Search results for: machine harvested grapes

Authors: Abdulatif Abdulsalam, Mohamed Shaban

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This paper introduces interfacing and replication of electronic tools based on the MATLAB/ SIMULINK mock-up package. Mock-up components contain dc-dc converters, power issue rectifiers, motivation machines, dc gear, synchronous gear, and more entire systems. Power issue rectifier model includes solid state device models. The tools are the clear-cut structure and mock-up of complex energetic systems connecting with power electronic machines.

Keywords: power electronics, machine, MATLAB, simulink

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Authors: Hoda Abugharsa

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In this paper we propose to design a sorter circuit using insertion sorting algorithm. The circuit will be designed using Algorithmic State Machines (ASM) method. That means converting the insertion sorting flowchart into an ASM chart. Then the ASM chart will be used to design the sorter circuit and the control unit.

Keywords: insert sorting algorithm, ASM chart, sorter circuit, state machine, control unit

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Authors: Essam Al Daoud

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Gradient boosting methods have been proven to be a very important strategy. Many successful machine learning solutions were developed using the XGBoost and its derivatives. The aim of this study is to investigate and compare the efficiency of three gradient methods. Home credit dataset is used in this work which contains 219 features and 356251 records. However, new features are generated and several techniques are used to rank and select the best features. The implementation indicates that the LightGBM is faster and more accurate than CatBoost and XGBoost using variant number of features and records.

Keywords: gradient boosting, XGBoost, LightGBM, CatBoost, home credit

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Authors: H. N. Alyaseen, S. E. Alalawi, T. Cordoba, É. Delisle, C. Cordoba, A. Odobescu

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Sternal dehiscence is defined as the persistent separation of sternal bones that are often complicated with mediastinitis. Etiologies that lead to sternal dehiscence vary, with cardiovascular and thoracic surgeries being the most common. Early diagnosis in susceptible patients is crucial to the management of such cases, as they are associated with high mortality rates. A recent meta-analysis of more than four hundred thousand patients concluded that deep sternal wound infections were the leading cause of mortality and morbidity in patients undergoing cardiac procedures. Long-term complications associated with sternal dehiscence include increased hospitalizations, cardiac infarctions, and renal and respiratory failures. Numerous osteosynthesis methods have been described in the literature. Surgical materials offer enough rigidity to support the sternum and can be flexible enough to allow physiological breathing movements of the chest; however, these materials fall short when managing patients with extensive bone loss, osteopenia, or general poor bone quality, for such cases, flaps offer a better closure system. Early utilization of flaps yields better survival rates compared to delayed closure or to patients treated with sternal rewiring and closed drainage. The utilization of pectoralis major flaps, rectus abdominus, and latissimus muscle flaps have all been described in the literature as great alternatives. Flap selection depends on a variety of factors, mainly the size of the sternal defect, infection, and the availability of local tissues. Free fibular flaps are commonly harvested flaps utilized in reconstruction around the body. In cases regarding sternal reconstruction with free fibular flaps, the literature exclusively discussed the flap applied vertically to the chest wall. We present a different technique applying the free fibular triple barrel flap oriented in a transverse manner, in parallel to the ribs. In our experience, this method could have enhanced results and improved prognosis as it contributes to the normal circumferential shape of the chest wall.

Keywords: sternal dehiscence, management, free fibular flaps, novel surgical techniques

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Authors: Sanjay Dhiraaj, Puneet Goyal, Aditya Kapoor, Gaurav Misra

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In electrocardiography, an ECG artefact is used to indicate something that is not heart-made. Although technological advancements have produced monitors with the potential of providing accurate information and reliable heart rate alarms, despite this, interference of the displayed electrocardiogram still occurs. These interferences can be from the various electrical gadgets present in the operating room or electrical signals from other parts of the body. Artefacts may also occur due to poor electrode contact with the body or due to machine malfunction. Knowing these artefacts is of utmost importance so as to avoid unnecessary and unwarranted diagnostic as well as interventional procedures. We report a case of ECG artefacts occurring due to patient warming blanket and its consequences. A 20-year-old male with a preoperative diagnosis of exstrophy epispadias complex was posted for surgery under epidural and general anaesthesia. Just after endotracheal intubation, we observed nonspecific ECG changes on the monitor. At a first glance, the monitor strip revealed broad QRs complexes suggesting a ventricular bigeminal rhythm. Closer analysis revealed these to be artefacts because although the complexes were looking broad on the first glance there was clear presence of normal sinus complexes which were immediately followed by 'broad complexes' or artefacts produced by some device or connection. These broad complexes were labeled as artefacts as they were originating in the absolute refractory period of the previous normal sinus beat. It would be physiologically impossible for the myocardium to depolarize so rapidly as to produce a second QRS complex. A search for the possible reason for the artefacts was made and after deepening the plane of anaesthesia, ruling out any possible electrolyte abnormalities, checking of ECG leads and its connections, changing monitors, checking all other monitoring connections, checking for proper grounding of anaesthesia machine and OT table, we found that after switching off the patient’s warming apparatus the rhythm returned to a normal sinus one and the 'broad complexes' or artefacts disappeared. As misdiagnosis of ECG artefacts may subject patients to unnecessary diagnostic and therapeutic interventions so a thorough knowledge of the patient and monitors allow for a quick interpretation and resolution of the problem.

Keywords: ECG artefacts, patient warming blanket, peri-operative arrhythmias, mobile messaging services

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Authors: Pavel Porubiak, Lukas Kudlacek

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Introduction Transplant tourism is a controversial method of obtaining an organ, and that goes all the more for a country such as China, where sources of evidence point out to the possibility of organs being harvested illegally. This research aimed at listing the individual countries these tourists come from, or which medical companies sell transplant related products in there, with China being used as an example. Materials and methods The methodology of scoping study was used for both parts of the research. The countries from which transplant tourists come to China were identified by a search through existing medical studies in the NCBI PubMed database, listed under the keyword ‘transplantation in China’. The search was not limited by any other criteria, but only the studies available for free – directly on PubMed or a linked source – were used. Other research studies on this topic were considered as well. The companies were identified through multiple methods. The first was an online search focused on medical companies and their products. The Bloomberg Service, used by stock brokers worldwide, was then used to identify the revenue of these companies in individual countries – if data were available – as well as their business presence in China. A search through the U.S. Securities and Exchange Commission was done in the same way. Also a search on the Chinese internet was done, and to obtain more results, a second online search was done as well. The results and discussion The extensive search has identified 14 countries with transplant tourists to China. The search for a similar studies or reports resulted in finding additional six countries. The companies identified by our research also amounted to 20. Eight of them are sourcing China with organ preservation products – of which one is just trying to enter the Chinese market, six with immunosuppressive drugs, four with transplant diagnostics, one with medical robots which Chinese doctors use for transplantation as well, and another one trying to enter the Chinese market with a consumable-type product also related to transplantation. The conclusion The question of the ethicality of transplant tourism may be very pressing, since as the research shows, just the sheer amount of participating countries, sourcing transplant tourists to another one, amounts to 20. The identified companies are facing risks due to the nature of transplantation business in China, as officially executed prisoners are used as sources, and widely cited pieces of evidence point out to illegal organ harvesting. Similar risks and ethical questions are also relevant to the countries sourcing the transplant tourists to China.

Keywords: China, illegal organ harvesting, transplant tourism, organ harvesting technology

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Authors: Maamar Yahiaoui, Abdelrrahmene Kechich, Ismail Elkhallile Bousserhene

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This article presents a study in simulation by means of MATLAB/Simulink software of the nonlinear control in positioning of a linear synchronous machine with the esteemed force of load, to have effective control in the estimator in all tests the wished trajectory follows and the disturbance of load start. The results of simulation prove clearly that the control proposed can detect the reference of positioning the value estimates of load force equal to the actual value.

Keywords: mathematical model, Matlab, PMLSM, control, linearization, estimator, force, load, current

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Authors: Reham Banwair, Lana Alshehri, Sara Hadrawi

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The purpose of this project is to identify user satisfaction with the AI functions on Snapchat, in order to generate improvement proposals that allow its development within the app. To achieve this, a qualitative analysis was carried out through interviews to people who usually use the application, revealing their satisfaction or dissatisfaction with the usefulness of the AI. In addition, the background of the company and its introduction in these algorithms were analyzed. Furthermore, the characteristics of the three main functions of AI were explained: identify songs, solve mathematical problems, and recognize plants. As a result, it was obtained that 50% still do not know the characteristics of AI, 50% still believe song recognition is not always correct, 41.7% believe that math problems are usually accurate and 91.7% believes the plant detection tool is working properly.

Keywords: artificial intelligence, scanning, Snapchat, machine learning

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Authors: Saradha Priya

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Developing virtual human is very important to meet the challenges occurred in many applications where human find difficult or risky to perform the task. A robot is a machine that can perform a task automatically or with guidance. Robotics is generally a combination of artificial intelligence and physical machines (motors). Computational intelligence involves the programmed instructions. This project proposes a robotic vehicle that has a camera, PIR sensor and text command based movement. It is specially designed to perform surveillance and other few tasks in the most efficient way. Serial communication has been occurred between a remote Base Station, GUI Application, and PC.

Keywords: Zigbee, camera, pirsensor, wireless transmission, DC motor

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Authors: Maha Azzam, Mahmoud Gabr, Mahmoud Zakaria, Ayman Refaie, Amani Ismail, Sherry Khater, Sylvia Ashamallah, Mohamed Ghoniem

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Evidence was provided that human bone marrow-derived mesenhymal stem cells (HBM-MSCs) could be differentiated to form insulin-producing cells (IPCs). Transplantation of these cells was able to cure chemically-induced diabetes in nude mice. The efficacy of these cells to control diabetes in large animals was carried out to evaluate the sufficient number of cells needed/Kg body weight and to determine the functional longevity in vivo. Materials/Methods: Ten male mongrel dogs weighing 15-20 Kg were used in this study. Diabetes was chemically-induced in 7 dogs by a mixture of alloxan and streptozotocin. Three non-diabetic served as normal controls. Differentiated HBM-MSCs (5 million/Kg) were encapsulated in theracyte capsules and transplanted beneath the rectus sheath. Each dog received 2 capsules. One dog died 4 days postoperative from inhalation pneumonia. The remaining 6 dogs were followed up for 6-18 months. Results: Four dogs became normoglycemic within 6-8 weeks with normal glucose tolerance curves providing evidence that the transplanted cells were glucose-sensitive and insulin-responsive. In the remaining 2 dogs, fasting blood glucose was reduced but did not reach euglycemic levels. The sera of all transplanted dogs contained human insulin and c-peptide but negligible levels of canine insulin. When the HBM-MSCs loaded capsules were removed, rapid return of diabetic state was noted. The harvested capsules were examined by immunofluorescence. IPCs were seen and co-expression of with c-peptide was confirmed. Furthermore, all the pancreatic endocrine genes were expressed by the transplanted cells. Conclusions: This study provided evidence that theracyte capsules could protect the xenogenic HBM-MSCs from the host immune response. This is an important issue when clinical stem cell therapy is considered for definitive treatment for T1DM.

Keywords: diabetes, mesenchymal stem cells, dogs, Insulin-producing cells

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Authors: Elionai Moura, José A. Cunha, César Analide

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This work introduces a proposal scheme for an Intelligent System applied to Pedagogical Advising using Case-Based Reasoning, to find consolidated solutions before used for the new problems, making easier the task of advising students to the pedagogical staff. We do intend, through this work, introduce the motivation behind the choices for this system structure, justifying the development of an incremental and smart web system who learns bests solutions for new cases when it’s used, showing technics and technology.

Keywords: case-based reasoning, pedagogical advising, educational data-mining (EDM), machine learning

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Authors: Assad Maalouf, Lunjin Lu, James Lynott

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We design and implement a precise model of string operations using finite state machine transformers and state transformers to approximate the values string variables can take throughout the execution of the program.We use our model to analyze Android program string variables. Our experimental results show that our string analysis is very efficient at detecting the contextual effect of string operations on the string variables. Our model proved to be very useful when it came to verifying statements about the string variables of the program.

Keywords: abstract interpretation, android, static analysis, string analysis

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Authors: Ammaarah Gamieldien

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Depression is a very common and serious mental illness that has a significant impact on both the social and economic aspects of sufferers worldwide. This study aimed to investigate the association between body mass index (BMI), blood pressure, and lipid profiles with mental health symptoms in university students. Secondary objectives included the associations between the variables (BMI, blood pressure, and lipids) with themselves, as they are key factors in cardiometabolic disease. Sixty-three (63) students participated in the study. Thirty-two (32) were assigned to the control group (minimal-mild depressive symptoms), while 31 were assigned to the depressive group (moderate to severe depressive symptoms). Montgomery-Asberg Depression Rating Scale (MADRS) and Beck Depression Inventory (BDI) were used to assess depressive scores. Anthropometric measurements such as weight (kg), height (m), waist circumference (WC), and hip circumference were measured. Body mass index (BMI) and ratios such as waist-to-hip ratio (WHR) and waist-to-height ratio (WtHR) were also calculated. Blood pressure was measured using an automated AfriMedics blood pressure machine, while lipids were measured using a CardioChek plus analyzer machine. Statistics were analyzed via the SPSS statistics program. There were no significant associations between anthropometric measurements and depressive scores (p > 0.05). There were no significant correlations between lipid profiles and depression when running a Spearman’s rho correlation (P > 0.05). However, total cholesterol and LDL-C were negatively associated with depression, and triglycerides were positively associated with depression after running a point-biserial correlation (P < 0.05). Overall, there were no significant associations between blood pressure measurements and depression (P > 0.05). However, there was a significant moderate positive correlation between systolic blood pressure and MADRS scores in males (P < 0.05). Depressive scores positively and strongly correlated to how long it takes participants to fall asleep. There were also significant associations with regard to the secondary objectives. This study indicates the importance of determining the prevalence of depression among university students in South Africa. If the prevalence and factors associated with depression are addressed, depressive symptoms in university students may be improved.

Keywords: depression, blood pressure, body mass index, lipid profiles, mental health symptoms

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Authors: Mouna Essaadi, Mohamed Khafallah, Abdallah Saad, Hamid Chaikhy

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This paper presents a comparative analysis between conventional direct torque control (C_DTC), Modified direct torque control (M_DTC) and twelve sectors direct torque control (12_DTC).Those different strategies are compared by simulation in term of torque, flux and stator current performances. Finally, a summary of the comparative analysis is presented.

Keywords: C_DTC, M_DTC, 12_DTC, torque dynamic, stator current, flux, performances

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Authors: Christian Neunzig, Simon Fahle, Jürgen Schulz, Matthias Möller, Bernd Kuhlenkötter

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The increasing use of deep learning applications in production is becoming a competitive advantage. Predictive quality enables the assurance of product quality by using data-driven forecasts via machine learning models as a basis for decisions on test results. The use of real Bosch production data along the value chain of hydraulic valves is a promising approach to classifying the leakage of directional valves.

Keywords: artificial neural networks, classification, hydraulics, predictive quality, deep learning

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Authors: Igor A. Bessmertny, Bidru C. Enkomaryam

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Active involving patients and communities in health decisions can improve both people’s health and the healthcare system. Adopting artificial intelligence can lead to more accurate and complete patient record management. This review aims to identify the current state of researches conducted using artificial intelligence techniques to improve patient engagement and wellbeing, medical domains used in patient engagement context, and lastly, to assess opportunities and challenges for patient engagement in the wellness process. A search of peer-reviewed publications, reviews, conceptual analyses, white papers, author’s manuscripts and theses was undertaken. English language literature published in 2013– 2022 period and publications, report and guidelines of World Health Organization (WHO) were also assessed. About 281 papers were retrieved. Duplicate papers in the databases were removed. After application of the inclusion and exclusion criteria, 41 papers were included to the analysis. Patient counseling in preventing adverse drug events, in doctor-patient risk communication, surgical, drug development, mental healthcare, hypertension & diabetes, metabolic syndrome and non-communicable chronic diseases are implementation areas in healthcare where patient engagement can be implemented using artificial intelligence, particularly machine learning and deep learning techniques and tools. The five groups of factors that potentially affecting patient engagement in safety are related to: patient, health conditions, health care professionals, tasks and health care setting. Active involvement of patients and families can help accelerate the implementation of healthcare safety initiatives. In sub-Saharan Africa, using digital technologies like artificial intelligence in patient engagement context is low due to poor level of technological development and deployment. The opportunities and challenges available to implement patient engagement strategies vary greatly from country to country and from region to region. Thus, further investigation will be focused on methods and tools using the potential of artificial intelligence to support more simplified care that might be improve communication with patients and train health care professionals.

Keywords: artificial intelligence, patient engagement, machine learning, patient involvement

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Authors: Tim Bolle, Franziska Kreimendahl, Thomas Gries, Stefan Jockenhoevel

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The therapeutic treatment of extensive, deep wounds is limited. Autologous split-skin grafts are used as a so-called ‘gold standard’. Most common deficits are the defects at the donor site, the risk of scarring as well as the limited availability and quality of the autologous grafts. The aim of this project is a tissue engineered dermal-epidermal skin replacement to overcome the limitations of the gold standard. A key requirement for the development of such a three-dimensional implant is the formation of a functional capillary-like network inside the implant to ensure a sufficient nutrient and gas supply. Tailored three-dimensional warp knitted spacer fabrics are used to reinforce the mechanically week fibrin gel-based scaffold and further to create a directed in vitro pre-vascularization along the parallel-oriented pile yarns within a co-culture. In this study various three-dimensional warp knitted spacer fabrics were developed in a factorial design to analyze the influence of the machine parameters such as the stitch density and the pattern of the fabric on the scaffold performance and further to determine suitable parameters for a successful fibrin gel-incorporation and a physiological performance of the scaffold. The fabrics were manufactured on a Karl Mayer double-bar raschel machine DR 16 EEC/EAC. A fine machine gauge of E30 was used to ensure a high pile yarn density for sufficient nutrient, gas and waste exchange. In order to ensure a high mechanical stability of the graft, the fabrics were made of biocompatible PVDF yarns. Key parameters such as the pore size, porosity and stress/strain behavior were investigated under standardized, controlled climate conditions. The influence of the input parameters on the mechanical and morphological properties as well as the ability of fibrin gel incorporation into the spacer fabric was analyzed. Subsequently, the pile yarns of the spacer fabrics were colonized with Human Umbilical Vein Endothelial Cells (HUVEC) to analyze the ability of the fabric to further function as a guiding structure for a directed vascularization. The cells were stained with DAPI and investigated using fluorescence microscopy. The analysis revealed that the stitch density and the binding pattern have a strong influence on both the mechanical and morphological properties of the fabric. As expected, the incorporation of the fibrin gel was significantly improved with higher pore sizes and porosities, whereas the mechanical strength decreases. Furthermore, the colonization trials revealed a high cell distribution and density on the pile yarns of the spacer fabrics. For a tailored reinforcing structure, the minimum porosity and pore size needs to be evaluated which still ensures a complete incorporation of the reinforcing structure into the fibrin gel matrix. That will enable a mechanically stable dermal graft with a dense vascular network for a sufficient nutrient and oxygen supply of the cells. The results are promising for subsequent research in the field of reinforcing mechanically weak biological scaffolds and develop functional three-dimensional scaffolds with an oriented pre-vascularization.

Keywords: fibrin-gel, skin replacement, spacer fabric, pre-vascularization

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Authors: Ákos Tótin

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In crop production the most modern hybrids are available for us, therefore the yield and yield stability is determined by the agro-technology. The purpose of the experiment is to adapt the modern agrotechnology to the new type of hybrids. The long-term experiment was set up in 2015-2016 on chernozem soil in the Hajdúság (eastern Hungary). The plots were set up in 75 thousand ha-1 plant density. We examined some mainly use hybrids of Hungary. The conducted studies are: germination dynamic, growing dynamic and the effect of annual weather for the yield. We use three different sowing date as early, average and late, and measure how many plant germinated during the germination process. In the experiment, we observed the germination dynamics in 6 hybrid in 4 replication. In each replication, we counted the germinated plants in 2m long 2 row wide area. Data will be shown in the average of the 6 hybrid and 4 replication. Growing dynamics were measured from the 10cm (4-6 leaf) plant highness. We measured 10 plants’ height in two weeks replication. The yield was measured buy a special plot harvester - the Sampo Rosenlew 2010 – what measured the weight of the harvested plot and also took a sample from it. We determined the water content of the samples for the water release dynamics. After it, we calculated the yield (t/ha) of each plot at 14% of moisture content to compare them. We evaluated the data using Microsoft Excel 2015. The annual weather in each crop year define the maize germination dynamics because the amount of heat is determinative for the plants. In cooler crop year the weather is prolonged the germination. At the 2015 crop year the weather was cold in the beginning what prolonged the first sowing germination. But the second and third sowing germinated faster. In the 2016 crop year the weather was much favorable for plants so the first sowing germinated faster than in the previous year. After it the weather cooled down, therefore the second and third sowing germinated slower than the last year. The statistical data analysis program determined that there is a significant difference between the early and late sowing date growing dynamics. In 2015 the first sowing date had the highest amount of yield. The second biggest yield was in the average sowing time. The late sowing date has lowest amount of yield.

Keywords: germination, maize, sowing date, yield

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Authors: Ana C. R. P. Ferreira, Adriano H. P. Pereira

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Given the frequent variation of mineral properties throughout the Araxá pyrochlore deposit, even if a good homogenization work has been carried out before feeding the processing plants, an operation with quality and performance’s high variety standard is expected. These results could be improved and standardized if the blend composition parameters that most influence the processing route are determined, and then the types of raw materials are grouped by them, finally presenting a great reference with operational settings for each group. Associating the physical and chemical parameters of a unit operation through benchmarking or even an optimal reference of metallurgical recovery and product quality reflects in the reduction of the production costs, optimization of the mineral resource, and guarantee of greater stability in the subsequent processes of the production chain that uses the mineral of interest. Conducting a comprehensive exploratory data analysis to identify which characteristics of the ore are most relevant to the process route, associated with the use of Machine Learning algorithms for grouping the raw material (ore) and associating these with reference variables in the process’ benchmark is a reasonable alternative for the standardization and improvement of mineral processing units. Clustering methods through Decision Tree and K-Means were employed, associated with algorithms based on the theory of benchmarking, with criteria defined by the process team in order to reference the best adjustments for processing the ore piles of each cluster. A clean user interface was created to obtain the outputs of the created algorithm. The results were measured through the average time of adjustment and stabilization of the process after a new pile of homogenized ore enters the plant, as well as the average time needed to achieve the best processing result. Direct gains from the metallurgical recovery of the process were also measured. The results were promising, with a reduction in the adjustment time and stabilization when starting the processing of a new ore pile, as well as reaching the benchmark. Also noteworthy are the gains in metallurgical recovery, which reflect a significant saving in ore consumption and a consequent reduction in production costs, hence a more rational use of the tailings dams and life optimization of the mineral deposit.

Keywords: mineral clustering, machine learning, process optimization, pyrochlore processing

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Authors: Ksenia Volkova, Artur Petrosyan, Ignatii Dubyshkin, Alexei Ossadtchi

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Brain-computer interfaces are a growing research field producing many implementations that find use in different fields and are used for research and practical purposes. Despite the popularity of the implementations using non-invasive neuroimaging methods, radical improvement of the state channel bandwidth and, thus, decoding accuracy is only possible by using invasive techniques. Electrocorticography (ECoG) is a minimally invasive neuroimaging method that provides highly informative brain activity signals, effective analysis of which requires the use of machine learning methods that are able to learn representations of complex patterns. Deep learning is a family of machine learning algorithms that allow learning representations of data with multiple levels of abstraction. This study explores the potential of deep learning approaches for ECoG processing, decoding movement intentions and the perception of proprioceptive information. To obtain synchronous recording of kinematic movement characteristics and corresponding electrical brain activity, a series of experiments were carried out, during which subjects performed finger movements at their own pace. Finger movements were recorded with a three-axis accelerometer, while ECoG was synchronously registered from the electrode strips that were implanted over the contralateral sensorimotor cortex. Then, multichannel ECoG signals were used to track finger movement trajectory characterized by accelerometer signal. This process was carried out both causally and non-causally, using different position of the ECoG data segment with respect to the accelerometer data stream. The recorded data was split into training and testing sets, containing continuous non-overlapping fragments of the multichannel ECoG. A deep convolutional neural network was implemented and trained, using 1-second segments of ECoG data from the training dataset as input. To assess the decoding accuracy, correlation coefficient r between the output of the model and the accelerometer readings was computed. After optimization of hyperparameters and training, the deep learning model allowed reasonably accurate causal decoding of finger movement with correlation coefficient r = 0.8. In contrast, the classical Wiener-filter like approach was able to achieve only 0.56 in the causal decoding mode. In the noncausal case, the traditional approach reached the accuracy of r = 0.69, which may be due to the presence of additional proprioceptive information. This result demonstrates that the deep neural network was able to effectively find a representation of the complex top-down information related to the actual movement rather than proprioception. The sensitivity analysis shows physiologically plausible pictures of the extent to which individual features (channel, wavelet subband) are utilized during the decoding procedure. In conclusion, the results of this study have demonstrated that a combination of a minimally invasive neuroimaging technique such as ECoG and advanced machine learning approaches allows decoding motion with high accuracy. Such setup provides means for control of devices with a large number of degrees of freedom as well as exploratory studies of the complex neural processes underlying movement execution.

Keywords: brain-computer interface, deep learning, ECoG, movement decoding, sensorimotor cortex

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Authors: Kishor Prasad Bhatta, Suman Ghimire, Durga Prasad Joshi

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Invasive Alien Species (IAS) are considered waste forest resources in Nepal. The rapid expansion of IAS is one of the nine main drivers of forest degradation, though the extent and distribution of this species are not well known. Further, the knowledge of the impact of IAS removal on forest plant diversity is hardly known, and the possibilities of income generation from them at the grass-root communities are rarely documented. Systematic sampling of 1% with nested circular plots of 500 square meters was performed in IAS removed and non-removed area, each of 30 hectares in Udayapur Community Forest User Group (CFUG), Chitwan, central Nepal to observe whether the removal of IAS contributed to an increase in plant diversity. In addition, ten entrepreneurs of Udaypur CFUG, involved in the charcoal production, briquette making and marketing were interviewed and interacted as well as their record keeping booklets were reviewed to understand if the charcoal production contributed to their income and employment. The average annual precipitation and temperature of the study area is 2100 mm and 34 degree Celsius respectively with Shorea robusta as main tree species and Eupatorium odoratum as dominant IAS. All the interviewed households were from the ̔below-poverty-line’ category as per Community Forestry Guidelines. A higher Shannon-Weiner plant diversity index at regeneration level was observed in IAS removed areas (2.43) than in control site (1.95). Furthermore, the number of tree seedlings and saplings in the IAS harvested blocks were significantly higher (p < 0.005) compared to the unharvested one. The sale of charcoal produced through the pyrolysis of IAS in ̔ Bio-energy kilns’ contributed for an average increased income of 30.95 % (Nepalese rupees 31,000) of the involved households. Despite above factors, some operational policy hurdles related to charcoal transport and taxation existed at field level. This study suggests that plant diversity could be increased through the removal of IAS, and considerable economic benefits could be achieved if charcoal is substantially produced and utilized.

Keywords: briquette, economic benefits, pyrolysis, regeneration

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Authors: Tamara Grujic, Mirjana Bonkovic

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Measurements and quantitative analysis of kinematic parameters of human hand movements have an important role in different areas such as hand function rehabilitation, modeling of multi-digits robotic hands, and the development of machine-man interfaces. In this paper the assessment and evaluation of the reach-to-grasp movement by using computerized and robot-assisted method is described. Experiment involved the measurements of hand positions of seven healthy subjects during grasping three objects of different shapes and sizes. Results showed that three dominant phases of reach-to-grasp movements could be clearly identified.

Keywords: human hand, kinematics, measurement and analysis, reach-to-grasp movement

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Authors: Goodness Onwuka, Khaled Abou-El-Hossein

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The increase in the demand for precision optics, coupled with the absence of much research output in the ultra high precision grinding of precision optics as compared to the ultrahigh precision diamond turning of optical metals has fostered the need for more research in the ultra high precision grinding of an optical lens. Furthermore, the increase in the stringent demands for nanometric surface finishes through lapping, polishing and grinding processes necessary for the use of borosilicate-crown glass in the automotive and optics industries has created the demand to effectively monitor the surface roughness during the production process. Acoustic emission phenomenon has been proven as useful monitoring technique in several manufacturing processes ranging from monitoring of bearing production to tool wear estimation. This paper introduces a rare and unique approach with the application of acoustic emission technique to monitor the surface roughness of borosilicate-crown glass during an ultra high precision grinding process. This research was carried out on a 4-axes Nanoform 250 ultrahigh precision lathe machine using an ultra high precision grinding spindle to machine the flat surface of the borosilicate-crown glass with the tip of the grinding wheel. A careful selection of parameters and design of experiment was implemented using Box-Behnken method to vary the wheel speed, feed rate and depth of cut at three levels with a 3-center point design. Furthermore, the average surface roughness was measured using Taylor Hobson PGI Dimension XL optical profilometer, and an acoustic emission data acquisition device from National Instruments was utilized to acquire the signals while the data acquisition codes were designed with National Instrument LabVIEW software for acquisition at a sampling rate of 2 million samples per second. The results show that the raw and root mean square amplitude values of the acoustic signals increased with a corresponding increase in the measured average surface roughness values for the different parameter combinations. Therefore, this research concludes that acoustic emission monitoring technique is a potential technique for monitoring the surface roughness in the ultra high precision grinding of borosilicate-crown glass.

Keywords: acoustic emission, borosilicate-crown glass, surface roughness, ultra high precision grinding

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Authors: Zahid Ullah, Atlas Khan

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The rapid growth of data in diverse domains has created an urgent need for effective utilization of mathematical and statistical sciences in data analytics. This abstract explores the journey from theory to practice, emphasizing the importance of harnessing mathematical and statistical innovations to unlock the full potential of data analytics. Drawing on a comprehensive review of existing literature and research, this study investigates the fundamental theories and principles underpinning mathematical and statistical sciences in the context of data analytics. It delves into key mathematical concepts such as optimization, probability theory, statistical modeling, and machine learning algorithms, highlighting their significance in analyzing and extracting insights from complex datasets. Moreover, this abstract sheds light on the practical applications of mathematical and statistical sciences in real-world data analytics scenarios. Through case studies and examples, it showcases how mathematical and statistical innovations are being applied to tackle challenges in various fields such as finance, healthcare, marketing, and social sciences. These applications demonstrate the transformative power of mathematical and statistical sciences in data-driven decision-making. The abstract also emphasizes the importance of interdisciplinary collaboration, as it recognizes the synergy between mathematical and statistical sciences and other domains such as computer science, information technology, and domain-specific knowledge. Collaborative efforts enable the development of innovative methodologies and tools that bridge the gap between theory and practice, ultimately enhancing the effectiveness of data analytics. Furthermore, ethical considerations surrounding data analytics, including privacy, bias, and fairness, are addressed within the abstract. It underscores the need for responsible and transparent practices in data analytics, and highlights the role of mathematical and statistical sciences in ensuring ethical data handling and analysis. In conclusion, this abstract highlights the journey from theory to practice in harnessing mathematical and statistical sciences in data analytics. It showcases the practical applications of these sciences, the importance of interdisciplinary collaboration, and the need for ethical considerations. By bridging the gap between theory and practice, mathematical and statistical sciences contribute to unlocking the full potential of data analytics, empowering organizations and decision-makers with valuable insights for informed decision-making.

Keywords: data analytics, mathematical sciences, optimization, machine learning, interdisciplinary collaboration, practical applications

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Authors: Saeid Jalilzadeh

Abstract:

Designing of PSS and SVC controller based on Bacterial Foraging Algorithm (BFA) to improve the stability of power system is proposed in this paper. Same controllers for PSS and SVC has been considered and Single machine infinite bus (SMIB) system with SVC located at the terminal of generator is used to evaluate the proposed controllers. BFA is used to optimize the coefficients of the controllers. Finally simulation for a special disturbance as an input power of generator with the proposed controllers in order to investigate the dynamic behavior of generator is done. The simulation results demonstrate that the system composed with optimized controllers has an outstanding operation in fast damping of oscillations of power system.

Keywords: PSS, SVC, SMIB, optimize controller

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Authors: Saeed Qaisrani

Abstract:

The study was conducted to observe carbon and nutrient loss by burning of rice residues on rice-wheat cropping system The rice crop was harvested to conduct the experiment in a randomized complete block design (RCBD) with factors and 4 replications with a net plot size of 10 m x 20 m. Rice stubbles were managed by two methods i.e. Incorporation & burning of rice residues. Soil samples were taken to a depth of 30 cm before sowing & after harvesting of wheat. Wheat was sown after harvesting of rice by three practices i.e. Conventional tillage, Minimum tillage and Zero tillage to observe best tillage practices. Laboratory and field experiments were conducted on wheat to assess best tillage practice and residues management method with estimation of carbon losses. Data on the following parameters; establishment count, plant height, spike length, number of grains per spike, biological yield, fat content, carbohydrate content, protein content, and harvest index were recorded to check wheat quality & ensuring food security in the region. Soil physico-chemical analysis i.e. pH, electrical conductivity, organic matter, nitrogen, phosphorus, potassium, and carbon were done in soil fertility laboratory. Substantial results were found on growth, yield and related parameters of wheat crop. The collected data were examined statistically with economic analysis to estimate the cost-benefit ratio of using different tillage techniques and residue management practices. Obtained results depicted that Zero tillage method have positive impacts on growth, yield and quality of wheat, Moreover, it is cost effective methodology. Similarly, Incorporation is suitable and beneficial method for soil due to more nutrients provision and reduce the need of fertilizers. Burning of rice stubbles has negative impact including air pollution, nutrient loss, microbes died and carbon loss. Recommended the zero tillage technology to reduce carbon losses along with food security in Pakistan.

Keywords: agricultural agronomy, food security, carbon sequestration, rice-wheat cropping system

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Authors: Nilki Upathissa, Torin Wirasinghe

Abstract:

This solution addresses the challenges faced by traditional bookstores and the limitations of digital media, striking a balance between the tactile experience of printed books and the convenience of modern technology. The book exchange system offers a sustainable alternative, empowering users to access a diverse range of books while promoting community engagement. The user-friendly interfaces incorporated into the book exchange system ensure a seamless and enjoyable experience for users. Intuitive features for book management, search, and messaging facilitate effortless exchanges and interactions between users. By streamlining the process, the system encourages readers to explore new books aligned with their interests, enhancing the overall reading experience. Central to the system's success is the hybrid recommendation engine, which leverages advanced technologies such as Long Short-Term Memory (LSTM) models. By analyzing user input, the engine accurately predicts genre preferences, enabling personalized book recommendations. The hybrid approach integrates multiple technologies, including user interfaces, machine learning models, and recommendation algorithms, to ensure the accuracy and diversity of the recommendations. The evaluation of the book exchange system with the hybrid recommendation engine demonstrated exceptional performance across key metrics. The high accuracy score of 0.97 highlights the system's ability to provide relevant recommendations, enhancing users' chances of discovering books that resonate with their interests. The commendable precision, recall, and F1score scores further validate the system's efficacy in offering appropriate book suggestions. Additionally, the curve classifications substantiate the system's effectiveness in distinguishing positive and negative recommendations. This metric provides confidence in the system's ability to navigate the vast landscape of book choices and deliver recommendations that align with users' preferences. Furthermore, the implementation of this book exchange system with a hybrid recommendation engine has the potential to revolutionize the way readers interact with printed books. By facilitating book exchanges and providing personalized recommendations, the system encourages a sense of community and exploration within the reading community. Moreover, the emphasis on sustainability aligns with the growing global consciousness towards eco-friendly practices. With its robust technical approach and promising evaluation results, this solution paves the way for a more inclusive, accessible, and enjoyable reading experience for book lovers worldwide. In conclusion, the developed book exchange system with a hybrid recommendation engine represents a progressive solution to the challenges faced by traditional bookstores and the limitations of digital media. By promoting sustainability, widening access to printed books, and fostering engagement with reading, this system addresses the evolving needs of book enthusiasts. The integration of user-friendly interfaces, advanced machine learning models, and recommendation algorithms ensure accurate and diverse book recommendations, enriching the reading experience for users.

Keywords: recommendation systems, hybrid recommendation systems, machine learning, data science, long short-term memory, recurrent neural network

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Authors: Frank Fan

Abstract:

PharmD (Doctor of Pharmacy) education has confronted an increasing challenge — curricular overload, a phenomenon resulting from the expansion of curricular requirements, as PharmD education strives to produce graduates who are practice-ready. The aftermath of the global pandemic has amplified the need for healthcare professionals, leading to a growing trend of assigning more responsibilities to them to address the global healthcare shortage. For instance, the pharmacist’s role has expanded to include not only compounding and distributing medication but also providing clinical services, including minor ailments management, patient counselling and vaccination. Consequently, PharmD programs have responded by continually expanding their curricula adding more requirements. While these changes aim to enhance the education and training of future professionals, they have also led to unintended consequences, including curricular overload, student burnout, and a potential decrease in program quality. To address the issue and ensure program quality, there is a growing need for evidence-based curriculum reforms. My research seeks to integrate Cognitive Load Theory, emerging machine learning algorithms within artificial intelligence (AI), and statistical approaches to develop a quantitative framework for optimizing curriculum design within the PharmD program at the University of Toronto, the largest PharmD program within Canada, to provide quantification and measurement of issues that currently are only discussed in terms of anecdote rather than data. This research will serve as a guide for curriculum planners, administrators, and educators, aiding in the comprehension of how the pharmacy degree program compares to others within and beyond the field of pharmacy. It will also shed light on opportunities to reduce the curricular load while maintaining its quality and rigor. Given that pharmacists constitute the third-largest healthcare workforce, their education shares similarities and challenges with other health education programs. Therefore, my evidence-based, data-driven curriculum analysis framework holds significant potential for training programs in other healthcare professions, including medicine, nursing, and physiotherapy.

Keywords: curriculum, curriculum analysis, health professions education, reflective writing, machine learning

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Authors: Desplanches Maxime

Abstract:

Accurate estimation of the remaining useful life of lithium-ion batteries for electronic devices is crucial. Data-driven methodologies encounter challenges related to data volume and acquisition protocols, particularly in capturing a comprehensive range of aging indicators. To address these limitations, we propose a hybrid approach that integrates an electrochemical model with state-of-the-art data analysis techniques, yielding a comprehensive database. Our methodology involves infusing an aging phenomenon into a Newman model, leading to the creation of an extensive database capturing various aging states based on non-destructive parameters. This database serves as a robust foundation for subsequent analysis. Leveraging advanced data analysis techniques, notably principal component analysis and t-Distributed Stochastic Neighbor Embedding, we extract pivotal information from the data. This information is harnessed to construct a regression function using either random forest or support vector machine algorithms. The resulting predictor demonstrates a 5% error margin in estimating remaining battery life, providing actionable insights for optimizing usage. Furthermore, the database was built from the Newman model calibrated for aging and performance using data from a European project called Teesmat. The model was then initialized numerous times with different aging values, for instance, with varying thicknesses of SEI (Solid Electrolyte Interphase). This comprehensive approach ensures a thorough exploration of battery aging dynamics, enhancing the accuracy and reliability of our predictive model. Of particular importance is our reliance on the database generated through the integration of the electrochemical model. This database serves as a crucial asset in advancing our understanding of aging states. Beyond its capability for precise remaining life predictions, this database-driven approach offers valuable insights for optimizing battery usage and adapting the predictor to various scenarios. This underscores the practical significance of our method in facilitating better decision-making regarding lithium-ion battery management.

Keywords: Li-ion battery, aging, diagnostics, data analysis, prediction, machine learning, electrochemical model, regression

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Authors: Aruma Baduge Kithma De Silva

Abstract:

Haskap (Lonicera caerulea L.), also known as blue honeysuckle, is a newly commercialized berry crop in Canada. Haskap berries are rich in polyphenols, including, anthocyanins, which are known for potential health-promoting properties. Cyanidin-3-O-glucoside (C3G) is the most abundant anthocyanin of haskap berries. The compound C3G has the ability to reduce the risk of type 2 diabetes (T2D), which has become an increasingly common health issue around the world. The T2D is characterized as a metabolic disorder of hyperglycemia and insulin resistance. It has been demonstrated that C3G has anti-diabetic effects through several ways, including inhibition of dipeptidyl peptidase-4 (DPP-4), reduction of gluconeogenesis, improvement in insulin sensitivity, and inhibition of activities of carbohydrate hydrolyzing enzymes, including α-amylase and α-glucosidase. The goal of this study was to investigate the influence of variety and harvests maturity of haskap on C3G, other fruit quality characteristics and anti-diabetic activities of haskap berries using in vitro studies. The polyphenols present in four commercially grown haskap cultivars, Aurora, Rebecca, Larissa, and Evie harvested at five harvesting dates (H1-H5) apart from 2-3 days, were extracted separately. High-performance liquid chromatography electrospray ionization mass spectrometry (HPLC-ESI-MS) analyzes of polyphenols revealed that haskap berries contain predominantly anthocyanins, flavonols, flavan-3-ols, and phenolic acids. The compound C3G was the most prominent anthocyanin, which is available in approximately 79% of total anthocyanin in four cultivars. The Larissa at H5 contained the highest C3G content. The antioxidant capacity of Evie at H5 was greater than other cultivars. Furthermore, Larissa H5 showed the greatest inhibition of carbohydrate hydrolyzing enzymes including alpha-glucosidase and alpha-amylase. In conclusion, the haskap variety and harvesting date influenced the polyphenol composition and biological properties. The variety Larissa, at H5 harvesting date, contained the highest polyphenol content and the ability of inhibition of the carbohydrate hydrolyzing enzyme as well as DPP4 enzyme in order to reduce type 2 diabetes.

Keywords: anthocyanin, Haskap, type 2 diabetes, polyphenol

Procedia PDF Downloads 129