Miracle-dz/UniFAR-FTUs huggingface dataset:
Antigen-Based Diagnosis of Human Giardiasis: A Systematic Review and Meta-Analysis

Background: We aimed to present a systematic review and meta-analysis of studies that used antigen-based assays for the diagnosis of human giardiasis. Methods: All the related published literature cited within PubMed, ISI web of science, Google Scholar, Embase, and Scopus, were searched up to December 2021. The search terms, both as MeSH terms and text words, were “Giardia”, “Giardia lamblia”, “Giardia intestinalis”, “giardiasis”, combined with “diagnosis”, “antigen detection”, serodiagnosis, or serological diagnosis. The required data was extracted from the papers. Pooled estimates of sensitivity and specificity were obtained and forest plots and summary receiver operating characteristics (SROC) plots were used to calculate sensitivity, specificity, positive predictive value (PPV), and negative predictive value (NPV). Results: The search of databases found 1683 papers, of which 46 articles fulfilled our eligibility criteria. The sensitivity of antigen-based methods for the diagnosis of human giardiasis ranged from 45% (95% CI: 31–59%) to 100% (95% CI: 100–100%) and the pooled estimate of sensitivity was 92% (95% CI: 90–93%). The pooled estimated specificity was 97% (95% CI: 96–98%), ranged from 81% (95% CI: 68–89%) to 100% (95% CI: 98–100%). The summary estimate of PPV and NPV were 92 % (95% CI: 90–93%) and 97% (95% CI: 96–98%) respectively. Comparing the performance of the antigen detection assays by region revealed a significant difference in the assay's performances in different regions of the world. Conclusion: The antigen-based detection methods have acceptable and satisfactory performance in the diagnosis of human giardiasis. The task ahead is to identify more specific target antigens and design simpler, cheaper, and more sensitive methods for the diagnosis of this common worldwide-distributed parasitic infection.
One-Year Survival after Cardiac Surgery in Frail Older People—Social Support Matters: A Prospective Cohort Study

There are increasing rates of cardiac surgery in the elderly. Frailty, depression, and social vulnerability are frequently present in older people, and should be considered while assessing risk and providing treatment options. We aimed to analyse the impact of clinically relevant variables on survival at one year, and identify areas of future intervention. We performed a prospective cohort study at a University Hospital, with a sample of 309 elective cardiac surgery patients 65 years old and over. Their socio-demographic and clinical variables were collected. Frailty prevalence was 61.3%, while depression was absent in the majority of patients. Mortality was 1.6% and 7.8% at 30 days and 12 months, respectively. After Kaplan–Meier analysis, severe frailty (p = 0.003), severe depression (p = 0.027), pneumonia until 30 days (p = 0.014), and re-operation until 12 months (p = 0.003) significantly reduced survival, while social support increased survival (p = 0.004). In the adjusted multivariable Cox regression model, EuroSCORE II (HR = 1.27 [95% CI 1.069–1.499] p = 0.006), pneumonia until 30 days (HR = 4.19 [95% CI 1.169–15.034] p = 0.028), re-intervention until 12 months (HR = 3.14 [95% CI 1.091–9.056] p = 0.034), and social support (HR = 0.24 [95% CI 0.079–0.727] p = 0.012) explained time until death. Regular screening for social support, depression, and frailty adds relevant information regarding risk stratification, perioperative interventions, and decision-making in older people considered for cardiac surgery.
Generalizing Semantic Lenses for Large Element-based Plots

Given a spatial embedding of multivariate relational data, we propose a semantic lens which selects a specific spatial and attribute-related data range. The lens keeps the selected data in focus unchanged and continuously deforms the data out of the selection range in order to maintain the context around the focus. Specific deformations include distance-based repulsion of scatter plot points, deforming straight-line node-link graph drawings, and as varying the simplification degree of bundled edge graph layouts. Our technique is simple to implement and provides real-time performance on large datasets.
Importance of the baseline risk in determining sample size and power of a randomized controlled trial

Accurate estimation of adequate sample size to reduce the possibility of a type II error (false negative) is important in planning a randomized controlled trial (RCT). It becomes apparent that many recent critical care trials could not reject the null hypothesis; so much so that the proportion of clinical trials rejecting the null hypothesis on crude mortality outcome was close to the conventional acceptable type I error (5%) (1).
Impact of Concept Mapping and Modular Learning Techniques on Pupils’ Achievement for the Selected Topics in Mathematics

This research emphasizes on an alternate instructional tools called “Concept Map and Modular learning techniques”. The goal of this study was to determine the effectiveness of using concept maps and modular learning techniques in improving the mathematics achievement of higher secondary students and compare it with a traditional approach for a mathematics unit. Both the control and the experimental groups were required to take a pre-test before instruction and a post-test at the end of instruction. The test consisting of 30 questions was used to assess learning gains on a Mathematics Unit about Trigonometry and Differential Calculus. Moreover, results showed that the levels of concept mapping and modular learning techniques ability were associated with the students’ learning gains. The study suggests that, when carefully integrated into the normal classroom procedure and when other contributing factors such as student motivation and preparedness, reading ability levels, time and classroom environment are considered, concept mapping and modular learning techniques has a potential to be an effective instructional strategy. To enforce the title of the study, the researcher had determined to verify the results from a different angle using the normative survey method to justify the impact of attitude towards mathematics homework. Based on the findings and discussions, XI standard students are having positive mathematics homework attitude towards concept mapping and modular learning techniques for the topics trigonometry and differential calculus.
Approximate solution for fractional Burgers equation with variable coefficient using Daftardar-Gejji-Jafaris method

Here, λ(x, t) is the given function. Equation (1) arises in the mathematical modeling of various physical phenomena, such as heat exchange in nanomaterials [1-12]. Moreover, the Burgers’ equation with variable coefficient can be used to describe the cylinder and spherical wave in overfall, and traffic flow, see for example [8, 13, 14]. The time-fractional term in eq. (1) implies that at the measured time period, time is discontinuous and it has intermittent property, for example, a traffic flows at the daytime and at the night-time have obvious difference. When α = 1 the traffic flow has the same properties throughout the whole day, when α = 0 the traffic flow does not change with time. Such intermittent motion can be best described by the time-fractional model. In eq. (1): 1– ( , ) D ( , ) J t t u x t u x t t α α ∂   =   ∂   (3)
Satellite Imageries for Detection of Bangladesh’s Rural and Urban Areas Using YOLOv5 and CNN

In recent years, there have been significant advancements in object identification in natural photos. However, when applying natural image object recognition techniques directly to satellite images, the results are often unsatisfactory. This is primarily due to inherent disparities in the object scale and orientation caused by the omniscient viewpoint of satellite imagery. The distinguishing factors between rural and urban areas lie in the objects that cover them. Furthermore, the complex backdrop of satellite photos poses challenges in accurately extracting features, leading to the omission of small objects in many regions. The performance of object detection, which is crucial for area identification, is also affected by dense object overlap and occlusion. To address these aforementioned issues, we made modifications to the generalized one-stage detector YOLOv5, specifically tailored for satellite photos. For this research, we manually collected data from Google Earth, meticulously labeling them and subsequently verifying them with human annotators. We then preprocessed the data using computer vision techniques, such as resizing and normalization. Next, we employed YOLOv5 and transfer learning-based CNN architectures of InceptionV3, DenseNet201, and Xception to compare their performances. The goal was to accurately identify rural and urban areas from remote sensing images.
A Study on the Regeneration of Cultural Tourism Resources in Historic Districts from the Perspective of Experience Economy - A Case Study of Macao's Lai Chi Vun Historic District

As a microcosm of the city's development, the historic district is a witness to the long years of a city. It is through the revitalization of the historic district that the district will not be eliminated in the process of rapid urbanization. The experience economy is an emerging economic model that emphasizes personalized design concepts and pays particular attention to consumer perceptions, and a historic district driven by the experience economy can be re-invigorated to bring its own connotations into line with modern perceptions and needs. This paper takes the Lai Chi Vun historic district in Macao as an example, and uses the concept of experience economy to revitalize it, with a view to saving the Lai Chi Vun shipbuilding industry from decline and exploring the development mode of industrial heritage in the wave of post-industrialization.
Research on Predictive Model Based on Ensemble Learning

Ensemble learning completes learning tasks by building and combining multiple learners. The use of ensemble learning can make accurate prediction. This paper used the dataset publicly available on kaggle platform. Firstly, this paper preprocessed and performed descriptive statistics on the dataset, based on which this research constructed the prediction model. Three ensemble learning models Random Forest, AdaBoost, and LightGBM were selected to study the data. To prevent overfitting, a 10-fold cross-validation method was used to train the classifiers and the models were tuned using the grid search method. Finally, the three models were compared in terms of Accuracy, Precision, Recall, F1-score, ROC curve and AUC values. The comparison shows that all three models have good performance, and the accuracy of all model predictions are higher than 80%. However, there is a slight difference in classification ability among the models. Random Forest performs best, with an Accuracy of 86.94, Precision of 85.91, Recall of 93.10, F1-score of 0.8936, and AUC of 0.8906. All evaluation indexes are higher, which also verify the feasibility of using ensemble learning algorithms in prediction.
Evaporation of strong coffee drops

Coffee stains remain when coffee drops evaporate. Commonly, dilute coffee exhibits ring-like stains after evaporation governed by self-pinning and vapor diffusion. Modern printing technology requires dense-ink printing, so that it is useful to understand the evaporation dynamics of dense inks. Here, we experimentally explore how strong coffee drops evaporate and how coffee stains are generated from strong coffee that can give an insight into dense inks. We find that evaporation rates of strong coffee drops become slower as coffee concentrations become higher. The evaporation dynamics of strong coffee deviate greatly from dilute coffee, revealing nonlinear evaporation dynamics, attributed to the concentration-dependent fluid property. The nonlinear evaporation of strong coffee can be important in understanding the drying-mediated printing of dense nanoinks.Coffee stains remain when coffee drops evaporate. Commonly, dilute coffee exhibits ring-like stains after evaporation governed by self-pinning and vapor diffusion. Modern printing technology requires dense-ink printing, so that it is useful to understand the evaporation dynamics of dense inks. Here, we experimentally explore how strong coffee drops evaporate and how coffee stains are generated from strong coffee that can give an insight into dense inks. We find that evaporation rates of strong coffee drops become slower as coffee concentrations become higher. The evaporation dynamics of strong coffee deviate greatly from dilute coffee, revealing nonlinear evaporation dynamics, attributed to the concentration-dependent fluid property. The nonlinear evaporation of strong coffee can be important in understanding the drying-mediated printing of dense nanoinks.
GRADUALLY INCREASING IN LIGHTING INTENSITY ON CHARACTERISTICS OF ROSS 308 BROILER CARCASS AND SOME INTERNAL ORGANS

This study was conducted to find out the health status of the internal organs of birds and some characteristics of the carcass using the system of gradual increase in the intensity of illumination in the chicks of the broiler. 224 Ross 308 chicks were used in this study at the age of one day. The chicks were randomly distributed to 4 treatments with 4 replications for each treatment (14 chicks/duplicate). The transactions included the following: T1 lighting program according to the guide for 308 ROSS company (control treatment). T2 Lighting program according to the guide and a gradual increase in the intensity of lighting and a gradual reality of light every 20 minutes. T3 Lighting program according to the guide and a gradual increase in the intensity of lighting and a gradual reality of light every 40 minutes. T4 Lighting program according to the guide and a gradual increase in the intensity of the lighting and a gradual reality of the light every 60 minutes. Both the characteristics of the carcass and the internal organs of the experimental birds were studied, and the results showed a significant increase in the grooming ratio of all experimental treatment birds compared with the control treatment, and a significant increase in the relative weight of the duodenum for the fourth treatment compared with the third and second treatment without a significant difference with the control treatment The relative weight of the spleen was significantly superior to the fourth and second treatment compared to the treatment. The rest of the traits did not show any significant differences, the different parameters, the back, the thigh, the drum stick, Abdominal, wings, legs, heart, liver, stomach, glandular, pancreas, pod of Fabricia, bile, jejunum, ileum, cecum is full, cecum is empty. Thus, we conclude that the intensity of lighting has a positive effect in raising the percentage of netting in the carcass of birds exposed to the gradual intensity of lighting.
A Spline Theory of Deep Networks (Extended Version)

We build a rigorous bridge between deep networks (DNs) and approximation theory via spline functions and operators. Our key result is that a large class of DNs can be written as a composition of max-affine spline operators (MASOs), which provide a powerful portal through which to view and analyze their inner workings. For instance, conditioned on the input signal, the output of a MASO DN can be written as a simple affine transformation of the input. This implies that a DN constructs a set of signal-dependent, class-specific templates against which the signal is compared via a simple inner product; we explore the links to the classical theory of optimal classification via matched filters and the effects of data memorization. Going further, we propose a simple penalty term that can be added to the cost function of any DN learning algorithm to force the templates to be orthogonal with each other; this leads to significantly improved classifi- cation performance and reduced overfitting with no change to the DN architecture. The spline partition of the input signal space that is implicitly induced by a MASO directly links DNs to the theory of vector quantization (VQ) and K-means clustering, which opens up new geometric avenue to study how DNs organize signals in a hierarchical fashion. To validate the utility of the VQ interpretation, we develop and validate a new distance metric for signals and images that quantifies the difference between their VQ encodings. (This paper is a significantly expanded version of a paper with the same title that will appear at ICML 2018.)
Non-recombining trinomial tree pricing model and calibration for the volatility smile

Abstract In this paper, we consider the non-recombining trinomial tree pricing model under the volatility, which is a function of time, establish the option pricing model and give the convergence rates of the non-recombining trinomial tree method. In addition, we research the calibration problem of volatility and adopt an exterior penalty method to transform this problem into a nonlinear unconstrained optimization problem. For the optimization problem, we use the quasi-Newton algorithm. Finally, we test our model by numerical examples and options data on the S&P 500 index. The results show the effectiveness of the non-recombining trinomial tree pricing model.
Active adults have thicker peripheral muscles and diaphragm: A cross-sectional study

Background: The association between exercise and muscle build-up is a long-run connection. Whereas limited physical activity doesn't do well with muscle build-up. But how much is that difference in muscle thickness between different levels of physical activity? Aim: To understand this we conducted a cross-sectional study to associate physical activity and sitting time with the muscle thickness of the lower limb and diaphragm. Methods: The research's participants ranged in age from 18 to 35. Out of 91 patients, 30 were found to smoke routinely and 6 had a drinking history after questions concerning lifestyle factors including smoking and drinking were questioned. In terms of occupation, 74.7% of people had an occupation and 25% were unemployed. We acquired ultrasonography for the quadriceps (rectus femoris and vastus intermedialis), soleus muscle, and diaphragm. Participants were divided into groups according to their self-reported levels of physical activity and sitting time based on IPAQ scores. Results: We found that the lower limb muscles have shown statistically significant differences between vigorous physical activity (VPA) and lower physical activity (LPA). We found that the quadriceps muscle(rectus femoris and vastus intermedialis) thickness was 1.3 cm in LPA whereas 2.8 cm in VPA with (p=<0.001) soleus muscle thickness being 1 cm in LPA and 2.2 cm. Conclusions: Physical activity levels are found to be positively related to the peripheral muscle thickness VPA (p=<0.001). Physical activity levels are found to be positively related to peripheral muscle thickness.
ALTERNATIVE METHODS FOR IMPROVING BALANCE IN STROKE RECOVERY: VISUAL AND VERBAL CUES AS COST-EFFECTIVE MODALITIES

Objective: Stroke is a significant global health concern, leading to high mortality and disability rates. Prompt assessment, management, and rehabilitation are crucial for improving clinical outcomes. Balance impairment is a common consequence of stroke, affecting the majority of survivors and increasing the risk of falls and disability. Methods: The study included 30 post-stroke hemiplegic individuals, randomly assigned to a control group and an experimental group. The control group received conventional physical therapy, while the experimental group received additional visual and verbal feedback. The Berg Balance Scale was used to assess balance on days 1, 15, and 30. Results: Both groups showed improvements in balance scores over time. However, the experimental group consistently had higher scores, indicating a greater effect of incorporating visual and verbal cues with conventional therapy. Conclusion: Our study demonstrates that the combination of visual and verbal cues with conventional therapy leads to improved balance outcomes in stroke recovery. These findings support the benefits of visual feedback, verbal feedback, and mirror therapy in stroke rehabilitation. Incorporating these alternative methods can be cost-effective and accessible interventions to enhance balance, reduce fall risk, and improve the overall recovery and quality of life in stroke survivors.
Thermoelectric performance of monolayer InSe improved by convergence of multivalley bands

We theoretically investigate a possibility of improving the thermoelectric performance of monolayer InSe through convergence of multivalley energy bands, in which some distinct valleys become almost degenerate. The convergence of energy bands is achieved by applying mechanical strain. We find that the thermoelectric power factor of monolayer InSe can be significantly enhanced by nearly a factor of 3 through the band convergence in both valence (p-type) and conduction (n-type) bands under a biaxial compressive stress of about 1.16 GPa. However, the maximum enhancement of the figure of merit Z T in the p-type and n-type InSe differs each other depending on how the valleys converge in each case. The optimal scenario is that the heavy valleys approach the light valleys in the band convergence, which leads to an increase in the power factor and, at the same time, a decrease in the thermal conductivity of an electron. This optimal condition can be obtained in the strained n-type InSe that gives the largest enhancement of Z T as high as 230% Z T of unstrained InSe. In contrast, the enhancement of Z T in the strained p-type InSe, which exhibits opposite valley convergence (light valleys joining heavy ones), gives only 26% Z T of unstrained InSe.We theoretically investigate a possibility of improving the thermoelectric performance of monolayer InSe through convergence of multivalley energy bands, in which some distinct valleys become almost degenerate. The convergence of energy bands is achieved by applying mechanical strain. We find that the thermoelectric power factor of monolayer InSe can be significantly enhanced by nearly a factor of 3 through the band convergence in both valence (p-type) and conduction (n-type) bands under a biaxial compressive stress of about 1.16 GPa. However, the maximum enhancement of the figure of merit Z T in the p-type and n-type InSe differs each other depending on how the valleys converge in each case. The optimal scenario is that the heavy valleys approach the light valleys in the band convergence, which leads to an increase in the power factor and, at the same time, a decrease in the thermal conductivity of an electron. This optimal condition can be obtained in the strained n-type InSe that gives the largest en.
The global infrared energy budget of the thermosphere from 1947 to 2016 and implications for solar variability

We present an empirical model of the global infrared energy budget of the thermosphere over the past 70 years. The F10.7, Ap, and Dst indices are used in linear regression fits to the 14.5 year time series of radiative cooling by carbon dioxide and nitric oxide measured by the Sounding of the Atmosphere using Broadband Emission Radiometry (SABER) instrument on the TIMED satellite. Databases of these indices are used to develop the radiative cooling time series from 1947. No consistent relation between the occurrence of peak sunspot number and peak infrared cooling is found over the past six solar cycles. The total infrared energy radiated by the thermosphere, integrated over a solar cycle, is nearly constant over five complete solar cycles studied. This is a direct consequence of the geoeffective solar energy also being nearly constant over the same intervals. These results provide a new metric for assessing the terrestrial context of the long‐term record of solar‐related indices.