitrage, and its solutions and the transition density are represented under a new ECIR martingale measure. the findings are employed to derive a closed-form ECIR bond option valuation formula which generalizes that obtained by CIR (1985). Copyright 1996 Blackwell Publishers.
‘IT’S COMPLICATED’: TEACHERS’ STRATEGIES TO DEAL WITH THE CHALLENGES OF THE ONLINE EFL TEACHING

The pandemic of Covid-19 has affected the education system, particularly in Indonesia. All teaching and learning activities should be set in an online learning environment. This situation requires the teachers to change their strategy in the learning and teaching process. This study aims to find out the challenges and also the strategies to deal with the challenges of teaching English in an online setting. The study applied a case study research design using a documentary video and semi-structured interview. The study showed that the teachers faced some challenges in online classes such as difficulty in monitoring the students, the lack of students’ motivation, the student’s network instability, and lack of students’ participation. The results of the study also indicates that the teachers applied the strategies in online setting classes such as combining synchronous and synchronous, playing a song, using other learning applications, giving an additional score, etc.
Accuracy-Aware Data Reduction for Internet of Things

The Internet of Things (IoT) generates an enormous amount of data. Usually, either in real-time or after their collection, these data are passed to already trained machine learning and deep learning models for making predictions. Sometimes these data are used for retraining models. While efforts for data reduction have been made, little attention has been given to the effects of data reduction on machine learning models in the IoT context. Furthermore, the works that have explored how accurately data is reduced have no mechanism to use the result as feedback for future data reduction. In this paper, we propose a new reduction technique that depends on feedback from machine learning and deep learning models. As a result, data is as much reduced as required to achieve the desired results for the models. When machine learning model feedback reports low performance, the reduction algorithm increases the amount of data sent. Similarly, when the feedback indicates that the data being sent is sufficient to achieve the desired level of performance, the proposed reduction algorithm attempts to reduce the amount of data further to explore the opportunity that a similar level of performance might be achieved with a comparatively smaller amount of data. Experiments on NASA's turbofan engine data revealed that the algorithm's reduction rate can be integrated with feedback from the machine learning model without significantly affecting the performance of the model.
The Dynamics of Wave-Induced Downslope Winds

Abstract A theory for the dynamics of strong surface winds on the lee side of a large mountain range is derived and compared with observations. The strong winds observed near Boulder, Colo. , are found to be surface manifestations of standing gravity waves whose wavelength is long compared to typical resonant lee wavelengths. The theory indicates that such waves can become very intense if an inversion is present neat mountain-top level in the upstream environment and if the stability and wind profiles are such that the waves approximately reverse phase between the surface and the tropopause. The theory is extended to the development of a numerical model for estimation of maximum surface winds from upstream sounding data. Comparisons of the model predictions with observations are sufficiently encouraging to suggest the future utilization of such a model for operational forecasting. The differences between the predictions of this theory and those of hydraulic jump models are explored.
Healthcare Workers' Well-Being: A Systematic Review of Positive Psychology Interventions

Given persistent occupational stressors and multiple challenges in the delivery of healthcare, there is an increased focus on the well-being of healthcare workers. Responding to these challenges will require a multipronged approach, focusing on system level, organization, and individual actions. Positive psychology interventions (PPIs) represent a promising area for individual action. This systematic review indicates that PPI, delivered via many methods, holds promise for improving the well-being of healthcare workers, although there is a clear need for additional randomized controlled trials utilizing defined and standardized outcome measures. In this review, the most commonly evaluated PPIs were mindfulness-based or gratitude-based interventions. These were delivered via different methods, with many administered in the workplace and commonly in the form of courses ranging from two days to eight weeks. Researchers documented measurable improvements in multiple studied outcomes, noting reductions in symptoms of depression, anxiety, burnout, and stress. Some interventions increased well-being, job and life satisfaction, self-compassion, relaxation, and resilience. Most studies emphasized that these are simple, accessible, low-cost interventions. Limitations included some nonrandomized or quasi-experimental designs, alongside generally small sample sizes and varying methods of intervention delivery. Another concern is the lack of standardized outcome assessments and long-term follow-up data. As almost all studies included were performed before the pandemic, further research will be required post-pandemic. Overall, however, PPI shows promise as one arm of a multipronged approach to improving the well-being of healthcare workers.
Significant Factors Identification for Particle Swarm Optimization Algorithm to Solving the Design Optimization Problem of a Four-Bar Linkage for Path Generation

Particle swarm optimization (PSO) is a well-known population-based searching algorithm to solving optimization problems. This paper aims at identifying significant control factors for PSO to solving the design optimization problem of a four-bar linkage for path generation. Control factors considered herein are inertial weight, acceleration coefficients, breeding operation, and the number of population. A full factorial design of experiments is used to construct a set of experiments. Experimental results are analyzed with the analysis of variance method. According to the results obtained in this paper, breeding operation and the interaction between breeding operation and acceleration coefficients are significant. Inertial weight, acceleration coefficients, the number of population, and the other interactions are not significant. For the design optimization problem discussed herein, it is suggested to adopt breeding operation strategy and apply constant acceleration coefficients to increase significantly PSO’s performance and robustness. Type of inertial weight and the number of population do not affect PSO’s performance and robustness significantly.
A hole-filling algorithm based on pixel labeling for DIBR

Depth Image Based Rendering (DIBR) technology is one of effective methods to generate stereoscopic image pairs in 3D image warping, however, the holes would be produced when using this method. Hole-filling algorithms are essential for improving image quality of stereoscopic image pairs. In this paper, a new hole-filling algorithm based on pixel labeling is proposed. Firstly, holes in stereoscopic image pairs produced by DIBR are marked as 0, whereas marked as 1. Then traversing the image pairs only once to fill pixel values of each hole according to the situation of hole’s eight neighborhood pixels, the hole would be filled by the average of no-hole pixel values when the number of no-holes greater than threshold, otherwise the hole is filled by the cross diamond search algorithm from every direction to find the closest no-holes until the number of no-holes greater than threshold. The proposed method is evaluated by existing objective assessment methods, such as PSNR and SSIM. Experiment results show that the proposed hole-filling algorithm provides an improvement in both of subjective and objective assessment by compared with the conventional hole-filling algorithm under the same source images. The proposed algorithm is not only simple, but also can effectively eliminate the holes generated by using the DIBR method.
Comparative Investigations on Properties of Three Kinds of FDM 3D-Printed Natural Plant Powder/Poly(lactic acid) Biocomposites

In order to further explore the feasibility of the application of the residue of Chinese herbal medicine in FDM 3D technology and enrich the kinds of printing materials, Astragalus residue powder(ARP)/poly(lactic acid) (PLA) biocomposite was FDM 3D-printed, meanwhile, two traditional biocomposites, i.e., wood flour (WF)/PLA and rice straw powder (RSP)/PLA, were prepared by the same method, and the properties of the biocomposites were comparatively investigated. The results showed that, the tensile and flexural strengths of ARP/PLA were 28.33 MPa and 97.60 MPa, respectively, which were 2.85% and 10.89% smaller than those of WF/PLA, while 15.73% and 7.04% greater than those of RSP/PLA. WF/PLA showed typical brittle fracture characteristics, ARP/PLA and RSP/PLA both showed ductile fracture, but not obviously. Among the three kinds of biocomposites, ARP/PLA was the most thermally stable, followed by WF/PLA and RSP/PLA in turn. The incorporation of natural plant powder had no significant effect on the glassy transition, melting, and cold-crystallization behaviors of PLA, but the crystallinity of PLA could be increased from 0.3% to 2.0% and 1.9%, respectively, by adding ARP and WF. At 20 °C, the storage modulus of ARP/PLA, WF/PLA and RSP/PLA was 2759.4 MPa, 3361.3 MPa, and 2691.5 MPa, respectively, indicating that WF/PLA has the greatest stiffness, and the stiffness of RSP/PLA was the least. In addition to these, all the biocomposites were hydrophilic, the contact angle of the distilled water on the surface of ARP/PLA, WF/PLA or RSP/PLA was correspondingly 73.5°, 77.6° and 71.2°. Overall, it can be concluded that ARP/PLA has moderate strengths, stiffness and wettability, meanwhile, it is the most thermal stable among the three biocomposites, and can be processed at a temperature close to that of PLA. ARP/PLA is suitable as a new kind of feedstock material for FDM 3D printing.
A stress-induced phase transition model for semi-crystallize shape memory polymer

The developments of constitutive models for shape memory polymer (SMP) have been motivated by its increasing applications. During cooling or heating process, the phase transition which is a continuous time-dependent process happens in semi-crystallize SMP and the various individual phases form at different temperature and in different configuration. Then, the transformation between these phases occurred and shape memory effect will emerge. In addition, stress applied on SMP is an important factor for crystal melting during phase transition. In this theory, an ideal phase transition model considering stress or pre-strain is the key to describe the behaviors of shape memory effect. So a normal distributed model was established in this research to characterize the volume fraction of each phase in SMP during phase transition. Generally, the experiment results are partly backward (in heating process) or forward (in cooling process) compared with the ideal situation considering delay effect during phase transition. So, a correction on the normal distributed model is needed. Furthermore, a nonlinear relationship between stress and phase transition temperature Tg is also taken into account for establishing an accurately normal distributed phase transition model. Finally, the constitutive model which taking the stress as an influence factor on phase transition was also established. Compared with the other expressions, this new-type model possesses less parameter and is more accurate. For the sake of verifying the rationality and accuracy of new phase transition and constitutive model, the comparisons between the simulated and experimental results were carried out.
Characterization and Safety Profile of a New Combined Advanced Therapeutic Medical Product Platelet Lysate-Based Fibrin Hydrogel for Mesenchymal Stromal Cell Local Delivery in Regenerative Medicine

Adipose-derived mesenchymal stromal cells (ASC) transplant to recover the optimal tissue structure/function relationship is a promising strategy to regenerate tissue lesions. Because filling local tissue defects by injection alone is often challenging, designing adequate cell carriers with suitable characteristics is critical for in situ ASC delivery. The aim of this study was to optimize the generation phase of a platelet–lysate-based fibrin hydrogel (PLFH) as a proper carrier for in situ ASC implantation and (1) to investigate in vitro PLFH biomechanical properties, cell viability, proliferation and migration sustainability, and (2) to comprehensively assess the local in vivo PLFH/ASC safety profile (local tolerance, ASC fate, biodistribution and toxicity). We first defined the experimental conditions to enhance physicochemical properties and microscopic features of PLFH as an adequate ASC vehicle. When ASC were mixed with PLFH, in vitro assays exhibited hydrogel supporting cell migration, viability and proliferation. In vivo local subcutaneous and subgingival PLFH/ASC administration in nude mice allowed us to generate biosafety data, including biodegradability, tolerance, ASC fate and engraftment, and the absence of biodistribution and toxicity to non-target tissues. Our data strongly suggest that this novel combined ATMP for in situ administration is safe with an efficient local ASC engraftment, supporting the further development for human clinical cell therapy.
Chemical vapor deposition for few‐layer two‐dimensional materials

Chemical vapor deposition (CVD) approach offers a controllable strategy for preparing large‐area and high‐quality few‐layer (mainly bilayer or trilayer) twisted or untwisted two‐dimensional (2D) materials, and is predicted to boost the development of 2D materials from laboratory research to industrial applications.
Dirichlet Process Mixed Generalized Linear Models

Abstract Although generalized linear models (GLM's) are an attractive and widely used class of models, they are limited in the range of density shapes that they can provide. For instance, they are unimodal exponential families of densities in the response variable with tail behavior determined by the implicit mean-variance relationship. Dirichlet process (DP) mixing adds considerable flexibility to these models. Using such mixing, we develop models that we call DPMGLM's, which still retain the GLM character with regard to the mean. Overdispersed GLM's (OGLM's) provide an alternative class of models to cope with extra variability in samples. We show that how OGLM's may be DP mixed, leading to what we call DPMOGLM's. These models are extremely rich. Moreover, recent computational advances enable them to be fitted straightforwardly. We illustrate this with both simulated and real datasets. We also address the question of choosing between the GLM, OGLM, DPMGLM, and DPMOGLM. Finally, we consider extensions, by.
Simple heterogeneity variance estimation for meta‐analysis

Summary. A simple method of estimating the heterogeneity variance in a random‐effects model for meta‐analysis is proposed. The estimator that is presented is simple and easy to calculate and has improved bias compared with the most common estimator used in random‐effects meta‐analysis, particularly when the heterogeneity variance is moderate to large. In addition, it always yields a non‐negative estimate of the heterogeneity variance, unlike some existing estimators. We find that random‐effects inference about the overall effect based on this heterogeneity variance estimator is more reliable than inference using the common estimator, in terms of coverage probability for an interval estimate.
Imitation Learning for Real-Time Job Shop Scheduling Using Graph-Based Representation

Scheduling of manufacturing systems in practice is challenging due to dynamic production environments, such as random job arrivals and machine breakdowns. Dispatching rules are often used because they can be easily applied even in such dynamic manufacturing environments. However, dispatching rules often fail to provide a satisfactory production schedule because they cannot consider overall system states when assigning jobs. Therefore, we develop a real-time scheduling method using imitation learning, especially behavior cloning, to solve job shop scheduling problems. We define a set of available actions, a target optimal policy, and a dynamic graph-based state representation method for imitation learning. The proposed method is size-agnostic, which then can be applied to unseen larger problems. The experimental results show that the proposed method performs better to minimize makespan than other dispatching rules in dynamic job shops.
Functional Characterization of the GNAT Family Histone Acetyltransferase Elp3 and GcnE in Aspergillus fumigatus

Post-translational modifications of chromatin structure by histone acetyltransferase (HATs) play a pivotal role in the regulation of gene expression and diverse biological processes. However, the function of GNAT family HATs, especially Elp3, in the opportunistic human pathogenic fungus Aspergillus fumigatus is largely unknown. To investigate the roles of the GNAT family HATs Elp3 and GcnE in the A. fumigatus, we have generated and characterized individual null Δelp3 and ΔgcnE mutants. The radial growth of fungal colonies was significantly decreased by the loss of elp3 or gcnE, and the number of asexual spores (conidia) in the ΔgcnE mutant was significantly reduced. Moreover, the mRNA levels of the key asexual development regulators were also significantly low in the ΔgcnE mutant compared to wild type (WT). Whereas both the Δelp3 and ΔgcnE mutants were markedly impaired in the formation of adherent biofilms, the ΔgcnE mutant showed a complete loss of surface structure and of intercellular matrix. The ΔgcnE mutant responded differently to oxidative stressors and showed significant susceptibility to triazole antifungal agents. Furthermore, Elp3 and GcnE function oppositely in the production of secondary metabolites, and the ΔgcnE mutant showed attenuated virulence. In conclusion, Elp3 and GcnE are associated with diverse biological processes and can be potential targets for controlling the pathogenic fungus.
Immunopharmacological Activities of Luteolin in Chronic Diseases

Flavonoids have been shown to have anti-oxidative effects, as well as other health benefits (e.g., anti-inflammatory and anti-tumor functions). Luteolin (3′, 4′, 5,7-tetrahydroxyflavone) is a flavonoid found in vegetables, fruits, flowers, and herbs, including celery, broccoli, green pepper, navel oranges, dandelion, peppermint, and rosemary. Luteolin has multiple useful effects, especially in regulating inflammation-related symptoms and diseases. In this paper, we summarize the studies about the immunopharmacological activity of luteolin on anti-inflammatory, anti-cardiovascular, anti-cancerous, and anti-neurodegenerative diseases published since 2018 and available in PubMed or Google Scholar. In this review, we also introduce some additional formulations of luteolin to improve its solubility and bioavailability.