Search results for: fine coal

Authors: Shih Yu Pan, Pao Chen Hung, Chuan Yao Lin, Charles C.-K. Chou, Yu Chi Lin, Kai Hsien Chi

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This study analyzed 16 atmospheric PAHs species which were controlled by USEPA and IARC. To measure the concentration of PAHs, four rural sampling sites and two urban sampling sites were selected in Central Taiwan during spring and summer. In central Taiwan, the rural sampling stations were located in the downstream of Da-An River, Da-Jang River, Wu River and Chuo-shui River. On the other hand, the urban sampling sites were located in Taichung district and close to the roadside. Ambient air samples of both vapor phase and particle phase of PAHs compounds were collected using high volume sampling trains (Analitica). The sampling media were polyurethane foam (PUF) with XAD2 and quartz fiber filters. Diagnostic ratio, Principal component analysis (PCA), Positive Matrix Factorization (PMF) models were used to evaluate the apportionment of PAHs in the atmosphere and speculate the relative contribution of various emission sources. Because of the high temperature and low wind speed, high PAHs concentration in the atmosphere was observed. The total PAHs concentration, especially in vapor phase, had significant change during summer. During the sampling periods the total PAHs concentration of atmospheric at four rural and two urban sampling sites in spring and summer were 3.70±0.40 ng/m3,3.40±0.63 ng/m3,5.22±1.24 ng/m3,7.23±0.37 ng/m3,7.46±2.36 ng/m3,6.21±0.55 ng/m3　; 15.0± 0.14 ng/m3,18.8±8.05 ng/m3,20.2±8.58 ng/m3,16.1±3.75 ng/m3,29.8±10.4 ng/m3,35.3±11.8 ng/m3, respectively. In order to identify PAHs sources, we used diagnostic ratio to classify the emission sources. The potential sources were diesel combustion and gasoline combustion in spring and summer, respectively. According to the principal component analysis (PCA), the PC1 and PC2 had 23.8%, 20.4% variance and 21.3%, 17.1% variance in spring and summer, respectively. Especially high molecular weight PAHs (BaP, IND, BghiP, Flu, Phe, Flt, Pyr) were dominated in spring when low molecular weight PAHs (AcPy, Ant, Acp, Flu) because of the dominating high temperatures were dominated in the summer. Analysis by using PMF model found the sources of PAHs in spring were stationary sources (34%), vehicle emissions (24%), coal combustion (23%) and petrochemical fuel gas (19%), while in summer the emission sources were petrochemical fuel gas (34%), the natural environment of volatile organic compounds (29%), coal combustion (19%) and stationary sources (18%).

Keywords: PAHs, source identification, diagnostic ratio, principal component analysis, positive matrix factorization

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Authors: Yousif M. Makeen

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This study presents the results of the sedimentological and petrographical analyses on the cored samples from the Bentiu Formation. The cored intervals consist of thick beds of sandstone, which are sometimes intercalated with beds of fine-grained sandstone and, in a minor case, with a siltstone bed. Detailed sedimentological facies analysis revealed the presence of six facies types, which can be clarified in order of their great percentage occurrences as follows: (i) Massive sandstone, (ii) Planar cross-bedded sandstone, (iii) Trough cross-bedded sandstone, (iv) Fine laminated sandstone (v) Fine laminated siltstone and (vi) Horizontally parted sandstone. The petrographical analyses under the plane polarized microscope and the scanning electron microscope (SEM) for the sandstone lithofacies types that exist within the cored intervals allowed classifying these lithofacies into Kaolinitic Subfeldspathic Arenites. Among the detrital components, quartz grains are the most abundant (mainly monocrystalline quartz), followed by feldspars, micas, detrital and authigenic clays, and carbonaceous debris. However, traces of lithic fragments, iron oxides and heavy minerals were observed in some of the analyzed samples, where they occur in minor amounts. Kaolinite is present mainly as an authigenic component in most of the analyzed samples, while quartz overgrowths occur in variable amounts in most of the investigated samples. Carbonates (calcite & siderite) are present in considerable amounts. The grain roundness in most of the investigated sandstone samples ranges from well-rounded to round, and, in fewer samples, is sub-angular to angular. Most of the sandstone samples are moderately compacted and display point, concavo-convex and long grain contacts, whereas the sutured grain contacts, which reflect a higher degree of compaction, are relatively observed in lesser amounts, while the float grain contact has also been observed in minor quantity. Pore types in the analyzed samples are dominantly primary and secondary interparticle forms. Point-counted porosity values range from 19.6% to 30%. Average pore sizes are highly variable and range from 20 to 350 microns. Pore interconnectivity ranges from good to very good.

Keywords: sandstone, sedimentological facies, porosity, quartz overgrowths

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Authors: Toktam Khatibi

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Self-supervised learning (SSL) includes machine learning models that are trained on one aspect and/or one part of the input to learn other aspects and/or part of it. SSL models are divided into two different categories, including pre-text task-based models and contrastive learning ones. Pre-text tasks are some auxiliary tasks learning pseudo-labels, and the trained models are further fine-tuned for downstream tasks. However, one important disadvantage of SSL using pre-text task solving is defining an appropriate pre-text task for each image dataset with a variety of image modalities. Therefore, it is required to design an appropriate pretext task automatically for each dataset and each downstream task. To the best of our knowledge, the automatic designing of pretext tasks for image analysis has not been considered yet. In this paper, we present a framework based on In-context learning that describes each task based on its input and output data using a pre-trained image transformer. Our proposed method combines the input image and its learned description for optimizing the pre-text task design and its hyper-parameters using Meta-learning models. The representations learned from the pre-text tasks are fine-tuned for solving the downstream tasks. We demonstrate that our proposed framework outperforms the compared ones on unseen tasks and image modalities in addition to its superior performance for previously known tasks and datasets.

Keywords: in-context learning (ICL), meta learning, self-supervised learning (SSL), vision-language domain, transformers

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Authors: Giselle Tran, Ralitza Parina, Phuong T. Nguyen

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Background/Aims: Pancreatic cysts (PC) have recently become an increasingly common entity, often diagnosed as incidental findings on cross-sectional imaging. Clinically, management of the lesions is difficult because of uncertainties in their potential for malignant degeneration. Prior series have reported that carcinoembryonic antigen (CEA), a biomarker collected from cyst fluid aspiration, has a high diagnostic accuracy for discriminating between mucinous and non-mucinous lesions, at the patient’s initial presentation. To the author’s best knowledge, no prior studies have reported PC CEA levels obtained from endoscopic ultrasound fine-needle aspiration (EUS-FNA) over years of serial EUS surveillance imaging. Methods: We report a consecutive retrospective series of 624 patients who underwent EUS evaluation for a PC between 11/20/2009 and 11/13/2018. Of these patients, 401 patients had CEA values obtained at the point of entry. Of these, 157 patients had two or more CEA values obtained over the course of their EUS surveillance. Of the 157 patients (96 F, 61 M; mean age 68 [range, 62-76]), the mean interval of EUS follow-up was 29.7 months [3.5-128]. The mean number of EUS procedures was 3 [2-7]. To assess CEA value fluctuations, we defined an appreciable increase in CEA as "spikes" – two-times increase in CEA on a subsequent EUS-FNA of the same cyst, with the second CEA value being greater than 1000 ng/mL. Using this definition, cysts with a spike in CEA were compared to those without a spike in a bivariate analysis to determine if a CEA spike is associated with poorer outcomes and the presence of high-risk features. Results: Of the 157 patients analyzed, 29 had a spike in CEA. Of these 29 patients, 5 had a cyst with size increase >0.5cm (p=0.93); 2 had a large cyst, >3cm (p=0.77); 1 had a cyst that developed a new solid component (p=0.03); 7 had a cyst with a solid component at any time during surveillance (p=0.08); 21 had a complex cyst (p=0.34); 4 had a cyst categorized as "Statistically Higher Risk" based on molecular analysis (p=0.11); and 0 underwent surgical resection (p=0.28). Conclusion: With serial EUS imaging in the surveillance of PC, an increase in CEA level defined as a spike did not predict poorer outcomes. Most notably, a spike in CEA did not correlate with the number of patients sent to surgery or patients with an appreciable increase in cyst size. A spike in CEA did not correlate with the development of a solid nodule within the PC nor progression on molecular analysis. Future studies should focus on the selected use of CEA analysis when patients undergo EUS surveillance evaluation for PCs.

Keywords: carcinoembryonic antigen (CEA), endoscopic ultrasound (EUS), fine-needle aspiration (FNA), pancreatic cyst, spike

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Authors: A. Beril Tugrul

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Demand on production and thereby the global need of energy is growing continuously. Each country has different trends on energy demand and supply according to their geopolitical and geographical locations, underground reserves, weather conditions and level of industrialization. Conventional energy resources such as oil, gas and coal –in other words fossil resources- remain dominant on primary energy supply in spite of causing of environmental problems. Energy supply and demand securities are essential within the energy importing and exporting countries. This concept affected all sectors, but especially impressed on political aspects of the countries and also global economic views.

Keywords: energy policy, energy economics, energy strategy, global trends, petro-dollar recycling

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Authors: Nilgun Kuskonmaz

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Ceramic molding can produce castings with fine detail, smooth surface and high degree of dimensional accuracy. All these features are the key factors for producing decorated glass moulds. In the ceramic mold casting process, the fundamental parameters affecting the mold-metal reactions are the composition and the properties of the refractory materials used in the production of ceramic mold. As a result of the reactions taking place between the liquid metal and mold surface, it is not possible to achieve a perfect surface quality, a fine surface detail and maintain a high standard dimensional tolerances. The present research examines the effects of the binder composition on the structural and physical properties of the zircon ceramic mold. In the experiment, the ceramic slurry was prepared by mixing the refractory powders (zircon(ZrSiO4), mullit(3Al2O32SiO2) and alumina (Al2O3)) with the low alkaline silica (ethyl silicate (C8H20O4Si)) and acidic type gelling material suitable binder and gelling agent. This was followed by pouring that ceramic slurry on to a silicon pattern. After being gelled, the mold was removed from the silicon pattern and dried. Then, the ceramic mold was subjected to the reaction sintering at 1600°C for 2 hours in the furnace. The stainless steel (SS) was cast into the sintered ceramic mold. At the end of this process it was observed that the surface quality of decorated glass mold.

Keywords: ceramic mold, stainless steel casting, decorated glass mold

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Authors: Sudha Shukla, Bablu Kumar, Gyan Prakash Gupta, U. C. Kulshrestha

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Air pollutants emitted from solid biofuels during cooking are the major contributors to poor air quality, respiratory problems, and radiative forcing, etc. in rural areas of most of developing countries. The present study reports the chemical characteristics and sources of ambient aerosols and traces gases during cooking and non-cooking hours emitted during biofuel combustion in a village in North-Central India. Fine aerosol samples along with gaseous species (Sox, NOx, and NH₃) were collected during September 2010-March 2011 at Khairatpur village (KPV) which is located in the Uttar Pradesh state in North-Central India. Results indicated that most of the major ions in aerosols and Sox, NOx, and NH₃ gases were found to be higher during cooking hours as compared to non-cooking hours suggesting that solid biofuel combustion is an important source of air pollution. Results of Principal Component Analysis (PCA) revealed that combustion of solid biofuel, vehicular emissions, and brick kilns were the major sources of fine aerosols and trace gases in the village. A health survey was conducted to find out the relation between users of biofuels and their health effects and the results revealed that most of the women in the village were suffering from diseases associated with biofuel combustion during cooking.

Keywords: ambient aerosols, biofuel combustion, cooking, health survey, rural area

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Authors: Carla Palencia-Aguilar

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In order to attain the 2050’s zero emissions goal, it is necessary to know the carbon dioxide changes over time either from pollution to attenuations in the mining industry versus at green zones to establish real goals and redirect efforts to reduce greenhouse effects. Two methods were used to compute the amount of CO2 tons in specific mining zones in Colombia. The former by means of NPP with MODIS MOD17A3HGF from years 2000 to 2021. The latter by using MODIS MYD021KM bands 33 to 36 with maximum values of 644 data points distributed in 7 sites corresponding to surface mineral mining of: coal, nickel, iron and limestone. The green zones selected were located at the proximities of the studied sites, but further than 1 km to avoid information overlapping. Year 2012 was selected for method 2 to compare the results with data provided by the Colombian government to determine range of values. Some data was compared with 2022 MODIS energy values and converted to kton of CO2 by using the Greenhouse Gas Equivalencies Calculator by EPA. The results showed that Nickel mining was the least pollutant with 81 kton of CO2 e.q on average and maximum of 102 kton of CO2 e.q. per year, with green zones attenuating carbon dioxide in 103 kton of CO2 on average and 125 kton maximum per year in the last 22 years. Following Nickel, there was Coal with average kton of CO2 per year of 152 and maximum of 188, values very similar to the subjacent green zones with average and maximum kton of CO2 of 157 and 190 respectively. Iron had similar results with respect to 3 Limestone sites with average values of 287 kton of CO2 for mining and 310 kton for green zones, and maximum values of 310 kton for iron mining and 356 kton for green zones. One of the limestone sites exceeded the other sites with an average value of 441 kton per year and maximum of 490 kton per year, eventhough it had higher attenuation by green zones than a close Limestore site (3.5 Km apart): 371 kton versus 281 kton on average and maximum 416 kton versus 323 kton, such vegetation contribution is not enough, meaning that manufacturing process should be improved for the most pollutant site. By comparing bands 33 to 36 for years 2012 and 2022 from January to August, it can be seen that on average the kton of CO2 were similar for mining sites and green zones; showing an average yearly balance of carbon dioxide emissions and attenuation. However, efforts on improving manufacturing process are needed to overcome the carbon dioxide effects specially during emissions’ peaks because surrounding vegetation cannot fully attenuate it.

Keywords: carbon dioxide, MODIS, surface mining, vegetation

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Authors: Rama Shanker, Anil Kumar Sachan

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Basic ingredients of concrete are cement, fine aggregate, coarse aggregate and water. To produce a concrete of certain specific properties, optimum proportion of these ingredients are mixed. The important factors which govern the mix design are grade of concrete, type of cement and size, shape and grading of aggregates. Concrete mix design method is based on experimentally evolved empirical relationship between the factors in the choice of mix design. Basic draw backs of this method are that it does not produce desired strength, calculations are cumbersome and a number of tables are to be referred for arriving at trial mix proportion moreover, the variation in attainment of desired strength is uncertain below the target strength and may even fail. To solve this problem, a lot of cubes of standard grades were prepared and attained 28 days strength determined for different combination of cement, fine aggregate, coarse aggregate and water. An artificial neural network (ANN) was prepared using these data. The input of ANN were grade of concrete, type of cement, size, shape and grading of aggregates and output were proportions of various ingredients. With the help of these inputs and outputs, ANN was trained using feed forward back proportion model. Finally trained ANN was validated, it was seen that it gave the result with/ error of maximum 4 to 5%. Hence, specific type of concrete can be prepared from given material properties and proportions of these materials can be quickly evaluated using the proposed ANN.

Keywords: aggregate proportions, artificial neural network, concrete grade, concrete mix design

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Authors: Beier Zhu, Rui Zhang, Qi Song

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Text contained within fixed-layout documents can be of great semantic value and so requires a high localization accuracy, such as ID cards, invoices, cheques, and passports. Recently, algorithms based on deep convolutional networks achieve high performance on text detection tasks. However, for text localization in fixed-layout documents, such algorithms detect word bounding boxes individually, which ignores the layout information. This paper presents a novel architecture built on convolutional neural networks (CNNs). A global text localization network and a regional bounding-box regression network are introduced to tackle the problem in a coarse-to-fine manner. The text localization network simultaneously locates word bounding points, which takes the layout information into account. The bounding-box regression network inputs the features pooled from arbitrarily sized RoIs and refine the localizations. These two networks share their convolutional features and are trained jointly. A typical type of fixed-layout documents: ID cards, is selected to evaluate the effectiveness of the proposed system. These networks are trained on data cropped from nature scene images, and synthetic data produced by a synthetic text generation engine. Experiments show that our approach locates high accuracy word bounding boxes and achieves state-of-the-art performance.

Keywords: bounding box regression, convolutional networks, fixed-layout documents, text localization

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Authors: Esra Arici, Duygu Olmez, Murat Ozkan, Nurcan Topcu, Furkan Capraz, Gokhan Deniz, Arman Altinyay

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Engineered quartz stone is a composite material comprising approximately 90 wt.% fine quartz aggregate with a variety of particle size ranges and `10 wt.% unsaturated polyester resin (UPR). In this study, the objective is to investigate the influence of particle size distribution on mechanical strength and physical properties of the engineered stone slabs. For this purpose, granular quartz with two particle size ranges of 63-200 µm and 100-300 µm were used individually and mixed with a difference in ratios of mixing. The void volume of each granular packing was measured in order to define the amount of filler; quartz powder with the size of less than 38 µm, and UPR required filling inter-particle spaces. Test slabs were prepared using vibration-compression under vacuum. The study reports that both impact strength and flexural strength of samples increased as the mix ratio of the particle size range of 63-200 µm increased. On the other hand, the values of water absorption rate, apparent density and abrasion resistance were not affected by the particle size distribution owing to vacuum compaction. It is found that increasing the mix ratio of the particle size range of 63-200 µm caused the higher porosity. This led to increasing in the amount of the binder paste needed. It is also observed that homogeneity in the slabs was improved with the particle size range of 63-200 µm.

Keywords: engineered quartz stone, fine quartz aggregate, granular packing, mechanical strength, particle size distribution, physical properties.

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Authors: Ankit Sinha, Soham Banerjee, Pratik Chattopadhyay

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Automated product recognition in retail stores is an important real-world application in the domain of Computer Vision and Pattern Recognition. In this paper, we consider the problem of automatically identifying the classes of the products placed on racks in retail stores from an image of the rack and information about the query/product images. We improve upon the existing approaches in terms of effectiveness and memory requirement by developing a two-stage object detection and recognition pipeline comprising of a Faster-RCNN-based object localizer that detects the object regions in the rack image and a ResNet-18-based image encoder that classifies the detected regions into the appropriate classes. Each of the models is fine-tuned using appropriate data sets for better prediction and data augmentation is performed on each query image to prepare an extensive gallery set for fine-tuning the ResNet-18-based product recognition model. This encoder is trained using a triplet loss function following the strategy of online-hard-negative-mining for improved prediction. The proposed models are lightweight and can be connected in an end-to-end manner during deployment to automatically identify each product object placed in a rack image. Extensive experiments using Grozi-32k and GP-180 data sets verify the effectiveness of the proposed model.

Keywords: retail stores, faster-RCNN, object localization, ResNet-18, triplet loss, data augmentation, product recognition

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Authors: Huinan Zhang, Wenjie Jiang

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Accurate estimation of tropical cyclone intensity is of great importance for disaster prevention and mitigation. Existing techniques are largely based on satellite imagery data, and research and utilization of the inner thermal core structure characteristics of tropical cyclones still pose challenges. This paper presents a two-stage tropical cyclone intensity estimation network based on the fusion of coarse and fine-grained features from microwave brightness temperature data. The data used in this network are obtained from the thermal core structure of tropical cyclones through the Advanced Technology Microwave Sounder (ATMS) inversion. Firstly, the thermal core information in the pressure direction is comprehensively expressed through the maximal intensity projection (MIP) method, constructing coarse-grained thermal core images that represent the tropical cyclone. These images provide a coarse-grained feature range wind speed estimation result in the first stage. Then, based on this result, fine-grained features are extracted by combining thermal core information from multiple view profiles with a distributed network and fused with coarse-grained features from the first stage to obtain the final two-stage network wind speed estimation. Furthermore, to better capture the long-tail distribution characteristics of tropical cyclones, focal loss is used in the coarse-grained loss function of the first stage, and ordinal regression loss is adopted in the second stage to replace traditional single-value regression. The selection of tropical cyclones spans from 2012 to 2021, distributed in the North Atlantic (NA) regions. The training set includes 2012 to 2017, the validation set includes 2018 to 2019, and the test set includes 2020 to 2021. Based on the Saffir-Simpson Hurricane Wind Scale (SSHS), this paper categorizes tropical cyclone levels into three major categories: pre-hurricane, minor hurricane, and major hurricane, with a classification accuracy rate of 86.18% and an intensity estimation error of 4.01m/s for NA based on this accuracy. The results indicate that thermal core data can effectively represent the level and intensity of tropical cyclones, warranting further exploration of tropical cyclone attributes under this data.

Keywords: Artificial intelligence, deep learning, data mining, remote sensing

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Authors: Luofu Liu, Fei Xiao Jr., Fei Xiao

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Through the analysis technology of gas chromatography-mass spectrometry (GC-MS), the composition and distribution characteristics of aromatic hydrocarbons in the Chepaizi area of the Junggar Basin were analyzed in detail. Based on that, the biological input, maturity of crude oils and sedimentary environment of the corresponding source rocks were determined and the origin types of crude oils were divided. The results show that there are three types of crude oils in the study area including Type I, Type II and Type III oils. The crude oils from the 1st member of the Neogene Shawan Formation are the Type I oils; the crude oils from the 2nd member of the Neogene Shawan Formation are the Type II oils; the crude oils from the Cretaceous Qingshuihe and Jurassic Badaowan Formations are the Type III oils. For the Type I oils, they show a single model in the late retention time of the chromatogram of total aromatic hydrocarbons. The content of triaromatic steroid series is high, and the content of dibenzofuran is low. Maturity parameters related to alkyl naphthalene, methylphenanthrene and alkyl dibenzothiophene all indicate low maturity for the Type I oils. For the Type II oils, they have also a single model in the early retention time of the chromatogram of total aromatic hydrocarbons. The content of naphthalene and phenanthrene series is high, and the content of dibenzofuran is medium. The content of polycyclic aromatic hydrocarbon representing the terrestrial organic matter is high. The aromatic maturity parameters indicate high maturity for the Type II oils. For the Type III oils, they have a bi-model in the chromatogram of total aromatic hydrocarbons. The contents of naphthalene series, phenanthrene series, and dibenzofuran series are high. The aromatic maturity parameters indicate medium maturity for the Type III oils. The correlation results of triaromatic steroid series fingerprint show that the Type I and Type III oils have similar source and are both from the Permian Wuerhe source rocks. Because of the strong biodegradation and mixing from other source, the Type I oils are very different from the Type III oils in aromatic hydrocarbon characteristics. The Type II oils have the typical characteristics of terrestrial organic matter input under oxidative environment, and are the coal oil mainly generated by the mature Jurassic coal measure source rocks. However, the overprinting effect from the low maturity Cretaceous source rocks changed the original distribution characteristics of aromatic hydrocarbons to some degree.

Keywords: oil source, geochemistry, aromatic hydrocarbons, crude oils, chepaizi area, Junggar Basin

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Authors: Yegnanew A. Shiferaw

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Background: commodity price volatility originating from excessive commodity price fluctuation has been a global problem especially after the recent financial crises. Volatility is a measure of risk or uncertainty in financial analysis. It plays a vital role in risk management, portfolio management, and pricing equity. Objectives: the core objective of this paper is to examine the relationship between the prices of agricultural commodities with oil price, gas price, coal price and exchange rate (USD/Rand). In addition, the paper tries to fit an appropriate model that best describes the log return price volatility and estimate Value-at-Risk and expected shortfall. Data and methods: the data used in this study are the daily returns of agricultural commodity prices from 02 January 2007 to 31st October 2016. The data sets consists of the daily returns of agricultural commodity prices namely: white maize, yellow maize, wheat, sunflower, soya, corn, and sorghum. The paper applies the three-state Markov-switching (MS) regression, the standard single-regime GARCH and the two regime Markov-switching GARCH (MS-GARCH) models. Results: to choose the best fit model, the log-likelihood function, Akaike information criterion (AIC), Bayesian information criterion (BIC) and deviance information criterion (DIC) are employed under three distributions for innovations. The results indicate that: (i) the price of agricultural commodities was found to be significantly associated with the price of coal, price of natural gas, price of oil and exchange rate, (ii) for all agricultural commodities except sunflower, k=3 had higher log-likelihood values and lower AIC and BIC values. Thus, the three-state MS regression model outperformed the two-state MS regression model (iii) MS-GARCH(1,1) with generalized error distribution (ged) innovation performs best for white maize and yellow maize; MS-GARCH(1,1) with student-t distribution (std) innovation performs better for sorghum; MS-gjrGARCH(1,1) with ged innovation performs better for wheat, sunflower and soya and MS-GARCH(1,1) with std innovation performs better for corn. In conclusion, this paper provided a practical guide for modelling agricultural commodity prices by MS regression and MS-GARCH processes. This paper can be good as a reference when facing modelling agricultural commodity price problems.

Keywords: commodity prices, MS-GARCH model, MS regression model, South Africa, volatility

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Authors: Anna Vanderbruggen, Kai Bachmann, Martin Rudolph, Rodrigo Serna

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Recycling of lithium-ion batteries has attracted a lot of attention in recent years and focuses primarily on valuable metals such as cobalt, nickel, and lithium. Despite the growth in graphite consumption and the fact that it is classified as a critical raw material in the European Union, USA, and Australia, there is little work focusing on graphite recycling. Thus, graphite is usually considered waste in recycling treatments, where graphite particles are concentrated in the “black mass”, a fine fraction below 1mm, which also contains the foils and the active cathode particles such as LiCoO2 or LiNiMnCoO2. To characterize the material, various analytical methods are applied, including X-Ray Fluorescence (XRF), X-Ray Diffraction (XRD), Atomic Absorption Spectrometry (AAS), and SEM-based automated mineralogy. The latter consists of the combination of a scanning electron microscopy (SEM) image analysis and energy-dispersive X-ray spectroscopy (EDS). It is a powerful and well-known method for primary material characterization; however, it has not yet been applied to secondary material such as black mass, which is a challenging material to analyze due to fine alloy particles and to the lack of an existing dedicated database. The aim of this research is to characterize the black mass depending on the metals recycling process in order to understand the liberation mechanisms of the active particles from the foils and their effect on the graphite particle surfaces and to understand their impact on the subsequent graphite flotation. Three industrial processes were taken into account: purely mechanical, pyrolysis-mechanical, and mechanical-hydrometallurgy. In summary, this article explores various and common challenges for graphite and secondary material characterization.

Keywords: automated mineralogy, characterization, graphite, lithium ion battery, recycling

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Authors: Arjun, Gautam, Sanjeev Naval

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Concrete technology has been changing rapidly and constantly since its discovery. Concrete is the most widely used man-made construction material, versatility of making concrete is the 2nd largest consumed material on earth. In this paper an effort has been made to use metal aggregates in concrete has been discussed, the metal aggregates has been named as “BAUT” which had outstandingly qualities to resist shear, tension and compression forces. In this paper, COARSE BAUT AGGREGATES (C.B.A.) 10mm & 20mm and FINE BAUT AGGREGATES (F.B.A.) 3mm were divided and used for making high performance concrete (H.P.C). This “BAUT” had cutting edge technology through draft and design by the use of Auto CAD, ANSYS software can be used effectively In this research paper we study high performance concrete (H.P.C) with “BAUT” and consider the grade of M65 and finally we achieved the result of 90-95 Mpa (high compressive strength) for mega structures and irregular structures where center of gravity (CG) is not balanced. High Performance BAUT Concrete is the extraordinary qualities like long-term performance, no sorptivity by BAUT AGGREGATES, better rheological, mechanical and durability proportion that conventional concrete. This high strength BAUT concrete using “BAUT” is applied in the construction of mega structure like skyscrapers, dam, marine/offshore structures, nuclear power plants, bridges, blats and impact resistance structures. High Performance BAUT Concrete which is a controlled concrete possesses invariable high strength, reasonable workability and negligibly permeability as compare to conventional concrete by the mix of Super Plasticizers (SMF), silica fume and fly ash.

Keywords: BAUT, High Strength Concrete, High Performance Concrete, Fine BAUT Aggregate, Coarse BAUT Aggregate, metal aggregates, cutting edge technology

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Authors: Nubwimana Rachel, Mugabowindekwe Maurice

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Trees play a crucial role in maintaining biodiversity and providing various ecological services. Traditional methods of counting trees are time-consuming, and there is a need for more efficient techniques. However, deep learning makes it feasible to identify the multi-scale elements hidden in aerial imagery. This research focuses on the application of deep learning techniques for tree detection and counting in both forest and non-forest areas through the exploration of the deep learning application for automated tree detection and counting using satellite imagery. The objective is to identify the most effective model for automated tree counting. We used different deep learning models such as YOLOV7, SSD, and UNET, along with Generative Adversarial Networks to generate synthetic samples for training and other augmentation techniques, including Random Resized Crop, AutoAugment, and Linear Contrast Enhancement. These models were trained and fine-tuned using satellite imagery to identify and count trees. The performance of the models was assessed through multiple trials; after training and fine-tuning the models, UNET demonstrated the best performance with a validation loss of 0.1211, validation accuracy of 0.9509, and validation precision of 0.9799. This research showcases the success of deep learning in accurate tree counting through remote sensing, particularly with the UNET model. It represents a significant contribution to the field by offering an efficient and precise alternative to conventional tree-counting methods.

Keywords: remote sensing, deep learning, tree counting, image segmentation, object detection, visualization

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Authors: Ghazala Anjum, Farooq Hussain Bhat, Ravi Kumar

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Multiferroic materials are vital for new application and memory devices, not only because of the presence of multiple types of domains but also as a result of cross correlation between coexisting forms of magnetic and electrical orders. In spite of wide studies done on multiferroic bulk ceramic materials their realization in thin film form is yet limited due to some crucial problems. During the last few years, special attention has been devoted to synthesis of thin films like of BiFeO₃. As they allow direct integration of the material into the device technology. Therefore owing to the process of exploration of new multiferroic thin films, preparation, and characterization of La₀.₈Bi₀.₂Fe₀.₇Mn₀.₃O₃ (LBFMO3) thin film on LaAlO₃ (LAO) substrate with LaNiO₃ (LNO) being the buffer layer has been done. The fact that all the electrical and magnetic properties are closely related to the electronic structure makes it inevitable to study the electronic structure of system under study. Without the knowledge of this, one may never be sure about the mechanism responsible for different properties exhibited by the thin film. Literature review reveals that studies on change in atomic and the hybridization state in multiferroic samples are still insufficient except few. The technique of x-ray absorption (XAS) has made great strides towards the goal of providing such information. It turns out to be a unique signature to a given material. In this milieu, it is time honoured to have the electronic structure study of the elements present in the LBFMO₃ multiferroic thin film on LAO substrate with buffer layer of LNO synthesized by RF sputtering technique. We report the electronic structure studies of well characterized LBFMO3 multiferroic thin film on LAO substrate with LNO as buffer layer using near-edge X-ray absorption fine structure (NEXAFS). Present exploration has been performed to find out the valence state and crystal field symmetry of ions present in the system. NEXAFS data of O K- edge spectra reveals a slight shift in peak position along with growth in intensities of low energy feature. Studies of Mn L₃,₂- edge spectra indicates the presence of Mn³⁺/Mn⁴⁺ network apart from very small contribution from Mn²⁺ ions in the system that substantiates the magnetic properties exhibited by the thin film. Fe L₃,₂- edge spectra along with spectra of reference compound reveals that Fe ions are present in +3 state. Electronic structure and valence state are found to be in accordance with the magnetic properties exhibited by LBFMO/LNO/LAO thin film.

Keywords: magnetic, multiferroic, NEXAFS, x-ray absorption fine structure, XMCD, x-ray magnetic circular dichroism

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Authors: М. Zh. Khazimov, А. B. Kudasheva

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The article presents the state of solid fuel reserves and their share in the world energy sector. The air pollution caused by the operation of heating devices using solid fuels is a significant problem. In order to improve the air quality, heating device producers take constant measures to improve their products. However, the emission results achieved during an initial test of heating devices in the laboratory may be much worse during operation in real operating conditions. The ways of increasing the efficiency of the boiler by improving its design for combustion in full-layer mode are shown. The results of the testing of the improved КВТС-0.2 hot water boiler is presented and the technical and economic indicators are determined, which indicate an increase in the efficiency of the boiler.

Keywords: boiler unit, grate, furnace, coal, ash

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Authors: Dina Kuttah

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It is well known that recycling of by-product materials saves natural resources, reduces by-product volumes, and reduces the need for virgin materials. The steel industry produces a myriad of metal components for industrial chains, which in turn generates mineral discarded sand molds. Although these sands are clean before their use, after casting, they may contain contaminants. Therefore, huge quantities of excess by-product foundry sand (BFS) end up occupying large volumes in landfills. In Sweden, approximately 200000 tonnes of excess BFS end up in landfills. The transportation and construction industries have the greatest potential for reuse by-products because they use vast quantities of earthen materials annually. Accordingly, experimental work has been undertaken to evaluate the possible use of two chosen BFS from two Swedish foundries in a conventional Swedish asphalt mixture. The experimental procedure of this research has focused on the dosage, environmental and technical properties of the same mixture type ABT 11 and the same bitumen (160/220) but at different replacement proportions of the conventional fine sand with the two BFS. The environmental requirements, in addition to the technical requirements, namely, void ratio, static indirect tensile strength ratio, and resilient modulus before and after moisture-induced sensitivity tests of the asphalt mixtures, have been investigated in the current study. The test results demonstrated that the BFS from both foundries can be incorporated in the selected asphalt mixture at specified replacement proportions of the conventional fine sand fraction 0-2 mm, as discussed in the paper.

Keywords: asphalt mixtures, by-product foundry sand, indirect tensile strength, moisture induced sensitivity tests, resilient modulus

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Authors: Adeoti Adebowale, Abduljaleel, Ejiogu Fidelis Onyekwo

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Form and image development are fundamental to creative expression in visual arts. The form is an element that distinguishes the difference between two-dimension and three-dimension among the branches of visual arts. Particularly, the sculpture is a three-dimensional form, while the textile design is a two-dimensional form of its visual appearance. The visual expression of each of them is embedded in the creative practice of the artist, which is easily understood and interpreted by the viewer. In this research, an attempt is made to explore and analyse sculptural forms adopted as a motif for wax print in textile design, aiming at breeding yet another pattern and motif suitable for various design uses. For instance, the dynamics of sculptural form adaptation into other areas of creativity, such as architecture, pictorial arts and pottery, as well as automobile bodies, is a discernible image everywhere. The research is studio exploratory, while a camera and descriptive analysis were used to process the data. Two sculptural forms were adopted from the Department of Fine and Applied Arts, Federal Capital Territory College of Education Zuba-Abuja, in this study due to the uniqueness of their technique of execution. The findings resulted in ten (10) paper designs showing the dexterity of studio practice in the development of design for various fashion and textile uses. However, the paper concludes that sculptural form is a source of inspiration for generating design concepts for a textile designer.

Keywords: exploration, design, motifs, sculptural forms, wax print

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Authors: Aashee Garg, Anusha Agarwal

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India, as a country is very rich in terms of natural resources however as citizens, we have not respected this fact and have been continuously exploiting nature’s gift to mankind. Further as the population is ever increasing, the load on the consumption of resources is unprecedented. This has led to the depletion of natural resources such as coal, oil, gas etc., apart from the pollution it causes. It is time that we shift from use of these conventional resources to more effective new ways of energy generation. We should develop and encourage usage of renewable resources such as wind and solar in households to conserve energy in place of the above mentioned nonrenewable energy sources. This paper deals with the most effective ways in which the households in India can conserve energy thus reducing effect on environment and depletion of limited resources.

Keywords: energy consumption, resources, India, renewable resources and environment

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Authors: Mian Huang, Chi Ma, Junyu Lin, William Lu

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Introduction: Bone health attracts more attention recently and an intelligent question and answer (QA) chatbot for osteoporosis is helpful for science popularization. With Generative Pretrained Transformer (GPT) technology developing, we build an osteoporosis corpus dataset and then fine-tune LLaMA, a famous open-source GPT foundation large language model(LLM), on our self-constructed osteoporosis corpus. Evaluated by clinical orthopedic experts, our fine-tuned model outperforms vanilla LLaMA on osteoporosis QA task in Chinese. Three-dimensional quantitative computed tomography (QCT) measured bone mineral density (BMD) is considered as more accurate than DXA for BMD measurement in recent years. We develop an automatic Phantom-less QCT(PL-QCT) that is more efficient for BMD measurement since no need of an external phantom for calibration. Combined with LLM on osteoporosis, our PL-QCT provides efficient and accurate BMD measurement for our chatbot users. Material and Methods: We build an osteoporosis corpus containing about 30,000 Chinese literatures whose titles are related to osteoporosis. The whole process is done automatically, including crawling literatures in .pdf format, localizing text/figure/table region by layout segmentation algorithm and recognizing text by OCR algorithm. We train our model by continuous pre-training with Low-rank Adaptation (LoRA, rank=10) technology to adapt LLaMA-7B model to osteoporosis domain, whose basic principle is to mask the next word in the text and make the model predict that word. The loss function is defined as cross-entropy between the predicted and ground-truth word. Experiment is implemented on single NVIDIA A800 GPU for 15 days. Our automatic PL-QCT BMD measurement adopt AI-associated region-of-interest (ROI) generation algorithm for localizing vertebrae-parallel cylinder in cancellous bone. Due to no phantom for BMD calibration, we calculate ROI BMD by CT-BMD of personal muscle and fat. Results & Discussion: Clinical orthopaedic experts are invited to design 5 osteoporosis questions in Chinese, evaluating performance of vanilla LLaMA and our fine-tuned model. Our model outperforms LLaMA on over 80% of these questions, understanding ‘Expert Consensus on Osteoporosis’, ‘QCT for osteoporosis diagnosis’ and ‘Effect of age on osteoporosis’. Detailed results are shown in appendix. Future work may be done by training a larger LLM on the whole orthopaedics with more high-quality domain data, or a multi-modal GPT combining and understanding X-ray and medical text for orthopaedic computer-aided-diagnosis. However, GPT model gives unexpected outputs sometimes, such as repetitive text or seemingly normal but wrong answer (called ‘hallucination’). Even though GPT give correct answers, it cannot be considered as valid clinical diagnoses instead of clinical doctors. The PL-QCT BMD system provided by Bone’s QCT(Bone’s Technology(Shenzhen) Limited) achieves 0.1448mg/cm2(spine) and 0.0002 mg/cm2(hip) mean absolute error(MAE) and linear correlation coefficient R2=0.9970(spine) and R2=0.9991(hip)(compared to QCT-Pro(Mindways)) on 155 patients in three-center clinical trial in Guangzhou, China. Conclusion: This study builds a Chinese osteoporosis corpus and develops a fine-tuned and domain-adapted LLM as well as a PL-QCT BMD measurement system. Our fine-tuned GPT model shows better capability than LLaMA model on most testing questions on osteoporosis. Combined with our PL-QCT BMD system, we are looking forward to providing science popularization and early morning screening for potential osteoporotic patients.

Keywords: GPT, phantom-less QCT, large language model, osteoporosis

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Authors: Sriram Venkatesh, V. Murali Mohan, T. V. Karthikeyan

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The main advantage of multi-directionally reinforced composites is the freedom to orient selected fibre types and hence derives the benefits of varying fibre volume fractions and there by accommodate the design loads of the final structure of composites. This technology provides the means to produce tailored composites with desired properties. Due to the high level of fibre integrity with through thickness reinforcement those composites are expected to exhibit superior load bearing characteristics with capability to carry load even after noticeable and apparent fracture. However a survey of published literature indicates inadequacy in the design and test data base for the complete characterization of the multidirectional composites. In this paper the research objective is focused on the development and testing of 4-D orthogonal composites with different preform configurations and resin systems. A preform is the skeleton 4D reinforced composite other than the matrix. In 4-D preforms fibre bundles are oriented in three directions at 1200 with respect to each other and they are on orthogonal plane with the fibre in 4th direction. This paper addresses the various types of 4-D composite manufacturing processes and the mechanical test methods followed for the material characterization. A composite analysis is also made, experiments on course and fine woven preforms are conducted and the findings of test results are discussed in this paper. The interpretations of the test results reveal several useful and interesting features. This should pave the way for more widespread use of the perform configurations for allied applications.

Keywords: multi-directionally reinforced composites, 4-D orthogonal preform, course weave, fine weave, fibre bundle spools, unit cell, fibre architecture, fibre volume fraction, fibre distribution

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Authors: Ruchi Sharma, Rajasekhar Balasubramanian

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Motor vehicles emit a number of air pollutants, among which fine particulate matter (PM₂.₅) is of major concern in cities with high population density due to its negative impacts on air quality and human health. Typically, people spend more than 80% of their time indoors. Consequently, human exposure to traffic-related PM₂.₅ in indoor environments has received considerable attention. Most of the public residential buildings in tropical countries are designed for natural ventilation where indoor air quality tends to be strongly affected by the migration of air pollutants of outdoor origin. However, most of the previously reported traffic-related PM₂.₅ exposure assessment studies relied on ambient PM₂.₅ concentrations and thus, the health impact of traffic-related PM₂.₅ on occupants in naturally ventilated buildings remains largely unknown. Therefore, a systematic field study was conducted to assess indoor human exposure to traffic-related PM₂.₅ with and without mitigation measures in a typical naturally ventilated residential apartment situated near a road carrying a large volume of traffic. Three PM₂.₅ exposure scenarios were simulated in this study, i.e., Case 1: keeping all windows open with a ceiling fan on as per the usual practice, Case 2: keeping all windows fully closed as a mitigation measure, and Case 3: keeping all windows fully closed with the operation of a portable indoor air cleaner as an additional mitigation measure. The indoor to outdoor (I/O) ratios for PM₂.₅ mass concentrations were assessed and the effectiveness of using the indoor air cleaner was quantified. Additionally, potential human health risk based on the bioavailable fraction of toxic trace elements was also estimated for the three cases in order to identify a suitable mitigation measure for reducing PM₂.₅ exposure indoors. Traffic-related PM₂.₅ levels indoors exceeded the air quality guidelines (12 µg/m³) in Case 1, i.e., under natural ventilation conditions due to advective flow of outdoor air into the indoor environment. However, while using the indoor air cleaner, a significant reduction (p < 0.05) in the PM₂.₅ exposure levels was noticed indoors. Specifically, the effectiveness of the air cleaner in terms of reducing indoor PM₂.₅ exposure was estimated to be about 74%. Moreover, potential human health risk assessment also indicated a substantial reduction in potential health risk while using the air cleaner. This is the first study of its kind that evaluated the indoor human exposure to traffic-related PM₂.₅ and identified a suitable exposure mitigation measure that can be implemented in densely populated cities to realize health benefits.

Keywords: fine particulate matter, indoor air cleaner, potential human health risk, vehicular emissions

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Authors: Roman Knizek, Denisa Knizkova

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This paper describes the development of a nanofiber membrane for sport and outdoor use at the Technical University of Liberec (TUL) and the following cooperation with a private Czech company which launched this product onto the market. For making this membrane, Polyurethan was electrospun on the Nanospider spinning machine, and a wire string electrode was used. The created nanofiber membrane with a nanofiber diameter of 150 nm was subsequently hydrophobisied using a low vacuum plasma and Fluorocarbon monomer C6 type. After this hydrophobic treatment, the nanofiber membrane contact angle was higher than 125o, and its oleophobicity was 6. The last step was a lamination of this nanofiber membrane with a woven or knitted fabric to create a 3-layer laminate. Gravure printing technology and polyurethane hot-melt adhesive were used. The gravure roller has a mesh of 17. The resulting 3-layer laminate has a water vapor permeability Ret of 1.6 [Pa.m2.W-1] (– measured in compliance with ISO 11092), it is 100% windproof (– measured in compliance with ISO 9237), and the water column is above 10 000 mm (– measured in compliance with ISO 20811). This nanofiber membrane which was developed in the laboratories of the Technical University of Liberec was then produced industrially by a private company. A low vacuum plasma line and a lamination line were needed for industrial production, and the process had to be fine-tuned to achieve the same parameters as those achieved in the TUL laboratories. The result of this work is a newly developed nanofiber membrane which offers much better properties, especially water vapor permeability, than other competitive membranes. It is an example of product development and the consequent fine-tuning for industrial production; it is also an example of the cooperation between a Czech state university and a private company.

Keywords: nanofiber membrane, start-up, state university, private company, product

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Authors: Alina Gilmanova

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China’s dependency on coal for energy is causing pollution in China and abroad. To supply the increasing energy demand and being under the pressure from international society to reduce the emissions, China was pushed to develop renewable energy. The increasing subsidies in Renewable energy sources (RES) led not only to the price-cutting but also affecting the international trade in green technology sector. In order to evaluate the role of RES in foreign policy of China, I am going to give an (i) overview of RES development in China and examine the cooperation between China and (ii) developed, (ii) developing and emerging countries. The conclusive remarks are intended to address the question of how the present Chinese renewable energy development is impacting its foreign policy and international society.

Keywords: renewable energy, China, foreign affairs, brics, cooperation

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Authors: Laila Seada, Ashraf Ibrahim, Amjad Al Shammari

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Background and Objectives: Breast carcinoma is the most common cancer of females in Hail region, accounting for 31% of all diagnosed cancer cases followed by thyroid carcinoma (25%) and colorectal carcinoma (13%). Methods: In the present retrospective study, all cases of breast lesions received at the histopathology department in King Khalid Hospital, Hail, during the period from May 2011 to April 2016 have been retrieved from department files. For all cases, a trucut biopsy, lumpectomy, or modified radical mastectomy was available for histopathologic diagnosis, while 105/140 (75%) had, as well, preoperative fine needle aspirates (FNA). Results: 49 cases out of 140 (35%) breast lesions were carcinomas: 44/49 (89.75%) was invasive ductal, 2/49(4.1%) invasive lobular carcinomas, 1/49(2.05%) intracystic low grade papillary carcinoma and 2/49 (4.1%) ductal carcinoma in situ (DCIS). Mean age for malignant cases was 45.06 (+/-10.58): 32.6% were below the age of 40 and 30.6 below 50 years, 18.3% below 60 and 16.3% below 70 years. For the benign group, mean age was 32.52 (+/10.5) years. Benign lesions were in order of frequency: 34 fibroadenomas, 14 fibrocystic disease, 12 chronic mastitis, five granulomatous mastitis, three intraductal papillomas, and three benign phyllodes tumor. Tubular adenoma, lipoma, skin nevus, pilomatrixoma, and breast reduction specimens constituted the remaining specimens. Conclusion: Breast lesions are common in our series and invasive carcinoma accounts for more than 1/3^rd of the lumps, with 63.2% incidence in pre-menopausal ladies, below the age of 50 years. FNA as a non-invasive procedure, proved to be an effective tool in diagnosing both benign and malignant/suspicious breast lumps and should continue to be used as a first assessment line of palpable breast masses.

Keywords: age incidence, breast carcinoma, fine needle aspiration, hail region

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Authors: Priyanka Mazumdar, Ananya Mukhopadhyay

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A lot of progress has been achieved in the research of turbidites during the last decades. However, their relationship to delta systems still deserves further attention. This paper addresses example of fine grained turbidite from a pro-deltaic deposit of a Proterozoic mixed energy delta system exposed along Chaossa-Baliana river section of the Chaossa Formation of the Simla Basin. Lithostratigraphic analysis of the Chaossa Formation reveals three major facies associations (prodelta deposit-FA1, delta slope deposit-FA2 and delta front deposit-FA3) based on lithofacies types, petrography and sedimentary structures. Detailed process-based facies and paleoenvironmental analysis of the study area have led to identification of more than150 m thick coarsening-upwards deltaic successions composed of fine grained turbidites overlain by delta slope deposits. Erosional features are locally common at the base of turbidite beds and still more widespread at the top. The complete sequence has eight sub-divisions that are here termed T1 to T8. The basal subdivision (T1) comprises a massive graded unit with a sharp, scoured base, internal parallel-lamination and cross-lamination. The overlying sequence shows textural and compositional grading through alternating silt and mud laminae (T2). T2 is overlying by T3 which is characterized by climbing ripple and cross lamination. Parallel laminae are the predominant facies attributes of T4 which caps the T3 unit. T5 has a loaded scour base and is mainly characterized laminated silt. The topmost three divisions, graded mud (T6), ungraded mud (T7) and laminated mud (T8). The proposed sequence is analogous to the Bouma (1962) structural scheme for sandy turbidites. Repetition of partial sequences represents deposition from different stages of evolution of a large, muddy, turbidity flow. Detailed facies analysis of the study area reveals that the sediments of the turbidites developed during normal regression at the stage of stable or marginally rising sea level. Thin-bedded turbidites were deposited predominantly by turbidity currents in the relatively shallower part of the Simla basin. The fine-grained turbidites are developed by resedimentation of delta-front sands and slumping of upper pro-delta muds.

Keywords: turbidites, prodelta, proterozoic, Simla Basin, Bouma sequence

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