Miracle-dz/UniFAR-FTUs huggingface dataset:
Novel Foaming-Agent Free Insulating Geopolymer Based on Industrial Fly Ash and Rice Husk

This study highlights the synthesis of a new thermal insulating geopolymer based on the alkaline activation of fly ashes. A porous geopolymer material can be prepared without the addition of a foaming agent, using high ratio solution/ashes (activating solutions used are water, sodium or potassium hydroxide). In order to increase the porosity of the material and to make it more ecological, rice husks are incorporated into the formulation. The geopolymer materials were prepared at room temperature and dried at moderate temperature (105 °C) by a simple procedure. The microstructural characteristics of these new porous geopolymers were assessed by optical microscopy, X-ray diffraction (XRD), thermogravimetric analysis (TGA) and X-ray fluorescence (XRF). Infrared spectroscopy (FTIR) was used to confirm the geopolymerisation. The effect of the ratio solution/ashes and the percentage of the rice husk addition on thermal and mechanical analysis was evaluated. An insulating material for a solution/ashes ratio of 0.9 and a rice husk content of 15% having a λ value of 0.087 W/(m·K), a porosity of 61.4% and an Rc value of 0.1 MPa was successfully prepared.
Determinants of Late Initiation for Antenatal Care Follow up Among Pregnant Mothers Attending Public Health Centers at Jimma Town, South West Ethiopia, 2021: Unmatched Case Control Study.

Abstract Background World Health Organization recommends that, a woman without complications should have at least four antenatal care visits, the first of which should take place during the first trimester (not > 16 weeks). But, high proportion of pregnant women in most of developing countries attended first ANC at a late time. In Ethiopia, magnitude of late ANC follow up initiation is 64% and in study area 68.7% however determinants of the problem were not identified particularly in the study area. Objective To identify determinants of late antenatal care initiation among pregnant women attending antenatal care in public health centers of Jimma town, south west Ethiopia 2021. Methods Facility based unmatched case control study was conducted from May 1-30. The cases (n=177) were mothers who attended their first antenatal care visit at >= 16 weeks of gestational age and the controls (n=177) were mothers who attended their first antenatal care visit at <16 weeks of gestational age during the data collection period. Data was collected through face to face interview using pre tested structured questionnaire. Descriptive and summary statistics was done. Bivariable and multivariable logistic regression analysis was done using SPSS version 21.0 to identify candidate variables at P-value <0.25 and determinants of late ANC initiation at P-value <0.05 respectively. Results The current study identified that, being age > 25 years (AOR= 2.623, 95% CI 1.059 - 6.496), poor knowledge about ANC (AOR= 3.856, 95% CI 1.266-11.750), lack of history of abortion (AOR= 3.326, 95% CI 1.082-10.224), place of previous delivery (AOR=3.926, 95%CI 1.023-15.063), recognizing pregnancy through missed period(AOR= 3.631, 95% CI 1.520-8.674), pregnancy related pain(AOR= 3.499, 95% CI 1.423 - 8.603) and lack of family support (AOR=2.647, 95% CI 1.092-6.415) were determinants of late ANC initiation. Conclusion Being age > 25 years, poor knowledge about ANC, lack of history of abortion, place of previous delivery, recognizing pregnancy through missed period, pregnancy related pain, and lack of family support during ANC booking were determinants of late ANC initiation.Health facilities and respective health care providers should provide health education at facility level regularly with due attention to late ANC initiation.
Specific Interleukin-1 Inhibitors, Specific Interleukin-6 Inhibitors, and GM-CSF Blockades for COVID-19 (at the Edge of Sepsis): A Systematic Review

Sepsis is a syndrome with high mortality, which seriously threatens human health. During the pandemic of coronavirus disease 2019 (COVID-19), some severe and critically ill COVID-19 patients with multiple organ dysfunction developed characteristics typical of sepsis and met the diagnostic criteria for sepsis. Timely detection of cytokine storm and appropriate regulation of inflammatory response may be significant in the prevention and treatment of sepsis. This study evaluated the efficacy and safety of specific interleukin (IL)-1 inhibitors, specific IL-6 inhibitors, and GM-CSF blockades in the treatment of COVID-19 (at the edge of sepsis) patients through systematic review and meta-analysis. Methodology: A literature search was conducted on PubMed, EMBASE, Clinical Key, Cochrane Library, CNKI, and Wanfang Database using proper keywords such as “SARS-CoV-2,” “Corona Virus Disease 2019,” “COVID-19,” “anakinra,” “tocilizumab,” “siltuximab,” “sarilumab,” “mavrilimumab,” “lenzilumab,” and related words for publications released until August 22, 2021. Other available resources were also used to identify relevant articles. The present systematic review was performed based on PRISMA protocol. Results: Based on the inclusion and exclusion criteria, 43 articles were included in the final review. The meta-analysis results showed that tocilizumab could reduce the mortality of patients with COVID-19 (at the edge of sepsis) [randomized controlled trials, RCTs: odds ratio (OR) 0.71, 95%CI: 0.52–0.97, low-certainty evidence; non-RCTs: risk ratio (RR) 0.68, 95%CI: 0.55–0.84, very low-certainty evidence) as was anakinra (non-RCTs: RR 0.47, 95%CI: 0.34–0.66, very low-certainty evidence). Sarilumab might reduce the mortality of patients with COVID-19 (at the edge of sepsis), but there was no statistical significance (OR 0.65, 95%CI: 0.36–1.2, low-certainty evidence). For safety outcomes, whether tocilizumab had an impact on serious adverse events (SAEs) was very uncertain (RCTs: OR 0.87, 95%CI: 0.38–2.0, low-certainty evidence; non-RCTs 1.18, 95%CI: 0.83–1.68, very low-certainty evidence) as was on secondary infections (RCTs: OR 0.71, 95%CI: 0.06–8.75, low-certainty evidence; non-RCTs: RR 1.15, 95%CI: 0.89–1.49, very low-certainty evidence). Conclusions: This systematic review showed that tocilizumab, sarilumab, and anakinra could reduce the mortality of people with COVID-19 (at the edge of sepsis), and tocilizumab did not significantly affect SAEs and secondary infections. The current evidence of the studies on patients treated with siltuximab, mavrilimumab, and lenzilumab is insufficient. In order to establish evidence with stronger quality, high-quality studies are needed. Systematic Review Registration: PROSPERO (https://www.crd.york.ac.uk/prospero/), identifier CRD42020226545
The Fiscal Burden of Wildfires: State Expenditures and Funding Mechanisms for Wildfire Suppression in the Western U.S. and Implications for Federal Policy

The number and size of wildfires in the western United States have increased dramatically in the last 30 years. The rising cost of wildfire suppression has become a significant concern for all levels of government, although most attention has been focused on the federal level. Much less is known about the financial impact of expenditures on states, which retain responsibility for suppression on over 480 million acres of state and private forests. This study collected data on state expenditures for wildfire suppression in the western United States from 2005 to 2015 to examine fiscal burdens and compare funding mechanisms used to cover those costs. Our analysis finds that western states expended $11.9 billion on wildfire suppression over the 11-year period and used own-source funds to cover 88 percent of these costs. States displayed a variety of mechanisms for covering their cost obligations with tradeoffs that may affect non-wildfire policy priorities.
Transcode: Detecting Status Code Mapping Errors in Large-Scale Systems

Status code mappings reveal state shifts of a program, mapping one status code to another. Due to careless programming or the lack of the system-wide knowledge of a whole program, developers can make incorrect mappings. Such errors are widely spread across modern software, some of which have even become critical vulnerabilities. Unfortunately, existing solutions merely focus on single status code values, while never considering the relationships, that is, mappings, among them. Therefore, it is imperative to propose an effective method to detect status code mapping errors.In this paper, we propose Transcode to detect potential status code mapping errors. It firstly conducts value flow analysis to efficiently and precisely collect candidate status code values, that is, the integer values, which are checked by following conditional comparisons. Then, it aggregates the correlated status codes according to whether they are propagated with the same variable. Finally, Transcode extracts mappings based on control dependencies and reports the mapping error if one status code is mapped to two others of the same kind. We have implemented Transcode as a prototype system, and evaluated it with 5 real-world software projects, each of which possesses in the order of a million lines of code. The experimental results show that Transcode is capable of handling large-scale systems in both a precise and efficient manner. Furthermore, it has discovered 59 new errors in the tested projects, among which 13 have been fixed by the community. We also deploy Transcode in WeChat, a widely-used instant messaging service, and have succeeded in finding real mapping errors in the industrial settings.
Automating User Notice Generation for Smart Contract Functions

Smart contracts have obtained much attention and are crucial for automatic financial and business transactions. For end-users who have never seen the source code, they can read the user notice shown in end-user client to understand what a transaction does of a smart contract function. However, due to time constraints or lack of motivation, user notice is often missing during the development of smart contracts. For end-users who lack the information of the user notices, there is no easy way for them to check the code semantics of the smart contracts. Thus, in this paper, we propose a new approach SMARTDOC to generate user notice for smart contract functions automatically. Our tool can help end-users better understand the smart contract and aware of the financial risks, improving the users’ confidence on the reliability of the smart contracts. SMARTDOC exploits the Transformer to learn the representation of source code and generates natural language descriptions from the learned representation. We also integrate the Pointer mechanism to copy words from the input source code instead of generating words during the prediction process. We extract 7,878 〈function, notice〉 pairs from 54,739 smart contracts written in Solidity. Due to the limited amount of collected smart contract functions (i.e., 7,878 functions), we exploit a transfer learning technique to utilize the learned knowledge to improve the performance of SMARTDOC. The learned knowledge obtained by the pre-training on a corpus of Java code, that has similar characteristics as Solidity code. The experimental results show that our approach can effectively generate user notice given the source code and significantly outperform the state-of-the-art approaches. To investigate human perspectives on our generated user notice, we also conduct a human evaluation and ask participants to score user notice generated by different approaches. Results show that SMARTDOC outperforms baselines from three aspects, naturalness, informativeness, and similarity.
Structural and functional insight into the regulation of kinesin-1 by microtubule associated protein MAP7

Microtubule (MT)–associated protein 7 (MAP7) is a required cofactor for kinesin-1–driven transport of intracellular cargoes. Using cryo–electron microscopy and single–molecule imaging, we investigated how MAP7 binds MTs and facilitates kinesin-1 motility. The MT-binding domain (MTBD) of MAP7 bound MTs as an extended α helix between the protofilament ridge and the site of lateral contact. Unexpectedly, the MTBD partially overlapped with the binding site of kinesin-1 and inhibited its motility. However, by tethering kinesin-1 to the MT, the projection domain of MAP7 prevented dissociation of the motor and facilitated its binding to available neighboring sites. The inhibitory effect of the MTBD dominated as MTs became saturated with MAP7. Our results reveal biphasic regulation of kinesin-1 by MAP7 in the context of their competitive binding to MTs. Description Biphasic regulation of kinesin-1 by MAP7 Motor proteins are differentially regulated by microtubule-associated proteins (MAPs) to deliver intracellular cargos to their destinations. MAP7 recruits the molecular motor kinesin-1 to microtubules and activates subsequent motility, but the underlying mechanism is unclear. Using cryo–electron microscopy, Ferro et al. determined a near-atomic structure of MAP7 on the microtubule. They found that the microtubule-binding site of MAP7 overlaps with kinesin. However, by tethering kinesin-1 to the microtubule, the MAP7 projection domain enabled the motor to diffuse to available sites on microtubules partially decorated by MAP7. These results reveal the mechanistic basis of MAP7-mediated activation of kinesin motility despite their competing binding to tubulin. —SMH MAP7 binds to microtubules and activates kinesin-1 motility despite the overlap between their tubulin-binding sites.
Comparing Performance and Efficiency of Designers and Design Intelligence

Intelligent design has been an emerging important area in the design. Existing works related to intelligent design use objective indicators to measure the quality of AI design by comparing the differences between AI-generated data and real data. However, the level of quality and efficiency of intelligent design compared to human designers remains unclear. We conducted user experiments to compare the design quality and efficiency of advanced design methods with that of junior designers. The conclusion is advanced intelligent design methods are comparable with junior designers on painting. Besides, intelligent design uses only 10% of the time spent by the junior designer in the tasks of layout design, color matching, and video editing.
Finding the Missing Piece: Permission Specification Analysis for Android NDK

The Android research community has long focused on building the permission specification for Android framework APIs, which can be referenced by app developers to request the necessary permissions for their apps. However, existing studies just analyze the permission specification for Java framework APIs in Android SDK, whereas the permission specification for native framework APIs in Android NDK remains intact. Since more and more apps implement their functionalities using native framework APIs, and the permission specification for these APIs is poorly documented, the permission specification analysis for Android NDK is in urgent need. To fill in the gap, in this paper, we conduct the first permission specification analysis for Android NDK. In particular, to automatically generate the permission specification for Android NDK, we design and develop PSGen, a new tool that statically analyzes the implementation of Android framework and Android kernel to correlate native framework APIs with their required permissions. Applying PSGen to 3 Android systems, including Android 9.0, 10.0, and 11.0, we find that PSGen can precisely build the permission specification. With the help of PSGen, we discover more than 200 native framework APIs that are correlated with at least one permission.
ELSV: An Effective Anomaly Detection System from Web Access Logs

As network technologies have been developing rapidly, web applications have been widely used. Meanwhile, more and more web attacks have appeared. These attacks have caused a lot of losses to individuals or organizations. A large number of web access logs also bring huge challenges to the attack detection. In this paper, we propose a system ELSV(Ensemble Learning classification with Semantic Vectorization) to detect anomaly web access logs. We use the difference of word occurrence between different types of web access logs to refine the formatted log data, strengthening the distinction between them. The Word2Vec method and TextCNN model are applied to extract vectorized features, making feature extraction more automated and intelligent. We use the ensemble learning classification algorithm as the final classifier, improving the overall classification performance. ELSV is tested on the HTTP DATASET CSIC 2010. The final experimental results show that ELSV achieved 98.48% F1-score and 99.74% AUC, proving the efficacy of it.
Transmission Performance Analysis of Cognitive Radio Based Backscatter Communication Systems

In this paper, optimal time allocation for the users in the cognitive radio based backscatter communication networks and the maximization problem of the total received data from these users is analyzed. In the case under consideration, it was assumed that the primary transmitter never interrupts its broadcast and transmits signals to users continuously, users can both modulate and backscatter the signal received from the primary transmitter and harvest energy from the primary signals. In this model, it is desired to transmit the data of multiple users to the receiver by using time division multiple access technique. First, the system was modeled mathematically and turned into an optimization problem. Then, backscattering, transmission and energy harvesting times that will maximize the total amount of data sent from the users to the receiver are determined and performance results are given in terms of the total data received by the receiver. Finally, the model was compared with the situation where users only backscatter and only energy harvest-then-transmit mode, and the model analyzed showed better performance.
Automatic watering of plants in a pot using plant recognition with CNN

The plant's growth is dependent on the soil moisture, but the required soil moisture threshold level is not the same for all the plant species. In the modern era with the combination of advanced Embedded and Computer Vision technologies, it is possible to detect the plant species on an Embedded platform and watering automatically. This paper discusses the implementation and working model which can detect the plant in a pot and water the plant automatically based on the threshold of the soil moisture and plant species. The complete model is built on Jetson Nano Nvidia GPU and a PCB which is de-signed according to the requirements, to control the motor pump and read sensor values. Python is used for the implementation of API in more customized way. The plant species and threshold values can be customized based on the climatic condition of any country. As the model is implemented for automatic watering of the plant in a pot, the calibration of the pot width is also done using the MobileNet SSD V2 algorithm to suggest the required soil moisture content and the water level in the tank.
A Review paper on Optimized Reconfigurable Cell Array

Optimized reconfigurable cell array (ORCA), which can support larger data path designs on a nibble basis, without decreasing its support for random logic control applications. This can be implemented by providing a programmable function unit that is equally adapted to environmental conditions. The routing structure is designed so that it can allow nibble of data to be moved around the chip efficiently, without interrupting other individual connections that characterize based on high logic data paths. ORCA-Top chip is implemented using various CMOS technologies. Based on the specifications of ORCA-Top, RTL logic is written and converted to gate-level netlist by synthesizing the RTL logic. In this review paper, the various ORCA architecture implementations has been discussed.
Dynamic Economic Dispatch Considering Wind Power Penetration Based on Wind Speed Forecasting and Stochastic Programming

With the increase of wind power in power systems, the influence of wind farms penetration should be considered in economic dispatch. Due to dynamic and stochastic of wind farms the traditional economic dispatch is not suitable for the power system with integration of wind farms with high capacities. A new dynamic economic dispatch method is proposed based on the wind speed forecasting and stochastic programming theory. A stochastic modeling for dynamic economic dispatch considering unit commitment is presented. The model is solved by the hybrid intelligent algorithm, which is combined with stochastic simulation, artificial neural network and genetic algorithm. The hybrid intelligent algorithm improves the convergence speed and search ability. The results on IEEE30 system with wind farms demonstrate the feasibility and effectiveness of the proposed method.
Diabetes Disease Prediction Using Artificial Neural Network with Machine Learning Approaches

Diabetes disease is caused because of an increase in the blood sugar level. The aim is to bring out a system that predicts patient's diabetes with good accuracy with the results of various techniques of machine learning. Further, the expansion of this research work gives the most effective algorithm for classification for identifying the chances of diabetes in an exceedingly person. The main idea of doing the project is to give a diabetes prediction model for the purpose of predicting the occurrence of diabetes. Further, the most effective algorithm is identified for classification of the chances of diabetes in an exceedingly men or women. Here an algorithm that can predict the diabetes with good accuracy is obatined. The developing model relies on categorization and classification methods like Naive Bayes, Support Vector Machine algorithm, and ANN. The main result of this work will be spotting most effective algorithm which is good at providing good and more accuracy when the classification of person is allotted. It's found that the neural network algorithm performs better in comparison to other algorithms for disease prediction. The project is meant using R Language.
Subtle Bugs Everywhere: Generating Documentation for Data Wrangling Code

Data scientists reportedly spend a significant amount of their time in their daily routines on data wrangling, i.e. cleaning data and extracting features. However, data wrangling code is often repetitive and error-prone to write. Moreover, it is easy to introduce subtle bugs when reusing and adopting existing code, which results in reduced model quality. To support data scientists with data wrangling, we present a technique to generate documentation for data wrangling code. We use (1) program synthesis techniques to automatically summarize data transformations and (2) test case selection techniques to purposefully select representative examples from the data based on execution information collected with tailored dynamic program analysis. We demonstrate that a JupyterLab extension with our technique can provide on-demand documentation for many cells in popular notebooks and find in a user study that users with our plugin are faster and more effective at finding realistic bugs in data wrangling code.