Miracle-dz/UniFAR-FTUs huggingface dataset:
Recycled Glass Cullet as Fine Aggregate and Partial Cement Replacement in Concrete

Utilisation of recycled glass cullet in concrete has obvious benefits with respect to environmental conservation and sustainable development. The problems of rising landfills can be reduced. Reduction of primary aggregates for concrete where alternative materials would suffice can also be fulfilled. Glass also contains large amounts of silicon and calcium which means that in theory, is pozzolanic. However, the possibility of alkali-silica reaction (ASR) due to the presence of silica must also be addressed. Tests were performed to investigate the suitability of using cullet in concrete. A sieve analysis was performed to determine the size distribution of the cullet and provided basis to the range of samples that could be cast. Using cullet as partial replacement aggregate in concrete has resulted in minor increases in concrete consistence (workability) and minor reductions in compressive strength. When ground glass cullet (GGC) was used as 30% partial cement replacement, the consistence increased considerably, whilst the later age compressive strength was close or higher (depending on the mix proportions used) compared to the corresponding control mixes, which did not have GGC incorporation. The early age strength was lower and strength development was slower at early ages further suggesting likelihood of pozzolanic activity. Accelerated ASR tests have shown the occurrence of ASR in concrete mixes using cullet as replacement aggregate to be more than control mixes which used conventional aggregate. The magnitude of ASR was also found to be colour dependant-green coloured cullet resulted in less expansion than when using amber cullet. Qualitative microstructural analysis of specimen micrographs recorded in Secondary Electron Imaging (SEI) mode using a Scanning Electron Microscope (SEM) have shown that mortar specimens using cullet displayed more cracking at longer curing periods. Cracks propagated from, and surrounded the glass particles, further suggesting the occurrence of ASR activity. Depending on size classification used, glass cullet can therefore be recommended to be used as partial aggregate replacement and partial cement replacement in concrete. The possibility of durability attack due to ASR reaction must however be taken into consideration.
An Occlusion Handling Evaluation Criterion for Deep Learning Object Segmentation

This paper introduces a novel evaluation criterion to occlusion handling for deep learning object segmentation. The occlusion is defined as objects blocking each other on an image. It affects deep learning object segmentation. More and more researches focus on occlusion handling for object segmentation. However, these researches do not clearly show the evaluation of their occlusion handling method, because there is no suitable evaluation criterion. Traditionally, people just use images results or use the entire object boundary accuracy to show the occlusion handling of their methods. Conversely, these ideas cannot give a numerical evaluation focusing on occlusion handling. This research reports an evaluation criterion to measure occlusion handling performance. This evaluation criterion uses the shortest distances between the pixels from the ground truth occlusion edges and the segmentation (result) shape. The shortest distances are segmentation errors. Then, the average value of these errors is the final parameter for occlusion handling evaluation criterion. The experiment uses a deep learning based segmentation model as an example. It shows that this criterion (or method) successfully measures the occlusion handling for deep learning based object segmentation.
The Fermat-Steiner problem in the space of compact subsets of endowed with the Hausdorff metric

The Fermat-Steiner problem consists in finding all points in a metric space at which the sum of the distances to fixed points of attains its minimum value. This problem is studied in the metric space of all nonempty compact subsets of the Euclidean space , and the are pairwise disjoint finite sets in . The set of solutions of this problem (which are called Steiner compact sets) falls into different classes in accordance with the distances to the . Each class contains an inclusion-greatest element and inclusion-minimal elements (a maximal Steiner compact set and minimal Steiner compact sets, respectively). We find a necessary and sufficient condition for a compact set to be a minimal Steiner compact set in a given class, provide an algorithm for constructing such compact sets and find a sharp estimate for their cardinalities. We also put forward a number of geometric properties of minimal and maximal compact sets. The results obtained can significantly facilitate the solution of specific problems, which is demonstrated by the well-known example of a symmetric set , for which all Steiner compact sets are asymmetric. The analysis of this case is significantly simplified due to the technique developed. Bibliography 16 titles.
A Method of Identifying the Critical Source Areas for Phosphorus Loss in Geological Phosphorus-rich Watersheds

The principal cause of eutrophication in some freshwater systems is diffuse phosphorus (P) loss. The identification of critical source areas (CSAs) is identified to be the most effective way to minimize the difficulty of nonpoint source (NPS) P pollution management in watersheds. How to efficaciously identify CSAs for phosphorus loss in geological phosphorus-rich (GPR) watersheds is still a challenging hassle. An assessment approach primarily based on QGIS was applied to assess the risk of P loss and identified CSAs for a case study in the Huangbai River watershed of south China, which is a typical geological P-rich (GPR) watershed. In the modified P ranking scheme, total soil P (TP) content in various land use types was used as a source factor; the topographic index (TI) and distance-to-streams were considered as transport factors. P index calculation results showed that 92.2% of the watershed was low risk area, while 7.8% for medium and high risk of P loss. Meanwhile, higher risk areas were associated with the shorter distances from the stream, higher TI, and higher soil P content. Also, CSAs are normally located in phosphate mining sites and concentrated arable land in the karst mountainous areas. The introduction of a simplified P index scheme provides a convenient and practical tool to target CSAs for P loss from GPR areas in data-poor watersheds, thus assisting decision makers to implement remediation actions as to reduce P loss to sensitive waterbodies.
The Possibility of Biogas production in Nigeria from Organic Waste Material: A Review

Biogas waste initiative is one big solution to Nigeria’s growing solid waste problem. This study explores the potential biogas processing of organic waste materials and their benefits to the areas where the excess of such organic waste is located. Based on this review, researchers were noted to be proposing lots of distinctive ways to contain generating solid waste and discoveries have been highlighted to show the importance of converting these organic wastes to biogas. Processing is a well-established technology mainly for sustainable energy use generation in addition to the valorization of organic residues. Through this analysis, these wastes were described as enormous sources of biogas that can assist to solve some of the electricity problems in the country.
Effect of plant growth regulators on the induction of peach resistance to leaf curl

The article presents data from studies of the effect of growth regulators when applied for three years in peach plantations in the humid subtropical zone of the Black Sea coast of Russia. The influence of drugs (Zerebra® Agro (colloidal silver + polyhexamethylenebiguanide hydrochloride), Epin-Extra® (24-epibrassinolide) and Biosil® (triterpene acids)) on the degree of development of peach leaf curl (pathogen – Taphrina deformans (Berk) Tul. ) was analyzed. The preparations were used in pure form and in combination with fungicides, the dosage of which was two times lower than in the standard. Already in the first year of the experiment, an increase in the resistance of the peach to leaf curl was noted. The maximum biological efficiency (up to 80.3%) in relation to leaf curl was observed when using Zerebra Agro in a tank mixture with fungicides. An inverse correlation was established between the degree of development of leaf curl and the level of antioxidant system enzymes activity (catalase and guaiacol peroxidase), which confirms the immunity-inducing effect of the growth regulators use.
Hierarchical Residual Embedding Network for Real-Time De-focus Blur Detection

Defocus blur detection, as an important pre-processing step of image processing, has attracted more and more attention. Albeit great success has been made, there are still several challenges for accurate defocus blur detection, such as the interference of background clutter, sensitivity to scales, missing boundary details and large computational burden. For handling these issues, we present a deep neural network which hierarchically embeds residual learning blocks for defocus blur detection. Based on the feature pyramid structure, we extract deep features with varying scales via utilizing a backbone fully convolutional network and generate a coarse score map by using the last layer of feature maps. Then we design a hierarchical residual embedding module to fuse different levels of features in a layer-wise manner. By embedding different layer-wise features in the top-down pathway, coarse-level semantic information from the deep layers can be seamlessly propagated to shallow layers, while fine details in the shallow layers can be used to refine the boundary between out-of-focus and in-focus regions. For each layer, a side output is generated by using a residual learning block. For capturing multi-scale information, the multiple side outputs of different layers are fed into a designed fusion block for yielding the final blur map result. Experimental results on two commonly used datasets show that our proposed network can more accurately locate the defocus blur regions with sharpened details being well preserved when compared to other previous state-of-the-arts. In addition, our approach is fast as well and can run at a speed of more than 25 FPS when processing an image with size 427 x 640.
Fats and oils authentication by FT-Raman : L'outil analytique : situation, problèmes et perspectives

The discrimination of virgin olive oils from other edible oils together with the detection and quantification of their adulteration is presented. FT-Raman spectra of 120 samples of fats and oils from 21 sources were analysed for these objectives. The usefulness of this technique is clearly demonstrated. An arborescent structure including 7 mathematical equations were designed to discriminate simultaneously saturated fats, corn, sunflower, soybean, canola, peanut, high oleic sunflower and virgin olive oils. The possibilities offered by spectral data in the detection of virgin olive oil was also investigated. Results show that genuine samples can be easily discriminated from adulterated samples including those spiked at 1%.
A new organic-inorganic MgZnO/Au/PEDOT:PSS hybrid heterojunction photodetector fabrication

This research reports oxygen zinc magnesium (MgZnO)/gold (Au) photodetectors (PD) and organic-inorganic semiconductors MgZnO/Au/poly(3,4-ethylenedioxythio-phene): poly(4-styrenesulfonate) (PEDOT:PSS) PD research. At room temperature, spin-coating and radio frequency (RF) magnetron sputtering were used to deposit PEDOT:PSS layer and MgZnO thin film on the substrate, respectively. The properties of the heterojunction formed on the MgZnO/PEDOT:PSS inorganic/organic contact interface were studied. In order to manufacture ultraviolet (UV) heterojunction PDs, PEDOT:PSS is used as the hole transport layer (HTL), and MgZnO is used as the electron transport layer (ETL). This heterojunction shows excellent ultraviolet light detection capability. Under 40 V bias, the responsivity of MgZnO/Au PD and MgZnO/Au/PEDOT:PSS heterojunction PD are 0.0473 A W−1 and 0.134 A W−1 under ultraviolet irradiation, respectively. The device shows good Schottky contact between metal semiconductor junctions. The excellent UV detector performance is due to the strong UV absorption of PEDOT: PSS.
Can reaction with amino acid turn Dimefox or Fluoroacetamide to nontoxic derivative: in Silico Study

To answer the title question, two ways can be applied. The first way is the experimental methods through using multistep, various techniques, different chemicals, characterization instruments, time, cost, and environmental considerations, and in vitro–in vivo testing. The second way is in Silico calculation. In this path of working, all steps, instruments, testing, chemicals …etc. will be canceled and only evaluate the numerical results to qualify any chemical to be a drug. The above points encourage me to take a hypothetical reaction between two insecticides (Dimefox (D) and Fluoroacetamide (F)) and several amino acids (aspartic acid, glutamic acid, glycine, alanine, phenylalanine, valine, isoleucine, proline, and methionine). The resulted P-N or C-N derivatives were subjected to ADMET and Druglikeness predications. They showed various important notes like increasing water solubility, mutagen character of Ames test to all 20 compounds, non–inhibition predication to P-glycoprotein, non–inhibition character of CYP-2C19 and CYP-2C9 except F. Many of 20 compounds showed negative response to Mouse or Rat Carcinogenic test, TA100-10RLI, TA100-NA, TA1535-10RLI, and TA1535-NA beside low risk to hREG inhibition. The other calculated characters were varied with influence of polarity, surface area, hydrogen bonding, and molecular structure. So, if these 18 compounds, if they formed in any biological system or in lab, have a toxic character.
Prediction of dissolved oxygen content in water based on EEMD-Pearson and LSTM hybrid models

Improving the accuracy of dissolved oxygen (DO) prediction and establishing a water body DO prediction model are of great importance in water environment pollution management and planning management. In this paper, we propose a hybrid model (EEMD-Pearson-LSTM) of ensemble empirical modal decomposition-Pearson analysis and long-short memory neural network (LSTM), which firstly uses EEMD to decompose the non-stationary dissolved oxygen data into several sub-series that are easy to analyze, and secondly uses Pearson correlation analysis method to The screened subsequences are input to the LSTM network for training and prediction. By establishing the conventional LSTM model, EEMD-LSTM model, EEMD-BP model, and EEMD-Pearson-BP model for comparison under different time periods, root mean square error (RMSE), mean absolute error (MAE), mean absolute percentage error (MAPE), and coefficient of determination (R2) were used as evaluation indicators. In predicting the first 90 days of data, the RMSE, MAE, MAPE, and R2 of the EEMD-Pearson-LSTM model were 0.2355, 0.1893, 2.4710, and 0.8787, respectively, which were optimized by 37.88%, 35.44%, 37.42%, and 28.15%, respectively, compared with the traditional LSTM model, and the EEMD- LSTM model by 13.74%, 16.46%, 16.82%, and 4.98%, respectively, and the error of EEMD-BP network by 23.93%, 22.70%, and 24.32%, respectively, and its R2 by 11.17%, and the error of EEMD-Pearson-BP network by 18.62%, 14.07%, and 14.44%, and its R2 improved by 7.58%. To further demonstrate the advantages of EEMD-Pearson-LSTM, the prediction models for 30-day and 60-day time periods were selected for comparison, and the results showed that EEMD-Pearson-LSTM outperformed other models for the prediction of dissolved oxygen content in different time periods.
Identification of spammer and fake accounts on social networks

The existence in the electronic world is dominated by rechargeable conversations. The emails from advertisers and online communication companies end up being junk most often because their receivers are bombarded with promotional messages which are meaningless to them. Unwanted emails are a form of email correspondence. Because of email spam, there is a requirement to block and separate undesign ned messages. Many email-snoise ltering algorithms and computing techniques have been developed, but spammers continuously adjust their spam methods to stay one step ahead of them. We present here a method which makes use of binary and continuous probability distributions for the creation of spam. Naive Bayes and Decision trees How much model error affects the decision tree’s efficiency and accuracy is measured. The classi er with the best accuracy to correctly distinguish non-spam and nonspam emails has been found. this error also results in increasing the roll-out of invalid information to unnecessary customers, as well as increasing the probability of including harmful elements. The problem of spammers and scammers in today’s online communities has been known in some circles for some time. At the moment, play out a search of ways to identify Twitter spam methods A conceptualisation of the Twitter recognitions shows that the techniques lean toward recognising fakes, (ii) in places, (iii) web clients, and (iv) spammers use URLs to assist in making the product. Furthermore, the procedures may be analysed, including, for example, customers, material, structure, and time. It’s reassuring to know that this exhibition would be a source of understanding for researchers on social media on their own.
The impact of STEM PjBL on students’ engineering practice, scientific practice, and scientific attitude

The study aimed to analyze the effect of Science Technology Engineering and Mathematics (STEM) Project-based Learning (PjBL) on students’ engineering practice, scientific practice, and scientific attitude. Scientific and engineering practice in this research focused on the planning and carrying out investigations (PCOI) aspect and obtaining, evaluating, and communicating information (OECI) aspect. This study employed the pretest-posttest control group design of experimental research. In this study, 70 of 8th-grade junior high school students in Yogyakarta were determined by a simple random sampling technique to become subjects. Data were obtained through (1) a multiple-choice test to measure the planning investigation and obtaining, evaluating, and communicating information as cognitive domains, (2) an observation sheet to observe the carrying out investigation as a psychomotor domain, (3) a questionnaire to obtain students’ scientific attitude of this study. The test instrument, the observation sheet, and the questionnaire had been developed and validated. Data from this study were analyzed by MANOVA in SPSS 16. The result of this study showed that STEM PjBL effected students’ scientific practice, engineering practice, and scientific attitude.
Investigation of Flow Growth in aDuct Flowsfor Higher Area Ratio

Base pressure is a crucial factor in evaluating flow parameters. The effect of flow regulation mechanism is through an experiment examined for the convergent-divergent (C-D) nozzle with abrupt enlargement in an axisymmetric duct. The microjet control is extended along the nozzle's exit diameter at a 90-degree interval having a circular cross-section in the shape of an orifice of 0.5 mm radius. This research's primary aim is to evaluate the flow quality near the wall in the absence and presence of control. The experimental investigation aims to record the data for area ratio 6.25 of the flow field at the separate flow zone's rear end. At ninety degrees apart, four tiny jets are situated at 6.5 mm from the axisof the primary shear layer emanating from the nozzle. The small control jets attain critical conditions. The real Mach values for the main jets were 1.1 and 1.5, respectively, in the present study. The experiments were done at different expansion in the range from 11 to 3. The duct length was in the range from 10 to 1. This research primarily aims to study the quality of the duct's flow and its influence on the pressure and the pressure level along the duct. It is realized that the duct pressure is not harshlyaffected by the control jets in the pipe founded on the current findings.
Evaluating the Performance of the state-of-the-art HybridSN Deep Learning Algorithm for Airborne Hyperspectral Image Classification

This study aims to evaluate the performance of state-of-the-art HybridSN deep learning algorithm versus standard machine learning (ML) and deep learning (DL) techniques using open-source Python libraries for producing hyperspectral land use and land cover (LULC) classification maps. Japanese Chikusei hyperspectral datasets captured by the airborne platform using Hyperspec-VNIR-C sensor were used in this study. Standard ML methods used in this study were support vector machine linear kernel (SVM-linear), support vector machine radial basis function kernel (SVM-RBF) and random forests (RFs) that were provided in Python’s Scikit-learn library. DL techniques used in this study were multilayer perceptron (MLP), two-dimensional convolutional neural network (2-D CNN) and hybrid spectral convolutional neural network (HybridSN), which integrates the 2-D and 3-D feature learning. These DL models were built based on the sequential model using Keras API. The results show that all the proposed methods obtained overall accuracies (OAs) above 95%. The HybridSN and 2-D CNN models gave the best score with 99.97% OAs for hyperspectral image classification using the Chikusei dataset.
Optical monitoring and IDV analysis of the blazars S5 0716+714 and 3C 273

S5 0716+714 and 3C 273 are frequently studied blazars, which show obvious optical variabilities with different timescales. Using the 1.0 m telescope at the Yunnan Observatory, we monitored the two sources. For S5 0716+714, we report 990 observations during the monitoring duration from JD 2458536 to JD 2458540. For 3C 273, there are 884 observations during the monitoring duration from JD 2458539 to JD 2458542. Based on those observations, we obtain the following results. (1) For S5 0716+714, there lie intra-day optical variabilities (IDVs), with timescales from 0.31 hours to 2.64 hours. For 3C 273, it is possible that there lie IDVs, with timescales from 0.36 hours to 0.49 hours. (2) The time delay of S5 0716+714 is τRI = 3.46 min between R and I bands, and the time delay of 3C273 is τIV = 6.42 min between I and V bands. (3) We find that, for S5 0716+714, there lies a suspected intra-day period, P ≈ 185.78 min; for 3C 273, there lie intra-day periods, which are about ∼60 min and ∼80 min.
Multi-dimensional object estimation by its deviation from goal

In the paper, an approach for estimation of objects relative to specified target that was proposed by Wierzbicki A. P. is analyzed. It is asserted that this approach does not contradict with using utility (value) functions and weight coefficients at generalizing function for solving the same task. This assertion is justified by a mutual inverse relationship between functions estimating the goal achievement and deviation from a goal. It is shown that the deviation functions additionally allow solving such problems as ordering objects by penalties and bonuses, as well as by deviation from the norm. Using a simple medical example, the results of ranking patients by deviation from the norm, obtained by the criterion of minimax optimization and the additive generalizing function, are compared with equal and different values of the weighting coefficients of the indicators.