Miracle-dz/UniFAR-FTUs huggingface dataset:
CCD Charge Collection Efficiency And The Photon Transfer Technique

The charge-coupled device (CCD) has shown unprecendented performance as a photon detector in the areas of spectral response, charge transfer and readout noise. Recent experience indicates, however, that the full potential for the CCD's charge collection efficiency (CCE) lies well beyond that which is realized in currently available devices. In this paper, we present a definition of CCE performance and introduce a standard test tool (the photon transfer technique) for measuring and optimizing this important COD parameter. We compare CCE characteristics for different types of CODs, discuss the primary limitations in achieving high CCE performance, and outline the prospects for future improvement.
Configuration Effect: Innovation Performance Evaluation and Modeling of Cooperatives

The essence of economic development is innovation, and an important source of innovation is the new configuration of multiple factors. Cooperative economic is such a typical model worthy of research. Under socialism with Chinese characteristics, Cooperatives' innovation is a key measure in rural governance. The author, therefore, examines 51 forestry cooperatives that have been modeled by the Forestry Department of the Chinese Government. By using the tools of qualitative comparative analysis, the author discusses how some factors lead to the difference of cooperative's innovation performance from the aspects of subject composition, operation mechanism and business characteristics. The research has confirmed three conclusions. Firstly, long-term planning is a core requirement for cooperatives to produce high innovation performance. Secondly, four combination mechanisms enable cooperatives to produce high innovation performance. Thirdly, the combination of factors between the mechanisms that produce different innovation performances is asymmetrical. Around these possible combination mechanisms, the research concludes with recommendations to improve cooperatives' innovation performance. All in all, the research attempts to demonstrate the widespread existence of Schumpeter-style economic growth in Chinese cooperatives. And it provides a partial basis for the future formulation of China's rural policies.
Somatic and Abdominal Acupuncture for Pain Treatment in Adolescent Complex Regional Pain Syndrome (CRPS) of the Upper Limb: A Case Report

Complex regional pain syndrome (CRPS) is still poorly understood. It is a pain disorder in which pain is disproportionate to the initial stimulus. There is no specific therapy for CRPS, but it can be managed by a combination of treatments. We report a 13-year-old girl with CRPS of the upper limb treated with somatic and abdominal acupuncture. She described a severe, pulsating pain in the left wrist and hand, with hypersensitivity, allodynia, a marked reduction in strength, and swelling and sweating at the level of the fingers. Pain began three months previously, after a trauma to the left wrist. The diagnostic tests performed were negative. At the first visit we recommended oral tramadol. During the first two sessions we used somatic acupuncture. At the third session, the girl reported suffering intolerable pain in the affected limb during the previous sessions, so we decided to use abdominal acupuncture. After 8 sessions of abdominal acupuncture the pain completely disappeared. Acupuncture could be a potential alternative when conservative therapy with physical and medical treatment fails, but more often parents and adolescents prefer this therapy since other comorbidities are often present in pediatric populations and abdominal acupuncture could be a valuable alternative aid.
EVALUATION OF THE CROPGRO-SOYBEAN MODEL FOR ASSESSING CLIMATE IMPACTS ON REGIONAL SOYBEAN YIELDS

Remote sensing using aerial images is very useful in obtaining large amounts of data in a short period of time. Vegetation indices derived from remotely sensed images often correlate well with plant characteristics. In our previous research, we found a relationship between modified normalized difference vegetation index (NDVI) and leaf area index (LAI). In this study, we have developed a model to predict the processing tomato yield based on soil, crop, and environmental parameters. LAI derived from modified NDVI and photosynthetically active radiation (PAR) are the two major inputs to this yield prediction model. The model was calibrated and validated using yield data gathered from a load cell based tomato yield monitor during the 2000 crop growing season. Although the actual and predicted yield maps did not have a very high correlation, the two maps showed similar yield patterns. The RMS error in yield prediction was about 6%.
Effect of simulation methodology on solder joint crack growth correlation

A generalized solder joint fatigue life model for surface mount packages was previously published by the author. The model is based on correlation to measured crack growth data on BGA joints during thermal cycling. It was subsequently discovered by Anderson et. al. that the ANSYS/sup TM/ 5.2 finite element code used in the model had an error in its method for calculating plastic work. It was shown that significant error in life prediction could result by using a recent version of the code where the bug has been fixed. The error comes about since the original crack growth constants were derived based on plastic work calculations that had the bug. In this paper, crack initiation and growth constants are recalculated using ANSYS/sup TM/ 5.6. In addition, several other model related issues are explored with respect to the crack growth correlations. For example, 3D slice models were compared to quarter symmetry models. Anand's constitutive model was compared with Darveaux's constitutive model. It was shown that the crack growth rate dependence on strain energy density always had an exponent of 1.10+/-0.15. This is in the range of the original correlation, so the accuracy of relative predictions should still be within+/-25%. However, the accuracy of absolute predictions could be off by a factor of 7 in the worst case, if the analyst uses a modeling procedure that is not consistent with that used for the crack growth correlation. The key to good accuracy is to maintain consistency in the modeling procedure.
Rapid Contrast Enhancement Algorithm for Natural Contrast- Distorted Color Images

Digital images are often obtained with contrast distortions due to different factors that cannot be avoided on many occasions. Various research works have been introduced on this topic, yet no conclusive findings have been made. Therefore, a low-intricacy multi-step algorithm is developed in this study for rapid contrast enhancement of color images. The developed algorithm consists of four steps, in that the first two steps include separate processing of the input image by the probability density function of the standard normal distribution and the softplus function. In the third step, the output of these two approaches is combined using a modified logarithmic image processing approach. In the fourth step, a gamma-controlled normalization function is applied to fully stretch the image intensities to the standard interval and correct its gamma. The results obtained by the developed algorithm have an improved contrast with preserved brightness and natural colors. The developed algorithm is evaluated with a dataset of various natural contrast degraded color images, compared against six different techniques, and assessed using three specialized image evaluation methods, in that the proposed algorithm performed the best among the comparators according to the used image evaluation methods, processing speed and perceived quality.
Multitask Learning for Classification Problem via New Tight Relaxation of Rank Minimization

Multitask learning (MTL) is a joint learning paradigm, which fuses multiple related tasks together to achieve the better performance than single-task learning methods. It has been observed by many researchers that different tasks with certain similarities share a low-dimensional common yet latent subspace. In order to get the low-rank structure shared across tasks, trace norm has been used as a convex relaxation of the rank minimization problem. However, trace norm is not a tight approximation for the rank function. To address this important issue, we propose two novel regularization-based models to approximate the rank minimization problem by minimizing the $k$ minimal singular values. For our new models, if the minimal singular values are suppressed to zeros, the rank would also be reduced. Compared with the standard trace norm, our new regularization-based models are the tighter approximations, which can help our models capture the low-dimensional subspace among multiple tasks better. Besides, it is an NP-hard problem to directly solve the exact rank minimization problem for our models. In this article, we proposed two simple but effective strategies to optimize our models, which tactically solves the exact rank minimization problem by setting a large penalizing parameter. Experimental results performed on synthetic and real-world benchmark datasets demonstrate that the proposed models have the ability of learning the low-rank structure shared across tasks and the better performance than other classical MTL methods.
A technique for determining the normalized impedance of slots in the image plane of the image NRDG

This paper presents a method for determining the normalized impedance of a transverse slot in the image plane of the image nonradiative dielectric guide using measurements of the standing wave. The method overcomes the problem of distortion caused by the scattered evanescent fields that are present in the vicinity of the slot. The measurement equipment, its optimum parameters, and aspects necessary for accurate measurements are also discussed. Moreover, the finite-difference time-domain technique is employed to determine the normalized slot impedance, and good agreement is obtained with measured results, confirming the reliability of the method.
Structure and Magnetic Properties of Thermodynamically Predicted Rapidly Quenched Fe85-xCuxB15 Alloys

In this work, based on the thermodynamic prediction, the comprehensive studies of the influence of Cu for Fe substitution on the crystal structure and magnetic properties of the rapidly quenched Fe85B15 alloy in the ribbon form are performed. Using thermodynamic calculations, the parabolic shape dependence of the ΔGamoprh with a minimum value at 0.6% of Cu was predicted. The ΔGamoprh from the Cu content dependence shape is also asymmetric, and, for Cu = 0% and Cu = 1.5%, the same ΔGamoprh value is observed. The heat treatment optimization process of all alloys showed that the least lossy (with a minimum value of core power losses) is the nanocomposite state of nanocrystals immersed in an amorphous matrix obtained by annealing in the temperature range of 300–330 °C for 20 min. The minimum value of core power losses P10/50 (core power losses at 1T@50Hz) of optimally annealed Fe85-xCuxB15 x = 0,0.6,1.2% alloys come from completely different crystallization states of nanocomposite materials, but it strongly correlates with Cu content and, thus, a number of nucleation sites. The TEM observations showed that, for the Cu-free alloy, the least lossy crystal structure is related to 2–3 nm short-ordered clusters; for the Cu = 0.6% alloy, only the limited value of several α-Fe nanograins are found, while for the Cu-rich alloy with Cu = 1.2%, the average diameter of nanograins is about 26 nm, and they are randomly distributed in the amorphous matrix. The only high number of nucleation sites in the Cu = 1.2% alloy allows for a sufficient level of grains’ coarsening of the α-Fe phase that strongly enhances the ferromagnetic exchange between the α-Fe nanocrystals, which is clearly seen with the increasing value of saturation induction up to 1.7T. The air-annealing process tested on studied alloys for optimal annealing conditions proves the possibility of its use for this type of material.
DETERMINANT MODELS AFFECTING FINANCIAL PERFORMANCE AND STOCK RETURN COMPANIES REGISTERED IN INDONESIA STOCK EXCHANGE

This research aims to analyze the effect of Capital Adequacy Ratio (CAR), Operating Expenses to Operating Income Ratio (BOPO), Loan to Deposits Ratio (LDR), Net Interest Margin (NIM), and Non-Performing Loan Ratio (NPL) variables on ROA and Stock Return of Banks That Listed in the Indonesia Stock Exchange. The population in this research is all banks listed on the Indonesia Stock Exchange. At the same time, the samples are 30 companies. The sampling uses the purposive sampling method. Secondary data was obtained in the Indonesia Stock Exchange and Yahoo! Finance. The independent variables used are CAR, BOPO, LDR, NIM, and NPL. The data analysis technique used is multiple linear regression analysis by SmartPLS software. This research indicates that the LDR, NIM, and NPL variables have a significant effect on ROA. The CAR, BOPO, and NPL variables have a significant effect on Stock Return. The predictive ability of the independent variables (CAR, BOPO, LDR, NIM, and NPL) on ROA is 59.5%, as indicated by the value of Adjusted R Square is 59.5%, while the remaining is 40.5% influenced by other variables not included in this research. The independent variables (CAR, BOPO, NIM, and NPL) on Stock Returns have 13.3% of Adjusted R Square while the remaining is 86.7% influenced by other variables.
Recalling Direct 2D-3D Matches for Large-Scale Visual Localization

Estimating the 6-DoF camera pose of an image with respect to a 3D scene model, known as visual localization, is a fundamental problem in many computer vision and robotics tasks. Among various visual localization methods, the direct 2D-3D matching method has become the preferred method for many practical applications due to its computational efficiency. When using direct 2D-3D matching methods in large-scale scenes, a vocabulary tree can be used to accelerate the matching process, which will also induce the quantization artifacts leading to reduce the inlier ratio and decrease the localization accuracy. To this end, in this paper two simple and effective mechanisms, called visibility-based recalling and space-based recalling, are proposed to recover lost matches caused by the quantization artifacts, thus can largely improve the localization accuracy and success rate without increasing too much computational time. Experimental results on long-term visual localization benchmarks demonstrate the effectiveness of our method compared with state-of-the-arts.
Short senolytic or senostatic interventions rescue progression of radiation-induced frailty and premature ageing in mice

Cancer survivors suffer from progressive frailty, multimorbidity and premature morbidity. We hypothesize that therapy-induced senescence and senescence progression via bystander effects is a significant cause of this premature ageing phenotype. Accordingly, the study addresses the question whether a short anti-senescence intervention is able to block progression of radiation-induced frailty and disability in a pre-clinical setting. Male mice were sub-lethally irradiated at 5 months of age and treated (or not) with either a senolytic drug (Navitoclax or dasatinib + quercetin) for 10 days or with the senostatic metformin for 10 weeks. Follow up was for one year. Treatments commencing within a month after irradiation effectively reduced frailty progression (p<0.05) and improved muscle (p<0.01) and liver (p<0.05) function as well as short-term memory (p<0.05) until advanced age with no need for repeated interventions. Senolytic interventions that started late, after radiation-induced premature frailty was manifest, still had beneficial effects on frailty (p<0.05) and short-term memory (p<0.05). Metformin was similarly effective as senolytics. At therapeutically achievable concentrations metformin acted as a senostatic neither via inhibition of mitochondrial complex I, nor via improvement of mitophagy or mitochondrial function, but by reducing non-mitochondrial ROS production via NOX4 inhibition in senescent cells. Our study suggests that the progression of adverse long-term health and quality-of-life effects of radiation exposure, as experienced by cancer survivors, might be rescued by short-term adjuvant anti-senescence interventions.
The Rare Disease Research Scholars Program: A training curriculum for clinical researchers with mixed methods evaluation study

Rare disease clinician investigators are essential to ensure appropriate diagnosis, care, and treatment for the rapidly growing rare disease population. As these researchers are spread across many specialties, learning the unique skill set for rare disease research (RDR) can be a hurdle and may hinder progress in the field. The need for an RDR focused training program for investigators in many specialties and backgrounds was identified in a needs assessment of trainees in the NIH funded Rare Diseases Clinical Research Network. Based on this information, the Rare Disease Research Scholars Program (RDRSP) was developed. We describe the needs assessment, curriculum creation, scholar recruitment, and outcome evaluation based on four years of programmatic data (2015–2019). This one year-long RDRSP uses a blended approach that includes in-person, web-based, synchronous and asynchronous learning. We evaluated the RDRSP using quantitative and qualitative approaches. Quantitative measures included pre and post questionnaires about knowledge, self-efficacy, and intent to remain in RDR. Data were analyzed using descriptive statistics and a paired t-test. Qualitative semi-structured interviews explored the RDR scholars’ perceptions of the RDRSP; thematic analysis examined the textual data. Quantitative pre- and post-measures were statistically significant in the following areas: 1) improved knowledge content in RDR, 2) enhanced self-efficacy in clinical research, and 3) intent to remain in the field of RDR. Qualitative data analysis found the program supported the development of the scholar’s research skills as well as ‘soft-skills’. By combining training of skills unique to RDR with the more general topics of leadership, mentorship and collaboration among participants in diverse specialties, we created a program that supports the development of the next generation of rare disease clinician investigators and serves as a model for training in other niche research areas.
New method to implement IF simulation for ocean radar altimeter

The IF simulator is an important equipment to test and calibrate spaceborne radar altimeter before launch. In this paper, a new IF simulation algorithm (IFSA) is introduced, based on which an IF simulator is developed successfully. The IF simulator is designed to provide simulated I/Q signals and related interface, so that the tracker of altimeter can be tested and calibrated in close-loop under the simulated circumstance. That means the IF simulator should have the ability to simulate the variety of the distance from the satellite to the sea surface, the significant wave height (SWH), the backscatter coefficient of the sea surface, the sea scattering noise, and the thermal noise of the radar altimeter electronic system. Besides IF simulation, IFSA can be used in RF simulator, and it has been proved to be correct and useful by lots of experiments. The IF simulator includes the monitor unit, the data processing unit, timing unit, D/A unit, buffer unit, and interface unit.
Emotional Anxiety, Frustration of Failure and the Chain Mediating Effect of Project-Based Learning Performance.

BACKGROUND Anxiety is one of the greatest emotional obstacles in language learning. College English teaching has shifted from focusing on external factors such as the improvement of teaching hardware facilities to focusing on learners' language learning awareness, language psychological changes, language cognitive ability, and language emotional performance. SUBJECTS AND METHODS This study was carried out on a sample of 318 Chinese undergraduates. This paper suggests that emotional anxiety is a key factor leading to low language learning efficiency and has a negative impact on project-based learning performance. Then two variables: frustration of failure and learning satisfaction, are introduced, and a multi-chain mediating model is established to explore the transmission mechanism of emotional anxiety and learning performance. RESULTS With the increase of emotional anxiety, students' learning performance will be significantly affected in the process of project-based learning, and the results verify the negative relationship between anxiety and learning acquisition. Three paths that affect emotional anxiety on learning performance are identified, namely, "Emotional Anxiety -- Frustration of Failure -- Learning Satisfaction -- Learning Performance", "Emotional Anxiety -- Frustration of Failure -- Learning Performance" and "Emotional Anxiety -- Learning Satisfaction -- Learning Performance". CONCLUSIONS English learning is not only a process of language cognition, but also a process of psychological changes in learners. The achievement of learning performance is not only affected by language characteristics, students' cognitive ability, and learning environments, but also by non-intellectual factors such as learning attitude, learning motivation and emotions.
SIDE : Self Driving Drones Embrace Uncertainty

Aerial drones are increasingly used to perform monitoring tasks in a large number of applications. Current solutions to trajectory planning rely on perfect knowledge of ongoing events requiring inspection. Nevertheless, in many scenarios the events’ time and position can only be estimated with some <italic>uncertainty</italic>. Unlike previous work, we consider critical scenarios where a squad of drones is required to autonomously inspect an area of interest under <italic>uncertainty</italic> of time and location of target events. The main goal of the squad is to ensure maximum coverage of event monitoring with minimum average inspection delay. With no initial knowledge, the drones share their local observations of the environment and apply the Parzen-Rosenblatt approach to manage a dynamic probabilistic map of ongoing events. This map is integrated into a virtual force approach for a joint solution to distributed dynamic trajectory planning and collision avoidance. Through extensive simulations and real-field experiments, we compare our proposal against <italic>AC-GAP</italic>, a state-of-art solution for UAVs, and <italic>Sweep</italic>, a sweep-based algorithm for multiple robots. We show that our proposal discovers new events 30-40<inline-formula><tex-math notation="LaTeX">$\%$</tex-math><alternatives><mml:math><mml:mo>%</mml:mo></mml:math><inline-graphic xlink:href="coletta-ieq1-3135894.gif"/></alternatives></inline-formula> faster than the other algorithms, and outperforms them in terms of percentage of visited events and inspection delay, under a wide variety of scenarios.
Oral care experiences of palliative care patients, their relatives/carers and healthcare professionals.

AIM To explore the oral care experiences of palliative care patients and of those who support or deliver oral care to these patients: nurses, doctors, dentists, their relatives and carers. DATA SOURCES An electronic search of the databases included: Embase, MEDLINE, DOSS, AMED and PsycINFO was performed. DATA SELECTION Six articles were included in the final synthesis. DATA EXTRACTION Thematic analysis was applied by two researchers. DATA SYNTHESIS Three themes were identified: 'performing oral care', 'effects on oral health' and 'challenges in palliative care'. CONCLUSIONS Despite similarities of oral care experiences, this review identified some differences, crucially in terms of the wider impact of oral symptoms, namely social interactions with others and challenges to oral care. This confirms the need to investigate these topics further in relation to different stakeholders, such as nurses; and to have consistent guidance to facilitate the provision of effective oral care to palliative care patients.