Search results for: multiple organ manifestation

Authors: Christopher Leung, Tony Kim, Rebecca Lendzion, Scott Mackenzie

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Retroperitoneal abscesses are a rare but serious condition with often delayed diagnosis, non-specific symptoms, multiple causes and high morbidity/mortality. With the advent of more readily available cross-sectional imaging, retroperitoneal abscesses are treated earlier and better outcomes are achieved. Occasionally, a retroperitoneal abscess is present as a huge retroperitoneal abscess, as evident in this 53-year-old male. With a background of chronic renal disease and left partial nephrectomy, this gentleman presented with a one-month history of left flank pain without any other symptoms, including fevers or abdominal pain. CT abdomen and pelvis demonstrated a huge retroperitoneal abscess spanning from the diaphragm, abutting the spleen, down to the iliopsoas muscle and abutting the iliac vessels at the pelvic brim. This large retroperitoneal abscess required open drainage as well as drainage by interventional radiology. A long course of intravenous antibiotics and multiple drainages was required to drain the abscess. His blood culture and fluid culture grew Proteus species suggesting a urinary source, likely from his non-functioning kidney, which had a partial nephrectomy. Such a huge retroperitoneal abscess has rarely been described in the literature. The learning point here is that the basic principle of source control and antibiotics is paramount in treating retroperitoneal abscesses regardless of the size of the abscess.

Keywords: retroperitoneal abscess, retroperitoneal mass, sepsis, genitourinary infection

Procedia PDF Downloads 222

Authors: Patience Muchini, Electdom Matandiroya, Emmanuel Mashonjowa

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A ground-end manifestation of space weather phenomena is known as geomagnetically induced currents (GICs). GICs flow along the electric power transmission cables connecting the transformers and between the grounding points of power transformers during significant geomagnetic storms. Geomagnetically induced currents have been studied in other regions and have been noted to affect the power grid network. In Zimbabwe, grid failures have been experienced, but it is yet to be proven if these failures have been due to GICs. The purpose of this paper is to characterize geomagnetically induced currents with a power grid network. This paper analyses data collected, which is geomagnetic data, which includes the Kp index, DST index, and the G-Scale from geomagnetic storms and also analyses power grid data, which includes reactive power, relay tripping, and alarms from high voltage substations and then correlates the data. This research analysis was first theoretically analyzed by studying geomagnetic parameters and then experimented upon. To correlate, MATLAB was used as the basic software to analyze the data. Latitudes of the substations were also brought into scrutiny to note if they were an impact due to the location as low latitudes areas like most parts of Zimbabwe, there are less severe geomagnetic variations. Based on theoretical and graphical analysis, it has been proven that there is a slight relationship between power system failures and GICs. Further analyses can be done by implementing measuring instruments to measure any currents in the grounding of high-voltage transformers when geomagnetic storms occur. Mitigation measures can then be developed to minimize the susceptibility of the power network to GICs.

Keywords: adverse space weather, DST index, geomagnetically induced currents, KP index, reactive power

Procedia PDF Downloads 115

Authors: Emmanuel Kamal Aziz Saba, Hussein Al-Moghazy Sultan

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The aim of this study was to evaluate clinically and electro physiologically the autonomic nervous system changes associated with rheumatoid arthritis (RA). The present study included 25 patients with RA [22 women (88%)] and 30 apparently healthy control subjects [27 women (90%)]. A thorough clinical examination was carried out. Disease activity and functional disability were assessed. Tests for assessment of autonomic functions include active and passive orthostatic stress tests, and sympathetic skin response (SSR). The presence of abnormality in 2 tests or more was a clue for the presence of autonomic neuropathy (AN). Sural sensory nerve conduction study and posterior tibial motor nerve conduction study were done. There was a statistically significant decrease in standing systolic and diastolic blood pressure (BP) components of the active orthostatic stress test and SSR amplitude as well as statistically significant prolongation of SSR latency of RA patients when compared to control. Three patients (12%) had clinical symptoms suggestive of AN; increased to 14 patients (56 %) when orthostatic stress tests and SSR were utilized. There were no statistically significant differences between patients with different disease activity score 28 with 4 variables grades of RA activity and SSR latency and amplitude. There were no statistically significant differences between patients with different Stanford Health Assessment Questionnaire Disability Index grades of RA functional disability and SSR latency and amplitude. In conclusion, autonomic neuropathy is a common extra-articular manifestation of RA affecting sympathetic and parasympathetic fibers.

Keywords: autonomic neuropathy, orthostatic stress test, rheumatoid arthritis, sympathetic skin response

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Authors: Vijaya Dixit Aasheesh Dixit

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Shipbuilding industry operates in Engineer Procure Construct (EPC) context. Product mix of a shipyard comprises of various types of ships like bulk carriers, tankers, barges, coast guard vessels, sub-marines etc. Each order is unique based on the type of ship and customized requirements, which are engineered into the product right from design stage. Thus, to execute every new project, a shipyard needs to upgrade its production expertise. As a result, over the long run, holistic learning occurs across different types of projects which contributes to the knowledge base of the shipyard. Simultaneously, in the short term, during execution of a project comprising of multiple sister ships, repetition of similar tasks leads to learning at activity level. This research aims to capture above learnings of a shipyard and incorporate learning curve effect in project scheduling and materials procurement to improve project performance. Extant literature provides support for the existence of such learnings in an organization. In shipbuilding, there are sequences of similar activities which are expected to exhibit learning curve behavior. For example, the nearly identical structural sub-blocks which are successively fabricated, erected, and outfitted with piping and electrical systems. Learning curve representation can model not only a decrease in mean completion time of an activity, but also a decrease in uncertainty of activity duration. Sister ships have similar material requirements. The same supplier base supplies materials for all the sister ships within a project. On one hand, this provides an opportunity to reduce transportation cost by batching the order quantities of multiple ships. On the other hand, it increases the inventory holding cost at shipyard and the risk of obsolescence. Further, due to learning curve effect the production scheduled of each consequent ship gets compressed. Thus, the material requirement schedule of every next ship differs from its previous ship. As more and more ships get constructed, compressed production schedules increase the possibility of batching the orders of sister ships. This work aims at integrating materials management with project scheduling of long duration projects for manufacturing of multiple sister ships. It incorporates the learning curve effect on progressively compressing material requirement schedules and addresses the above trade-off of transportation cost and inventory holding and shortage costs while satisfying budget constraints of various stages of the project. The activity durations and lead time of items are not crisp and are available in the form of probabilistic distribution. A Stochastic Mixed Integer Programming (SMIP) model is formulated which is solved using evolutionary algorithm. Its output provides ordering dates of items and degree of order batching for all types of items. Sensitivity analysis determines the threshold number of sister ships required in a project to leverage the advantage of learning curve effect in materials management decisions. This analysis will help materials managers to gain insights about the scenarios: when and to what degree is it beneficial to treat a multiple ship project as an integrated one by batching the order quantities and when and to what degree to practice distinctive procurement for individual ship.

Keywords: learning curve, materials management, shipbuilding, sister ships

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Authors: Naragain Phumchusri, Kittitach Pongpairoj

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Overbooking is a very useful revenue management technique that could help reduce costs caused by either undersales or oversales. In this paper, we propose an overbooking model for two types of cars that can minimize the total cost for car rental service. With two types of cars, there is an upgrade possibility for lower type to upper type. This makes the model more complex than one type of cars scenario. We have found that convexity can be proved in this case. Sensitivity analysis of the parameters is conducted to observe the effects of relevant parameters on the optimal solution. Model simplification is proposed using multiple linear regression analysis, which can help estimate the optimal overbooking level using appropriate independent variables. The results show that the overbooking level from multiple linear regression model is relatively close to the optimal solution (with the adjusted R-squared value of at least 72.8%). To evaluate the performance of the proposed model, the total cost was compared with the case where the decision maker uses a naïve method for the overbooking level. It was found that the total cost from optimal solution is only 0.5 to 1 percent (on average) lower than the cost from regression model, while it is approximately 67% lower than the cost obtained by the naïve method. It indicates that our proposed simplification method using regression analysis can effectively perform in estimating the overbooking level.

Keywords: overbooking, car rental industry, revenue management, stochastic model

Procedia PDF Downloads 173

Authors: Shaik Ayesha Fathima, Shaik Noor Jahan, Duvvada Rajeswara Rao

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Earth's environment and its evolution can be seen through satellite images in near real-time. Through satellite imagery, remote sensing data provide crucial information that can be used for a variety of applications, including image fusion, change detection, land cover classification, agriculture, mining, disaster mitigation, and monitoring climate change. The objective of this project is to propose a method for classifying satellite images according to multiple predefined land cover classes. The proposed approach involves collecting data in image format. The data is then pre-processed using data pre-processing techniques. The processed data is fed into the proposed algorithm and the obtained result is analyzed. Some of the algorithms used in satellite imagery classification are U-Net, Random Forest, Deep Labv3, CNN, ANN, Resnet etc. In this project, we are using the DeepLabv3 (Atrous convolution) algorithm for land cover classification. The dataset used is the deep globe land cover classification dataset. DeepLabv3 is a semantic segmentation system that uses atrous convolution to capture multi-scale context by adopting multiple atrous rates in cascade or in parallel to determine the scale of segments.

Keywords: area calculation, atrous convolution, deep globe land cover classification, deepLabv3, land cover classification, resnet 50

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Authors: Jamie R. Chua, Christina Irene D. Mejia, Regina P. Berba

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Disseminated tuberculosis is an infectious disease caused by Mycobacterium tuberculosis involving two or more non-contiguous sites identified through bacteriologic confirmation or clinical diagnosis. Over the five year period included in the study, the UP-PGH TB DOTS clinic had total of 3,967 referrals, and the prevalence of disseminated tuberculosis is 1% (68/3967). The mean age was 33.9 years (range 19-64 years) with a male: female ratio of 1:1. 67% (52 patients) had no predisposing comorbid illness or immune disorder. The most common presenting symptoms were abdominal pain (19%), back pain (13%), abdominal enlargement (11%) and mass (10.2%). Anemia, leukocytosis, hypoalbuminemia, and high-normal serum calcium were common biochemical and hematologic findings. Around 36% (25) of patients were diagnosed clinically with disseminated tuberculosis despite lacking bacteriologic evidence of multi-organ involvement. The lungs (86%) is still the most commonly involved site, followed by intestinal (22%), vertebral/Pott’s (27%), and pelvic/genital (19%). The mean time from presentation to initiation of therapy was 22 days (SD 32.7). Only 18 patients (29.3%) were properly recorded to have been referred to local TB DOTs facilities. Of the 68 patients, only 16% (11 patients) continued follow-up at PGH, and all had documented treatment completion. Treatment outcomes of the remaining were unknown. Due to the variety of involved sites, a high index of suspicion is required. Knowledge on clinical features, common radiographic findings, and histopathologic characteristics of disseminated TB is important as bacteriologic evidence of infection is not always apparent.

Keywords: disseminated tuberculosis, Mycobacterium tuberculosis, miliary tuberculosis, tuberculosis

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Authors: Ashish Kumar Agrahari, Amit Kumar

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Paroxysmal nocturnal hemoglobinuria (PNH) is an acquired clonal blood disorder that manifests with hemolytic anemia, thrombosis, and peripheral blood cytopenias. The disease is caused by the deficiency of two glycosylphosphatidylinositols (GPI)-anchored proteins (CD55 and CD59) in the hemopoietic stem cells. The deficiency of GPI-anchored proteins has been associated with the somatic mutations in phosphatidylinositol glycan class A (PIGA). However, the mutations that do not cause PNH is associated with the multiple congenital anomalies-hypotonia-seizures syndrome 2 (MCAHS2). To best of our knowledge, no computational study has been performed to explore the atomistic level impact of PIGA mutations on the structure and dynamics of the protein. In the current work, we are mainly interested to get insights into the molecular mechanism of PIGA mutations. In the initial step, we screened the most pathogenic mutations from the pool of publicly available mutations. Further, to get a better understanding, pathogenic mutations were mapped to the modeled structure and subjected to 50ns molecular dynamics simulation. Our computational study suggests that four mutations are highly vulnerable to altering the structural conformation and stability of the PIGA protein, which illustrates its association with PNH and MCAHS2 phenotype.

Keywords: homology modeling, molecular dynamics simulation, missense mutations PNH, MCAHS2, PIGA

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Authors: Ali Kuzu

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There are about 800 thousand chemicals in our environment and the number is increasing more than a thousand every year. While most of these chemicals are used as components in various consumer products, some are faced as industrial waste in the environment. Unfortunately, many of these chemicals are hazardous and affect humans. According to the “International Program on Chemical Safety” of World Health Organization; Among the chronic health effects of chemicals, cancer is of major concern. Many substances have found in recent years to be carcinogenic in one or more species of laboratory animals. Especially with respect to long-term effects, the response to a chemical may vary, quantitatively or qualitatively, in different groups of individuals depending on predisposing conditions, such as nutritional status, disease status, current infection, climatic extremes, and genetic features, sex and age of the individuals. Understanding the response of such specific risk groups is an important area of toxicology research. People with age 65+ is defined as “aged (or elderly)”. The elderly population in the world is about 600 million, which corresponds to ~8 percent of the world population. While every 1 of each 4 people is aged in Japan, the elderly population is quite close to 20 percent in many developed countries. And elderly population in these countries is growing more rapidly than the total population. The negative effects of chemicals on elderly take an important place in health-care related issues in last decades. The aged population is more susceptible to the harmful effects of environmental chemicals. According to the poor health of the organ systems in elderly, the ability of their body to eliminate the harmful effects and chemical substances from their body is also poor. With the increasing life expectancy, more and more people will face problems associated with chemical residues.

Keywords: elderly, chemicals’ effects, aged care, care need

Procedia PDF Downloads 457

Authors: Pooja Khanal, Huaming Zhang

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Labeling of issues/bugs, also known as bug classification, plays a vital role in software engineering. Some known labels/classes of bugs are 'User Interface', 'Security', and 'API'. Most of the time, when a reporter reports a bug, they try to assign some predefined label to it. Those issues are reported for a project, and each project is a repository in GitHub/GitLab, which contains multiple issues. There are many software project repositories -ranging from individual projects to commercial projects. The labels assigned for different repositories may be dependent on various factors like human instinct, generalization of labels, label assignment policy followed by the reporter, etc. While the reporter of the issue may instinctively give that issue a label, another person reporting the same issue may label it differently. This way, it is not known mathematically if a label in one repository is similar or different to the label in another repository. Hence, the primary goal of this research is to find the semantic differences between bug labeling of different repositories via machine learning. Independent optimal classifiers for individual repositories are built first using the text features from the reported issues. The optimal classifiers may include a combination of multiple classifiers stacked together. Then, those classifiers are used to cross-test other repositories which leads the result to be deduced mathematically. The produce of this ongoing research includes a formalized open-source GitHub issues database that is used to deduce the similarity of the labels pertaining to the different repositories.

Keywords: bug classification, bug labels, GitHub issues, semantic differences

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Authors: Rithika Sriram, Kiran M. Bhojwani

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Introduction : Tuberculosis of the head and neck has proved to be a diagnostic challenge for otorhinolarynologists around the world. These lesions are often misdiagnosed as cancer. So in order to contribute to a better understanding of these lesions, we have conducted our study among patients affected by TB in the head and neck region with the objective of assessing the various manifestations, presentations, diagnostic techniques, risk factors such as smoking and alcohol consumption, coexisting illnesses and treatment modalities. Materials and Methods: This was a retrospective study conducted over a three year period (2012-2014) in 2 hospitals affliated to Kasturba Medical College in Mangalore, South India. A semi structured proforma was used to capture information from the medical records pertaining to the various objectives of the study such as clinical features and history of smoking. Data was analysed using SPSS version 16.0 and results obtained were depicted as percentages. Chi square test was used to find association between the variables and p<0.05 was considered statistically significant. Results: 104 patients were found to have TB of the head and neck and among them,the most common manifestation was found to be Tubercular Lymphadenitis (86.53%), followed by laryngeal TB (4.8%), submandibular gland TB (3.8%), deep neck space abscess(3.8%) and adenotonsillar TB. FNAC was found to be the gold standard for the diagnosis of TB disease of the lymph node.26% of the patients had coexisting HIV infection and 16.3% of the patients had associated pulmonary TB. More than 20% of the patients were smokers. Most patients were treated using ATT. Conclusion: Tuberculosis affecting regions of head and neck is no longer uncommon. Sufficient knowledge and appropriate diagnostic means is required while dealing with these lesions and must be included in the differential diagnosis of pathological lesions of head and neck.

Keywords: FNAC, Mangalore, smoking, tuberculosis

Procedia PDF Downloads 278

Authors: Mohanad Alhabo, Li Zhang, Naveed Nawaz

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Small cell deployment in 5G networks is a promising technology to enhance capacity and coverage. However, unplanned deployment may cause high interference levels and high number of unnecessary handovers, which in turn will result in an increase in the signalling overhead. To guarantee service continuity, minimize unnecessary handovers, and reduce signalling overhead in heterogeneous networks, it is essential to properly model the handover decision problem. In this paper, we model the handover decision according to Multiple Attribute Decision Making (MADM) method, specifically Technique for Order Preference by Similarity to an Ideal Solution (TOPSIS). In this paper, we propose a hybrid TOPSIS method to control the handover in heterogeneous network. The proposed method adopts a hybrid weighting, which is a combination of entropy and standard deviation. A hybrid weighting control parameter is introduced to balance the impact of the standard deviation and entropy weighting on the network selection process and the overall performance. Our proposed method shows better performance, in terms of the number of frequent handovers and the mean user throughput, compared to the existing methods.

Keywords: handover, HetNets, interference, MADM, small cells, TOPSIS, weight

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Authors: Xinyuan Y. Song, Kai Kang

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Joint models for analyzing longitudinal and survival data are widely used to investigate the relationship between a failure time process and time-variant predictors. A common assumption in conventional joint models in the survival analysis literature is that all predictors are observable. However, this assumption may not always be supported because unobservable traits, namely, latent variables, which are indirectly observable and should be measured through multiple observed variables, are commonly encountered in the medical, behavioral, and financial research settings. In this study, a joint modeling approach to deal with this feature is proposed. The proposed model comprises three parts. The first part is a dynamic factor analysis model for characterizing latent variables through multiple observed indicators over time. The second part is a random coefficient trajectory model for describing the individual trajectories of latent variables. The third part is a proportional hazard model for examining the effects of time-invariant predictors and the longitudinal trajectories of time-variant latent risk factors on hazards of interest. A Bayesian approach coupled with a Markov chain Monte Carlo algorithm to perform statistical inference. An application of the proposed joint model to a study on the Alzheimer's disease neuroimaging Initiative is presented.

Keywords: Bayesian analysis, joint model, longitudinal data, time-to-event data

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Authors: Sarah K. Hagi, Majdi Alnowaimi

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In the last decade, there were immense advancements in the medical imaging modalities. These advancements can scan a whole volume of the lung organ in high resolution images within a short time. According to this performance, the physicians can clearly identify the complicated anatomical and pathological structures of lung. Therefore, these advancements give large opportunities for more advance of all types of lung cancer treatment available and will increase the survival rate. However, lung cancer is still one of the major causes of death with around 19% of all the cancer patients. Several factors may affect survival rate. One of the serious effects is the breathing process, which can affect the accuracy of diagnosis and lung tumor treatment plan. We have therefore developed a semi automated algorithm to localize the 3D lung tumor positions across all respiratory data during respiratory motion. The algorithm can be divided into two stages. First, a lung tumor segmentation for the first phase of the 4D computed tomography (CT). Lung tumor segmentation is performed using an active contours method. Then, localize the tumor 3D position across all next phases using a 12 degrees of freedom of an affine transformation. Two data set where used in this study, a compute simulate for 4D CT using extended cardiac-torso (XCAT) phantom and 4D CT clinical data sets. The result and error calculation is presented as root mean square error (RMSE). The average error in data sets is 0.94 mm ± 0.36. Finally, evaluation and quantitative comparison of the results with a state-of-the-art registration algorithm was introduced. The results obtained from the proposed localization algorithm show a promising result to localize alung tumor in 4D CT data.

Keywords: automated algorithm , computed tomography, lung tumor, tumor localization

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Authors: Justin Orimisan Ijigbade

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The study was conducted to assess the effect of climate variability on honeybee’s production in Ondo State, Nigeria. Multistage sampling technique was employed to collect the data from 60 beekeepers across six Local Government Areas in Ondo State. Data collected were subjected to descriptive statistics and multiple regression model analyses. The results showed that 93.33% of the respondents were male with 80% above 40 years of age. Majority of the respondents (96.67%) had formal education and 90% produced honey for commercial purpose. The result revealed that 90% of the respondents admitted that low temperature as a result of long hours/period of rainfall affected the foraging efficiency of the worker bees, 73.33% claimed that long period of low humidity resulted in low level of nectar flow, while 70% submitted that high temperature resulted in improper composition of workers, dunes and queen in the hive colony. The result of multiple regression showed that beekeepers’ experience, educational level, access to climate information, temperature and rainfall were the main factors affecting honey bees production in the study area. Therefore, beekeepers should be given more education on climate variability and its adaptive strategies towards ensuring better honeybees production in the study area.

Keywords: climate variability, honeybees production, humidity, rainfall and temperature

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Authors: Yoonjung An, Yongtae Park

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Offering a Product-Service System (PSS) is a well-accepted strategy that companies may adopt to provide a set of systemic solutions to customers. PSSs were initially provided in a simple form but now take diversified and complex forms involving multiple services, products and technologies. With the growing interest in the PSS, frameworks for the PSS development have been introduced by many researchers. However, most of the existing frameworks fail to examine various relations existing in a complex PSS. Since designing a complex PSS involves full integration of multiple products and services, it is essential to identify not only product-service relations but also product-product/ service-service relations. It is also equally important to specify how they are related for better understanding of the system. Moreover, as customers tend to view their purchase from a more holistic perspective, a PSS should be developed based on the whole system’s requirements, rather than focusing only on the product requirements or service requirements. Thus, we propose a framework to develop a complex PSS that is coordinated fully with the requirements of both worlds. Specifically, our approach adopts a multi-domain matrix (MDM). A MDM identifies not only inter-domain relations but also intra-domain relations so that it helps to design a PSS that includes highly desired and closely related core functions/ features. Also, various dependency types and rating schemes proposed in our approach would help the integration process.

Keywords: inter-domain relations, intra-domain relations, multi-domain matrix, product-service system design

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Authors: Bilal Arshad, Johan Barthelemy, Elliott Pilton, Pascal Perez

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Getting reliable, informative, and up-to-date information about the location, mobility, and behavioural patterns of animals will enhance our ability to research and preserve biodiversity. The fusion of infra-red sensors and camera traps offers an inexpensive way to collect wildlife data in the form of images. However, extracting useful data from these images, such as the identification and counting of animals remains a manual, time-consuming, and costly process. In this paper, we demonstrate that such information can be automatically retrieved by using state-of-the-art deep learning methods. Another major challenge that ecologists are facing is the recounting of one single animal multiple times due to that animal reappearing in other images taken by the same or other camera traps. Nonetheless, such information can be extremely useful for tracking wildlife and understanding its behaviour. To tackle the multiple count problem, we have designed a meshed network of camera traps, so they can share the captured images along with timestamps, cumulative counts, and dimensions of the animal. The proposed method takes leverage of edge computing to support real-time tracking and monitoring of wildlife. This method has been validated in the field and can be easily extended to other applications focusing on wildlife monitoring and management, where the traditional way of monitoring is expensive and time-consuming.

Keywords: computer vision, ecology, internet of things, invasive species management, wildlife management

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Authors: Yanjun Chen, Chuanping Hu, Jie Shao, Lin Mei, Chongyang Zhang

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Traditional image classification is based on encoding scheme (e.g. Fisher Vector, Vector of Locally Aggregated Descriptor) with low-level image features (e.g. SIFT, HoG). Compared to these low-level local features, deep convolutional features obtained at the mid-level layer of convolutional neural networks (CNN) have richer information but lack of geometric invariance. For scene classification, there are scattered objects with different size, category, layout, number and so on. It is crucial to find the distinctive objects in scene as well as their co-occurrence relationship. In this paper, we propose a method to take advantage of both deep convolutional features and the traditional encoding scheme while taking object-centric and scene-centric information into consideration. First, to exploit the object-centric and scene-centric information, two CNNs that trained on ImageNet and Places dataset separately are used as the pre-trained models to extract deep convolutional features at multiple scales. This produces dense local activations. By analyzing the performance of different CNNs at multiple scales, it is found that each CNN works better in different scale ranges. A scale-wise CNN adaption is reasonable since objects in scene are at its own specific scale. Second, a fisher kernel is applied to aggregate a global representation at each scale and then to merge into a single vector by using a post-processing method called scale-wise normalization. The essence of Fisher Vector lies on the accumulation of the first and second order differences. Hence, the scale-wise normalization followed by average pooling would balance the influence of each scale since different amount of features are extracted. Third, the Fisher vector representation based on the deep convolutional features is followed by a linear Supported Vector Machine, which is a simple yet efficient way to classify the scene categories. Experimental results show that the scale-specific feature extraction and normalization with CNNs trained on object-centric and scene-centric datasets can boost the results from 74.03% up to 79.43% on MIT Indoor67 when only two scales are used (compared to results at single scale). The result is comparable to state-of-art performance which proves that the representation can be applied to other visual recognition tasks.

Keywords: deep convolutional features, Fisher Vector, multiple scales, scale-specific normalization

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Authors: Huafeng Wei

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Dementia in Alzheimer’s Disease (AD) is the number one cause of dementia internationally, without effective treatments. Increasing evidence suggest that disruption of intracellular calcium homeostasis, primarily pathological elevation of cytosol and mitochondria but reduction of endoplasmic reticulum (ER) calcium concentrations, play critical upstream roles on multiple pathologies and associated neurodegeneration, impaired neurogenesis, synapse, and cognitive dysfunction in various AD preclinical studies. The last federal drug agency (FDA) approved drug for AD dementia treatment, memantine, exert its therapeutic effects by ameliorating N-methyl-D-aspartate (NMDA) glutamate receptor overactivation and subsequent calcium dysregulation. More research works are needed to develop other drugs targeting calcium dysregulation at multiple pharmacological acting sites for future effective AD dementia treatment. Particularly, calcium channel blockers for the treatment of hypertension and dantrolene for the treatment of muscle spasm and malignant hyperthermia can be repurposed for this purpose. In our own research work, intranasal administration of dantrolene significantly increased its brain concentrations and durations, rendering it a more effective therapeutic drug with less side effects for chronic AD dementia treatment. This review summarizesthe progress of various studies repurposing drugs targeting calcium dysregulation for future effective AD dementia treatment as potentially disease-modifying drugs.

Keywords: alzheimer, calcium, cognitive dysfunction, dementia, neurodegeneration, neurogenesis

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Authors: Tarek Abdelkader, Suresh Selvaraj, Prasad Iyer, Yong Mun Hin, Hajmath Begum, P. Gopalakrishnakone

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Nowadays, more and more educational institutes, as well as students, rely on 3D anatomy programs as an important tool that helps students correlate the actual locations of anatomical structures in a 3D dimension. Lately, virtual reality (VR) is gaining more favor from the younger generations due to its higher interactive mode. As a result, using virtual reality as a gamified learning platform for anatomy became the current goal. We present a model where a Virtual Human Anatomy Program (VHAP) was developed to assist with the anatomy learning experience of students. The anatomy module has been built, mostly, from real patient CT scans. Segmentation and surface rendering were used to create the 3D model by direct segmentation of CT scans for each organ individually and exporting that model as a 3D file. After acquiring the 3D files for all needed organs, all the files were introduced into a Virtual Reality environment as a complete body anatomy model. In this ongoing experiment, students from different Allied Health orientations are testing the VHAP. Specifically, the cardiovascular system has been selected as the focus system of study since all of our students finished learning about it in the 1st trimester. The initial results suggest that the VHAP system is adding value to the learning process of our students, encouraging them to get more involved and to ask more questions. Involved students comments show that they are excited about the VHAP system with comments about its interactivity as well as the ability to use it solo as a self-learning aid in combination with the lectures. Some students also experienced minor side effects like dizziness.

Keywords: 3D construction, health sciences, teaching pedagogy, virtual reality

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Authors: Niklas Panten, Eberhard Abele

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This paper presents a novel approach for real-time and near-optimal control of industrial smart grids by deep reinforcement learning (DRL). To achieve highly energy-efficient factory systems, the energetic linkage of machines, technical building equipment and the building itself is desirable. However, the increased complexity of the interacting sub-systems, multiple time-variant target values and stochastic influences by the production environment, weather and energy markets make it difficult to efficiently control the energy production, storage and consumption in the hybrid industrial smart grids. The studied deep reinforcement learning approach allows to explore the solution space for proper control policies which minimize a cost function. The deep neural network of the DRL agent is based on a multilayer perceptron (MLP), Long Short-Term Memory (LSTM) and convolutional layers. The agent is trained within multiple Modelica-based factory simulation environments by the Advantage Actor Critic algorithm (A2C). The DRL controller is evaluated by means of the simulation and then compared to a conventional, rule-based approach. Finally, the results indicate that the DRL approach is able to improve the control performance and significantly reduce energy respectively operating costs of industrial smart grids.

Keywords: industrial smart grids, energy efficiency, deep reinforcement learning, optimal control

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Authors: Szymon Kowieski, Zygmunt Mikno

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The study involved the optimization of process parameters during resistance spot welding of Al-coated martensitic boron steel 22MnB5, applied in hot stamping, performed using a programme with a multiple current impulse mode and a programme with variable pressure force. The aim of this research work was to determine the possibilities of a growth in welded joint strength and to identify the expansion of a welding lobe. The process parameters were adjusted on the basis of welding process simulation and confronted with experimental data. 22MnB5 steel is known for its tendency to obtain high hardness values in weld nuggets, often leading to interfacial failures (observed in the study-related tests). In addition, during resistance spot welding, many production-related factors can affect process stability, e.g. welding lobe narrowing, and lead to the deterioration of quality. Resistance spot welding performed using the above-named welding programme featuring 3 levels of force made it possible to achieve 82% of welding lobe extension. Joints made using the multiple current impulse program, where the total welding time was below 1.4s, revealed a change in a peeling mode (to full plug) and an increase in weld tensile shear strength of 10%.

Keywords: 22MnB5, hot stamping, interfacial fracture, resistance spot welding, simulation, single lap joint, welding lobe

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Authors: Hyun-Kyung Lee, Jehyung Oh, Young Eun Jeong, Hyun-Shik Lee

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Perfluoroalkyl compounds (PFCs) are environmental toxicants that persistently accumulate in the human blood. Their widespread detection and accumulation in the environment raise concerns about whether these chemicals might be developmental toxicants and teratogens in the ecosystem. We evaluated and compared the toxicity of PFCs of containing various numbers of carbon atoms (C8-11 carbons) on vertebrate embryogenesis. We assessed the developmental toxicity and teratogenicity of various PFCs. The toxic effects on Xenopus embryos were evaluated using different methods. We measured teratogenic indices (TIs) and investigated the mechanisms underlying developmental toxicity and teratogenicity by measuring the expression of organ-specific biomarkers such as xPTB (liver), Nkx2.5 (heart), and Cyl18 (intestine). All PFCs that we tested were found to be developmental toxicants and teratogens. Their toxic effects were strengthened with increasing length of the fluorinated carbon chain. Furthermore, we produced evidence showing that perfluorodecanoic acid (PFDA) and perfluoroundecanoic acid (PFuDA) are more potent developmental toxicants and teratogens in an animal model compared to the other PFCs we evaluated [perfluorooctanoic acid (PFOA) and perfluorononanoic acid (PFNA)]. In particular, severe defects resulting from PFDA and PFuDA exposure were observed in the liver and heart, respectively, using the whole mount in situ hybridization, real-time PCR, pathologic analysis of the heart, and dissection of the liver. Our studies suggest that most PFCs are developmental toxicants and teratogens, however, compounds that have higher numbers of carbons (i.e., PFDA and PFuDA) exert more potent effects.

Keywords: PFC, xenopus, fetax, development

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Authors: W. Mughees, M. Alahmad

Abstract:

Water is the most significant entity that controls local and global development. For the Gulf region, especially Saudi Arabia, with its limited potable water resources, the potential of the fresh water problem is highly considerable. In this research, the study involves the design and analysis of pinch-based water/wastewater networks. Multiple water/wastewater networks were developed using pinch analysis involving direct recycle/material recycle method. Property-integration technique was adopted to carry out direct recycle method. Particularly, a petroleum refinery was considered as a case study. In direct recycle methodology, minimum water discharge and minimum fresh water resource targets were estimated. Re-design (or retrofitting) of water allocation in the networks was undertaken. Chemical Oxygen Demand (COD) and hardness properties were taken as pollutants. This research was based on single and double contaminant approach for COD and hardness and the amount of fresh water was reduced from 340.0 m3/h to 149.0 m3/h (43.8%), 208.0 m3/h (61.18%) respectively. While regarding double contaminant approach, reduction in fresh water demand was 132.0 m3/h (38.8%). The required analysis was also carried out using mathematical programming technique. Operating software such as LINGO was used for these studies which have verified the graphical method results in a valuable and accurate way. Among the multiple water networks, the one possible water allocation network was developed based on mass exchange.

Keywords: minimization, water pinch, water management, pollution prevention

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Authors: Badriah Alayidi, Emad Alyahya

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Background: Most people with Multiple Sclerosis (MS) report worsening balance as the condition progresses. Poor balance control is also well known to be a significant risk factor for both falling and fear of falling. The increased risk of falls with disease progression thus makes balance control an essential target of gait rehabilitation amongst people with MS. Intervention programs have developed various methods to improve balance control, and accumulating evidence suggests that exercise programs may help people with MS improve their balance. Among these methods, virtual reality (VR) is growing in popularity as a balance-training technique owing to its potential benefits, including better compliance and greater user happiness. However, it is not clear if a VR environment will induce different balance control mechanisms in MS as compared to healthy individuals or traditional environments. Therefore, this study aims to examine how individuals with MS control their balance in a VR setting. Methodology: The proposed study takes an empirical approach to estimate and determine the role of balance response in persons with MS using a VR environment. It will use primary data collected through patient observations, physiological and biomechanical evaluation of balance, and data analysis. Results: The preliminary systematic review and meta-analysis indicated that there was variability in terms of the outcome assessing balance response in people with MS. The preliminary results of these assessments have the potential to provide essential indicators of the progression of MS and contribute to the individualization of treatment and evaluation of the interventions’ effectiveness. The literature describes patients who have had the opportunity to experiment in VR settings and then used what they have learned in the real world, suggesting that this VR setting could be more appealing than conditional settings. The findings of the proposed study will be beneficial in estimating and determining the effect of VR on balance control in persons with MS. In previous studies, VR was shown to be an interesting approach to neurological rehabilitation, but more data are needed to support this approach in MS. Conclusions: The proposed study enables an assessment of balance and evaluations of a variety of physiological implications related to neural activity as well as biomechanical implications related to movement analysis.

Keywords: multiple sclerosis, virtual reality, postural control, balance

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Authors: Touil Issam, Bouraghda Skander

Abstract:

The human hand is a vital yet intricate organ, essential for tasks ranging from grasping to executing fine motor skills. It serves as the most advanced and natural interface for interaction between humans and their surroundings. Upper-limb deficiencies, caused by conditions such as illness, accidents, or congenital factors, are prevalent worldwide. These deficiencies are categorized into seven types: partial hand, wrist disarticulation, transradial, elbow disarticulation, transhumeral, shoulder disarticulation, and forequarter, with transradial amputations being the most common and often well-suited for prosthetic hands. Advancements in technology and healthcare services have amplified the need for affordable, user-friendly, and functional prosthetic hands capable of restoring essential hand and finger functions. As a critical subset of medical robotics, prosthetic hands have seen notable design and development progress. However, challenges remain in achieving widespread user acceptance and satisfaction, highlighting the need for a holistic approach to their design and implementation. This study aims to consolidate the various factors involved in the development of prosthetic hands, focusing particularly on transradial prosthetics. It considers all types of prosthetic hands, whether actively powered, passively powered, or nonpowered. By presenting a comprehensive concept map, we aim to integrate these factors into a cohesive framework, guiding the development of prosthetic hands that offer enhanced functionality, improved user acceptance, and better alignment with user.

Keywords: prosthetic hands, user-centeren design, human machine interaction design, assistive technologies, meical robotics

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Authors: Wei Wen Yang, Emily Chia Yu Su

Abstract:

Dengue fever is a mosquito-borne tropical infectious disease caused by the dengue virus. After infection, symptoms usually start from three to fourteen days. Dengue virus may cause a high fever and at least two of the following symptoms, severe headache, severe eye pain, joint pains, muscle or bone pain, vomiting, feature skin rash, and mild bleeding manifestation. In addition, recovery will take at least two to seven days. Dengue fever has rapidly spread in tropical and subtropical areas in recent years. Several phenomena around the world such as global warming, urbanization, and international travel are the main reasons in boosting the spread of dengue. In Taiwan, epidemics occur annually, especially during summer and fall seasons. On the other side, Singapore government also has announced the amounts number of dengue cases spreading in Singapore. As the serious epidemic of dengue fever outbreaks in Taiwan and Singapore, countries around the Asia-Pacific region are becoming high risks of susceptible to the outbreaks and local hub of spreading the virus. To improve public safety and public health issues, firstly, we are going to use Microsoft Excel and SAS EG to do data preprocessing. Secondly, using support vector machines and decision trees builds predict model, and analyzes the infectious cases between Taiwan and Singapore. By comparing different factors causing vector mosquito from model classification and regression, we can find similar spreading patterns where the disease occurred most frequently. The result can provide sufficient information to predict the future dengue infection outbreaks and control the diffusion of dengue fever among countries.

Keywords: dengue fever, Taiwan, Singapore, Aedes aegypti

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Authors: Shayma Munqith Al-Baker, Fadhl Ahmed Saeed Al-Gasha’a, Samira Hamid Hanash, Ahmed Ali Al-Hazmi

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A total of 200 samples (180 fecal materials and 20 organ samples) were collected from (5 different poultry farms, 10 local poultry shops, 5 houses poultry, 5 Eggs stores shops and 5 hand slaughters centers) in Ibb city, Yemen, 2014. According to morphological, cultural, as well as biochemical characterization and serological tests, 59 29.5% isolates were identified as Salmonella spp. and all Salmonella isolates were categorized by serotype, which comprised of, 37 62.71% Salmonella Typhimurium serovar, 21 35.59%. Salmonella Enteritidis serovar and 11.69% Salmonella Heidelberg serovar. Antibiotic sensitivity test was done for bacterial isolates and the results showed there were clear differences in antibiotic resistant. Antimicrobial susceptibility of the isolates varies as follows: Ofloxacin 79.66%, Ciprofloxacin 67.80%, Colistin 59.32% and Gentamycin 52.54%. All of isolates were resistant to Erythromycin, Penicillin and Lincomycin. Antibacterial activity was done for both aqueous and ethanol extracts of Dodonaea viscosa plant by using well and disc diffusion assay. The results indicated that well diffusion assay had best results than disc diffusion assay, the highest inhibition zone was 22 mm for well diffusion and 15 mm for disc diffusion assay, the results observed that ethanol extract had best antibacterial effect than aqueous extract which the percentage of bacterial isolates affected with ethanol extract was 71.19% comparing with aqueous extract 28.81% by using disc diffusion assay, while the percentage of bacterial isolates affected with ethanol extract was 88.13% comparing with aqueous extract 52.54% by using will diffusion assay.

Keywords: Salmonella spp, Dodonaea viscosa, antimicrobial and salmonellosis

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Authors: Kang-Hyun Leem, Myung-Gyou Kim, Hye Kyung Kim

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Eggs have long been an important contributor to the nutritional quality of the human, and recognized as a very valuable source of proteins for human nutrition. Egg yolk is composed of various important chemical substances for human health. Growth means not only the increase of body weight but also the elongation of height and the enlargement of each organ's anatomical and morphological size. A calcium shortage causes the growth retardation on the body growth. In this study, we examined the therapeutic effects of egg yolk peptide (EYP) on the retardation of the longitudinal bone growth induced by low-calcium diet (0.05%) in adolescent rats. Low calcium diets were administrated for 15 days. During the last five days, calcium and/or vitamin D and/or EYP were administrated. The body weights, longitudinal bone growth rates, the heights of growth plates, and bone morphogenetic protein (BMP)-2 and insulin-like growth factor (IGF)-1 expressions were measured using histochemical analysis. Low calcium diets caused the significant reduction in body weight gains and the longitudinal bone growth. The heights of growth plates and the expressions of BMP-2 and IGF-1 showed the impairment of body growth as well. Calcium and/or vitamin D administration could not significantly increase the longitudinal bone growth. However, calcium, vitamin D, and EYP administration significantly increased the bone growth, the growth plate height, and BMP-2 and IGF-1 expressions. These results suggest that EYP enhances the longitudinal bone growth in the calcium and/or vitamin D deficiency and it could be a promising agent for the treatment of children suffering from malnutrition.

Keywords: egg yolk peptide, low-calcium diet, longitudinal bone growth, morphogenetic protein-2, insulin-like growth factor-1, vitamin D

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Authors: Khaled A. shady, Fagr B. Bazeed, Nashwa K. Abousamra, Ihab H. Elberawe, Ashraf E. shaalan, Mohamed A. Sobh

Abstract:

Physical activity activates a variety of adult stem cells which might be released into the circulation or might be activated in their organ-resident state. A variety of stimuli such as metabolic, mechanical, and hormonal stimuli might by responsible for the mobilization. This study was done to know the changes in hematopoietic stem cells and endothelial progenitor in athletes in the 24 hours following 30 min of aerobic exercise. Methods: Ten healthy male's athlete's (age 20.7± 0.61 y) performed moderate running with 30 min at 80% of velocity of The IAT. Blood samples taken pre-, and immediately, 30 min, 2h, 6h and 24h post-exercise were analyzed for hematopoietic stem cells (HSCs ), endothelial progenitor cells (EPCs(, vascular endothelial growth factor (VEGF), nitric oxide (NO), lactic acid (LA), and white blood cells . HSCs and EPCs were quantified by flow cytometry. Results: After 30min of aerobic exercise significant increases in HSCs, EPC, VEGF, NO, LA and WBCs (p ˂ 0.05). This increase will be at different rates according to the timing of taking blood sample and was in the maximum rate of increase after 30 min of aerobic exercise. HSCs, EPC, NO and WBCs were in the maximum rate of increase 2h post exercise. In addition, VEGF was in the maximum rate of increase immediately post exercise and LA concentration not affected after exercise. Conclusion: These data suggest that HSCs and EPCs increased after aerobic exercise due to increase of VEGF which play an important role in mobilization of stem cells and promotes NO increase which contributes to increase EPCs.

Keywords: physical activity, hematopoietic stem cells, mobilization, athletes

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