Search results for: deep oxidation

Authors: Harold Mauricio Díaz-Vargas, Cristian Alfonso Jimenez-Castaño, David Augusto Cárdenas-Peña, Guillermo Alberto Ortiz-Gómez, Alvaro Angel Orozco-Gutierrez

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Precise identification of the nerves is a crucial task performed by anesthesiologists for an effective Peripheral Nerve Blocking (PNB). Now, anesthesiologists use ultrasound imaging equipment to guide the PNB and detect nervous structures. However, visual identification of the nerves from ultrasound images is difficult, even for trained specialists, due to artifacts and low contrast. The recent advances in deep learning make neural networks a potential tool for accurate nerve segmentation systems, so addressing the above issues from raw data. The most widely spread U-Net network yields pixel-by-pixel segmentation by encoding the input image and decoding the attained feature vector into a semantic image. This work proposes a conditioning approach and encoder pre-training to enhance the nerve segmentation of traditional U-Nets. Conditioning is achieved by the one-hot encoding of the kind of target nerve a the network input, while the pre-training considers five well-known deep networks for image classification. The proposed approach is tested in a collection of 619 US images, where the best C-UNet architecture yields an 81% Dice coefficient, outperforming the 74% of the best traditional U-Net. Results prove that pre-trained models with the conditional approach outperform their equivalent baseline by supporting learning new features and enriching the discriminant capability of the tested networks.

Keywords: nerve segmentation, U-Net, deep learning, ultrasound imaging, peripheral nerve blocking

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Authors: Ageliki I. Katsarou, Andriana C. Kaliora, Antonia Chiou, Apostolos Papalois, Nick Kalogeropoulos, Nikolaos K. Andrikopoulos

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Oxidative stress is a major mechanism underlying CVDs while inflammation, an intertwined process with oxidative stress, is also linked to CVDs. Extra virgin olive oil (EVOO) is widely known to play a pivotal role in CVD prevention and CVD reduction. However, in most studies, olive oil constituents are evaluated individually and not as part of the native food, hence potential synergistic effects as drivers of EVOO beneficial properties may be underestimated. In this study, EVOO lipidic and polar phenolics fractions were evaluated for their effect on inflammatory (TNF-alpha) and oxidation (malondialdehyde/MDA) markers, in cholesterol-fed rats. Thereat, oils with discernible lipidic profile and polar phenolic content were used. Wistar rats were fed on either a high-cholesterol diet (HCD) or a HCD supplemented with oils, either commercially available, i.e. EVOO, sunflower oil (SO), or modified as to their polar phenol content, i.e. phenolics deprived-EVOO (EVOOd), SO enriched with the EVOO phenolics (SOe). After 9 weeks of dietary intervention, heart and blood samples were collected. HCD induced dylipidemia shown by increase in serum total cholesterol, low-density lipoprotein cholesterol (LDL-c) and triacylglycerols. Heart tissue has been affected by dyslipidemia; oxidation was indicated by increase in MDA in cholesterol-fed rats and inflammation by increase in TNF-alpha. In both cases, this augmentation was attenuated in EVOO and SOe diets. With respect to oxidation, SO enrichment with the EVOO phenolics brought its lipid peroxidation levels as low as in EVOO-fed rats. This suggests that phenolic compounds may act as antioxidant agents in rat heart. A possible mechanism underlying this activity may be the protective effect of phenolics in mitochondrial membrane against oxidative damage. This was further supported by EVOO/EVOOd comparison with the former presenting lower heart MDA content. As for heart inflammation, phenolics naturally present in EVOO as well as phenolics chemically added in SO, exhibited quenching abilities in heart TNF-alpha levels of cholesterol-fed rats. TNF-alpha may have played a causative role in oxidative stress induction while the opposite may have also happened, hence setting up a vicious cycle. Overall, diet supplementation with EVOO or SOe attenuated hypercholesterolemia-induced increase in MDA and TNF-alpha in Wistar rat hearts. This is attributed to phenolic compounds either naturally existing in olive oil or as fortificants in seed oil.

Keywords: extra virgin olive oil, hypercholesterolemic rats, MDA, polar phenolics, TNF-alpha

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Authors: Revoti Prasad Bora, Saurabh Yadav, Nikita Katyal

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In the context of educational data mining, the use case of extracting information from images containing both text and diagrams is of high importance. Hence, document analysis requires the extraction of diagrams from such images and processes the text and diagrams separately. To the author’s best knowledge, none among plenty of approaches for extracting tables, figures, etc., suffice the need for real-time processing with high accuracy as needed in multiple applications. In the education domain, diagrams can be of varied characteristics viz. line-based i.e. geometric diagrams, chemical bonds, mathematical formulas, etc. There are two broad categories of approaches that try to solve similar problems viz. traditional computer vision based approaches and deep learning approaches. The traditional computer vision based approaches mainly leverage connected components and distance transform based processing and hence perform well in very limited scenarios. The existing deep learning approaches either leverage YOLO or faster-RCNN architectures. These approaches suffer from a performance-accuracy tradeoff. This paper proposes a U-Net based architecture that formulates the diagram extraction as a segmentation problem. The proposed method provides similar accuracy with a much faster extraction time as compared to the mentioned state-of-the-art approaches. Further, the segmentation mask in this approach allows the extraction of diagrams of irregular shapes.

Keywords: computer vision, deep-learning, educational data mining, faster-RCNN, figure extraction, image segmentation, real-time document analysis, text extraction, U-Net, YOLO

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Authors: Parthiban Srinivasan

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With recent advances in computational power and access to enough data in biosciences, artificial intelligence methods are increasingly being used in drug discovery research. These methods are essentially a series of advanced statistics based exercises that review the past to indicate the likely future. Our goal is to develop a model that accurately predicts biological activity and toxicity parameters for novel compounds. We have compiled a robust library of over 150,000 chemical compounds with different pharmacological properties from literature and public domain databases. The compounds are stored in simplified molecular-input line-entry system (SMILES), a commonly used text encoding for organic molecules. We utilize an automated process to generate an array of numerical descriptors (features) for each molecule. Redundant and irrelevant descriptors are eliminated iteratively. Our prediction engine is based on a portfolio of machine learning algorithms. We found Random Forest algorithm to be a better choice for this analysis. We captured non-linear relationship in the data and formed a prediction model with reasonable accuracy by averaging across a large number of randomized decision trees. Our next step is to apply deep neural network (DNN) algorithm to predict the biological activity and toxicity properties. We expect the DNN algorithm to give better results and improve the accuracy of the prediction. This presentation will review all these prominent machine learning and deep learning methods, our implementation protocols and discuss these techniques for their usefulness in biomedical and health informatics.

Keywords: deep learning, drug discovery, health informatics, machine learning, toxicity prediction

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Authors: Yves Mann Elate Lea Mbassi, Marie Solange Evehe Bebandoue, Wilfred Fon Mbacham

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Improving the catalytic performance of enzymes has been a long-standing theme of analytical biochemistry research. Induction of peroxidase activity by metals is a common reaction in higher plants. We thought that this increase in peroxidase activity may be due, on the one hand, to the stimulation of the gene expression of these enzymes but also to a modification of their chemical reactivity following the binding of some metal ions on their active site. We tested the effect of some metal salts (MgCl₂, MnCl₂, ZnCl₂, CaCl₂ and CuSO₄) on the activity and thermostability of peroxidase A6, a thermostable peroxidase that we discovered and purified in a previous study. The chromogenic substrate used was 3,3′,5,5′-tetramethylbenzidine. Of all the metals tested for their effect on A6, only magnesium and copper had a significant effect on the activity of the enzyme at room temperature. The Mann-Whitney test shows a slight inhibitory effect of activity by the magnesium salt (P = 0.043), while the activity of the enzyme is 5 times higher in the presence of the copper salt (P = 0.002). Moreover, the thermostability of peroxidase A6 is increased when calcium and copper salts are present. The activity in the presence of CaCl₂ is 8 times higher than the residual activity of the enzyme alone after incubation at 80°C for 10 min and 35 times higher in the presence of CuSO4 under the same conditions. In addition, manganese and zinc salts slightly reduce the thermostability of the enzyme. The activity and structural stability of peroxidase A6 can clearly be activated by Cu₂+, which therefore enhance the oxidation of 3,3′,5,5′-tetramethylbenzidine, which was used in this study as a chromogenic substrate. Ca₂+ likely has a more stabilizing function for the catalytic site.

Keywords: peroxidase activity, copper ions, calcium ions, thermostability

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Authors: Shekher Kummari, V. Sunil Kumar, K. Vengatajalabathy Gobi

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A simple, cost-effective, reusable and reagent-free electrochemical biosensor is developed with functionalized multiwall carbon nanotube paste electrode (f-CNTPE) for the sensitive and selective determination of the important chemotherapeutic drug methotrexate (MTX), which is widely used for the treatment of various cancer and autoimmune diseases. The electrochemical response of the fabricated electrode towards the detection of MTX is examined by cyclic voltammetry (CV), differential pulse voltammetry (DPV) and square wave voltammetry (SWV). CV studies have shown that f-CNTPE electrode system exhibited an excellent electrocatalytic activity towards the oxidation of MTX in phosphate buffer (0.2 M) compared with a conventional carbon paste electrode (CPE). The oxidation peak current is enhanced by nearly two times in magnitude. Applying the DPV method under optimized conditions, a linear calibration plot is achieved over a wide range of concentration from 4.0×10⁻⁷ M to 5.5×10⁻⁶ M with the detection limit 1.6×10⁻⁷ M. further, by applying the SWV method a parabolic calibration plot was achieved starting from a very low concentration of 1.0×10⁻⁸ M, and the sensor could detect as low as 2.9×10⁻⁹ M MTX in 10 s and 10 nM were detected in steady state current-time analysis. The f-CNTPE shows very good selectivity towards the specific recognition of MTX in the presence of important biological interference. The electrochemical biosensor detects MTX in-vitro directly from pharmaceutical sample, undiluted urine and human blood serum samples at a concentration range 5.0×10⁻⁷ M with good recovery limits.

Keywords: amperometry, electrochemical detection, human blood serum, methotrexate, MWCNT, SWV

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Authors: Rajnarayan Damor, Gayatri Swarnakar

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Fasciola gigantica are present in the biliary ducts of liver and gall bladder of domestic buffaloes. They are very harmful and causes significant lose to live stock owners, on account of poor growth and lower productivity of domestic buffaloes. Synthetic veterinary drugs have been used to eliminate parasites from cattle but these drugs are unaffordable and inaccessible for poor cattle farmers. The in vitro anthelmintic effect of Citrullus colocynthis fruit extract against Fasciola gigantica parasites were observed by light and scanning electron microscopy. Fruit extracts of C. colocynthis exhibit highest mortality 100% at 50 mg/ml in 15th hour of exposure. The oral and ventral sucker appeared to be slightly more swollen than control and synthetic drug albendazole. The tegument showed submerged spines by the swollen tegument around them. The tegument of the middle region showed deep furrows, folding and submerged spines which either lied very flat against the surface or had become submerged in the tegument by the swollen tegument around them leaving deep furrows. Posterior region showed with deep folding in the tegument, completely disappearance of spines and swelling of the tegument led to completely submerged spines leaving spine socket. The present study revealed that fruit extracts of Citrullus colocynthis found to be potential sources for novel anthelmintic and justify their ethno-veterinary use.

Keywords: anthelmintic, buffalo, Citrullus colocynthis, Fasciola gigantica, mortality, tegument

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Authors: Saba Ebrahimi, Saeed Ahmadian, Hedie Ashrafi

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World-wide spread of coronavirus grows the concern about planning for the excess demand of hospital services in response to COVID-19 pandemic. The surge in the hospital services demand beyond the current capacity leads to shortage of ICU beds and ventilators in some parts of US. In this study, we forecast the required number of hospital beds and possible shortage of beds in US during COVID-19 pandemic to be used in the planning and hospitalization of new cases. In this paper, we used a data on COVID-19 deaths and patients’ hospitalization besides the data on hospital capacities and utilization in US from publicly available sources and national government websites. we used a novel ensemble modelling of deep learning networks, based on stacking different linear and non-linear layers to predict the shortage in hospital beds. The results showed that our proposed approach can predict the excess hospital beds demand very well and this can be helpful in developing strategies and plans to mitigate this gap.

Keywords: COVID-19, deep learning, ensembled models, hospital capacity planning

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Authors: Muhammad Zaman Shakir, Mingfa Yao, Zohaib Iqbal

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This study proposed four Jet fuel surrogate (S1, S2 S3, and 4) with careful selection of seven large hydrocarbon fuel components, ranging from C₉-C₁₆ of higher molecular weight and higher boiling point, adapting the standard molecular distribution size of the actual jet fuel. The surrogate was composed of seven components, including n-propyl cyclohexane (C₉H₁₈), n- propylbenzene (C₉H₁₂), n-undecane (C₁₁H₂₄), n- dodecane (C₁₂H₂₆), n-tetradecane (C₁₄H₃₀), n-hexadecane (C₁₆H₃₄) and iso-cetane (iC₁₆H₃₄). The skeletal jet fuel surrogate reaction mechanism was developed by two approaches, firstly based on a decoupling methodology by describing the C₄ -C₁₆ skeletal mechanism for the oxidation of heavy hydrocarbons and a detailed H₂ /CO/C₁ mechanism for prediction of oxidation of small hydrocarbons. The combined skeletal jet fuel surrogate mechanism was compressed into 128 species, and 355 reactions and thereby can be used in computational fluid dynamics (CFD) simulation. The extensive validation was performed for individual single-component including ignition delay time, species concentrations profile and laminar flame speed based on various fundamental experiments under wide operating conditions, and for their blended mixture, among all the surrogate, S1 has been extensively validated against the experimental data in a shock tube, rapid compression machine, jet-stirred reactor, counterflow flame, and premixed laminar flame over wide ranges of temperature (700-1700 K), pressure (8-50 atm), and equivalence ratio (0.5-2.0) to capture the properties target fuel Jet-A, while the rest of three surrogate S2, S3 and S4 has been validated for Shock Tube ignition delay time only to capture the ignition characteristic of target fuel S-8 & GTL, IPK and RP-3 respectively. Based on the newly proposed HyChem model, another four surrogate with similar components and composition, was developed and parallel validations data was used as followed for previously developed surrogate but at high-temperature condition only. After testing the mechanism prediction performance of surrogates developed by the decoupling methodology, the comparison was done with the results of surrogates developed by the HyChem model. It was observed that all of four proposed surrogates in this study showed good agreement with the experimental measurements and the study comes to this conclusion that like the decoupling methodology HyChem model also has a great potential for the development of oxidation mechanism for heavy alkanes because of applicability, simplicity, and compactness.

Keywords: computational fluid dynamics, decoupling methodology Hychem, jet fuel, surrogate, skeletal mechanism

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Authors: Kyoungrean Kim

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Importance of deep-sea mineral resources is dramatically increasing due to the depletion of land mineral resources corresponding to increasing human’s economic activities. Korea has acquired exclusive exploration licenses at four areas which are the Clarion-Clipperton Fracture Zone in the Pacific Ocean (2002), Tonga (2008), Fiji (2011) and Indian Ocean (2014). The preparation for commercial mining of Nautilus minerals (Canada) and Lockheed martin minerals (USA) is expected by 2020. The London Protocol 1996 (LP) under International Maritime Organization (IMO) and International Seabed Authority (ISA) will set environmental guidelines for deep-sea mining until 2020, to protect marine environment. In this research, the applicability of washing/extraction treatment for the remediation of deep-sea mining tailings was mainly evaluated in order to present preliminary data to develop practical remediation technology in near future. Polymetallic nodule samples were collected at the Clarion-Clipperton Fracture Zone in the Pacific Ocean, then stored at room temperature. Samples were pulverized by using jaw crusher and ball mill then, classified into 3 particle sizes (> 63 µm, 63-20 µm, < 20 µm) by using vibratory sieve shakers (Analysette 3 Pro, Fritsch, Germany) with 63 µm and 20 µm sieve. Only the particle size 63-20 µm was used as the samples for investigation considering the lower limit of ore dressing process which is tens to 100 µm. Rhamnolipid and sodium alginate as biosurfactant and aluminum sulfate which are mainly used as flocculant were used as environmentally friendly additives. Samples were adjusted to 2% liquid with deionized water then mixed with various concentrations of additives. The mixture was stirred with a magnetic bar during specific reaction times and then the liquid phase was separated by a centrifugal separator (Thermo Fisher Scientific, USA) under 4,000 rpm for 1 h. The separated liquid was filtered with a syringe and acrylic-based filter (0.45 µm). The extracted heavy metals in the filtered liquid were then determined using a UV-Vis spectrometer (DR-5000, Hach, USA) and a heat block (DBR 200, Hach, USA) followed by US EPA methods (8506, 8009, 10217 and 10220). Polymetallic nodule was mainly composed of manganese (27%), iron (8%), nickel (1.4%), cupper (1.3 %), cobalt (1.3%) and molybdenum (0.04%). Based on remediation standards of various countries, Nickel (Ni), Copper (Cu), Cadmium (Cd) and Zinc (Zn) were selected as primary target materials. Throughout this research, the use of rhamnolipid was shown to be an effective approach for removing heavy metals in samples originated from manganese nodules. Sodium alginate might also be one of the effective additives for the remediation of deep-sea mining tailings such as polymetallic nodules. Compare to the use of rhamnolipid and sodium alginate, aluminum sulfate was more effective additive at short reaction time within 4 h. Based on these results, sequencing particle separation, selective extraction/washing, advanced filtration of liquid phase, water treatment without dewatering and solidification/stabilization may be considered as candidate technologies for the remediation of deep-sea mining tailings.

Keywords: deep-sea mining tailings, heavy metals, remediation, extraction, additives

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Authors: Yildiray Korkmaz, Mehmet Aksoy

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In today's operations, countries want to reach their aims in the shortest way due to economical, political and humanitarian aspects. The most effective way of achieving this goal is to be able to penetrate strategic targets. Strategic targets are generally located deep inside of the countries and are defended by modern and efficient surface to air missiles (SAM) platforms which are operated as integrated with Intelligence, Surveillance and Reconnaissance (ISR) systems. On the other hand, these high valued targets are buried deep underground and hardened with strong materials against attacks. Therefore, to penetrate these targets requires very detailed intelligence. This intelligence process should include a wide range that is from weaponry to threat assessment. Accordingly, the framework of the attack package will be determined. This mission package has to execute missions in a high threat environment. The way to minimize the risk which depends on loss of life is to use packages which are formed by UAVs. However, some limitations arising from the characteristics of UAVs restricts the performance of the mission package consisted of UAVs. So, the mission package should be formed with UAVs under the leadership of a fifth generation manned aircraft. Thus, we can minimize the limitations, easily penetrate in the deep inside of the enemy territory with minimum risk, make a decision according to ever-changing conditions and finally destroy the strategic targets. In this article, the strengthens and weakness aspects of UAVs are examined by SWOT analysis. And also, it revealed features of a mission package and presented as an example what kind of a mission package we should form in order to get marginal benefit and penetrate into strategic targets with the development of autonomous mission execution capability in the near future.

Keywords: UAV, autonomy, mission package, strategic attack, mission planning

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Authors: Maham Malik, Amjad Ali, Muhammad Asif

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Current study aims to examine the relationship of emotional labor strategies - deep acting and surface acting - with employees' job satisfaction, organizational commitment and intentions to quit. The study also examines the mediating role of job satisfaction and organizational commitment for relationship of emotional labor strategies with intentions to quit. Data were conveniently collected from 307 nurses by using self-administered questionnaire. Linear regression test was applied to find the relationship between the variables. Mediation was checked through Baron and Kenny Model and Sobel test. Results prove the existence of partial mediation of job satisfaction between the emotional labor strategies and quitting intentions. The study recommends that deep acting should be promoted because it is positively associated with quality of work life, work engagement and organizational citizenship behavior of employees.

Keywords: emotional labor strategies, intentions to quit, job satisfaction, organizational commitment, nursing

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Authors: Harpreet Singh

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Finding relevant information on the World Wide Web is becoming highly challenging day by day. Web usage mining is used for the extraction of relevant and useful knowledge, such as user behaviour patterns, from web access log records. Web access log records all the requests for individual files that the users have requested from the website. Web usage mining is important for Customer Relationship Management (CRM), as it can ensure customer satisfaction as far as the interaction between the customer and the organization is concerned. Web usage mining is helpful in improving website structure or design as per the user’s requirement by analyzing the access log file of a website through a log analyzer tool. The focus of this paper is to enhance the accessibility and usability of a guitar selling web site by analyzing their access log through Deep Log Analyzer tool. The results show that the maximum number of users is from the United States and that they use Opera 9.8 web browser and the Windows XP operating system.

Keywords: web usage mining, web mining, log file, data mining, deep log analyzer

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Authors: Asimina Paparrigopoulou, John Pavlopoulos, Maria Konstantinidou

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Dating papyri accurately is crucial not only to editing their texts but also for our understanding of palaeography and the history of writing, ancient scholarship, material culture, networks in antiquity, etc. Most ancient manuscripts offer little evidence regarding the time of their production, forcing papyrologists to date them on palaeographical grounds, a method often criticized for its subjectivity. By experimenting with data obtained from the Collaborative Database of Dateable Greek Bookhands and the PapPal online collections of objectively dated Greek papyri, this study shows that deep learning dating models, pre-trained on generic images, can achieve accurate chronological estimates for a test subset (67,97% accuracy for book hands and 55,25% for documents). To compare the estimates of these models with those of humans, experts were asked to complete a questionnaire with samples of literary and documentary hands that had to be sorted chronologically by century. The same samples were dated by the models in question. The results are presented and analysed.

Keywords: image classification, papyri images, dating

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Authors: Anum Ali, Kashaf ad Dooja, Asif Saleem

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The future smart cities trend will be towards Internet of Things (IoT); IoT creates dynamic connections in a ubiquitous manner. Smart cities offer ease and flexibility for daily life matters. By using small devices that are connected to cloud servers based on IoT, network traffic between these devices is growing exponentially, whose security is a concerned issue, since ratio of cyber attack may make the network traffic vulnerable. This paper discusses the latest machine learning approaches in related work further to tackle the increasing rate of cyber attacks, machine learning algorithm is applied to IoT-based network traffic data. The proposed algorithm train itself on data and identify different sections of devices interaction by using supervised learning which is considered as a classifier related to a specific IoT device class. The simulation results clearly identify the attacks and produce fewer false detections.

Keywords: IoT, traffic security, deep learning, classification

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Authors: Ubon Asuquo Essien, Okwudili Bismark Ibeagwa, Daberechi Peace Ubabuko

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The Broiler Poultry subsector is dominated by small scale production with low aggregate output. The high cost of inputs currently experienced in Nigeria tends to aggravate the situation; hence many broiler farmers struggle to break-even. This study was designed to examine income and input factors in small scale deep liter broiler production in Imo state, Nigeria. Specifically, the study examined; socio-economic characteristics of small scale deep liter broiler producing Poultry farmers; estimate cost and returns of broiler production in the area; analyze input factors in broiler production in the area and examined marketability, age and profitability of the enterprise. A multi-stage sampling technique was adopted in selecting 60 small scale broiler farmers who use deep liter system from 6 communities through the use of structured questionnaire. The socioeconomic characteristics of the broiler farmers and the profitability/ marketability age of the birds were described using descriptive statistical tools such as frequencies, means and percentages. Gross margin analysis was used to analyze the cost and returns to broiler production, while Cobb Douglas production function was employed to analyze input factors in broiler production. The result of the study revealed that the cost of feed (P<0.1), deep liter material (P<0.05) and medication (P<0.05) had a significant positive relationship with the gross return of broiler farmers in the study area, while cost of labour, fuel and day old chicks were not significant. Furthermore, Gross profit margin of the farmers who market their broiler at the 8th week of rearing was 80.7%; and 78.7% and 60.8% for farmers who market at the 10th week and 12th week of rearing, respectively. The business is, therefore, profitable but at varying degree. Government and Development partners should make deliberate efforts to curb the current rise in the prices of poultry feeds, drugs and timber materials used as bedding so as to widen the profit margin and encourage more farmers to go into the business. The farmers equally need more technical assistance from extension agents with regards to timely and profitable marketing.

Keywords: broilers, factor analysis, income, small scale

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Authors: Sarita Agarwal, Deepika Delsa Dean

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Like the task of transferring photo images to artistic images, image-to-image translation aims to translate the data to the imitated data which belongs to the target domain. Neural Style Transfer and CycleGAN are two well-known deep learning architectures used for photo image-to-art image transfer. However, studies involving these two models concentrate on one-to-one domain translation, not one-to-multi domains translation. Our study tries to investigate deep learning architectures, which can be controlled to yield multiple artistic style translation only by adding a conditional vector. We have expanded CycleGAN and constructed Conditional CycleGAN for 5 kinds of categories translation. Our study found that the architecture inserting conditional vector into the middle layer of the Generator could output multiple artistic images.

Keywords: genetic counseling, FMR1 gene, fragile x-associated primary ovarian insufficiency, premutation

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Authors: Rong Sun, Laihong Shen

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La-based perovskite oxygen carriers, especially the doped-La(M)FeO₃, showed excellent performances during chemical looping processes. However, the mechanisms of the undoped and doped La(M)FeO₃ are not clear at present, making the mechanisms clear may help the development of chemical looping technologies. In this paper, the method based on the density function theory (DFT) was used to analysis the influence of Ca, Sr, and Ba doping of La on the electronic structure, while the CO oxidation mechanisms on the surface of LaFeO₃ and Ca-doped LaFeO₃ oxygen carriers were also analyzed. The results showed that the band gap was decreased by the doping of low valence. While the doping of low valence element Ca, Sr, and Ba at La site simultaneously resulted to the moving of the valence band toward high energy and made the valence band cross the Fermi energy level. This was resulted from the holes generated by divalent ion substitution. The holes can change the total magnetization from antiferromagnet to weakly ferromagnetism. The calculation results about the formation of oxygen vacancy showed that substitutions of Ca, Sr, and Ba caused a large drop in oxygen vacancy formation energy, indicating that the bulk oxygen transport was improved. Based on the optimized bulk of the undoped and Ca-doped LaFeO₃(010) surface, the CO adsorption was analyzed. The results indicated that the adsorption energy increased by divalent ion substitution, meaning that the adsorption stability decreased. The results can provide a certain theoretical basis for the development of perovskite oxides in chemical looping technologies.

Keywords: chemical looping technologies, lanthanum ferrate (LaFeO₃), divalent ion substitution, CO oxidation

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Authors: Danute Karcauskiene, Dalia Ambrazaitiene, Regina Skuodiene, Monika Vilkiene, Regina Repsiene, Ieva Jokubauskaite

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The main ambition for nowadays agriculture is to get the economically effective yield and to secure the soil ecological sustainability. According to the effect on the main soil quality indexes, tillage systems may be separated into two types, conventional and conservation tillage. The goal of this study was to determine the impact of conservation and conventional primary soil tillage methods and soil fertility improvement measures on soil properties and crop productivity. Methods: The soil of the experimental site is Dystric Glossic Retisol (WRB 2014) with texture of sandy loam. The trial was established in 2003 in the experimental field of crop rotation of Vėžaičiai Branch of Lithuanian Research Centre for Agriculture and Forestry. Trial factors and treatments: factor A- primary soil tillage in (autumn): deep ploughing (20-25cm), shallow ploughing (10-12cm), shallow ploughless tillage (8-10cm); factor B – soil fertility improvement measures: plant residues, plant residues + straw, green manure 1st cut + straw, farmyard manure 40tha-1 + straw. The four - course crop rotation consisted of red clover, winter wheat, spring rape and spring barley with undersown. Results: The tillage had no statistically significant effect on topsoil (0-10 cm) pHKCl level, it was 5.5 - 5.7. During all experiment period, the highest soil pHKCl level (5.65) was in the shallow ploughless tillage. The organic fertilizers particularly the biomass of grass and farmyard manure had tendency to increase the soil pHKCl. The content of plant - available phosphorus and potassium significantly increase in the shallow ploughing compared with others tillage systems. The farmyard manure increases those elements in whole arable layer. The dissolved organic carbon concentration was significantly higher in the 0 - 10 cm soil layer in the shallow ploughless tillage compared with deep ploughing. After the incorporation of clover biomass and farmyard manure the concentration of dissolved organic carbon increased in the top soil layer. During all experiment period the largest amount of water stable aggregates was determined in the soil where the shallow ploughless tillage was applied. It was by 12% higher compared with deep ploughing. During all experiment time, the soil moisture was higher in the shallow ploughing and shallow ploughless tillage (9-27%) compared to deep ploughing. The lowest emission of CO2 was determined in the deep ploughing soil. The highest rate of CO2 emission was in shallow ploughless tillage. The addition of organic fertilisers had a tendency to increase the CO2 emission, but there was no statistically significant effect between the different types of organic fertilisers. The crop yield was larger in the deep ploughing soil compared to the shallow and shallow ploughless tillage.

Keywords: reduced tillage, soil structure, soil pH, biological activity, crop productivity

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Authors: Chamika Chiran Perera, Dananjaya Perera, Chirath Dasanayake, Banuka Athuraliya

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Male infertility is a major problem in the world, and it is a neglected and sensitive health issue in Sri Lanka. It can be determined by analyzing human semen samples. Sperm motility is one of many factors that can evaluate male’s fertility potential. In Sri Lanka, this analysis is performed manually. Manual methods are time consuming and depend on the person, but they are reliable and it can depend on the expert. Machine learning and deep learning technologies are currently being investigated to automate the spermatozoa motility analysis, and these methods are unreliable. These automatic methods tend to produce false positive results and false detection. Current automatic methods support different techniques, and some of them are very expensive. Due to the geographical variance in spermatozoa characteristics, current automatic methods are not reliable for motility analysis in Sri Lanka. The suggested system, DeepMotile, is to explore a method to analyze motility of human spermatozoa automatically and present it to the andrology laboratories to overcome current issues. DeepMotile is a novel deep learning method for analyzing spermatozoa motility parameters in the Sri Lankan population. To implement the current approach, Sri Lanka patient data were collected anonymously as a dataset, and glass slides were used as a low-cost technique to analyze semen samples. Current problem was identified as microscopic object detection and tackling the problem. YOLOv5 was customized and used as the object detector, and it achieved 94 % mAP (mean average precision), 86% Precision, and 90% Recall with the gathered dataset. StrongSORT was used as the object tracker, and it was validated with andrology experts due to the unavailability of annotated ground truth data. Furthermore, this research has identified many potential ways for further investigation, and andrology experts can use this system to analyze motility parameters with realistic accuracy.

Keywords: computer vision, deep learning, convolutional neural networks, multi-target tracking, microscopic object detection and tracking, male infertility detection, motility analysis of human spermatozoa

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Authors: Samiah Alammari, Nassim Ammour

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When providing a massive number of tasks successively to a deep learning process, a good performance of the model requires preserving the previous tasks data to retrain the model for each upcoming classification. Otherwise, the model performs poorly due to the catastrophic forgetting phenomenon. To overcome this shortcoming, we developed a successful continual learning deep model for remote sensing hyperspectral image regions classification. The proposed neural network architecture encapsulates two trainable subnetworks. The first module adapts its weights by minimizing the discrimination error between the land-cover classes during the new task learning, and the second module tries to learn how to replicate the data of the previous tasks by discovering the latent data structure of the new task dataset. We conduct experiments on HSI dataset Indian Pines. The results confirm the capability of the proposed method.

Keywords: continual learning, data reconstruction, remote sensing, hyperspectral image segmentation

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Authors: Gabriel K. Nzulu, Lina Rostorm, Hans Högberg, Jun Liu, per Eklund, Lars Hultman, Martin Magnuson

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Altered and gangue quartz in hydrothermal veins from the Kubi Gold deposit in Dunkwa on Offin in the central region of Ghana are investigated for possible Au associated pathfinder minerals and to provide understanding and increase the knowledge of the mineral hosting and alteration processes in quartz. X-ray diffraction, air annealing furnace, differential scanning calorimetry, energy dispersive X-ray spectroscopy, and transmission electron microscopy have been applied on different quartz types outcropping from surface and bed rocks at the Kubi Gold Mining to reveal the material properties at different temperatures. From the diffraction results of the fresh and annealed quartz samples, we find that the samples contain pathfinder and the impurity minerals FeS₂, biotite, TiO₂, and magnetite. These minerals, under oxidation process between 574-1400 °C temperatures experienced hematite alterations and a transformation from α-quartz to β-quartz and further to cristobalite as observed from the calorimetry scans for hydrothermally exposed materials. The energy dispersive spectroscopy revealed elemental species of Fe, S, Mg, K, Al, Ti, Na, Si, O, and Ca contained in the samples and these are attributed to the impurity phase minerals observed in the diffraction. The findings also suggest that during the hydrothermal flow regime, impurity minerals and metals can be trapped by voids and faults. Under favorable temperature conditions the trapped minerals can be altered to change color at different depositional stages by oxidation and reduction processes leading to hematite alteration which is a useful pathfinder in mineral exploration.

Keywords: quartz, hydrothermal, minerals, hematite, x-ray diffraction, crystal-structure, defects

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Authors: Fahad Aljasass, Hamza Abu-Tarboush, Salah Aleid, Siddig Hamad

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The effects of different high hydrostatic pressure treatments on the shelf life of camel’s milk were studied. Treatments at 300 to 350 MPa for 5 minutes at 40°C reduced microbial contamination to levels that prolonged the shelf life of refrigerated (3° C) milk up to 28 days. The treatment resulted in a decrease in the proteolytic activity of the milk. The content of proteolytic enzymes in the untreated milk sample was 4.23 µM/ml. This content decreased significantly to 3.61 µM/ml when the sample was treated at 250 MPa. Treatment at 300 MPa decreased the content to 3.90 which was not significantly different from the content of the untreated sample. The content of the sample treated at 350 MPa dropped to 2.98 µM/ml which was significantly lower than the contents of all other treated and untreated samples. High pressure treatment caused a slight but statistically significant increase in the pH of camel’s milk. The pH of the untreated sample was 6.63, which increased significantly to 6.70, in the samples treated at 250 and 350 MPa, but insignificantly in the sample treated at 300 MPa. High pressure treatment resulted in some degree of milk fat oxidation. The thiobarbituric acid (TBA) value of the untreated sample was 0.86 mg malonaldehyde/kg milk. This value remained unchanged in the sample treated at 250 MPa, but then it increased significantly to 1.25 and 1.33 mg/kg in the samples treated at 300 and 350 MPa, respectively. High pressure treatment caused a small increase in the greenness (a* value) of camel’s milk. The value of a* was reduced from -1.17 for the untreated sample to -1.26, -1.21 and -1.30 for the samples treated at 250, 300 and 350 MPa, respectively. Δa* at the 250 MPa treatment was -0.09, which then decreased to -0.04 at the 300 MPa treatment to increase again to -0.13 at the 350 MPa treatment. The yellowness (b* value) of camel’s milk increased significantly as a result of high pressure treatment. The b* value of the untreated sample was 1.40, this value increased to 2.73, 2.31 and 2.18 after treatments at 250, 300 and 350 MPa, respectively. The Δb* value was +1.33 at the treatment 250 MPa, decreased to +0.91 at 300 MPa and further to +0.78 at 350 MPa. The pressure treatment caused slight effect on color, slight decrease in protease activity and a slight increase in the oxidation products of lipids.

Keywords: high hydrostatic pressure, camel’s milk, mesophilic aerobic bacteria, clotting, protease

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Authors: Eki Farlen, Akas

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Condition assessment of oil-paper-insulated power transformers, particularly of water content, is becoming increasingly important for aged transformers. As insulation ages, it can produce water, which reduces its dielectric strength, accelerates the cellulose ageing process, and causes gas bubbles to form at high temperatures. This paper mainly assesses the life condition of oil-paper insulation system of Inter Bus Transformer (IBT) 30 MVA, 150/30 kV in PT PLN-Substation Jelok that has been operating for 41 years, since 1974. Valuable information about the condition of high voltage insulation may be obtained by measuring its dielectric response. This paper describes in detail the interpretation of Dielectric Response Analysis (DIRANA) measurements and the test result compared to other insulation tests to get deep information for diagnostic, such as Tan delta test, oil characteristic test and Dissolve Gas Analysis (DGA) test. This paper mainly discusses the parameter relationship between moisture content, water content, acidity, oil conductivity and dissipation factor. The result and analysis show that IBT 30 MVA Jelok phase U and W had just been ageing due to high acidity level (>0.2 mgKOH/g) which cause high moisture in cellulose/paper (%) are in wet category about 4.7% and 5% and water content in oil (ppm) about 3.13 ppm and 3.33 ppm at temperature 20°C. High acidity level can make oxidation process and produce water in paper and particle which can decrease the value of Interfacial Tension (IFT) below 22 mN/m (poor category) for both phase U and W. Even if paper insulation of transformer are in wet condition, dissipation factor and capacitance at the same frequency (50 Hz) from both measurement DIRANA test and Tangent delta test give the same result (almost), the results are 0.69% and 0.71% (<1%), it may be acceptable and should not be investigated. The DGA results show that TDCG are in level one (1) condition and there are no found a Key Gases, it means that transformers had no failure during operation like arching, partial discharge and thermal in oil or cellulose.

Keywords: diagnostic, inter-bus transformer, oil-paper insulation, moisture, dissipation factor

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Authors: Chuluundorj Begz

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This paper presents the dynamic, psycho-cognitive approach to study of human verbal thinking on the basis of typologically different languages /as a Mongolian, English and Russian/. Topological equivalence in verbal communication serves as a basis of Universality of mental structures and therefore deep structures. Mechanism of verbal thinking consisted at the deep level of basic concepts, rules for integration and classification, neural networks of vocabulary. In neuro cognitive study of language, neural architecture and neuro psychological mechanism of verbal cognition are basis of a vector-based modeling. Verbal perception and interpretation of the infinite set of meanings and propositions in mental continuum can be modeled by applying tensor methods. Euclidean and non-Euclidean spaces are applied for a description of human semantic vocabulary and high order structures.

Keywords: Euclidean spaces, isomorphism and homomorphism, mental lexicon, mental mapping, semantic memory, verbal cognition, vector space

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Authors: Jacob Green, Cecilia Cabrera, Maximilian Jakobs, Andrea Dimitracopoulos, Mark van der Wilk, Ryan Greenhalgh

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Predicting drug properties is key in drug discovery to enable de-risking of assets before expensive clinical trials and to find highly active compounds faster. Interest from the machine learning community has led to the release of a variety of benchmark datasets and proposed methods. However, it remains unclear for practitioners which method or approach is most suitable, as different papers benchmark on different datasets and methods, leading to varying conclusions that are not easily compared. Our large-scale empirical study links together numerous earlier works on different datasets and methods, thus offering a comprehensive overview of the existing property classes, datasets, and their interactions with different methods. We emphasise the importance of uncertainty quantification and the time and, therefore, cost of applying these methods in the drug development decision-making cycle. To the best of the author's knowledge, it has been observed that the optimal approach varies depending on the dataset and that engineered features with classical machine learning methods often outperform deep learning. Specifically, QSAR datasets are typically best analysed with classical methods such as Gaussian Processes, while ADMET datasets are sometimes better described by Trees or deep learning methods such as Graph Neural Networks or language models. Our work highlights that practitioners do not yet have a straightforward, black-box procedure to rely on and sets a precedent for creating practitioner-relevant benchmarks. Deep learning approaches must be proven on these benchmarks to become the practical method of choice in drug property prediction.

Keywords: activity (QSAR), ADMET, classical methods, drug property prediction, empirical study, machine learning

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Authors: Theo H. G. Moundzounga

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Arsenic and 2-chlorophenol are priority pollutants that pose serious health threats to humans and ecology. An electrochemical sensor, based on graphitic carbon nitride (g-C₃N₄) and carbon dots (CDs), was fabricated and used for the determination of arsenic and 2-chlorophenol. The g-C₃N₄/CDs nanocomposite was prepared via microwave irradiation heating method and was dropped-dried on the surface of the glassy carbon electrode (GCE). Transmission electron microscopy (TEM), X-ray diffraction (XRD), photoluminescence (PL), Fourier transform infrared spectroscopy (FTIR), UV-Vis diffuse reflectance spectroscopy (UV-Vis DRS) were used for the characterization of structure and morphology of the nanocomposite. Electrochemical characterization was done by electrochemical impedance spectroscopy (EIS) and cyclic voltammetry (CV). The electrochemical behaviors of arsenic and 2-chlorophenol on different electrodes (GCE, CDs/GCE, and g-C₃N₄/CDs/GCE) was investigated by differential pulse voltammetry (DPV). The results demonstrated that the g-C₃N₄/CDs/GCE significantly enhanced the oxidation peak current of both analytes. The analytes detection sensitivity was greatly improved, suggesting that this new modified electrode has great potential in the determination of trace level of arsenic and 2-chlorophenol. Experimental conditions which affect the electrochemical response of arsenic and 2-chlorophenol were studied, the oxidation peak currents displayed a good linear relationship to concentration for 2-chlorophenol (R²=0.948, n=5) and arsenic (R²=0.9524, n=5), with a linear range from 0.5 to 2.5μM for 2-CP and arsenic and a detection limit of 2.15μM and 0.39μM respectively. The modified electrode was used to determine arsenic and 2-chlorophenol in spiked tap and effluent water samples by the standard addition method, and the results were satisfying. According to the measurement, the new modified electrode is a good alternative as chemical sensor for determination of other phenols.

Keywords: electrochemistry, electrode, limit of detection, sensor

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Authors: Wann-Yun Shieh, Chin-Man Wang, Ya-Cheng Shieh

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This study aims to develop an objective scoring system to evaluate hand-eye coordination and hand dexterity for Parkinson’s disease. This system contains three boards, and each of them is implemented with the sensors to sense a user’s finger operations. The operations include the peg test, the block test, and the blind block test. A user has to use the vision, hearing, and tactile abilities to finish these operations, and the board will record the results automatically. These results can help the physicians to evaluate a user’s reaction, coordination, dexterity function. The results will be collected to a cloud database for further analysis and statistics. A researcher can use this system to obtain systematic, graphic reports for an individual or a group of users. Particularly, a deep learning model is developed to learn the features of the data from different users. This model will help the physicians to assess the Parkinson’s disease symptoms by a more intellective algorithm.

Keywords: deep learning, hand-eye coordination, reaction, hand dexterity

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Authors: Adebisi Moruf Ademola

Abstract:

Hematite (Fe2O3),commonly known as ‘rust’ which usually surfaced on metal when exposed to some climatic materials. This emerges as a promising candidate for photoelectrochemical (PEC) water splitting due to its favorable physiochemical properties of the narrow band gap (2.1–2.2 eV), chemical stability, nontoxicity, abundance, and low cost. However, inherent limitations such as short hole diffusion length (2–4 nm), high charge recombination rate, and slow oxygen evolution reaction kinetics inhibit the PEC performances of a-Fe2O3 photoanodes. As such, given the narrow bandgap enabling excellent optical absorption, increased charge carrier density and accelerated surface oxidation reaction kinetics become the key points for improved photoelectrochemical performances for a-Fe2O3 photoanodes and metal ion doping as an effective way to promote charge transfer by increasing donor density and improving the electronic conductivity of a-Fe2O3. Hematite attracts enormous efforts with a number of metal ions (Ti, Zr, Sn, Pt ,etc.) as dopants. A facile deposition-annealing process showed greatly enhanced PEC performance due to the increased donor density and reduced electron-hole recombination at the time scale beyond a few picoseconds. Zr doping was also found to enhance the PEC performance of a-Fe2O3 nanorod arrays by reducing the rate of electron-hole recombination. Slow water oxidation reaction kinetics, another main factor limiting the PEC water splitting efficiency of aFe2O3 as photoanodes, was previously found to be effectively improved by surface treatment.

Keywords: deposition-annealing, hematite, metal ion doping, nanorod

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Authors: Airy Huang, Fuji Foo, Aries Prasetya Wibowo

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In recent years, the focus of Natural Language Processing (NLP) development has been gradually shifting from the semantics-based approach to deep learning one, which performs faster with fewer resources. Although it performs well in many applications, the deep learning approach, due to the lack of semantics understanding, has difficulties in noticing and expressing a novel business case with a pre-defined scope. In order to meet the requirements of specific robotic services, deep learning approach is very labor-intensive and time consuming. It is very difficult to improve the capabilities of conversational AI in a short time, and it is even more difficult to self-learn from experiences to deliver the same service in a better way. In this paper, we present an adaptive conversational AI algorithm that combines both semantic knowledge and deep learning to address this issue by learning new business cases through conversations. After self-learning from experience, the robot adapts to the business cases originally out of scope. The idea is to build new or extended robotic services in a systematic and fast-training manner with self-configured programs and constructed dialog flows. For every cycle in which a chat bot (conversational AI) delivers a given set of business cases, it is trapped to self-measure its performance and rethink every unknown dialog flows to improve the service by retraining with those new business cases. If the training process reaches a bottleneck and incurs some difficulties, human personnel will be informed of further instructions. He or she may retrain the chat bot with newly configured programs, or new dialog flows for new services. One approach employs semantics analysis to learn the dialogues for new business cases and then establish the necessary ontology for the new service. With the newly learned programs, it completes the understanding of the reaction behavior and finally uses dialog flows to connect all the understanding results and programs, achieving the goal of self-learning process. We have developed a chat bot service mounted on a kiosk, with a camera for facial recognition and a directional microphone array for voice capture. The chat bot serves as a concierge with polite conversation for visitors. As a proof of concept. We have demonstrated to complete 90% of reception services with limited self-learning capability.

Keywords: conversational AI, chatbot, dialog management, semantic analysis

Procedia PDF Downloads 126