Search results for: Genetic Algorithm

Authors: Faisal Muhibuddin, Ani Dijah Rahajoe

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This in-depth exploration, through a systematic literature review, scrutinizes the nuanced role of data mining in the classification of criminal activities. The research focuses on investigating various methodological aspects and recent developments in leveraging data mining techniques to enhance the effectiveness and precision of crime categorization. Commencing with an exposition of the foundational concepts of crime classification and its evolutionary dynamics, this study details the paradigm shift from conventional methods towards approaches supported by data mining, addressing the challenges and complexities inherent in the modern crime landscape. Specifically, the research delves into various data mining techniques, including K-means clustering, Naïve Bayes, K-nearest neighbour, and clustering methods. A comprehensive review of the strengths and limitations of each technique provides insights into their respective contributions to improving crime classification models. The integration of diverse data sources takes centre stage in this research. A detailed analysis explores how the amalgamation of structured data (such as criminal records) and unstructured data (such as social media) can offer a holistic understanding of crime, enriching classification models with more profound insights. Furthermore, the study explores the temporal implications in crime classification, emphasizing the significance of considering temporal factors to comprehend long-term trends and seasonality. The availability of real-time data is also elucidated as a crucial element in enhancing responsiveness and accuracy in crime classification.

Keywords: data mining, classification algorithm, naïve bayes, k-means clustering, k-nearest neigbhor, crime, data analysis, sistematic literature review

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Authors: Namwinwelbere Dabire, Eugene C. Ezin, Adandedji M. Firmin

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The prediction of hydrological flows (rainfall-depth or rainfall-discharge) is becoming increasingly important in the management of hydrological risks such as floods. In this study, the Long Short-Term Memory (LSTM) network, a state-of-the-art algorithm dedicated to time series, is applied to predict the daily water level of Nokoue Lake in Benin. This paper aims to provide an effective and reliable method enable of reproducing the future daily water level of Nokoue Lake, which is influenced by a combination of two phenomena: rainfall and river flow (runoff from the Ouémé River, the Sô River, the Porto-Novo lagoon, and the Atlantic Ocean). Performance analysis based on the forecasting horizon indicates that LSTM can predict the water level of Nokoué Lake up to a forecast horizon of t+10 days. Performance metrics such as Root Mean Square Error (RMSE), coefficient of correlation (R²), Nash-Sutcliffe Efficiency (NSE), and Mean Absolute Error (MAE) agree on a forecast horizon of up to t+3 days. The values of these metrics remain stable for forecast horizons of t+1 days, t+2 days, and t+3 days. The values of R² and NSE are greater than 0.97 during the training and testing phases in the Nokoué Lake basin. Based on the evaluation indices used to assess the model's performance for the appropriate forecast horizon of water level in the Nokoué Lake basin, the forecast horizon of t+3 days is chosen for predicting future daily water levels.

Keywords: forecasting, long short-term memory cell, recurrent artificial neural network, Nokoué lake

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Authors: Asghar Arif

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Lung cancer has been recognized as one of the greatest common cancers, causing the annual mortality rate of about 1.2 million people in the world. Lung cancer is the most prevalent cancer in men and the third-most common cancer among women (after breast and digestive cancers).Recent evidences have shown the inflammatory process as one of the potential factors of cancer. Tuberculosis (TB), pneumonia, and chronic bronchitis are among the most important inflammation-inducing factors in the lungs, among which TB has a more profound role in the emergence of cancer.TB is one of the important mortality factors throughout the world, and 205,000 death cases are reported annually due to this disease. Chronic inflammation and fibrosis due to TB can induce genetic mutation and alternations. Parenchyma tissue of lung is involved in both diseases of TB and lung cancer, and continuous cough in lung cancer, morphological vascular variations, lymphocytosis processes, and generation of immune system mediators such as interleukins, are all among the factors leading to the hypothesis regarding the role of TB in lung cancer Some reports have shown that the induction of necrosis and apoptosis or TB reactivation, especially in patients with immune-deficiency, may result in increasing IL-17 and TNF_α, which will either decrease P53 activity or increase the expression of Bcl-2, decrease Bax-T, and cause the inhibition of caspase-3 expression due to decreasing the expression of mitochondria cytochrome oxidase. It has been also indicated that following the injection of BCG vaccine, the host immune system will be reinforced, and in particular, the rates of gamma interferon, nitric oxide, and interleukin-2 are increased. Therefore, CD4 + lymphocyte function will be improved, and the person will be immune against cancer.Numerous prospective studies have so far been conducted on the role of TB in lung cancer, and it seems that this disease is effective in that particular cancer.One of the main challenges of lung cancer is its correct and timely diagnosis. Unfortunately, clinical symptoms (such as continuous cough, hemoptysis, weight loss, fever, chest pain, dyspnea, and loss of appetite) and radiological images are similar in TB and lung cancer. Therefore, anti-TB drugs are routinely prescribed for the patients in the countries with high prevalence of TB, like Pakistan. Regarding the similarity in clinical symptoms and radiological findings of lung cancer, proper diagnosis is necessary for TB and respiratory infections due to nontuberculousmycobacteria (NTM). Some of the drug resistive TB cases are, in fact, lung cancer or NTM lung infections. Acid-fast staining and histological study of phlegm and bronchial washing, culturing and polymerase chain reaction TB are among the most important solutions for differential diagnosis of these diseases. Briefly, it is assumed that TB is one of the risk factors for cancer. Numerous studies have been conducted in this regard throughout the world, and it has been observed that there is a significant relationship between previous TB infection and lung cancer. However, to prove this hypothesis, further and more extensive studies are required. In addition, as the clinical symptoms and radiological findings of TB, lung cancer, and non-TB mycobacteria lung infections are similar, they can be misdiagnosed as TB.

Keywords: TB and lung cancer, TB people, TB servivers, TB and HIV aids

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Authors: Spira A., Marabelle A., Kientop D., Moser E., Mumm J.

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Background: Both interleukin-2 (IL-2) and interleukin-10 (IL-10) have been extensively studied for their stimulatory function on T cells and their potential to obtain sustainable tumor control in RCC, melanoma, lung, and pancreatic cancer as monotherapy, as well as combination with PD-1 blockers, radiation, and chemotherapy. While approved, IL-2 retains significant toxicity, preventing its widespread use. The significant efforts undertaken to uncouple IL-2 toxicity from its anti-tumor function have been unsuccessful, and early phase clinical safety observed with PEGylated IL-10 was not met in a blinded Phase 3 trial. Deka Biosciences has engineered a novel molecule coupling wild-type IL-2 to a high affinity variant of Epstein Barr Viral (EBV) IL-10 via a scaffold (scFv) that binds to epidermal growth factor receptors (EGFR). This patented molecule, termed DK210 (EGFR), is retained at high levels within the tumor microenvironment for days after dosing. In addition to overlapping and non-redundant anti-tumor function, IL-10 reduces IL-2 mediated cytokine release syndrome risks and inhibits IL-2 mediated T regulatory cell proliferation. Methods: DK210 (EGFR) is being evaluated in an open-label, dose-escalation (Phase 1) study with 5 (0.025-0.3 mg/kg) monotherapy dose levels and (expansion cohorts) in combination with PD-1 blockers, or radiation or chemotherapy in patients with advanced solid tumors overexpressing EGFR. Key eligibility criteria include 1) confirmed progressive disease on at least one line of systemic treatment, 2) EGFR overexpression or amplification documented in histology reports, 3) at least a 4 week or 5 half-lives window since last treatment, and 4) excluding subjects with long QT syndrome, multiple myeloma, multiple sclerosis, myasthenia gravis or uncontrolled infectious, psychiatric, neurologic, or cancer disease. Plasma and tissue samples will be investigated for pharmacodynamic and predictive biomarkers and genetic signatures associated with IFN-gamma secretion, aiming to select subjects for treatment in Phase 2. Conclusion: Through successful coupling of wild-type IL-2 with a high affinity IL-10 and targeting directly to the tumor microenvironment, DK210 (EGFR) has the potential to harness IL-2 and IL-10’s known anti-cancer promise while reducing immunogenicity and toxicity risks enabling safe concomitant cytokine treatment with other anti-cancer modalities.

Keywords: cytokine, EGFR over expression, interleukine-2, interleukine-10, clinical trial

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Authors: Chia-Chi Chang, Chuan-Bi Lin, Chia-Min Chan

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Most ZigBee sensor networks to date make use of nodes with limited processing, communication, and energy capabilities. Energy consumption is of great importance in wireless sensor applications as their nodes are commonly battery-driven. Once ZigBee nodes are deployed outdoors, limited power may make a sensor network useless before its purpose is complete. At present, there are two strategies for long node and network lifetime. The first strategy is saving energy as much as possible. The energy consumption will be minimized through switching the node from active mode to sleep mode and routing protocol with ultra-low energy consumption. The second strategy is to evaluate the energy consumption of sensor applications as accurately as possible. Erroneous energy model may render a ZigBee sensor network useless before changing batteries. In this paper, we present a ZigBee wireless sensor node with four key modules: a processing and radio unit, an energy harvesting unit, an energy storage unit, and a sensor unit. The processing unit uses CC2530 for controlling the sensor, carrying out routing protocol, and performing wireless communication with other nodes. The harvesting unit uses a 2W solar panel to provide lasting energy for the node. The storage unit consists of a rechargeable 1200 mAh Li-ion battery and a battery charger using a constant-current/constant-voltage algorithm. Our solution to extend node lifetime is implemented. Finally, a long-term sensor network test is used to exhibit the functionality of the solar powered system.

Keywords: ZigBee, Li-ion battery, solar panel, CC2530

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Authors: Rudy Bahouth, Farouk Benmeddour, Emmanuel Moulin, Jamal Assaad

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Guided ultrasonic waves are used in Non-Destructive Testing (NDT) and Structural Health Monitoring (SHM) for inspection and damage detection. Recently, wireless data transmission using ultrasonic waves in solid metallic channels has gained popularity in some industrial applications such as nuclear, aerospace and smart vehicles. The idea is to find a good substitute for electromagnetic waves since they are highly attenuated near metallic components due to Faraday shielding. The proposed solution is to use ultrasonic guided waves such as Lamb waves as an information carrier due to their capability of propagation for long distances. In addition to this, valuable information about the health of the structure could be extracted simultaneously. In this work, the reliable frequency bandwidth for communication is extracted experimentally from dispersion curves at first. Then, an experimental platform for wireless communication using Lamb waves is described and built. After this, coherent demodulation algorithm used in telecommunications is tested for Amplitude Shift Keying, On-Off Keying and Binary Phase Shift Keying modulation techniques. Signal processing parameters such as threshold choice, number of cycles per bit and Bit Rate are optimized. Experimental results are compared based on the average Bit Error Rate. Results have shown high sensitivity to threshold selection for Amplitude Shift Keying and On-Off Keying techniques resulting a Bit Rate decrease. Binary Phase Shift Keying technique shows the highest stability and data rate between all tested modulation techniques.

Keywords: lamb waves communication, wireless communication, coherent demodulation, bit error rate

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Authors: Shi-xia Xu, Zai-wen Zhang, Ru-xue Chen, Shan Zhou, Xiang-feng Tang

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Objective: The clinical influence of HLA-DPB1 mismatches on clinical outcome of HSCT is less clear. This is the first meta-analysis to study the HLA-DPB1 matching statues on clinical outcomes after unrelated donor HSCT. Methods: We searched the CIBMTR, Cochrane Central Register of Controlled Trials (CENTRAL) and related databases (1995.01–2017.06) for all relevant articles. Comparative studies were used to investigate the HLA-DPB1 loci mismatches on clinical outcomes after unrelated donor HSCT, such as the disease-free survival (DFS), overall survival, GVHD, relapse, and transplant-related mortality (TRM). We performed meta-analysis using Review Manager 5.2 software and funnel plot to assess the bias. Results: At first, 1246 articles were retrieved, and 18 studies totaling 26368 patients analyzed. Pooled comparisons of studies found that the HLA-DPB1 mismatched group had a lower rate of DFS than the DPB1-matched group, and lower OS in non-T cell depleted transplantation. The DPB1 mismatched group has a higher incidence of aGVHD and more severe ( ≥ III degree) aGvHD, lower rate of relapse and higher TRM. Moreover, compared with 1-antigen mismatch, 2-antigen mismatched led to a higher risk of TRM and lower relapse rate. Conclusions: This meta-analysis indicated HLA-DPB1 has important influence on survival and transplant-related complications during unrelated donor HSCT and HLA-DPB1 donor selection strategies have been proposed based on a personalized algorithm.

Keywords: human leukocyte antigen, DPB1, transplant, meta-analysis, outcome

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Authors: Yasin Tadayonrad

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Most of the supply chain networks have many nodes starting from the suppliers’ side up to the customers’ side that each node sends/receives the raw materials/products from/to the other nodes. One of the major concerns in this kind of supply chain network is finding the best schedule for loading /unloading the shipments through the whole network by which all the constraints in the source and destination nodes are met and all the shipments are delivered on time. One of the main constraints in this problem is loading/unloading capacity in each source/ destination node at each time slot (e.g., per week/day/hour). Because of the different characteristics of different products/groups of products, the capacity of each node might differ based on each group of products. In most supply chain networks (especially in the Fast-moving consumer goods industry), there are different planners/planning teams working separately in different nodes to determine the loading/unloading timeslots in source/destination nodes to send/receive the shipments. In this paper, a mathematical problem has been proposed to find the best timeslots for loading/unloading the shipments minimizing the overall delays subject to respecting the capacity of loading/unloading of each node, the required delivery date of each shipment (considering the lead-times), and working-days of each node. This model was implemented on python and solved using Python-MIP on a sample data set. Finally, the idea of a heuristic algorithm has been proposed as a way of improving the solution method that helps to implement the model on larger data sets in real business cases, including more nodes and shipments.

Keywords: supply chain management, transportation, multiple-multiple network, timeslots management, mathematical modeling, mixed integer programming

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Authors: Mulugeta K. Tefera, Xiaolong Yang, Jian Liu

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Detection and tracking of moving objects are very important in many application contexts such as detection and recognition of people, visual surveillance and automatic generation of video effect and so on. However, the task of detecting a real shape of an object in motion becomes tricky due to various challenges like dynamic scene changes, presence of shadow, and illumination variations due to light switch. For such systems, once the moving object is detected, tracking is also a crucial step for those applications that used in military defense, video surveillance, human computer interaction, and medical diagnostics as well as in commercial fields such as video games. In this paper, an object presents in dynamic background is detected using adaptive mixture of Gaussian based analysis of the video sequences. Then the detected moving object is tracked using the region based moving object tracking and inter-frame differential mechanisms to address the partial overlapping and occlusion problems. Firstly, the detection algorithm effectively detects and extracts the moving object target by enhancing and post processing morphological operations. Secondly, the extracted object uses region based moving object tracking and inter-frame difference to improve the tracking speed of real-time moving objects in different video frames. Finally, the plotting method was applied to detect the moving objects effectively and describes the object’s motion being tracked. The experiment has been performed on image sequences acquired both indoor and outdoor environments and one stationary and web camera has been used.

Keywords: background modeling, Gaussian mixture model, inter-frame difference, object detection and tracking, video surveillance

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Authors: Kwaku Damoah

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The agricultural sector is characterized by continuous transformation, driven by factors such as demographic shifts, evolving consumer preferences, climate change, and migration trends. This dynamic environment presents complex challenges for key stakeholders including farmers, governments, and investors, who must navigate these changes to achieve optimal investment returns. To effectively predict market trends and uncover promising investment opportunities, a systematic, data-driven approach is essential. This paper introduces the Structural Evolution Index (SEI), a machine learning-based methodology. SEI is specifically designed to analyse long-term trends and forecast the potential of emerging agricultural products for investment. Versatile in application, it evaluates various agricultural metrics such as production, yield, trade, land use, and consumption, providing a comprehensive view of the evolution within agricultural markets. By harnessing data from the UN Food and Agricultural Organisation (FAOSTAT), this study demonstrates the SEI's capabilities through Comparative Exploratory Analysis and evaluation of international trade in agricultural products, focusing on Malaysia and Singapore. The SEI methodology reveals intricate patterns and transitions within the agricultural sector, enabling stakeholders to strategically identify and capitalize on emerging markets. This predictive framework is a powerful tool for decision-makers, offering crucial insights that help anticipate market shifts and align investments with anticipated returns.

Keywords: agricultural investment, algorithm, comparative exploratory analytics, machine learning, market trends, predictive analytics, structural evolution index

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Authors: Ariq Bani Hardi

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Empowerment of smartphone technology is growing very rapidly in various fields of science. One of the mobile operating systems that dominate the smartphone market today is Android by Google. Unfortunately, the expansion of mobile technology is misused by criminals to hide the information that they store or exchange with each other. It makes law enforcement more difficult to prove crimes committed in the judicial process (anti-forensic). One of technique that used to hide the information is encryption, such as the usages of SMS encryption applications. A Mobile Forensic Examiner or an investigator should prepare a countermeasure technique if he finds such things during the investigation process. This paper will discuss an extension procedure if the investigator found unreadable SMS in android evidence because of encryption. To define the extended procedure, we create and analyzing a dataset of android SMS encryption application. The dataset was grouped by application characteristics related to communication permissions, as well as the availability of source code and the documentation of encryption scheme. Permissions indicate the possibility of how applications exchange the data and keys. Availability of the source code and the encryption scheme documentation can show what the cryptographic algorithm specification is used, how long the key length, how the process of key generation, key exchanges, encryption/decryption is done, and other related information. The output of this paper is an extended or alternative procedure for examination and analysis process of android digital forensic. It can be used to help the investigators while they got a confused cause of SMS encryption during examining and analyzing. What steps should the investigator take, so they still have a chance to discover the encrypted SMS in android evidence?

Keywords: anti-forensic countermeasure, SMS encryption android, examination and analysis, digital forensic

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Authors: María Reveles-Miranda, Daniella Pacheco-Catalán

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Sierra Papacal commissary is located north of Merida, Yucatan, México, where the indigenous Maya population predominates. Due to its location, the region has an elevation of fewer than 4.5 meters above sea level, with a high risk of flooding associated with storms and hurricanes and a high vulnerability of infrastructure and housing in the presence of strong gusts of wind. In environmental contingencies, the challenge is providing an autonomous electrical supply using renewable energy sources that cover vulnerable populations' health, food, and water pumping needs. To address this challenge, a hybrid energy storage module is proposed for the emergency photovoltaic (PV) system of the community shelter in Sierra Papacal, Yucatán, which combines high-energy-density batteries and high-power-density supercapacitors (SC) in a single module, providing a quick response to energy demand, reducing the thermal stress on batteries and extending their useful life. Incorporating SC in energy storage modules can provide fast response times to power variations and balanced energy extraction, ensuring a more extended period of electrical supply to vulnerable populations during contingencies. The implemented control strategy increases the module's overall performance by ensuring the optimal use of devices and balanced energy exploitation. The operation of the module with the control algorithm is validated with MATLAB/Simulink® and experimental tests.

Keywords: batteries, community shelter, environmental contingencies, hybrid energy storage, isolated photovoltaic system, supercapacitors

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Authors: Iman A. Saad, Loay E. George

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Iris pattern is an important biological feature of human body; it becomes very hot topic in both research and practical applications. In this paper, an algorithm is proposed for iris recognition and a simple, efficient and fast method is introduced to extract a set of discriminatory features using first order gradient operator applied on grayscale images. The gradient based features are robust, up to certain extents, against the variations may occur in contrast or brightness of iris image samples; the variations are mostly occur due lightening differences and camera changes. At first, the iris region is located, after that it is remapped to a rectangular area of size 360x60 pixels. Also, a new method is proposed for detecting eyelash and eyelid points; it depends on making image statistical analysis, to mark the eyelash and eyelid as a noise points. In order to cover the features localization (variation), the rectangular iris image is partitioned into N overlapped sub-images (blocks); then from each block a set of different average directional gradient densities values is calculated to be used as texture features vector. The applied gradient operators are taken along the horizontal, vertical and diagonal directions. The low order norms of gradient components were used to establish the feature vector. Euclidean distance based classifier was used as a matching metric for determining the degree of similarity between the features vector extracted from the tested iris image and template features vectors stored in the database. Experimental tests were performed using 2639 iris images from CASIA V4-Interival database, the attained recognition accuracy has reached up to 99.92%.

Keywords: iris recognition, contrast stretching, gradient features, texture features, Euclidean metric

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Authors: Yogendra K. Karna, Lauren T. Bennett

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Fire-tolerant eucalypt forests are one of the major forest ecosystems of south-eastern Australia and thought to be highly resistant to frequent high severity wildfires. However, the impact of different severity wildfires on the canopy structure of fire-tolerant forest type is under-studied, and there are significant knowledge gaps in relation to the assessment of tree and stand level canopy structural dynamics and recovery after fire. Assessment of canopy structure is a complex task involving accurate measurements of the horizontal and vertical arrangement of the canopy in space and time. This study examined the utility of multitemporal, small-footprint lidar data to describe the changes in the horizontal and vertical canopy structure of fire-tolerant eucalypt forests seven years after wildfire of different severities from the tree to stand level. Extensive ground measurements were carried out in four severity classes to describe and validate canopy cover and height metrics as they change after wildfire. Several metrics such as crown height and width, crown base height and clumpiness of crown were assessed at tree and stand level using several individual tree top detection and measurement algorithm. Persistent effects of high severity fire 8 years after both on tree crowns and stand canopy were observed. High severity fire increased the crown depth but decreased the crown projective cover leading to more open canopy.

Keywords: canopy gaps, canopy structure, crown architecture, crown projective cover, multi-temporal lidar, wildfire severity

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Authors: Yu-Ting Tsai, Chiun-Chieh Hsu, Yu-Chun Chu

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In recent years, due to the development of wireless network technology, many researchers have devoted to the study of wireless sensor networks. The applications of wireless sensor network mainly use the sensor nodes to collect the required information, and send the information back to the users. Since the sensed area is difficult to reach, there are many restrictions on the design of the sensor nodes, where the most important restriction is the limited energy of sensor nodes. Because of the limited energy, researchers proposed a number of ways to reduce energy consumption and balance the load of sensor nodes in order to increase the network lifetime. In this paper, we proposed the Energy-Balanced Clustering method with Auxiliary Members on Wireless Sensor Networks（EBCAM）based on the cluster routing. The main purpose is to balance the energy consumption on the sensed area and average the distribution of dead nodes in order to avoid excessive energy consumption because of the increasing in transmission distance. In addition, we use the residual energy and average energy consumption of the nodes within the cluster to choose the cluster heads, use the multi hop transmission method to deliver the data, and dynamically adjust the transmission radius according to the load conditions. Finally, we use the auxiliary cluster members to change the delivering path according to the residual energy of the cluster head in order to its load. Finally, we compare the proposed method with the related algorithms via simulated experiments and then analyze the results. It reveals that the proposed method outperforms other algorithms in the numbers of used rounds and the average energy consumption.

Keywords: auxiliary nodes, cluster, load balance, routing algorithm, wireless sensor network

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Authors: Zhaopeng Zhu, Xianzhi Song, Gensheng Li, Shuo Zhu, Shiming Duan, Xuezhe Yao

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Accurate calculation of wellbore pressure is of great significance to prevent wellbore risk during drilling. The traditional mechanism model needs a lot of iterative solving procedures in the calculation process, which reduces the calculation efficiency and is difficult to meet the demand of dynamic control of wellbore pressure. In recent years, many scholars have introduced artificial intelligence algorithms into wellbore pressure calculation, which significantly improves the calculation efficiency and accuracy of wellbore pressure. However, due to the ‘black box’ property of intelligent algorithm, the existing intelligent calculation model of wellbore pressure is difficult to play a role outside the scope of training data and overreacts to data noise, often resulting in abnormal calculation results. In this study, the multi-phase flow mechanism is embedded into the objective function of the neural network model as a constraint condition, and an intelligent prediction model of wellbore pressure under the constraint condition is established based on more than 400,000 sets of pressure measurement while drilling (MPD) data. The constraint of the multi-phase flow mechanism makes the prediction results of the neural network model more consistent with the distribution law of wellbore pressure, which overcomes the black-box attribute of the neural network model to some extent. The main performance is that the accuracy of the independent test data set is further improved, and the abnormal calculation values basically disappear. This method is a prediction method driven by MPD data and multi-phase flow mechanism, and it is the main way to predict wellbore pressure accurately and efficiently in the future.

Keywords: multiphase flow mechanism, pressure while drilling data, wellbore pressure, mechanism constraints, combined drive

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Authors: Ajibola Isola, Shattri Mansor, Ojogbane Sani, Olugbemi Tope

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The cadastral map is the rationale behind city management planning and development. For years, cadastral maps have been produced by ground and photogrammetry platforms. Recent evolution in photogrammetry and remote sensing sensors ignites the use of Unmanned Aerial Vehicle systems (UAVs) for cadastral mapping. Despite the time-saving and multi-dimensional cost-effectiveness of the UAV platform, issues related to cadastral map accuracy are a hindrance to the wide applicability of UAVs' cadastral mapping. This study aims to present an approach leading to the generation and assessing the credibility of UAV cadastral mapping. Different sets of Red, Green, and Blue (RGB) photos were obtained from the Tarot 680-hexacopter UAV platform flown over the Universiti Putra Malaysia campus sports complex at an altitude range of 70 m, 100 m, and 250. Before flying the UAV, twenty-eight ground control points were evenly established in the study area with a real-time kinematic differential global positioning system. The second phase of the study utilizes an image-matching algorithm for photos alignment wherein camera calibration parameters and ten of the established ground control points were used for estimating the inner, relative, and absolute orientations of the photos. The resulting orthoimages are exported to ArcGIS software for digitization. Visual, tabular, and graphical assessments of the resulting cadastral maps showed a different level of accuracy. The results of the study show a gradual approach for generating UAV cadastral mapping and that the cadastral map acquired at 70 m altitude produced better results.

Keywords: aerial mapping, orthomosaic, cadastral map, flying altitude, image processing

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Authors: Stephen L. Roberts

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This article offers a critical analysis of the rise of syndromic surveillance systems for the advanced detection of pandemic threats within contemporary global health security frameworks. The article traces the iterative evolution and ascendancy of three such novel syndromic surveillance systems for the strengthening of health security initiatives over the past two decades: 1) The Program for Monitoring Emerging Diseases (ProMED-mail); 2) The Global Public Health Intelligence Network (GPHIN); and 3) HealthMap. This article demonstrates how each newly introduced syndromic surveillance system has become increasingly oriented towards the integration of digital algorithms into core surveillance capacities to continually harness and forecast upon infinitely generating sets of digital, open-source data, potentially indicative of forthcoming pandemic threats. This article argues that the increased centrality of the algorithm within these next-generation syndromic surveillance systems produces a new and distinct form of infectious disease surveillance for the governing of emergent pathogenic contingencies. Conceptually, the article also shows how the rise of this algorithmic mode of infectious disease surveillance produces divergences in the governmental rationalities of global health security, leading to the rise of an algorithmic governmentality within contemporary contexts of Big Data and these surveillance systems. Empirically, this article demonstrates how this new form of algorithmic infectious disease surveillance has been rapidly integrated into diplomatic, legal, and political frameworks to strengthen the practice of global health security – producing subtle, yet distinct shifts in the outbreak notification and reporting transparency of states, increasingly scrutinized by the algorithmic gaze of syndromic surveillance.

Keywords: algorithms, global health, pandemic, surveillance

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Authors: Julia Gontar, Natalia Buderatskaya, Igor Ilyin, Olga Parnitskaya, Sergey Lavrynenko, Eduard Kapustin, Ekaterina Ilyina, Yana Lakhno

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Introduction: It is well known that oocytes obtained from older reproductive women have accumulated mitochondrial DNA mutations, which negatively affects the morphology of a developing embryo and may lead to the birth of a child with mitochondrial disease. Special techniques have been developed to allow a donor oocyte cytoplasmic transfer with the parents’ biological nuclear DNA retention. At the same time, it is important to understand whether the procedure affects the future embryonic chromosome sets as the nuclear DNA is the transfer subject in this new complex procedure. Material and Methods: From July 2015 to July 2016, the investigation was carried out in the Medical Centre IGR. 34 donor oocytes (group A) were used for the manipulation with the aim of donating cytoplasm: 21 oocytes were used for zygotes pronuclear transfer and oocytes 13 – for the spindle transfer. The mean age of the oocyte donors was 28.4±2.9 years. The procedure was performed using Nikon Ti Eclipse inverted microscope equipped with the micromanipulators Narishige system (Japan), Saturn 3 laser console (UK), Oosight imaging systems (USA). For the preimplantation genetic screening (PGS) blastocyst biopsy was performed, trophectoderm samples were diagnosed using fluorescent in situ hybridization on chromosomes 9, 13, 15, 16, 17, 18, 21, 22, X, Y. For comparison of morphological characteristics and euploidy, was chosen a group of embryos (group B) with the amount of 121 blastocysts obtained from 213 oocytes, which were gotten from the donor programs of assisted reproductive technologies (ART). Group B was not subjected to donor oocyte cytoplasmic transfer procedure and studied on the above mentioned chromosomes. Statistical analysis was carried out using the criteria t, x^2 at a significance levels p<0.05, p<0.01, p<0.001. Results: After the donor cytoplasm transfer process the amount of the third day developing embryos was 27 (79.4%). In this stage, the group B consisted of 189 (88.7%) developing embryos, and there was no statistically significant difference (SSD) between the two groups (p>0.05). After a comparative analysis of the morphological characteristics of the embryos on the fifth day, we also found no SSD among the studied groups (p>0.05): from 34 oocytes exposed to manipulation, 14 (41.2%) blastocysts was obtained, while the group B blastocyst yield was 56.8% (n=121) from 213 oocytes. The following results were obtained after PGS performing: in group A euploidy in studied chromosomes were 28.6%(n=4) blastocysts, whereas in group B this rate was 40.5%(n=49), 28.6%(n=4) and 21.5%(n=26) of mosaic embryos and 42.8%(n=6) and 38.0%(n=46) aneuploid blastocysts respectively were identified. None of these specified parameters had an SSD (p>0.05). But attention was drawn by the blastocysts in group A with identified mosaicism, which was chaotic without any cell having euploid chromosomal set, in contrast to the mosaic embryos in group B where identified chaotic mosaicism was only 2.5%(n=3). Conclusions: According to the obtained results, there is no direct procedural effect on the chromosome in embryos obtained following donor oocyte cytoplasmic transfer. Thus, the technology introduction will enhance the infertility treating effectiveness as well as avoiding having a child with mitochondrial disease.

Keywords: donor oocyte cytoplasmic transfer, embryos’ chromosome set, oocyte spindle transfer, pronuclear transfer

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Authors: J. K Adedeji, O. H Olowomofe, C. O Alo, S.T Ijatuyi

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The issue of high blood sugar level, the effects of which might end up as diabetes mellitus, is now becoming a rampant cardiovascular disorder in our community. In recent times, a lack of awareness among most people makes this disease a silent killer. The situation calls for urgency, hence the need to design a device that serves as a monitoring tool such as a wrist watch to give an alert of the danger a head of time to those living with high blood glucose, as well as to introduce a mechanism for checks and balances. The neural network architecture assumed 8-15-10 configuration with eight neurons at the input stage including a bias, 15 neurons at the hidden layer at the processing stage, and 10 neurons at the output stage indicating likely symptoms cases. The inputs are formed using the exclusive OR (XOR), with the expectation of getting an XOR output as the threshold value for diabetic symptom cases. The neural algorithm is coded in Java language with 1000 epoch runs to bring the errors into the barest minimum. The internal circuitry of the device comprises the compatible hardware requirement that matches the nature of each of the input neurons. The light emitting diodes (LED) of red, green, and yellow colors are used as the output for the neural network to show pattern recognition for severe cases, pre-hypertensive cases and normal without the traces of diabetes mellitus. The research concluded that neural network is an efficient Accu-Chek design tool for the proper monitoring of high glucose levels than the conventional methods of carrying out blood test.

Keywords: Accu-Check, diabetes, neural network, pattern recognition

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Authors: Hu Zhenxing, Gao Jianxin

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Stereo-based digital image correlation (also referred to as three-dimensional (3D) digital image correlation (DIC)) is a technique for both 3D shape and surface deformation measurement of a component, which has found increasing applications in academia and industries. The accuracy of the reconstructed coordinate depends on many factors such as configuration of the setup, stereo-matching, distortion, etc. Most of these factors have been investigated in literature. For instance, the configuration of a binocular vision system determines the systematic errors. The stereo-matching errors depend on the speckle quality and the matching algorithm, which can only be controlled in a limited range. And the distortion is non-linear particularly in a complex imaging acquisition system. Thus, the distortion correction should be carefully considered. Moreover, the distortion function is difficult to formulate in a complex imaging acquisition system using conventional models in such cases where microscopes and other complex lenses are involved. The errors of the distortion correction will propagate to the reconstructed 3D coordinates. To address the problem, an accurate mapping method based on 2D B-spline functions is proposed in this study. The mapping functions are used to convert the distorted coordinates into an ideal plane without distortions. This approach is suitable for any image acquisition distortion models. It is used as a prior process to convert the distorted coordinate to an ideal position, which enables the camera to conform to the pin-hole model. A procedure of this approach is presented for stereo-based DIC. Using 3D speckle image generation, numerical simulations were carried out to compare the accuracy of both the conventional method and the proposed approach.

Keywords: distortion, stereo-based digital image correlation, b-spline, 3D, 2D

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Authors: Rowane May A. Fesalbon, Greyland C. Agno, Jodel L. Cuasay, Dindo A. Malonzo, Ma. Rosario Concepcion O. Ang

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The Department of Energy of the Republic of the Philippines estimates that the country’s energy reserves for 2015 are dwindling– observed in the rotating power outages in several localities. To aid in the energy crisis, a national hydropower resource assessment scheme is developed. Hydropower is a resource that is derived from flowing water and difference in elevation. It is a renewable energy resource that is deemed abundant in the Philippines – being an archipelagic country that is rich in bodies of water and water resources. The objectives of this study is to develop a methodology for a national hydropower resource assessment using hydrologic modeling and geospatial techniques in order to generate resource maps for future reference and use of the government and other stakeholders. The methodology developed for this purpose is focused on two models – the implementation of the Soil and Water Assessment Tool (SWAT) for the river discharge and the use of geospatial techniques to analyze the topography and obtain the head, and generate the theoretical hydropower potential sites. The methodology is highly coupled with Geographic Information Systems to maximize the use of geodatabases and the spatial significance of the determined sites. The hydrologic model used in this workflow is SWAT integrated in the GIS software ArcGIS. The head is determined by a developed algorithm that utilizes a Synthetic Aperture Radar (SAR)-derived digital elevation model (DEM) which has a resolution of 10-meters. The initial results of the developed workflow indicate hydropower potential in the river reaches ranging from pico (less than 5 kW) to mini (1-3 MW) theoretical potential.

Keywords: ArcSWAT, renewable energy, hydrologic model, hydropower, GIS

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Authors: Vivek Rangarajan, Kim G. Klarke

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With overpopulation threatening the world’s ability to feed itself, food production and protection has become a major issue, especially in developing countries. Almost one-third of the food produced for human consumption, around 1.3 billion tonnes, is either wasted or lost annually. Postharvest decay in particular constitutes a major cause of crop loss with about 20% of fruits and vegetables produced lost during postharvest storage, mainly due to fungal disease. Some of the major phytopathogenic fungi affecting postharvest fruit crops in South Africa include Aspergillus, Botrytis, Penicillium, Alternaria and Sclerotinia spp. To date control of fungal phytopathogens has primarily been dependent on synthetic chemical fungicides, but these chemicals pose a significant threat to the environment, mainly due to their xenobiotic properties and tendency to generate resistance in the phytopathogens. Here, an environmentally benign alternative approach to control postharvest fungal phytopathogens in perishable fruit crops has been presented, namely the application of a bio-fungicide in the form of lipopeptide molecules. Lipopeptides are biosurfactants produced by Bacillus spp. which have been established as green, nontoxic and biodegradable molecules with antimicrobial properties. However, since the Bacillus are capable of producing a large number of lipopeptide homologues with differing efficacies against distinct target organisms, the lipopeptide production conditions and strategy are critical to produce the maximum lipopeptide concentration with homologue ratios to specification for optimum bio-fungicide efficacy. Process conditions, and their impact on Bacillus lipopeptide production, were evaluated in fully instrumented laboratory scale bioreactors under well-regulated controlled and defined environments. Factors such as the oxygen availability and trace element and nitrate concentrations had profound influences on lipopeptide yield, productivity and selectivity. Lipopeptide yield and homologue selectivity were enhanced in cultures where the oxygen in the sparge gas was increased from 21 to 30 mole%. The addition of trace elements, particularly Fe2+, increased the total concentration of lipopeptides and a nitrate concentration equivalent to 8 g/L ammonium nitrate resulted in optimum lipopeptide yield and homologue selectivity. Efficacy studies of the culture supernatant containing the crude lipopeptide mixture were conducted using phytopathogens isolated from fruit in the field, identified using genetic sequencing. The supernatant exhibited antifungal activity against all the test-isolates, namely Lewia, Botrytis, Penicillium, Alternaria and Sclerotinia spp., even in this crude form. Thus the lipopeptide product efficacy has been confirmed to control the main diseases, even in the basic crude form. Future studies will be directed towards purification of the lipopeptide product and enhancement of efficacy.

Keywords: antifungal efficacy, biocontrol, lipopeptide production, perishable crops

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Authors: Minhas Alam, Muhammad Hidayat Rasool, Mohsin Khurshid, Bilal Aslam

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The genus Acinetobacter has emerged as a significant concern in hospital-acquired infections, particularly due to the versatility of Acinetobacter baumannii in causing nosocomial infections. The organism's remarkable metabolic adaptability allows it to thrive in various environments, including the environment, animals, and humans. However, the extent of antimicrobial resistance in Acinetobacter species from veterinary settings, especially in developing countries like Pakistan, remains unclear. This study aimed to isolate and characterize Acinetobacter spp. from veterinary settings in Punjab, Pakistan. A total of 2,230 specimens were collected, including 1,960 samples from veterinary settings (nasal and rectal swabs from dairy and beef cattle), 200 from the environment, and 70 from human clinical settings. Isolates were identified using routine microbiological procedures and confirmed by polymerase chain reaction (PCR). Antimicrobial susceptibility was determined by the disc diffusion method, and minimum inhibitory concentration (MIC) was measured by the micro broth dilution method. Molecular techniques, such as PCR and DNA sequencing, were used to screen for antimicrobial-resistant determinants. Genetic diversity was assessed using standard techniques. The results showed that the overall prevalence of A. baumannii in cattle was 6.63% (65/980). However, among cattle, a higher prevalence of A. baumannii was observed in dairy cattle, 7.38% (54/731), followed by beef cattle, 4.41% (11/249). Out of 65 A. baumannii isolates, the carbapenem resistance was found in 18 strains, i.e. 27.7%. The prevalence of A. baumannii in nasopharyngeal swabs was higher, i.e., 87.7% (57/65), as compared to rectal swabs, 12.3% (8/65). Class D β-lactamases genes blaOXA-23 and blaOXA-51 were present in all the CRAB from cattle. Among carbapenem-resistant isolates, 94.4% (17/18) were positive for class B β-lactamases gene blaIMP, whereas the blaNDM-1 gene was detected in only one isolate of A. baumannii. Among 70 clinical isolates of A. baumannii, 58/70 (82.9%) were positive for the blaOXA-23-like gene, and 87.1% (61/70) were CRAB isolates. Among all clinical isolates of A. baumannii, blaOXA-51-like gene was present. Hence, the co-existence of blaOXA-23 and blaOXA-51 was found in 82.85% of clinical isolates. From the environmental settings, a total of 18 A. baumannii isolates were recovered; among these, 38.88% (7/18) strains showed carbapenem resistance. All environmental isolates of A. baumannii harbored class D β-lactamases genes, i.e., blaOXA-51 and blaOXA-23 were detected in 38.9% (7/18) isolates. Hence, the co-existence of blaOXA-23 and blaOXA-51 was found in 38.88% of isolates. From environmental settings, 18 A. baumannii isolates were recovered, with 38.88% showing carbapenem resistance. All environmental isolates harbored blaOXA-51 and blaOXA-23 genes, with co-existence in 38.88% of isolates. MLST results showed ten different sequence types (ST) in clinical isolates, with ST 589 being the most common in carbapenem-resistant isolates. In veterinary isolates, ST2 was most common in CRAB isolates from cattle. Immediate control measures are needed to prevent the transmission of CRAB isolates among animals, the environment, and humans. Further studies are warranted to understand the mechanisms of antibiotic resistance spread and implement effective disease control programs.

Keywords: Acinetobacter baumannii, carbapenemases, drug resistance, MSLT

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Authors: P.P. Rupasinghe, A.H. Farid

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The Aleutian mink disease virus (AMDV, Carnivore amdoparvovirus 1) causes persistent infection, plasmacytosis, and formation and deposition of immune complexes in various organs in adult mink, leading to glomerulonephritis, arteritis and sometimes death. The disease has no cure nor an effective vaccine, and identification and culling of mink positive for anti-AMDV antibodies have not been successful in controlling the infection in many countries. The failure to eradicate the virus from infected farms may be caused by keeping false-negative individuals on the farm, virus transmission from wild animals, or neighboring farms. The identification of sources of infection, which can be performed by comparing viral sequences, is important in the success of viral eradication programs. High mutation rates could cause inaccuracies when viral sequences are used to trace back an infection to its origin. There is no published information on the mutation rate of AMDV either in vivo or in vitro. The in vivo estimation is the most accurate method, but it is difficult to perform because of the inherent technical complexities, namely infecting live animals, the unknown numbers of viral generations (i.e., infection cycles), the removal of deleterious mutations over time and genetic drift. The objective of this study was to determine the mutation rate of AMDV on which no information was available. A homogenate was prepared from the spleen of one naturally infected American mink (Neovison vison) from Nova Scotia, Canada (parental template). The near full-length genome of this isolate (91.6%, 4,143 bp) was bidirectionally sequenced. A group of black mink was inoculated with this homogenate (descendant mink). Spleen sampled were collected from 10 descendant mink after 16 weeks post-inoculation (wpi) and from anther 10 mink after 176 wpi, and their near-full length genomes were bi-directionally sequenced. Sequences of these mink were compared with each other and with the sequence of the parental template. The number of nucleotide substitutions at 176 wpi was 3.1 times greater than that at 16 wpi (113 vs 36) whereas the estimates of mutation rate at 176 wpi was 3.1 times lower than that at 176 wpi (2.85×10-3 vs 9.13×10-4 substitutions/ site/ year), showing a decreasing trend in the mutation rate per unit of time. Although there is no report on in vivo estimate of the mutation rate of DNA viruses in animals using the same method which was used in the current study, these estimates are at the higher range of reported values for DNA viruses determined by various techniques. These high estimates are logical based on the wide range of diversity and pathogenicity of AMDV isolates. The results suggest that increases in the number of nucleotide substitutions over time and subsequent divergence make it difficult to accurately trace back AMDV isolates to their origin when several years elapsed between the two samplings.

Keywords: Aleutian mink disease virus, American mink, mutation rate, nucleotide substitution

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Authors: Wassim Jerbi, Abderrahmen Guermazi, Hafedh Trabelsi

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The optimum use of coverage in wireless sensor networks (WSNs) is very important. LEACH protocol called Low Energy Adaptive Clustering Hierarchy, presents a hierarchical clustering algorithm for wireless sensor networks. LEACH is a protocol that allows the formation of distributed cluster. In each cluster, LEACH randomly selects some sensor nodes called cluster heads (CHs). The selection of CHs is made with a probabilistic calculation. It is supposed that each non-CH node joins a cluster and becomes a cluster member. Nevertheless, some CHs can be concentrated in a specific part of the network. Thus, several sensor nodes cannot reach any CH. to solve this problem. We created an O-LEACH Orphan nodes protocol, its role is to reduce the sensor nodes which do not belong the cluster. The cluster member called Gateway receives messages from neighboring orphan nodes. The gateway informs CH having the neighboring nodes that not belong to any group. However, Gateway called (CH') attaches the orphaned nodes to the cluster and then collected the data. O-Leach enables the formation of a new method of cluster, leads to a long life and minimal energy consumption. Orphan nodes possess enough energy and seeks to be covered by the network. The principal novel contribution of the proposed work is O-LEACH protocol which provides coverage of the whole network with a minimum number of orphaned nodes and has a very high connectivity rates.As a result, the WSN application receives data from the entire network including orphan nodes. The proper functioning of the Application requires, therefore, management of intelligent resources present within each the network sensor. The simulation results show that O-LEACH performs better than LEACH in terms of coverage, connectivity rate, energy and scalability.

Keywords: WSNs; routing; LEACH; O-LEACH; Orphan nodes; sub-cluster; gateway; CH’

Procedia PDF Downloads 346

Authors: Ayten Ekin Meşe, Selahattin Şentürk, Melike Duvanoğlu

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Petroleum refineries are a highly complex process industry with continuous production and high operating costs. Physical separation of crude oil starts with the crude oil distillation unit, continues with various conversion and purification units, and passes through many stages until obtaining the final product. To meet the desired product specification, process parameters are strictly followed. To be able to ensure the quality of distillates, routine analyses are performed in quality control laboratories based on appropriate international standards such as American Society for Testing and Materials (ASTM) standard methods and European Standard (EN) methods. The cut point of distillates in the crude distillation unit is very crucial for the efficiency of the upcoming processes. In order to maximize the process efficiency, the determination of the quality of distillates should be as fast as possible, reliable, and cost-effective. In this sense, an alternative study was carried out on the crude oil distillation unit that serves the entire refinery process. In this work, studies were conducted with three different crude oil distillates which are Light Straight Run Naphtha (LSRN), Heavy Straight Run Naphtha (HSRN), and Kerosene. These products are named after separation by the number of carbons it contains. LSRN consists of five to six carbon-containing hydrocarbons, HSRN consist of six to ten, and kerosene consists of sixteen to twenty-two carbon-containing hydrocarbons. Physical properties of three different crude distillation unit products (LSRN, HSRN, and Kerosene) were determined using Near-Infrared Spectroscopy with multivariate calibration. The absorbance spectra of the petroleum samples were obtained in the range from 10000 cm⁻¹ to 4000 cm⁻¹, employing a quartz transmittance flow through cell with a 2 mm light path and a resolution of 2 cm⁻¹. A total of 400 samples were collected for each petroleum sample for almost four years. Several different crude oil grades were processed during sample collection times. Extended Multiplicative Signal Correction (EMSC) and Savitzky-Golay (SG) preprocessing techniques were applied to FT-NIR spectra of samples to eliminate baseline shifts and suppress unwanted variation. Two different multivariate calibration approaches (Partial Least Squares Regression, PLS and Genetic Inverse Least Squares, GILS) and an ensemble model were applied to preprocessed FT-NIR spectra. Predictive performance of each multivariate calibration technique and preprocessing techniques were compared, and the best models were chosen according to the reproducibility of ASTM reference methods. This work demonstrates the developed models can be used for routine analysis instead of conventional analytical methods with over 90% accuracy.

Keywords: crude distillation unit, multivariate calibration, near infrared spectroscopy, data preprocessing, refinery

Procedia PDF Downloads 95

Authors: A. A. Sadia, A. Emdad, E. Hossain

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The increasing importance of mushrooms as a source of nutrition, health benefits, and even potential cancer treatment has raised awareness of the impact of climate-sensitive variables on their cultivation. Factors like temperature, relative humidity, air quality, and substrate composition play pivotal roles in shaping mushroom growth, especially in Bangladesh. Oyster mushrooms, a commonly cultivated variety in this region, are particularly vulnerable to climate fluctuations. This research explores the climatic dynamics affecting oyster mushroom cultivation and, presents an approach to address these challenges and provides tangible solutions to fortify the agro-economy, ensure food security, and promote the sustainability of this crucial food source. Using climate and production data, this study evaluates the performance of three clustering algorithms -KMeans, OPTICS, and BIRCH- based on various quality metrics. While each algorithm demonstrates specific strengths, the findings provide insights into their effectiveness for this specific dataset. The results yield essential information, pinpointing the optimal temperature range of 13°C-22°C, the unfavorable temperature threshold of 28°C and above, and the ideal relative humidity range of 75-85% with the suitable production regions in three different seasons: Kharif-1, 2, and Robi. Additionally, a user-friendly web application is developed to support mushroom farmers in making well-informed decisions about their cultivation practices. This platform offers valuable insights into the most advantageous periods for oyster mushroom farming, with the overarching goal of enhancing the efficiency and profitability of mushroom farming.

Keywords: climate variability, mushroom cultivation, clustering techniques, food security, sustainability, web-application

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Authors: Deepak Kumar, Jahangeer, Brijesh Kumar Yadav, Shashi Mathur

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In-situ remediation is a technique which can remediate either surface or groundwater at the site of contamination. In the present study, simulation optimization approach has been used to develop management strategy for remediating LNAPL (Light Non-Aqueous Phase Liquid) contaminated aquifers. Benzene, toluene, ethyl benzene and xylene are the main component of LNAPL contaminant. Collectively, these contaminants are known as BTEX. In in-situ bioremediation process, a set of injection and extraction wells are installed. Injection wells supply oxygen and other nutrient which convert BTEX into carbon dioxide and water with the help of indigenous soil bacteria. On the other hand, extraction wells check the movement of plume along downstream. In this study, optimal design of the system has been done using PSO (Particle Swarm Optimization) algorithm. A comprehensive management strategy for pumping of injection and extraction wells has been done to attain a maximum allowable concentration of 5 ppm and 4.5 ppm. The management strategy comprises determination of pumping rates, the total pumping volume and the total running cost incurred for each potential injection and extraction well. The results indicate a high pumping rate for injection wells during the initial management period since it facilitates the availability of oxygen and other nutrients necessary for biodegradation, however it is low during the third year on account of sufficient oxygen availability. This is because the contaminant is assumed to have biodegraded by the end of the third year when the concentration drops to a permissible level.

Keywords: groundwater, in-situ bioremediation, light non-aqueous phase liquid, BTEX, particle swarm optimization

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Authors: Jeanne Leblond, Warren Viricel, Amira Mbarek

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Although several products have reached the market, gene therapeutics are still in their first stages and require optimization. It is possible to improve their lacking efficiency by the use of carefully engineered vectors, able to carry the genetic material through each of the biological barriers they need to cross. In particular, getting inside the cell is a major challenge, because these hydrophilic nucleic acids have to cross the lipid-rich plasmatic and/or endosomal membrane, before being degraded into lysosomes. It takes less than one hour for newly endocytosed liposomes to reach highly acidic lysosomes, meaning that the degradation of the carried gene occurs rapidly, thus limiting the transfection efficiency. We propose to use a new pH-sensitive lipid able to change its conformation upon protonation at endosomal pH values, leading to the disruption of the lipidic bilayer and thus to the fast release of the nucleic acids into the cytosol. It is expected that this new pH-sensitive mechanism promote endosomal escape of the gene, thereby its transfection efficiency. The main challenge of this work was to design a preparation presenting fast-responding lipidic bilayer destabilization properties at endosomal pH 5 while remaining stable at blood pH value and during storage. A series of pH-sensitive lipids able to perform a conformational switch upon acidification were designed and synthesized. Liposomes containing these switchable lipids, as well as co-lipids were prepared and characterized. The liposomes were stable at 4°C and pH 7.4 for several months. Incubation with siRNA led to the full entrapment of nucleic acids as soon as the positive/negative charge ratio was superior to 2. The best liposomal formulation demonstrated a silencing efficiency up to 10% on HeLa cells, very similar to a commercial agent, with a lowest toxicity than the commercial agent. Using flow cytometry and microscopy assays, we demonstrated that drop of pH was required for the transfection efficiency, since bafilomycin blocked the transfection efficiency. Additional evidence was brought by the synthesis of a negative control lipid, which was unable to switch its conformation, and consequently exhibited no transfection ability. Mechanistic studies revealed that the uptake was mediated through endocytosis, by clathrin and caveolae pathways, as reported for previous lipid nanoparticle systems. This potent system was used for the treatment of hypercholesterolemia. The switchable lipids were able to knockdown PCSK9 expression on human hepatocytes (Huh-7). Its efficiency is currently evaluated on in vivo mice model of PCSK9 KO mice. In summary, we designed and optimized a new cationic pH-sensitive lipid for gene delivery. Its transfection efficiency is similar to the best available commercial agent, without the usually associated toxicity. The promising results lead to its use for the treatment of hypercholesterolemia on a mice model. Anticancer applications and pulmonary chronic disease are also currently investigated.

Keywords: liposomes, siRNA, pH-sensitive, molecular switch

Procedia PDF Downloads 184