Search results for: deep cryogenic treatment

Authors: Zumra Mehmedovic, Susan Luhrmann

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Treatment-resistant depression (TRD) is a debilitating mental health disorder for which there are very few available treatment options. Current research suggests that ketamine may be a safe and effective option for the treatment of TRD. Research utilizing a review of the literature was conducted to determine if ketamine in conjunction with antidepressants is more effective than antidepressants alone in the treatment of TRD. The literature consists of ten journal articles which include quantitative studies based on primary research. A critique of the literature was done to determine whether the findings are reliable, critiquing elements influencing the believability and robustness of the research. The research was based on the neuroplasticity theory of depression, hypothesizing that ketamine, in conjunction with antidepressants, will be more effective than antidepressants alone as they have different mechanisms of action. All the studies except one found ketamine in conjunction with antidepressants to be a more effective treatment than antidepressants alone in the treatment of TRD. Results of the studies indicate that ketamine is effective in treating TRD at various doses, settings, and routes of administration. Further research is necessary, though, to further explore and confirm the findings. Several gaps in literature were identified, including the optimal dose of ketamine, its long-term efficacy and safety, and effects of ketamine in repeated doses. The research topic is highly significant to advanced practice nursing, as based on the findings, ketamine can be utilized as a safe and effective treatment for TRD.

Keywords: ketamine, major depressive disorder, treatment-resistant depression, treatment

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Authors: Ruby Siwach, Manish Kumar, Raman Seth

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Extent of inactivation of pectin methylesterase (PME) in mosambi juice during thermal and thermosonication treatments was studied to obtain a minimally processed product. Effect of both treatments on cloud value, pH, titratable acidity, oBrix, and sensory attributes (flavour and taste) was studied. Thermal treatments (HT) were carried out at three temperatures 60, 70, and 80°C in a serological water bath for 5, 10, 15, and 20 min at each temperature. Thermosonication treatments (TS) were also given for same time-temperature combinations in water bath of a thermosonicator. Treated samples were stored in a deep freezer at 18°C for PME assay. PME activity of untreated sample was also assayed and residual PME activity and % loss in PME activity was calculated at each time-temperature combination. The extent of inactivation of PME increased with increase in treatment temperature and duration. Thermosonication treatments were found far more effective than thermal treatments of same time temperature combination in PME inactivation and retention of sensory attributes.

Keywords: pectin methylesterase, heat inactivation kinetics, thermosonication, thermal treatment

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Authors: Walid El Kamash

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Sand columns (or granular piles) can be employed as soil strengthening for flexible constructions such as road embankments, oil storage tanks in addition to multistory structures. The challenge of embedding the sand columns in soft soil is that the surrounding soft soil cannot avail the enough confinement stress in order to keep the form of the sand column. Therefore, the sand columns which were installed in such soil will lose their ability to perform needed load-bearing capacity. The encasement, besides increasing the strength and stiffness of the sand column, prevents the lateral squeezing of sands when the column is installed even in extremely soft soils, thus enabling quicker and more economical installation. This paper investigates the improvement in load capacity of the sand column by encasement through a comprehensive parametric study using the 3-D finite difference analysis for the soft clay of soil in Egypt. Moreover, the study was extended to include a comparison study between encased sand column and Deep Mixed columns (DM). The study showed that confining the sand by geosynthetic resulted in an increment of shear strength. That result paid the attention to use encased sand stone rather than deep mixed columns due to relative high permeability of the first material.

Keywords: encased sand column, Deep mixed column, numerical analysis, improving soft soil

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Authors: Jinil Patel, Sarthak Patel, Sarthak Thakkar, Deepti Saraswat

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Recently, the COVID-19 outbreak has spread across the world, leading the World Health Organization to classify it as a global pandemic. To save the patient’s life, the COVID-19 symptoms have to be identified. But using an AI (Artificial Intelligence) model to identify COVID-19 symptoms within the allotted time was challenging. The RT-PCR test was found to be inadequate in determining the COVID status of a patient. To determine if the patient has COVID-19 or not, a Computed Tomography Scan (CT scan) of patient is a better alternative. It will be challenging to compile and store all the data from various hospitals on the server, though. Federated learning, therefore, aids in resolving this problem. Certain deep learning models help to classify Covid-19. This paper will have detailed work of certain deep learning models like VGG19, ResNet50, MobileNEtv2, and Deep Learning Aggregation (DLA) along with maintaining privacy with encryption.

Keywords: federated learning, COVID-19, CT-scan, homomorphic encryption, ResNet50, VGG-19, MobileNetv2, DLA

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Authors: Babak Bahri, Fatemeh Yassaee Meybodi, Changiz Eslahchi

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In the pursuit of effective cancer therapies, the exploration of combinatorial drug regimens is crucial to leverage synergistic interactions between drugs, thereby improving treatment efficacy and overcoming drug resistance. However, identifying synergistic drug pairs poses challenges due to the vast combinatorial space and limitations of experimental approaches. This study introduces ClusterSyn, a machine learning (ML)-powered framework for classifying anti-cancer drug synergy scores. ClusterSyn employs a two-step approach involving drug clustering and synergy score prediction using a fully connected deep neural network. For each cell line in the training dataset, a drug graph is constructed, with nodes representing drugs and edge weights denoting synergy scores between drug pairs. Drugs are clustered using the Markov clustering (MCL) algorithm, and vectors representing the similarity of drug pairs to each cluster are input into the deep neural network for synergy score prediction (synergy or antagonism). Clustering results demonstrate effective grouping of drugs based on synergy scores, aligning similar synergy profiles. Subsequently, neural network predictions and synergy scores of the two drugs on others within their clusters are used to predict the synergy score of the considered drug pair. This approach facilitates comparative analysis with clustering and regression-based methods, revealing the superior performance of ClusterSyn over state-of-the-art methods like DeepSynergy and DeepDDS on diverse datasets such as Oniel and Almanac. The results highlight the remarkable potential of ClusterSyn as a versatile tool for predicting anti-cancer drug synergy scores.

Keywords: drug synergy, clustering, prediction, machine learning., deep learning

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Authors: G. L. Devnani, Shishir Sinha

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Natural fiber reinforced polymer composite is a very attractive area among the scientific community because of their low cost, eco-friendly and sustainable in nature. Among all advantages there are few issues which need to be addressed, those issues are the poor adhesion and compatibility between two opposite nature materials that is fiber and matrix and their relatively high water absorption. Therefore, natural fiber modifications are necessary to improve their adhesion with different matrices. Excellent properties could be achieved with the surface treatment of these natural fibers ultimately leads to property up-gradation of their reinforced composites with different polymer matrices. Lot of work is going on to improve the adhesion between reinforced fiber phase and polymer matrix phase to improve the properties of composites. Researchers have suggested various methods for natural fiber treatment like silane treatment, treatment with alkali, acetylation, acrylation, maleate coupling, etc. In this study a review is done on the different methods used for the surface treatment of natural fibers and what are the advance treatment methodologies for natural fiber surface treatment for property improvement of natural fiber reinforced polymer composites.

Keywords: composites, acetylation, natural fiber, surface treatment

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Authors: Vinayaka Gude Divya Sampath

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The research introduces an integrated prediction model to assess the susceptibility of roads in a future flooding event. The model consists of deep learning algorithm for forecasting gauge height data and Flood Inundation Mapper (FIM) for spatial flooding. An optimal architecture for Long short-term memory network (LSTM) was identified for the gauge located on Tangipahoa River at Robert, LA. Dropout was applied to the model to evaluate the uncertainty associated with the predictions. The estimates are then used along with FIM to identify the spatial flooding. Further geoprocessing in ArcGIS provides the susceptibility values for different roads. The model was validated based on the devastating flood of August 2016. The paper discusses the challenges for generalization the methodology for other locations and also for various types of flooding. The developed model can be used by the transportation department and other emergency response organizations for effective disaster management.

Keywords: deep learning, disaster management, flood prediction, urban flooding

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Authors: Adrià Arbués-Sangüesa, Coloma Ballester, Gloria Haro

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Tracking sports players is a widely challenging scenario, specially in single-feed videos recorded in tight courts, where cluttering and occlusions cannot be avoided. This paper presents an analysis of several geometric and semantic visual features to detect and track basketball players. An ablation study is carried out and then used to remark that a robust tracker can be built with Deep Learning features, without the need of extracting contextual ones, such as proximity or color similarity, nor applying camera stabilization techniques. The presented tracker consists of: (1) a detection step, which uses a pretrained deep learning model to estimate the players pose, followed by (2) a tracking step, which leverages pose and semantic information from the output of a convolutional layer in a VGG network. Its performance is analyzed in terms of MOTA over a basketball dataset with more than 10k instances.

Keywords: basketball, deep learning, feature extraction, single-camera, tracking

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Authors: Jie Zhang, Qianyu Guo, Tieyi Zhang, Zhiyong Feng, Xiaohong Li

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With the widespread popularity of mobile devices and the development of artificial intelligence (AI), deep learning (DL) has been extensively applied in Android apps. Compared with traditional Android apps (traditional apps), deep learning based Android apps (DL-based apps) need to use more third-party application programming interfaces (APIs) to complete complex DL inference tasks. However, existing methods (e.g., FlowDroid) for detecting sensitive information leakage in Android apps cannot be directly used to detect DL-based apps as they are difficult to detect third-party APIs. To solve this problem, we design DLtrace; a new static information flow analysis tool that can effectively recognize third-party APIs. With our proposed trace and detection algorithms, DLtrace can also efficiently detect privacy leaks caused by sensitive APIs in DL-based apps. Moreover, using DLtrace, we summarize the non-sequential characteristics of DL inference tasks in DL-based apps and the specific functionalities provided by DL models for such apps. We propose two formal definitions to deal with the common polymorphism and anonymous inner-class problems in the Android static analyzer. We conducted an empirical assessment with DLtrace on 208 popular DL-based apps in the wild and found that 26.0% of the apps suffered from sensitive information leakage. Furthermore, DLtrace has a more robust performance than FlowDroid in detecting and identifying third-party APIs. The experimental results demonstrate that DLtrace expands FlowDroid in understanding DL-based apps and detecting security issues therein.

Keywords: mobile computing, deep learning apps, sensitive information, static analysis

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Authors: Lihong Wu, Raymond Young

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The ability to learn is one of the most critical skills in the information age. However, many students do not have a clear understanding of what learning is, what they are learning, and why they are learning. Many students study simply to pass rather than to learn something useful for their career and their life. They have a misconception about learning and a wrong attitude towards learning. This research explores student attitudes to study in management education and explores how to intercede to lead students from shallow to deeper modes of learning.

Keywords: knowledge, surface learning, deep learning, education

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Authors: Evelyn Smith, Susan Simpson

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Background: Eating disorder (ED) treatment is complicated by high rates of chronicity, comorbidity, complex personality traits and client dropout. Given these complexities, Schema Therapy (ST) has been identified as a suitable treatment option. The study primarily aims to evaluate the efficacy of group ST for the treatment of EDs. The study further evaluated the effectiveness of ST in reducing schemas and improving quality of life. Method: Participant suitability was ascertained using the Eating Disorder Examination. Following this, participants attended 90-minute weekly group sessions over 25 weeks. Groups consisted of six to eight participants and were facilitated by two psychologists, at least one of who is trained in ST. Measures were completed at pre, mid and post-treatment. Measures assessed ED symptoms, cognitive schemas, schema mode presentations, quality of life, self-compassion and psychological distress. Results: As predicted, measures of ED symptoms were significantly reduced following treatment. No significant changes were observed in early maladaptive schema severity; however, reductions in schema modes were observed. Participants did not report improvements in general quality of life measures following treatment, though improvement in psychological well-being was observed. Discussion: Overall, the findings from the current study support the use of group ST for the treatment of EDs. It is expected that lengthier treatment is needed for the reduction in schema severity. Given participant dropout was considerably low, this has important treatment implications for the suitability of ST for the treatment of EDs.

Keywords: eating disorders, schema therapy, treatment, quality of life

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Authors: Amir Mabhout, Toktam Ghafarian, Amirhossein Farzin, Zahra Makki, Sajjad Alizadeh, Amirhossein Ghavi

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The high number of Netflix subscribers makes it attractive for data scientists to extract valuable knowledge from the viewers' behavioural analyses. This paper presents a set of statistical insights into viewers' viewing history. After that, a deep learning model is used to predict the future watching behaviour of the users based on previous watching history within the Netflixlatte data pool. Netflixlatte in an aggregated and anonymized data pool of 320 Netflix viewers with a length 250 000 data points recorded between 2008-2022. We observe insightful correlations between the distribution of viewing time and the COVID-19 pandemic outbreak. The presented deep learning model predicts future movie and TV series viewing habits with an average loss of 0.175.

Keywords: data analysis, deep learning, LSTM neural network, netflix

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Authors: Ricky Saputra, Rashmi Walvekar, Mohammad Khalid

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Landfills, water contamination, and toxic gas emission are a few impacts faced by the environment due to the increasing number of αof waste rubber tires (WRT). In spite of such concerning issue, only minimal efforts are taken to reclaim or recycle these wastes as their products are generally not-profitable for companies. Unlike the typical reclamation process, devulcanization is a method to selectively cleave sulfidic bonds within vulcanizates to avoid polymeric scissions that compromise elastomer’s mechanical and tensile properties. The process also produces devulcanizates that are re-processable similar to virgin rubber. Often, a devulcanizing agent is needed. In the current study, novel and sustainable ammonium chloride-based ternary deep eutectic solvents (TDES), with a different number of α-hydrogens, were utilised to devulcanize ground rubber tire (GRT) as an effort to implement green chemistry to tackle such issue. 40-mesh GRT were soaked for 1 day with different TDESs and sonicated at 37-80 kHz for 60-120 mins and heated at 100-140oC for 30-90 mins. Devulcanizates were then filtered, dried, and evaluated based on the percentage of by means of Flory-Rehner calculation and swelling index. The result shows that an increasing number of α-Hs increases the degree of devulcanization, and the value achieved was around eighty-percent, thirty percent higher than the typical industrial-autoclave method. Resulting bondages of devulcanizates were also analysed by Fourier transform infrared spectrometer (FTIR), Horikx fitting, and thermogravimetric analyser (TGA). The earlier two confirms only sulfidic scissions were experienced by GRT through the treatment, while the latter proves the absence or negligibility of carbon-chains scission.

Keywords: ammonium, sustainable, deep eutectic solvent, α-hydrogen, waste rubber tire

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Authors: Ng Mei Han, Nu'man Abdul Hadi

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Extraction of ferulic acid from palm pressed fiber using deep eutectic solvent (DES) of choline chloride-acetic acid (ChCl-AA) and choline chloride-citric acid (ChCl-CA) are reported. Influence of water content in DES on the extraction efficiency was investigated. ChCl-AA and ChCl-CA experienced a drop in viscosity from 9.678 to 1.429 and 22.658 ± 1.655 mm2/s, respectively as the water content in the DES increased from 0 to 50 wt% which contributed to higher extraction efficiency for the ferulic acid. Between 41,155 ± 940 mg/kg ferulic acid was obtained after 6 h reflux when ChCl-AA with 30 wt% water was used for the extraction compared to 30,940 ± 621 mg/kg when neat ChCl-AA was used. Although viscosity of the DES could be improved with the addition of water, there is a threshold where the DES could tolerate the presence of water without changing its solvent behavior. The optimum condition for extraction of ferulic acid from palm pressed fiber was heating for 6 h with DES containing 30 wt% water.

Keywords: deep eutectic solvent, extraction, ferulic acid, palm fibre

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Authors: Zabeehullah, Fahim Arif, Yawar Abbas

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Software Defined Network (SDN) is a next genera-tion networking model which simplifies the traditional network complexities and improve the utilization of constrained resources. Currently, most of the SDN based Internet of Things(IoT) environments use traditional network routing strategies which work on the basis of max or min metric value. However, IoT network heterogeneity, dynamic traffic flow and complexity demands intelligent and self-adaptive routing algorithms because traditional routing algorithms lack the self-adaptions, intelligence and efficient utilization of resources. To some extent, SDN, due its flexibility, and centralized control has managed the IoT complexity and heterogeneity but still Software Defined IoT (SDIoT) lacks intelligence. To address this challenge, we proposed a model called Deep Routing Strategy (DRS) which uses Deep Learning algorithm to perform routing in SDIoT intelligently and efficiently. Our model uses real-time traffic for training and learning. Results demonstrate that proposed model has achieved high accuracy and low packet loss rate during path selection. Proposed model has also outperformed benchmark routing algorithm (OSPF). Moreover, proposed model provided encouraging results during high dynamic traffic flow.

Keywords: SDN, IoT, DL, ML, DRS

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Authors: Godwin Ojochogu Adejo, Evelyn UnekwuOjo Adejo, Sunday UnenwOjo Adejo

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The mainstream contemporary approach to controlling the impact of diseases among poultry birds rely largely on curative measures through the administration of drugs to infected birds. Most times as observed in the deep liter poultry farming system, entire flocks including uninfected birds receive the treatment they do not need. As such, unguarded use of chemical drugs and antibiotics has led to wastage and accumulation of chemical residues in poultry products with associated health hazards to humans. However, wanton and frequent drug usage in poultry is avoidable if feeding and drinking equipment are designed to curb infection transmission among birds. Using toxicological assays as guide and with efficiency and simplicity in view, two newly field-tested and recently patented equipments called 'healthy liquid drinking trough (HDT)' and 'healthy feeding trough (HFT)' that systematically eliminate contamination of the feeding and drinking channels, thereby, curbing wide-spread infection and transmission of diseases in the (intensive) deep litter poultry farming system were designed. Upon combined usage, they automatically and drastically reduced both the amount and frequency of antibiotics use in poultry by over > 50%. Additionally, they conferred optimization of feed and water utilization/elimination of wastage by > 80%, reduced labour by > 70%, reduced production cost by about 15%, and reduced chemical residues in poultry meat or eggs by > 85%. These new and cheap technologies which require no energy input are likely to elevate safety of poultry products for consumers' health, increase marketability locally and for export, and increase output and profit especially among poultry farmers and poor people who keep poultry or inevitably utilize poultry products in developing countries.

Keywords: healthy, trough, toxicological, assay-guided, poultry

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Authors: Chiau Yuan Lim, Hayyiratul Fatimah Mohd Zaid, Fai Kait Chong

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Deep Eutectic Solvent (DES) is used in various applications due to its simplicity in synthesis procedure, biodegradable, inexpensive and easily available chemical ingredients. Graphene Oxide is a popular catalyst that being used in various processes due to its stacking carbon sheets in layer which theoretically rapid up the catalytic processes. In this study, choline chloride based DESs were synthesized and ChCl-PEG(1:4) was found to be the most effective DES in performing desulfurization, which it is able to remove up to 47.4% of the sulfur content in the model oil in just 10 minutes, and up to 95% of sulfur content after repeat the process for six times. ChCl-PEG(1:4) able to perform up to 32.7% desulfurization on real diesel after 6 multiple stages. Thus, future research works should focus on removing the impurities on real diesel before utilising DESs in petroleum field.

Keywords: choline chloride, deep eutectic solvent, fuel desulfurization, graphene oxide

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Authors: Hitendra Raj Joshi, Bipin Lamichhane

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Groundwater is the main source of water for the industries of Balaju Industrial District (BID) and the denizens of Balaju-Boratar area. The quantity of groundwater is in a fatal condition in the area than earlier days. Water levels in shallow wells have highly lowered and deep wells are not providing an adequate amount of water as before because of higher extraction rate than the recharge rate. The main recharge zone of the shallow aquifer lies at the foot of Nagarjuna mountain, where recent colluvial debris are accumulated. Urbanization in the area is the main reason for decreasing water table. Recharge source for the deep aquifer in the region is aquiclude leakage. Sand layer above the Kalimati clay is the shallow aquifer zone, which is limited only in Balaju and eastern part of the Boratar, while the layer below the Kalimati clay spreading around Gongabu, Machhapohari, and Balaju area is considered as a potential area of deep aquifer. Over extraction of groundwater without considering water balance in the aquifers may dry out the source and can initiate the land subsidence problem. Hence, all the responsible of the industries in BID area and the denizens of Balaju-Boratar area should be encouraged to practice artificial groundwater recharge.

Keywords: aquiclude leakage, Kalimati clay, groundwater recharge

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Authors: James A. Adeyanju, John O. Olajide, Akinbode A. Adedeji

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A predictive mathematical model based on the fundamental principles of mass transfer was developed to simulate the moisture content and oil content during Deep-Fat Frying (DFF) process of dodo. The resulting governing equation, that is, partial differential equation that describes rate of moisture loss and oil uptake was solved numerically using explicit Finite Difference Technique (FDT). Computer codes were written in MATLAB environment for the implementation of FDT at different frying conditions and moisture loss as well as oil uptake simulation during DFF of dodo. Plantain samples were sliced into 5 mm thickness and fried at different frying oil temperatures (150, 160 and 170 ⁰C) for periods varying from 2 to 4 min. The comparison between the predicted results and experimental data for the validation of the model showed reasonable agreement. The correlation coefficients between the predicted and experimental values of moisture and oil transfer models ranging from 0.912 to 0.947 and 0.895 to 0.957, respectively. The predicted results could be further used for the design, control and optimization of deep-fat frying process.

Keywords: frying, moisture loss, modelling, oil uptake

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Authors: Chigbo Ikechukwu Emmanuel

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Microalgae can be used for tertiary treatment of wastewater due to their capacity to assimilate nutrients. The pH increase which is mediated by the growing algae also induces phosphorus precipitation and ammonia stripping to the air, and may in addition act disinfecting on the wastewater. Domestic wastewater is ideal for algal growth since it contains high concentrations of all necessary nutrients. The growth limiting factor is rather light, especially at higher latitudes. The most important operational factors for successful wastewater treatment with microalgae are depth, turbulence and hydraulic retention time.

Keywords: microalgae, wastewater treatment, phosphorus, nitrogen, light, operation, ponds, growth

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Authors: Shraddha Acharya, Sharad Kumar Sharma, Ratna Bhattarai, Bhagwan Maharjan, Deepak Dahal, Serpahine Kaminsa

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Background: Drug-resistant (DR) tuberculosis (TB) continues to be a threat in Nepal, with an estimated 2800 new cases every year. The treatment of DR-TB with second line TB drugs is complex and takes longer time with comparatively lower treatment success rate than drug-susceptible TB. Delay in treatment initiation for DR-TB patients might further result in unfavorable treatment outcomes and increased transmission. This study thus aims to determine median time taken to initiate second-line treatment among Rifampicin Resistant (RR) diagnosed TB patients and to assess the proportion of treatment delays among various type of DR-TB cases. Method: A retrospective cohort study was done using national routine electronic data (DRTB and TB Laboratory Patient Tracking System-DHIS2) on drug resistant tuberculosis patients between January 2020 and December 2022. The time taken for treatment initiation was computed as– days from first diagnosis as RR TB through Xpert MTB/Rif test to enrollment on second-line treatment. The treatment delay (>7 days after diagnosis) was calculated. Results: Among total RR TB cases (N=954) diagnosed via Xpert nationwide, 61.4% were enrolled under shorter-treatment regimen (STR), 33.0% under longer treatment regimen (LTR), 5.1% for Pre-extensively drug resistant TB (Pre-XDR) and 0.4% for Extensively drug resistant TB (XDR) treatment. Among these cases, it was found that the median time from diagnosis to treatment initiation was 6 days (IQR:2-15.8). The median time was 5 days (IQR:2.0-13.3) among STR, 6 days (IQR:3.0-15.0) among LTR, 30 days (IQR:5.5-66.8) among Pre-XDR and 4 days (IQR:2.5-9.0) among XDR TB cases. The overall treatment delay (>7 days after diagnosis) was observed in 42.4% of the patients, among which, cases enrolled under Pre-XDR contributed substantially to treatment delay (72.0%), followed by LTR (43.6%), STR (39.1%) and XDR (33.3%). Conclusion: Timely diagnosis and prompt treatment initiation remain fundamental focus of the National TB program. The findings of the study, however suggest gaps in timeliness of treatment initiation for the drug-resistant TB patients, which could bring adverse treatment outcomes. Moreover, there is an alarming delay in second line treatment initiation for the Pre-XDR TB patients. Therefore, this study generates evidence to identify existing gaps in treatment initiation and highlights need for formulating specific policies and intervention in creating effective linkage between the RR TB diagnosis and enrollment on second line TB treatment with intensified efforts from health providers for follow-ups and expansion of more decentralized, adequate, and accessible diagnostic and treatment services for DR-TB, especially Pre-XDR TB cases, due to the observed long treatment delays.

Keywords: drug-resistant, tuberculosis, treatment initiation, Nepal, treatment delay

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Authors: Samuel Capintero, Ole H. Petersen

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This paper examines the utilization of public-private partnerships for the building and operation of wastewater treatment plants. Our research focuses on risk allocation in this kind of projects. Our analysis builds on more than hundred wastewater treatment plants built and operated through PPP projects in Aragon (Spain). The paper illustrates the consequences of an inadequate management of construction risk and an unsuitable transfer of demand risk in wastewater treatment plants. It also shows that the involvement of many public bodies at local, regional and national level further increases the complexity of this kind of projects and make time delays more likely.

Keywords: wastewater, treatment plants, PPP, construction

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Authors: A. M. Gsiea, J. P. Goss, P. R. Briddon, Ramadan. M. Al-habashi, K. M. Etmimi, Khaled. A. S. Marghani

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Using first-principles methods based on density functional theory and pseudopotentials, we have performed a details study of native defects in ZnO. Native point defects are unlikely to be cause of the unintentional n-type conductivity. Oxygen vacancies, which considered most often been invoked as shallow donors, have high formation energies in n-type ZnO, in edition are a deep donors. Zinc interstitials are shallow donors, with high formation energies in n-type ZnO, and thus unlikely to be responsible on their own for unintentional n-type conductivity under equilibrium conditions, as well as Zn antisites which have higher formation energies than zinc interstitials. Zinc vacancies are deep acceptors with low formation energies for n-type and in which case they will not play role in p-type coductivity of ZnO. Oxygen interstitials are stable in the form of electrically inactive split interstitials as well as deep acceptors at the octahedral interstitial site under n-type conditions. Our results may provide a guide to experimental studies of point defects in ZnO.

Keywords: DFT, native, n-type, ZnO

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Authors: Hailyie Tekleselase

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Deep learning is increasingly used as a building block of security systems. However, neural networks are hard to interpret and typically solid to the practitioner. This paper presents a detail survey of computing methods in cyber security, and analyzes the prospects of enhancing the cyber security capabilities by suggests that of accelerating the intelligence of the security systems. There are many AI-based applications used in industrial scenarios such as Internet of Things (IoT), smart grids, and edge computing. Machine learning technologies require a training process which introduces the protection problems in the training data and algorithms. We present machine learning techniques currently applied to the detection of intrusion, malware, and spam. Our conclusions are based on an extensive review of the literature as well as on experiments performed on real enterprise systems and network traffic. We conclude that problems can be solved successfully only when methods of artificial intelligence are being used besides human experts or operators.

Keywords: artificial intelligence, machine learning, deep learning, cyber security, big data

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Authors: Chinlun Lai, Lunjyh Jiang

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Due to the heavy burden and pressure of caring for infants, an integrated automatic baby watching system based on IoT smart sensing and deep learning machine vision techniques is proposed in this paper. By monitoring infant body conditions such as heartbeat, breathing, body temperature, sleeping posture, as well as the surrounding conditions such as dangerous/sharp objects, light, noise, humidity and temperature, the proposed system can analyze and predict the obvious/potential dangerous conditions according to observed data and then adopt suitable actions in real time to protect the infant from harm. Thus, reducing the burden of the caregiver and improving safety efficiency of the caring work. The experimental results show that the proposed system works successfully for the infant care work and thus can be implemented in various life fields practically.

Keywords: baby care system, Internet of Things, deep learning, machine vision

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Authors: Priscila M. Lima, Corine A. P. de Almeida, Muriele R. de Lima, Fernando J. C. Magalhães Filho

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This work characterized the preliminary treatment in WWTPs in the state of Mato Grosso Do Sul (Brazil) and analyzed aspects of operation and maintenance of solid waste retained, and was evaluated the interference of this step in treatment efficiency beyond the relationship between solid waste generation with rainfall and seasonality in the region of each WTPs. The results shown that the standard setting in the preliminary treatment consists of grid along with Sand Trap, followed by Parshall that is used in 94.12% of WWTPs analyzed, and in 5.88% of WWTPs it was added the air-lift to the Sand Trap. Was concluded that the influence of rainfall, flow and seasonality associated with the rate of waste generation in the preliminary treatment, had little relation to the operation and maintenance of the primary treatment. But in some cases, precipitation data showed increased rainfall converging with increased flow and solid waste generation.

Keywords: pretreatment, sewage, solid waste, wastewater

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Authors: C. Pearcy, C. Rees

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Cognitive behavioural therapy (CBT) and exposure and response prevention (ERP) are currently the most efficacious treatments for Obsessive-compulsive disorder (OCD). Many clients, however, remain symptomatic following treatment. As a result, refusal of treatment, withdrawal from treatment, and partial adherence to treatment are common amongst ERP. Such limitations have caused few professionals to actually engage in ERP therapy, which has warranted the exploration of alternative treatments. This study evaluated an online self-help treatment program for OCD (the OCD Doctor Online); a 4-week Metacognitive Therapy (MCT) program which has implemented strategies from Wells’ Metacognitive model of OCD. The aim of the present study was to investigate whether an online self-help treatment using MCT would reduce symptoms of OCD, reduce unhelpful metacognitions and improve quality of life. Treatment effectiveness was assessed using a case series methodology in 3 consecutively referred individuals. At post-treatment, all participants showed reductions in unhelpful metacognitive beliefs (MCQ-30) and improvements in quality of life (Q-LES-Q), which were maintained through to 4 week follow-up. Two of the three participants showed reductions in OCD symptomology (OCI-R), which were further reduced at 4-week follow-up. The present study suggests that internet-based self-help treatment may be an effective means of delivering MCT to adults with OCD.

Keywords: internet-based, metacognitive therapy, obsessive-compulsive disorder, self-help

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Authors: Caspar S. Carson, Gabriel W. Prather, Nicholas E. Wong, Jeffery R. Anton, William H. McCoy

Abstract:

Cutibacterium acnes is a commensal bacterium found on human skin that has been linked to acne. C. acnes can also be an opportunistic pathogen when it infiltrates the body during surgery. This pathogen can cause dangerous infections of medical implants, such as shoulder replacements, leading to life-threatening blood infections. Compounding this issue, C. acnes resistance to many antibiotics has become an increasing problem worldwide, creating a need for special forms of treatment. C. acnes expresses the protein RoxP, and it requires this protein to colonize human skin. Though this protein is required for C. acnes skin colonization, its function is not yet understood. Inhibition of RoxP function might be an effective treatment for C. acnes infections. To develop such reagents, the McCoy Laboratory generated four unique anti-RoxP antibodies. Preliminary studies in the McCoy Lab have established that each antibody binds a distinct site on RoxP. To assess the potential of these antibodies as therapeutics, it is necessary to specifically characterize these antibody epitopes and evaluate them in assays that assess their ability to inhibit RoxP-dependent C. acnes growth. To provide material for these studies, an antibody expression construct, Fv-clasp(v2), was adapted to encode anti-RoxP antibody sequences. The author hypothesizes that this expression strategy can produce sufficient amounts of >95% pure antibody fragments for further characterization of these antibodies. Four anti-RoxP Fv-clasp(v2) expression constructs (pET vector-based) were transformed into E. coli BL21-Gold(DE3) cells and a small-scale expression and purification trial was performed for each construct to evaluate anti-RoxP Fv-clasp(v2) yield and purity. Successful expression and purification of these antibody constructs will allow for their use in structural studies, such as protein crystallography and cryogenic electron microscopy. Such studies would help to define the antibody binding sites on RoxP, which could then be leveraged in the development of certain methods to treat C. acnes infection through RoxP inhibition.

Keywords: structural biology, protein expression, infectious disease, antibody, therapeutics, E. coli

Procedia PDF Downloads 31

Authors: Ian Omung'a

Abstract:

Breast cancer remains to be one of the deadliest cancers for women worldwide, with the risk of developing tumors being as high as 50 percent in Sub-Saharan African countries like Kenya. With as many as 42 percent of these cases set to be diagnosed late when cancer has metastasized and or the prognosis has become terminal, Full Field Digital [FFD] Mammography remains an effective screening technique that leads to early detection where in most cases, successful interventions can be made to control or eliminate the tumors altogether. FFD Mammograms have been proven to multiply more effective when used together with Computer-Aided Detection and Diagnosis [CADe] systems, relying on algorithmic implementations of Deep Learning techniques in Computer Vision to carry out deep pattern recognition that is comparable to the level of a human radiologist and decipher whether specific areas of interest in the mammogram scan image portray abnormalities if any and whether these abnormalities are indicative of a benign or malignant tumor. Within this paper, we review emergent Deep Learning techniques that will prove relevant to the development of State-of-The-Art FFD Mammogram CADe systems. These techniques will span self-supervised learning for context-encoded occlusion, self-supervised learning for pre-processing and labeling automation, as well as the creation of a standardized large-scale mammography dataset as a benchmark for CADe systems' evaluation. Finally, comparisons are drawn between existing practices that pre-date these techniques and how the development of CADe systems that incorporate them will be different.

Keywords: breast cancer diagnosis, computer aided detection and diagnosis, deep learning, whole mammogram classfication, ultrasound classification, computer vision

Procedia PDF Downloads 67

Authors: M. Hadji, A. Haddad, Y. Hadji

Abstract:

The effects of boronizing treatment on the friction coefficient and wear behavior of 0.35 Vf TiC- Ti3 SiC2 composite were investigated. In order to modity the surface properties of Ti3SiC2, boronizing treatment was carried out through powder pack cementation in the 1150-1350 °C temperature range. After boronizing treatment, one mixture layer, composed of TiB2 and SiC, forms on the surface of Ti3SiC2. The growth of the coating is processed by inward diffusion of Boron and obeys a linear rule. The Boronizing treatment increases the hardness of Ti3SiC2 from 6 GPa to 13 GPa. In the pin-on-disc test, i twas found that the material undergoes a steady-state coefficient of friction of around 0.8 and 0.45 in case of Ti3SiC2/Al2O3 tribocouple under 7N load for the non treated and the boronized samples, respectively. The wear resistance of Ti3SiC2 underAl2O3 ball sliding has been significantly improved, which indicated that the boronizing treatment is a promising surface modification way of Ti3SiC2.

Keywords: MAX phase, wearing, friction, boronizing

Procedia PDF Downloads 424