Miracle-dz/UniFAR-FTUs huggingface dataset:
Impact of Telemedicine on Heart Failure Readmissions and Mortality: A Literature Review

Background: Heart failure is a chronic condition that commonly results in frequent hospitalizations and increases risk of mortality (Kotb et al., 2015; Ong et al., 2016; Pekmezaris et al., 2018). In addition, heart failure places a significant burden on healthcare costs and utilization of resources (Hale et al., 2016; Kotb et al., 2015; Ong et al., 2016). Measures to reduce heart failure readmissions, mortality, and overall costs are needed. Telemedicine interventions may be beneficial in the management of heart failure; however, the current research provides unclear and inconclusive findings in regard to the effectiveness of telemedicine interventions on heart failure readmissions and mortality (Kraai et al., 2016). Aim: The aim of this literature review is to evaluate the impact of telemedicine interventions on heart failure readmissions and mortality. Methods: A literature review was conducted using five healthcare-related databases for data collection. A systematic search process was employed to extract literature that was relevant and answered the identified clinical question using keywords and specific inclusion and exclusion criteria. A total of 10 studies met inclusion criteria for critical appraisal. Results: Study outcomes related to heart failure readmissions and mortality were mixed. Limitations of the studies as well as direction for future studies were identified. Conclusions: Synthesized findings from this literature review identify that telemedicine interventions may improve outcomes in heart failure patients. However, further research studies are warranted to evaluate the efficacy of telemedicine on heart
Reducing RRT metric sensitivity for motion planning with differential constraints

The Rapidly-Exploring Random Tree (RRT) is a search structure constructed incrementally in a way that quickly reduces the expected distance to nodes in the search structure, of randomly-chosen points in the search space. Although it has recently shown promising results in trajectory design problems, its performance is not reliable for very challenging problems with differential constraints. One reason is metric sensitivity, which measures the degrading of the performance of the planner due to poor guidance information provided by the metric function. It is typical for robotics problems with high-dimensional, nonlinear systems, in which insufficient problem domain knowledge can be provided to construct a good metric function. The thesis presents improved methods to reduce the negative effect of a poor metric function. Two main ideas are employed: 1) Combining systematic search with a randomized algorithm. 2) Collecting the constraint violation frequency during the exploration. The constraint violation frequency gathers global constraint information and helps planners to make better choices in the search. The derived planners possess both the completeness from systematic search and fast exploration properties from the RRT. Our analysis shows that the new methods: 1) are resolution complete, 2) are more reliable than the original RRT, and 3) have a deterministic upper bound on the required space and iterations. The resolution completeness theorem is also applicable to other discrete time and discretized input space planners. We have implemented the algorithm and demonstrated that it can solve challenging trajectory design problems involving a 9-dimensional nonlinear car-like robot and a 12-dimensional underactuated spacecraft.
Effect of Pad Design and Bracket Manufacturing Method on Bracket Bond Strength

Effect of Pad Design and Bracket Manufacturing Method on Bracket Bond Strength Dustin Osborne, D.D.S. Objective: The objective is to determine if there is a significant difference in shear bond strength and debonding characteristics between one-piece metal brackets made by metal injection molding (MIM) and multi-piece metal brackets made by machining then soldering/brazing with differing pad designs. Methods: A total of thirteen metal orthodontic brackets manufactured by 8 different companies were divided into two groups: one-piece MIM and multi-piece brackets. Each group consisted of 10 brackets for a total of 130 brackets. Brackets were bonded to stainless steel tubes with a universal bonding technique. Samples were tested on the universal Instron testing machine to measure the shear bond strength. Force required to debond each bracket was recorded. Brackets were observed after debond and an ARI score assigned. Data were analyzed using a two sample t-test. Results: The mean shear bond strength and ARI scores for the entire study sample were 3.7±1.7 and 4.2±0.7, respectively. The results of two sample t test showed that there was no significant difference between the one-piece MIM brackets (3.5±1.7) and the multi-piece brackets (4.0±1.8) in mean shear bond strength (t(128)=1.33, p=0.18). But the mean of ARI scores of the multipiece brackets (4.4±0.7) was significantly higher than the one-piece MIM brackets (4.0±0.7) (t(128)=3.5, p=0.006). Conclusion: One-piece metal orthodontic brackets manufactured by MIM and multi-piece brackets manufactured by machining then soldering/brazing exhibit similar shear bond strengths. However, one-piece brackets showed a significantly lower ARI score than multi-piece brackets, indicating a higher percentage of adhesive left on the one-piece bracket surface after debond.
Inventory Management System using Struts Framework Architecture

Every оrgаnizаtiоn needs inventоry fоr smооth running оf its асtivities. It serves аs а link between рrоduсtiоn аnd distribution рrосesses. The System maintains соmрuterized infоrmаtiоn relаted tо the Оrgаnizаtiоn. Whiсh mаintаins аnd sаves dаtа in the fоrm оf exсel sheets whiсh аre being ассessed by the оrgаnizаtiоn аdmin аnd its emрlоyees whiсh is а very tediоus jоb. Seсurity is very lоw аs аnyоne саn ассess the dаtа, even the оnes whо аre nоt hаving рermissiоns tо ассess the dаtаbаse. Tо reduсe these рrоblems the new system is рrоviding а level-wise аuthоrizаtiоn fоr the seсurity рurроses. The System is relаted tо the рrосessing оf infоrmаtiоn оn NРD (New Рrоjeсt Develорment) whiсh will be inсluding the рrоduсt асtivity trасking till the рrоduсt disраtсh. This system аlsо will be рrоviding
The “Ground-Glass” Mimicker in The Pandemic: A Novel Radiomics-Based Machine Learning Model Differentiates COVID-19 Pneumonia from Acute Non-COVID-19 Lung Disease

Ground-Glass Opacities (GGOs) are a non-specific CT finding observed in the early phase of COVID-19 pneumonia. However, GGOs are also seen in other acute interstitial and alveolar lung diseases, thus making the differential diagnosis a diagnostic challenge. In this poof-of-concept study, we aimed to differentiate COVID-19 pneumonia presenting with GGOs from acute non-COVID-19 lung disease using a novel radiomic-based model in patients who underwent a high-resolution CT (HRCT) scan at hospital admission during the first pandemic peak in Italy. HRCT scans of 28 RT-PCR diagnosed COVID-19 pneumonia (COVID) and 30 acute non-COVID-lung disease (nCOVID) were retrospectively included. All patients showed GGOs as the predominant CT pattern. Two readers, blinded to the final diagnosis, independently segmented GGOs on CT scans by using a semi-automated approach, and radiomic features were extracted from segmented images. Partial least square (PLS) regression was used as the multivariate machine-learning algorithm. A leave-one-out nested cross-validation was implemented to optimize the hyperparameter of PLS and to assess the model generalization. The diagnostic performance of the radiomic model to differentiate between COVID and nCOVID lung disease was assessed through receiver operating characteristic (ROC) analysis. The radiomics-based machine learning model differentiated COVID and nCOVID with an AUC = 0.868 (p = 4.2·10− 7). After a careful prospective evaluation in larger multicentric studies, it may help radiologists to rule out COVID-19 pneumonia thus improving the COVID-19 triaging in epidemic areas.
Laboratory Grade Radiometers

light-curing units (LCU) (Elipar S10, Elipar DeepCure-S, Corded VALO and Bluephase Style) using different types of radiometers. The devices used to analyze the LCU were classified as either handheld analog (Henry Schein, Spring, Demetron 100A, Demetron 100B and Demetron 200), handheld digital (Bluephase 1, Bluephase II, Coltolux, CureRite and Hilux), or laboratory instruments (Thermopile and Integrating Sphere). The laboratory instruments and the Bluephase II radiometer were also used to measure the LCU’s power (mW). The LCU’s were activated for 20 s (n=5). Data were analyzed using Kruskal-Wallis and Student-Newman-Keuls multiple comparison test (α=0.05). Among the LCU, the laboratory instruments presented different irradiance values, except for Corded VALO. The Coltolux and Hilux radiometers measured greater irradiance values compared to the laboratory instruments for the four LCUs tested. Within a given LCU, handheld analog units measured lower irradiance values, compared to handheld digital and laboratory instruments, except using the Spring radiometer for the Elipar S10 LCU. None of the handheld radiometers were able to measure similar irradiance values compared to laboratory instruments, except for Elipar S10 when comparing Bluephase 1 and Thermopile. Regarding power measurement, Bluephase II always presented the lowest values compared to the laboratory instruments. These findings suggest that the handheld radiometers utilized by practitioners (analog or digital) exhibit a wide range of irradiance values and may show lower outcomes compared to laboratory based instruments. Ac cu r a cy o f I r r a d i an c e and Power o f L ight-Cur ing Uni t s Measu red Wi th Handhe ld o r Laboratory Grade Radiometers
Charge and Light Production in the Charge Readout System of a Dual Phase LAr TPC

For the future neutrino oscillation experiment DUNE, liquid argon time projection chambers with a fiducial mass of 10 kton each are foreseen. The dual phase concept is one of the two implementations considered, wherein electrons produced by ionization in the liquid are extracted to a gaseous region above the liquid where they are amplified. For the amplification, large electron multipliers will be used. The technology was tested in various prototypes, most recently with a 3 x 1 x 1 m3 large setup. An even larger prototype of 6 x 6 x 6 m3 is currently being constructed and will start operation in 2019. An intensive R&D program was carried out with the focus on achieving an effective gain of at least 20. In the simulation study here presented for the first time not only the electron signal is considered but also the ion backflow and the expected production of secondary scintillation light is studied, because the latter might limit the capability of the detector to trigger on low energetic no-beam physics. It is found that the ion backflow and the light yield can be expected to be very large. The results for the effective gain show a discrepancy with experimental data, both in size and shape of the gain curve. Based on literature studies, it is argued that photon feedback contributes to the gain in detectors filled with pure noble gases, especially in the case of pure argon.
CHAPTER 3:Crystallization of PLA-based Materials

If we consider the large number of possible applications of poly(lactic acid) (PLA) in the biomedical field and in the biodegradable packaging applications, just to name two of the most important ones, the study of their crystallization will always be decisive to tailor their properties. Additionally, the production of a high impact PLA with improved thermal stability and processability is still under investigation. Crystallization is likely to play a vital role in the development of future PLA-based materials. The morphology and crystallization behaviour of PLA have been extensively studied by different analytical techniques, such as WAXS/SAXS, TEM, PLOM, AFM and DSC. The extensive review of the most recent literature results indicates some general features and they are presented in this chapter.
Can Temporal-Difference and Q-Learning Learn Representation? A Mean-Field Analysis

Temporal-difference and Q-learning play a key role in deep reinforcement learning, where they are empowered by expressive nonlinear function approximators such as neural networks. At the core of their empirical successes is the learned feature representation, which embeds rich observations, e.g., images and texts, into the latent space that encodes semantic structures. Meanwhile, the evolution of such a feature representation is crucial to the convergence of temporal-difference and Q-learning. In particular, temporal-difference learning converges when the function approxi-mator is linear in a feature representation, which is ﬁxed throughout learning, and possibly diverges otherwise. We aim to answer the following questions: When the function approximator is a neural network, how does the associated feature representation evolve? If it converges, does it converge to the optimal one? We prove that, utilizing an overparameterized two-layer neural network, temporal-difference and Q-learning globally minimize the mean-squared projected Bellman error at a sublinear rate. Moreover, the associated feature representation converges to the optimal one, generalizing the previous analysis of [21] in the neural tangent kernel regime, where the associated feature representation stabilizes at the initial one. The key to our analysis is a mean-ﬁeld perspective, which connects the evolution of a ﬁnite-dimensional parameter to its limiting counterpart over an inﬁnite-dimensional Wasserstein space. Our analysis generalizes to soft Q-learning, which is further connected to policy gradient.
Drought in the Breadbasket of America and the Influence of Oceanic Teleconnections

From 1980 to 2020, drought events accounted for only 11.4% of the billion-dollar disasters in the United States (U.S.), yet caused the second-highest total amount in damages, at USD 236.6 billion. With the average cost of a drought being upwards of USD 9.5 billion, these natural disasters can create serious problems in agriculture. Drought is defined as a period of below-average precipitation that causes damage to agriculture and water supplies. Previous research has linked drought events in the U.S. Great Plains to oceanic teleconnections in the Pacific and Atlantic basins, indicating the influence of El Niño—Southern Oscillation (ENSO), the Pacific Decadal Oscillation (PDO), and the Atlantic Multidecadal Oscillation (AMO). This study looks to identify areas of the Great Plains where drought, as measured by PDSI, has the strongest relationship to ENSO, PDO, and AMO from 1950 to 2019. The states studied are Iowa, Illinois, Minnesota, Texas, Nebraska, and Kansas because these rank as the second through seventh most agriculturally productive states in terms of crop and livestock production. Results show that most of this region displays a relationship between drought and the ENSO and PDO, with less of the region displaying a relationship with the AMO.
Scales of Effect for Urban Mammals Vary Among Species and Environmental Attributes

Context. Cities are increasingly recognized for their biodiversity conservation potential. Incomplete understanding of urban species habitat requirements and of the areal extents over which species-habitat interactions occur (“scale of effect”), however, restricts such conservation.Objectives. Better-understanding scales of effect for urban species could improve urban conservation by identifying areal extents to target in management. We sought to identify scales of effect for urban mammals and if and how these scales differ among species and environmental covariates.Methods. We constructed Eastern fox squirrel (Sciurus niger), red fox (Vulpes vulpes), and Virginia opossum (Didelphis virginianus) occupancy models using data from trail cameras in Iowa City, Iowa, USA. We compared models constructed using environmental covariates estimated over different extents and identified appropriate scales of effect for each covariate based on model fit.Results. Scales of effect varied among species and covariates. Environmental covariates based on home ranges sometimes provided the best model fit; however, best-fit model covariates were often estimated over other extents. The best-fit fox squirrel model included covariates estimated at different, but relatively local scales (<250 m). Both opossum and red fox models included local to landscape-level covariates. Thus, scales of effect differed among species, possibly with body size, and environmental attributes, offering clear evidence that urban mammals exhibit a multi-scalar response to their environments.Conclusions. Our findings suggest that habitat models should include covariates estimated over a range of biologically-informed extents to more accurately identify habitat relationships. Such modeling will inform the choice of extent in urban habitat management.
A Short Review on Electrochemical Sensing of Commercial Dyes in Real Samples Using Carbon Paste Electrodes

Synthetic dyes are commonly used in food products like soft drinks, vegetable sauces, jellies, etc. Most artificial dyes can cause cancer, therefore it is very important to develop sensors to detect them in food samples. Voltammetric methods with carbon paste electrodes (CPEs) are promising for this purpose. However, modification of CPEs is necessary to detect the commercial dyes in food samples in the presence of interferents. In the current review, we have discussed the different previous research in which detection of dyes is performed in real samples with good detection limits. The current review will be helpful for readers who are interested in developing low-cost electrodes for the effective determination of dyes in commercial products like soft drinks and vegetable sauces.
Novel Poly-(Lactic-Co-Glycolic Acid) Targeted Nanoparticles Conjunct with Antibody for the Enhancement of Antibacterial Activity against Ralstonia solanacearum

Due to the strong pathogenicity of Ralstonia solanacearum, a variety of strategies have been used to develop antibacterial agents; however, antibacterial drugs with targeted effects on R. solanacearum remain lacking. Herein, we present a nanoagent targeting R. solanacearum based on our previous research on poly-(lactic-co-glycolic acid) (PLGA) particles (PLGA-NPs) loaded with methyl caffeate and caffeic acid phenethyl ester. Antibodies that have specific effects on R. solanacearum, which were verified using immuno-PCR, were first used to prepare PLGA-targeted nanoparticles (PLGA-TNPs). The antibody coupling process was investigated in terms of antibody binding degree and antibacterial activity. The EC50 value of PLGA-TNPs was 0.021 mg/mL, which was significantly reduced by 92% in comparison to PLGA-NPs. PLGA-TNPs had a perforating effect on the cell membrane of R. solanacearum, but no effects on Escherichia coli according to the SEM results. In addition, a downregulation of the pathogenicity-related genes compared to PLGA-NP treatment was observed, and the expression of egl, phcA, phcB, pilT, polA-238, and pehC decreased by 78, 79, 87, 61, 58, and 41%, respectively. Therefore, PLGA-targeted nanoparticles not only enhance the activity against R. solanacearum, but also provide a new idea for controlling bacterial wilt.
Genetic analysis of rice seedling traits related to machine transplanting under different seeding densities

Dan Zhu China National Rice Research Institute Yuping Zhang China National Rice Research Institute Jing Xiang China National Rice Research Institute Yaliang Wang China National Rice Research Institute Defeng Zhu China National Rice Research Institute Yikai Zhang China National Rice Research Institute Huizhe Chen (  chenhuizhe@163.com ) China National Rice Research Institute https://orcid.org/0000-0003-0984-9927
Phytochemical analysis and antifungal activity of Tithonia diversifolia and Kigelia africana extracts against Fusarium oxysporum in tomato

Fusarium wilt can cause severe losses in many vegetables and flowers, field crops and plantation crops. The main method for controlling Fusarium wilt is through chemical methods which have the disadvantage of polluting the environment and are expensive. Thus, there is a need for more environmentally friendly and cheaper techniques for controlling Fusarium Wilt. This study screened the phytochemical compounds of two plant extracts; Tithonia diversifolia and Kigelia africana and assessed their potency in controlling plant fungal pathogen F. oxysporum, which causes fusarium wilt in tomatoes. The phytochemical analysis revealed that the two plant extracts contained saponins, tannins, terpenoids, flavonoids, glycosides and phenolics except alkaloids and steroids which were only found in T. diversifolia. The mean inhibitory zones ranged from 7.93 to 10.44 mm for T. diversifolia at 25 to 100 g/l. The mean inhibitory zones for K. Africana ranged from 12.07 to 15.56 mm at 25 to 100 g/l. K. Africana extracts had the highest inhibitory (antimicrobial) activity. Combining both extracts was more effective compared to the single extract. The effect of the combined plant extract and positive control was statistically significant (p < 0.05). This study provides scientific evidence that K. africana and T. diversifolia extract possess antifungal activity and can be used as a broad-spectrum in managing microbial diseases. Key words: Plant extracts, phytochemical screening, antimicrobial and antifungal properties, Fusarium oxysporum.
Simultaneous Biological Nutrient Removal from Municipal Wastewater and CO2-biofixation using Chlorella kessleri

Mohammed Omar Faruque Chemical Engineering Department, King Fahd University of Petroleum and Minerals, Dhahran, Saudi Arabia Kazeem Ayodeji Mohammed Chemical Engineering Department, King Fahd University of Petroleum and Minerals, Dhahran, Saudi Arabia Mohammad Mozahar Hossain Chemical Engineering Department, King Fahd University of Petroleum and Minerals, Dhahran, Saudi Arabia Shaikh Abdur Razzak (  srazzak@kfupm.edu.sa ) King Fahd University of Petroleum & Minerals https://orcid.org/0000-0003-4316-7882