Search results for: semantic textual similarity binary task

Authors: A. Idawaty, O. Mohamed, A. Z. Zuriati

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This paper presents the development of an event based Discrete Event Simulation (DES) for a recovery algorithm known Backward Recovery Global Preemptive Utility Accrual Scheduling (BR_GPUAS). This algorithm implements the Backward Recovery (BR) mechanism as a fault recovery solution under the existing Time/Utility Function/ Utility Accrual (TUF/UA) scheduling domain for multiprocessor environment. The BR mechanism attempts to take the faulty tasks back to its initial safe state and then proceeds to re-execute the affected section of the faulty tasks to enable recovery. Considering that faults may occur in the components of any system; a fault tolerance system that can nullify the erroneous effect is necessary to be developed. Current TUF/UA scheduling algorithm uses the abortion recovery mechanism and it simply aborts the erroneous task as their fault recovery solution. None of the existing algorithm in TUF/UA scheduling domain in multiprocessor scheduling environment have considered the transient fault and implement the BR mechanism as a fault recovery mechanism to nullify the erroneous effect and solve the recovery problem in this domain. The developed BR_GPUAS simulator has derived the set of parameter, events and performance metrics according to a detailed analysis of the base model. Simulation results revealed that BR_GPUAS algorithm can saved almost 20-30% of the accumulated utilities making it reliable and efficient for the real-time application in the multiprocessor scheduling environment.

Keywords: real-time system (RTS), time utility function/ utility accrual (TUF/UA) scheduling, backward recovery mechanism, multiprocessor, discrete event simulation (DES)

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Authors: Anukul K. Thakur, Ram Bilash Choudhary, Mandira Majumder

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In this technologically driven world, it is requisite to develop better, faster and smaller electronic devices for various applications to keep pace with fast developing modern life. In addition, it is also required to develop sustainable and clean sources of energy in this era where the environment is being threatened by pollution and its severe consequences. Supercapacitor has gained tremendous attention in the recent years because of its various attractive properties such as it is essentially maintenance-free, high specific power, high power density, excellent pulse charge/discharge characteristics, exhibiting a long cycle-life, require a very simple charging circuit and safe operation. Binary and ternary composites of conducting polymers with carbon and other layered transition metal dichalcogenides have shown tremendous progress in the last few decades. Compared with bulk conducting polymer, these days conducting polymers have gained more attention because of their high electrical conductivity, large surface area, short length for the ion transport and superior electrochemical activity. These properties make them very suitable for several energy storage applications. On the other hand, carbon materials have also been studied intensively, owing to its rich specific surface area, very light weight, excellent chemical-mechanical property and a wide range of the operating temperature. These have been extensively employed in the fabrication of carbon-based energy storage devices and also as an electrode material in supercapacitors. Incorporation of carbon materials into the polymers increases the electrical conductivity of the polymeric composite so formed due to high electrical conductivity, high surface area and interconnectivity of the carbon. Further, polymeric composites based on layered transition metal dichalcogenides such as molybdenum disulfide (MoS2) are also considered important because they are thin indirect band gap semiconductors with a band gap around 1.2 to 1.9eV. Amongst the various 2D materials, MoS2 has received much attention because of its unique structure consisting of a graphene-like hexagonal arrangement of Mo and S atoms stacked layer by layer to give S-Mo-S sandwiches with weak Van-der-Waal forces between them. It shows higher intrinsic fast ionic conductivity than oxides and higher theoretical capacitance than the graphite.

Keywords: supercapacitor, layered transition-metal dichalcogenide, conducting polymer, ternary, carbon

Procedia PDF Downloads 254

Authors: Mohammadreza Akbari, Sara Akbari, Davood Domiri Ganji, Pooya Solimani, Reza Khalili

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In this paper, three complicated nonlinear differential equations(PDE,ODE) in the field of engineering and non-vibration have been analyzed and solved completely by new method that we have named it Akbari-Ganji's Method (AGM) . As regards the previous published papers, investigating this kind of equations is a very hard task to do and the obtained solution is not accurate and reliable. This issue will be emerged after comparing the achieved solutions by Numerical Method. Based on the comparisons which have been made between the gained solutions by AGM and Numerical Method (Runge-Kutta 4th), it is possible to indicate that AGM can be successfully applied for various differential equations particularly for difficult ones. Furthermore, It is necessary to mention that a summary of the excellence of this method in comparison with the other approaches can be considered as follows: It is noteworthy that these results have been indicated that this approach is very effective and easy therefore it can be applied for other kinds of nonlinear equations, And also the reasons of selecting the mentioned method for solving differential equations in a wide variety of fields not only in vibrations but also in different fields of sciences such as fluid mechanics, solid mechanics, chemical engineering, etc. Therefore, a solution with high precision will be acquired. With regard to the afore-mentioned explanations, the process of solving nonlinear equation(s) will be very easy and convenient in comparison with the other methods. And also one of the important position that is explored in this paper is: Trigonometric and exponential terms in the differential equation (the method AGM) , is no need to use Taylor series Expansion to enhance the precision of the result.

Keywords: new method (AGM), complex non-linear partial differential equations, damping ratio, energy lost per cycle

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Authors: Oluwafunmibi Omotayo Fasanya, Augustine Kena Adjei

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To develop a machine learning model to predict diabetes (DM) prevalence in the U.S. population using demographic characteristics, physical indicators, and lifestyle habits, and to analyze how these factors contribute to the likelihood of diabetes. We analyzed data from 23,546 participants aged 20 and older, who were non-pregnant, from the 2009-2018 National Health and Nutrition Examination Survey (NHANES). The dataset included key demographic (age, sex, ethnicity), physical (BMI, leg length, total cholesterol [TCHOL], fasting plasma glucose), and lifestyle indicators (smoking habits). A weighted sample was used to account for NHANES survey design features such as stratification and clustering. A classification machine learning model was trained to predict diabetes status. The target variable was binary (diabetes or non-diabetes) based on fasting plasma glucose measurements. The following models were evaluated: Logistic Regression (baseline), Random Forest Classifier, Gradient Boosting Machine (GBM), Support Vector Machine (SVM). Model performance was assessed using accuracy, F1-score, AUC-ROC, and precision-recall metrics. Feature importance was analyzed using SHAP values to interpret the contributions of variables such as age, BMI, ethnicity, and smoking status. The Gradient Boosting Machine (GBM) model outperformed other classifiers with an AUC-ROC score of 0.85. Feature importance analysis revealed the following key predictors: Age: The most significant predictor, with diabetes prevalence increasing with age, peaking around the 60s for males and 70s for females. BMI: Higher BMI was strongly associated with a higher risk of diabetes. Ethnicity: Black participants had the highest predicted prevalence of diabetes (14.6%), followed by Mexican-Americans (13.5%) and Whites (10.6%). TCHOL: Diabetics had lower total cholesterol levels, particularly among White participants (mean decline of 23.6 mg/dL). Smoking: Smoking showed a slight increase in diabetes risk among Whites (0.2%) but had a limited effect in other ethnic groups. Using machine learning models, we identified key demographic, physical, and lifestyle predictors of diabetes in the U.S. population. The results confirm that diabetes prevalence varies significantly across age, BMI, and ethnic groups, with lifestyle factors such as smoking contributing differently by ethnicity. These findings provide a basis for more targeted public health interventions and resource allocation for diabetes management.

Keywords: diabetes, NHANES, random forest, gradient boosting machine, support vector machine

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Authors: Rosmini Yanti, Safira Aviolita, Marsetio

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Indonesia has several straits that are very important as a shipping lane, including the Sunda Strait and the Lombok Strait, which are the part of the Indonesian Archipelagic Sea Lane (IASL). An increase in traffic on the Marine Archipelago makes the task of monitoring sea routes increasingly difficult. Indonesia has proposed the establishment of a Traffic Separation Scheme (TSS) in the Sunda Strait and the Lombok Strait and the country now has the right to be able to conceptualize the TSS as well as the obligation to regulate it. Indonesia has the right to maintain national safety and sovereignty. In setting the TSS, Indonesia needs to issue national regulations that are in accordance with international law and the general provisions of the IMO (International Maritime Organization) can then be used as guidelines for maritime safety and security in the Sunda Strait and the Lombok Strait. The research method used is a qualitative method with the concept of linguistic and visual data collection. The source of the data is the analysis of documents and regulations. The results show that the determination of TSS was justified by International Law, in accordance with article 22, article 41, and article 53 of the United Nations Convention on the Law of the Sea (UNCLOS) 1982. The determination of TSS by the Indonesian government would be in accordance with COLREG (International Convention on Preventing Collisions at Sea) 10, which has been designed to follow IASL. Thus, TSS can provide a function as a safety and monitoring medium to minimize ship accidents or collisions, including the warship and aircraft of other countries that cross the IASL.

Keywords: archipelago state, maritime law, maritime security, traffic separation scheme

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Authors: Djamel Fawzi Hadj Sadok, Marrone Silvério Melo Dantas Pedro Henrique Dreyer, Gabriel Fonseca Reis de Souza, Daniel Bezerra, Ricardo Souza, Silvia Lins, Judith Kelner

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A radio base station (RBS), part of the radio access network, is a particular type of equipment that supports the connection between a wide range of cellular user devices and an operator network access infrastructure. Nowadays, most of the RBS maintenance is carried out manually, resulting in a time consuming and costly task. A suitable candidate for RBS maintenance automation is repairing faulty links between devices caused by missing or unplugged connectors. A suitable candidate for RBS maintenance automation is repairing faulty links between devices caused by missing or unplugged connectors. This paper proposes and compares two deep learning solutions to identify attached RJ45 connectors on network ports. We named connector detection, the solution based on object detection, and connector classification, the one based on object classification. With the connector detection, we get an accuracy of 0:934, mean average precision 0:903. Connector classification, get a maximum accuracy of 0:981 and an AUC of 0:989. Although connector detection was outperformed in this study, this should not be viewed as an overall result as connector detection is more flexible for scenarios where there is no precise information about the environment and the possible devices. At the same time, the connector classification requires that information to be well-defined.

Keywords: radio base station, maintenance, classification, detection, deep learning, automation

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Authors: Ralph Adaimy, Wassim El-Hajj, Ghassen Ben Brahim, Hazem Hajj, Haidar Safa

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Huge amounts of data and personal information are being sent to and retrieved from web applications on daily basis. Every application has its own confidentiality and integrity policies. Violating these policies can have broad negative impact on the involved company’s financial status, while enforcing them is very hard even for the developers with good security background. In this paper, we propose a framework that enforces security-by-construction in web applications. Minimal developer effort is required, in a sense that the developer only needs to annotate database attributes by a security class. The web application code is then converted into an intermediary representation, called Extended Program Dependence Graph (EPDG). Using the EPDG, the provided annotations are propagated to the application code and run against generic security enforcement rules that were carefully designed to detect insecure information flows as early as they occur. As a result, any violation in the data’s confidentiality or integrity policies is reported. As a proof of concept, two PHP web applications, Hotel Reservation and Auction, were used for testing and validation. The proposed system was able to catch all the existing insecure information flows at their source. Moreover and to highlight the simplicity of the suggested approaches vs. existing approaches, two professional web developers assessed the annotation tasks needed in the presented case studies and provided a very positive feedback on the simplicity of the annotation task.

Keywords: web applications security, secure information flow, program dependence graph, database annotation

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Authors: Harish Krishnamurthy, Anna Lafontant, Ren Yi

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Unidentified Flying Objects (UFOs) have been an interesting topic for most enthusiasts and hence people all over the United States report such findings online at the National UFO Report Center (NUFORC). Some of these reports are a hoax and among those that seem legitimate, our task is not to establish that these events confirm that they indeed are events related to flying objects from aliens in outer space. Rather, we intend to identify if the report was a hoax as was identified by the UFO database team with their existing curation criterion. However, the database provides a wealth of information that can be exploited to provide various analyses and insights such as social reporting, identifying real-time spatial events and much more. We perform analysis to localize these time-series geospatial events and correlate with known real-time events. This paper does not confirm any legitimacy of alien activity, but rather attempts to gather information from likely legitimate reports of UFOs by studying the online reports. These events happen in geospatial clusters and also are time-based. We look at cluster density and data visualization to search the space of various cluster realizations to decide best probable clusters that provide us information about the proximity of such activity. A random forest classifier is also presented that is used to identify true events and hoax events, using the best possible features available such as region, week, time-period and duration. Lastly, we show the performance of the scheme on various days and correlate with real-time events where one of the UFO reports strongly correlates to a missile test conducted in the United States.

Keywords: time-series clustering, feature extraction, hoax prediction, geospatial events

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Authors: R. Marhenke, M. Martini

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The distraction of attentional resources after learning hinders long-term memory consolidation compared to several minutes of post-encoding inactivity in form of wakeful resting. We tested whether an 8-minute period of wakeful resting, compared to performing an adapted version of the d2 test of attention after learning, supports memory retention. Participants encoded and immediately recalled a word list followed by either an 8 minute period of wakeful resting (eyes closed, relaxed) or by performing an adapted version of the d2 test of attention (scanning and selecting specific characters while ignoring others). At the end of the experimental session (after 12-24 min) and again after 7 days, participants were required to complete a surprise free recall test of both word lists. Our results showed no significant difference in memory retention between the experimental conditions. However, we found that participants who completed the first lines of the d2 test in less than the given time limit of 20 seconds and thus had short unfilled intervals before switching to the next test line, remembered more words over the 12-24 minute and over the 7 days retention interval than participants who did not complete the first lines. This interaction occurred only for the first test lines, with the highest temporal proximity to the encoding task and not for later test lines. Differences in retention scores between groups (completed first line vs. did not complete) seem to be widely independent of the general performance in the d2 test. Implications and limitations of these exploratory findings are discussed.

Keywords: long-term memory, retroactive interference, attention, forgetting

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Authors: Mirjana Ruppel, Rajendra Persad, Amit Bahl, Sanja Dogramadzi, Chris Melhuish, Lyndon Smith

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Multimodal image registration is a profoundly complex task which is why deep learning has been used widely to address it in recent years. However, two main challenges remain: Firstly, the lack of ground truth data calls for an unsupervised learning approach, which leads to the second challenge of defining a feasible loss function that can compare two images of different modalities to judge their level of alignment. To avoid this issue altogether we implement a generative adversarial network consisting of two registration networks GAB, GBA and two discrimination networks DA, DB connected by spatial transformation layers. GAB learns to generate a deformation field which registers an image of the modality B to an image of the modality A. To do that, it uses the feedback of the discriminator DB which is learning to judge the quality of alignment of the registered image B. GBA and DA learn a mapping from modality A to modality B. Additionally, a cycle-consistency loss is implemented. For this, both registration networks are employed twice, therefore resulting in images ˆA, ˆB which were registered to ˜B, ˜A which were registered to the initial image pair A, B. Thus the resulting and initial images of the same modality can be easily compared. A dataset of liver CT and MRI was used to evaluate the quality of our approach and to compare it against learning and non-learning based registration algorithms. Our approach leads to dice scores of up to 0.80 ± 0.01 and is therefore comparable to and slightly more successful than algorithms like SimpleElastix and VoxelMorph.

Keywords: cycle consistency, deformable multimodal image registration, deep learning, GAN

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Authors: Hamed Yousefi, Farnaz Mohammadi, Niloufar Mirian, Navid Amini

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Investigating possible capacities of visual functions where adapted mechanisms can enhance the capability of sports trainees is a promising area of research, not only from the cognitive viewpoint but also in terms of unlimited applications in sports training. In this paper, the visual evoked potential (VEP) and event-related potential (ERP) signals of amateur and trained volleyball players in a pilot study were processed. Two groups of amateur and trained subjects are asked to imagine themselves in the state of receiving a ball while they are shown a simulated volleyball field. The proposed method is based on a set of time-frequency features using algorithms such as Gabor filter, continuous wavelet transform, and a multi-stage wavelet decomposition that are extracted from VEP signals that can be indicative of being amateur or trained. The linear discriminant classifier achieves the accuracy, sensitivity, and specificity of 100% when the average of the repetitions of the signal corresponding to the task is used. The main purpose of this study is to investigate the feasibility of a fast, robust, and reliable feature/model determination as a neurofeedback parameter to be utilized for improving the volleyball players’ performance. The proposed measure has potential applications in brain-computer interface technology where a real-time biomarker is needed.

Keywords: visual evoked potential, time-frequency feature extraction, short-time Fourier transform, event-related spectrum potential classification, linear discriminant analysis

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Authors: Shilpa Sahoo, Michel Louge, Anthony Reeves, Olivier Desjardins, Susan Daniel, Sadik Omowunmi

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The microgravity on the International Space Station (ISS) was exploited to study the imbibition of water into a network of hydrophilic cylindrical capillaries on time and length scales long enough to observe details hitherto inaccessible under Earth gravity. When a drop touches a porous medium, it spreads as if laid on a composite surface. The surface first behaves as a hydrophobic material, as liquid must penetrate pores filled with air. When contact is established, some of the liquid is drawn into pores by a capillarity that is resisted by viscous forces growing with length of the imbibed region. This process always begins with an inertial regime that is complicated by possible contact pinning. To study imbibition on Earth, time and distance must be shrunk to mitigate gravity-induced distortion. These small scales make it impossible to observe the inertial and pinning processes in detail. Instead, in the International Space Station (ISS), astronaut Luca Parmitano slowly extruded water spheres until they touched any of nine capillary plates. The 12mm diameter droplets were large enough for high-speed GX1050C video cameras on top and side to visualize details near individual capillaries, and long enough to observe dynamics of the entire imbibition process. To investigate the role of contact pinning, a text matrix was produced which consisted nine kinds of porous capillary plates made of gold-coated brass treated with Self-Assembled Monolayers (SAM) that fixed advancing and receding contact angles to known values. In the ISS, long-term microgravity allowed unambiguous observations of the role of contact line pinning during the inertial phase of imbibition. The high-speed videos of spreading and imbibition on the porous plates were analyzed using computer vision software to calculate the radius of the droplet contact patch with the plate and height of the droplet vs time. These observations are compared with numerical simulations and with data that we obtained at the ESA ZARM free-fall tower in Bremen with a unique mechanism producing relatively large water spheres and similarity in the results were observed. The data obtained from the ISS can be used as a benchmark for further numerical simulations in the field.

Keywords: droplet imbibition, hydrophilic surface, inertial phase, porous medium

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Authors: Amani Tahat, Serdal Kirmizialtin

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Trajectory fragment methods such as Markov State Models (MSM), Milestoning (MS) and Transition Path sampling are the prime choice of extending the timescale of all atom Molecular Dynamics simulations. In these approaches, a set of structures that covers the accessible phase space has to be chosen a priori using cluster analysis. Structural clustering serves to partition the conformational state into natural subgroups based on their similarity, an essential statistical methodology that is used for analyzing numerous sets of empirical data produced by Molecular Dynamics (MD) simulations. Local transition kernel among these clusters later used to connect the metastable states using a Markovian kinetic model in MSM and a non-Markovian model in MS. The choice of clustering approach in constructing such kernel is crucial since the high dimensionality of the biomolecular structures might easily confuse the identification of clusters when using the traditional hierarchical clustering methodology. Of particular interest, in the case of MS where the milestones are very close to each other, accurate determination of the milestone identity of the trajectory becomes a challenging issue. Throughout this work we present two cluster analysis methods applied to the cis–trans isomerism of dinucleotide AA. The choice of nucleic acids to commonly used proteins to study the cluster analysis is two fold: i) the energy landscape is rugged; hence transitions are more complex, enabling a more realistic model to study conformational transitions, ii) Nucleic acids conformational space is high dimensional. A diverse set of internal coordinates is necessary to describe the metastable states in nucleic acids, posing a challenge in studying the conformational transitions. Herein, we need improved clustering methods that accurately identify the AA structure in its metastable states in a robust way for a wide range of confused data conditions. The single linkage approach of the hierarchical clustering available in GROMACS MD-package is the first clustering methodology applied to our data. Self Organizing Map (SOM) neural network, that also known as a Kohonen network, is the second data clustering methodology. The performance comparison of the neural network as well as hierarchical clustering method is studied by means of computing the mean first passage times for the cis-trans conformational rates. Our hope is that this study provides insight into the complexities and need in determining the appropriate clustering algorithm for kinetic analysis. Our results can improve the effectiveness of decisions based on clustering confused empirical data in studying conformational transitions in biomolecules.

Keywords: milestoning, self organizing map, single linkage, structure clustering

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Authors: Subham Kharel, Sudha Ravindranath, A. Vidya, B. Chandrasekaran, K. Ganesha Raj, T. Shesadri

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Digitization of urban features is a tedious and time-consuming process when done manually. In addition to this problem, Indian cities have complex habitat patterns and convoluted clustering patterns, which make it even more difficult to map features. This paper makes an attempt to classify urban objects in the satellite image using object-oriented classification techniques in which various classes such as vegetation, water bodies, buildings, and shadows adjacent to the buildings were mapped semi-automatically. Building layer obtained as a result of object-oriented classification along with already available building layers was used. The main focus, however, lay in the extraction of high-rise buildings using spatial technology, digital image processing, and modeling, which would otherwise be a very difficult task to carry out manually. Results indicated a considerable rise in the total number of buildings in the city. High-rise buildings were successfully mapped using satellite imagery, spatial technology along with logical reasoning and mathematical considerations. The results clearly depict the ability of Remote Sensing and GIS to solve complex problems in urban scenarios like studying urban sprawl and identification of more complex features in an urban area like high-rise buildings and multi-dwelling units. Object-Oriented Technique has been proven to be effective and has yielded an overall efficiency of 80 percent in the classification of high-rise buildings.

Keywords: object oriented classification, shadow extraction, high-rise buildings, satellite imagery, spatial technology

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Authors: Ghazala Kausar

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This study investigates Pakistani first year university students’ perception of the role of pragmatics in their general approach to learning English. The research is triggered by National Curriculum’s initiative to provide holistic opportunities to the students for language development and to equip them with competencies to use English language in academic and social contexts (New English National Curriculum for I-XII). The traditional grammar translation and examination oriented method is believed to reduce learners to silent listener (Zhang, 2008: Zhao 2009). This lead to the inability of the students to interpret discourse by relating utterances to their meaning, understanding the intentions of the users and how language is used in specific setting (Bachman & Palmer, 1996, 2010). Pragmatic competence is a neglected area as far as teaching and learning English in Pakistan is concerned. This study focuses on the different types of pragmatic knowledge, learners perception of such knowledge and learning strategies employed by different learners to process the learning in general and pragmatic in particular. This study employed three data collecting tools; a questionnaire, discourse completion task and interviews to elicit data from first year university students regarding their perception of pragmatic competence. Results showed that Pakistani first year university learners have limited pragmatic knowledge. Although they acknowledged the importance of linguistic knowledge for linguistic competence in the students but argued that insufficient English proficiency, limited knowledge of pragmatics, insufficient language material and tasks were major reasons of pragmatic failure.

Keywords: pragmatic competence, Pakistani college learners, linguistic competence

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Authors: S. Aberkane, N. Djabali, S. Fafi, A. Baghezza

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Chronic illnesses affect many Algerians. It is possible to investigate the impact of illness representations and coping on quality of life and whether illness representations are indirectly associated with quality of life through their influence on coping. This study aims at investigating the relationship between illness perception, coping strategies and quality of life with chronic illness. Illness perceptions are indirectly associated with the quality of life through their influence on coping mediation. A sample of 316 participants with chronic illness living in the region of Batna, Algeria, has been adopted in this study. A correlation statistical analysis is used to determine the relationship between illness perception, coping strategies, and quality of life. Multiple regression analysis was employed to highlight the predictive ability of the dimensions of illness perception and coping strategies on the dependent variables of quality of life, where mediation analysis is considered in the exploration of the indirect effect significance of the mediator. This study provides insights about the relationship between illness perception, coping strategies and quality of life in the considered sample (r = 0.39, p < 0.01). Therefore, it proves that there is an effect of illness identity perception, external and medical attributions related to emotional role, physical functioning, and mental health perceived, and these were fully mediated by the asking for assistance (c’= 0.04, p < 0.05), the guarding (c’= 0.00, p < 0.05), and the task persistence strategy (c’= 0.05, p < 0.05). The findings imply partial support for the common-sense model of illness representations in a chronic illness population. Directions for future research are highlighted, as well as implications for psychotherapeutic interventions which target unhelpful beliefs and maladaptive coping strategies (e.g., cognitive behavioral therapy).

Keywords: chronic illness, coping, illness perception, quality of life, self- regulation model

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Authors: Josiah Damisa, Michelle Louise Richardson, Morenike Adewuyi

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Lower back pain is a significant cause of disability worldwide and associated with great implications in terms of the well-being of affected individuals and society as a whole due to its undeniable socio-economic impact. With its prevalence on the increase as a result of an aging global population, the need for novel forms of pain management is ever paramount. This review aims to provide further insight into current research regarding a role for the endocannabinoid signaling pathway as a target in the treatment of chronic pain, with particular emphasis on its potential use as part of the treatment of lower back pain. Potential advantages and limitations of cannabis-based medicines over other forms of analgesia currently licensed for medical use are discussed in addition to areas that require ongoing consideration and research. To evaluate the efficacy of cannabis-based medicines in chronic pain, studies pertaining to the role of medical cannabis in chronic disease were reviewed. Standard searches of PubMed, Google Scholar and Web of Science databases were undertaken with peer-reviewed journal articles reviewed based on the indication for pain management, cannabis treatment modality used and study outcomes. Multiple studies suggest an emerging role for cannabis-based medicines as therapeutic agents in the treatment of chronic back pain. A potential synergistic effect has also been purported if these medicines are co-administered with opiate analgesia due to the similarity of the opiate and endocannabinoid signaling pathways. However, whilst recent changes to legislation in the United Kingdom mean that cannabis is now licensed for medicinal use on NHS prescription for a number of chronic health conditions, concerns remain as to the efficacy and safety of cannabis-based medicines. Research is lacking into both their side effect profiles and the long-term effects of cannabis use. Legal and ethical considerations to the use of these products in standardized medical practice also persist due to the notoriety of cannabis as a drug of abuse. Despite this, cannabis is beginning to gain traction as an alternative or even complementary drug to opiates, with some preclinical studies showing opiate-sparing effects. Whilst there is a paucity of clinical trials in this field, there is scope for cannabinoids to be successful anti-nociceptive agents in managing chronic back pain. The ultimate aim would be to utilize cannabis-based medicines as alternative or complementary therapies, thereby reducing opiate over-reliance and providing hope to individuals who have exhausted all other forms of standard treatment.

Keywords: endocannabinoids, cannabis-based medicines, chronic pain, lower back pain

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Authors: Zhiwei Wang

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Much emphasis has been placed on the political dimension of digitised Chinese national(ist) discourses and their embodied national identities, which neglects other important dimensions constitutive of their discursive nature. A further investigation into how Chinese national(ist) discourses are daily (re)shaped online by diverse socio-political actors (especially ordinary users) is crucial, which can contribute to not only deeper understandings of Chinese national sentiments on China’s Internet beyond the excessive focus on their passionate, political-charged facet but also richer insights into the socio-technical ecology of the contemporary Chinese digital (and physical) world. This research adopts an ethnographic methodology, by which ‘fieldsites’ are Sina Weibo and bilibili. The primary data collection method is virtual ethnographic observation on everyday national(ist) discussions on both platforms. If data obtained via observations do not suffice to answer research questions, in-depth online qualitative interviews with ‘key actors’ identified from those observations in discursively (re)producing Chinese national identity on each ‘fieldsite’ will be conducted, to complement data gathered through the first method. Critical discourse analysis is employed to analyse data. During the process of data coding, NVivo is utilised. From November 2021 to December 2022, 35 weeks’ digital ethnographic observations have been conducted, with 35 sets of fieldnotes obtained. The strategy adopted for the initial stage of observations was keyword searching, which means typing into the search box on Sina Weibo and bilibili any keywords related to China as a nation and then observing the search results. Throughout 35 weeks’ online ethnographic observations, six keywords have been employed on Sina Weibo and two keywords on bilibili. For 35 weeks’ observations, textual content created by ordinary users have been concentrated much upon. Based on the fieldnotes of the first week’s observations, multifarious national(ist) discourses on Sina Weibo and bilibili have been found, targeted both at national ‘Others’ and ‘Us’, both on the historical and real-world dimension, both aligning with and differing from or even conflicting with official discourses, both direct national(ist) expressions and articulations of sentiments in the name of presentation of national(ist) attachments but for other purposes. Second, Sina Weibo and bilibili users have agency in interpreting and deploying concrete national(ist) discourses despite the leading role played by the government and the two platforms in deciding on the basic framework of national expressions. Besides, there are also disputes and even quarrels between users in terms of explanations for concrete components of ‘nation-ness’ and (in)direct dissent to officially defined ‘mainstream’ discourses to some extent, though often expressed much more mundanely, discursively and playfully. Third, the (re)production process of national(ist) discourses on Sina Weibo and bilibili depends upon not only technical affordances and limitations of the two sites but also, to a larger degree, some established socio-political mechanisms and conventions in the offline China, e.g., the authorities’ acquiescence of citizens’ freedom in understanding and explaining concrete elements of national discourses while setting the basic framework of national narratives to the extent that citizens’ own national(ist) expressions do not reach political bottom lines and develop into mobilising power to shake social stability.

Keywords: national identity, national(ist) discourse(s), everyday nationhood/nationalism, Chinese nationalism, digital nationalism

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Authors: John Gibbs, Benjamin Flanders, Dylan Pozorski, Weixuan Liu

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Weight Painting–adjusting the influence a skeletal joint has on a given vertex in a character mesh–is an arduous and time con- suming part of the 3D animation pipeline. This process generally requires a trained technical animator and many hours of work to complete. Our skiNNer plug-in, which works within Autodesk’s Maya 3D animation software, uses Machine Learning and data pro- cessing techniques to create a deep neural network model that can accomplish the weight painting task in seconds rather than hours for bipedal quasi-humanoid character meshes. In order to create a properly trained network, a number of challenges were overcome, including curating an appropriately large data library, managing an arbitrary 3D mesh size, handling arbitrary skeletal architectures, accounting for extreme numeric values (most data points are near 0 or 1 for weight maps), and constructing an appropriate neural network model that can properly capture the high frequency alter- ation between high weight values (near 1.0) and low weight values (near 0.0). The arrived at neural network model is a cross between a traditional CNN, deep residual network, and fully dense network. The resultant network captures the unusually hard-edged features of a weight map matrix, and produces excellent results on many bipedal models.

Keywords: 3d animation, animation, character, rigging, skinning, weight painting, machine learning, artificial intelligence, neural network, deep neural network

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Authors: Ziad Abdeldayem, Jakub Markiewicz, Kunal Kansara, Laura Edwards

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Airborne Laser Scanning (ALS) is one of the main technologies for generating high-resolution digital terrain models (DTMs). DTMs are crucial to several applications, such as topographic mapping, flood zone delineation, geographic information systems (GIS), hydrological modelling, spatial analysis, etc. Laser scanning system generates irregularly spaced three-dimensional cloud of points. Raw ALS data are mainly ground points (that represent the bare earth) and non-ground points (that represent buildings, trees, cars, etc.). Removing all the non-ground points from the raw data is referred to as filtering. Filtering heavily forested areas is considered a difficult and challenging task as the canopy stops laser pulses from reaching the terrain surface. This research presents an approach for removing non-ground points from raw ALS data in densely forested areas. Smoothing splines are exploited to interpolate and fit the noisy ALS data. The presented filter utilizes a weight function to allocate weights for each point of the data. Furthermore, unlike most of the methods, the presented filtering algorithm is designed to be automatic. Three different forested areas in the United Kingdom are used to assess the performance of the algorithm. The results show that the generated DTMs from the filtered data are accurate (when compared against reference terrain data) and the performance of the method is stable for all the heavily forested data samples. The average root mean square error (RMSE) value is 0.35 m.

Keywords: airborne laser scanning, digital terrain models, filtering, forested areas

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Authors: Nadine Abuqtaish

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Females’ participation in athletic sports events has increased in the last decades, and the discovery of eating disorders and menstrual dysfunction has been evident since the early 1980s. The term “Female athlete triad” was initially defined by the Task Force on Women’s Issues of the American College of Sports Medicine (ACSM) in 1992. Menstrual irregularities have been prevalent in competitive female athletes, especially in their adolescence and early adulthood age. Nutritional restrictions to maintain a certain physique and lean look are sought to be advantageous in female athletes such as gymnastics, cheerleading, or weight-sensitive sports such as endurance sports (cycling and marathoners). This stress places the female at risk of irregularities in their menstrual cycle which can lead them to lose their circadian estrogen levels. Estrogen is an important female reproductive hormone that plays a role in maintaining bone mass. Bone mineral density peaks by the age 25. Inadequate estrogen due to missed menstrual cycle or amenorrhea has been estimated to cause a yearly loss of 2% of bone mass, increasing the risk of osteoporosis in the postmenopausal phase. This paper is intended to have a better depth understanding of whether female athletes are being monitored by their official entities or coaches. A qualitative research method through online search engines and keywords “females, athletes, triad, amenorrhea, anorexia, osteoporosis” were used to collect the available primary sources from official public library databases. The latest consensus was published in 2014 by the Female Athlete Triad Coalition and the need for further research and emphasis on this issue is still lacking.

Keywords: female, athlete, triad, amenorrhea, anorexia, bone loss

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Authors: Dongseok Cho, Mitchell Driedger, Sera Han, Noman Khan, Mohammed Elmorsy, Mohamad El-Hajj

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Since the approval of the COVID-19 vaccine in late 2020, vaccination rates have varied around the globe. Access to a vaccine supply, mandated vaccination policy, and vaccine hesitancy contribute to these rates. This study used COVID-19 vaccination data from Our World in Data and the Multilateral Leaders Task Force on COVID-19 to create two COVID-19 vaccination indices. The first index is the Vaccine Utilization Index (VUI), which measures how effectively each country has utilized its vaccine supply to doubly vaccinate its population. The second index is the Vaccination Acceleration Index (VAI), which evaluates how efficiently each country vaccinated its population within its first 150 days. Pearson correlations were created between these indices and country indicators obtained from the World Bank. The results of these correlations identify countries with stronger health indicators, such as lower mortality rates, lower age dependency ratios, and higher rates of immunization to other diseases, displaying higher VUI and VAI scores than countries with lesser values. VAI scores are also positively correlated to Governance and Economic indicators, such as regulatory quality, control of corruption, and GDP per capita. As represented by the VUI, proper utilization of the COVID-19 vaccine supply by country is observed in countries that display excellence in health practices. A country’s motivation to accelerate its vaccination rates within the first 150 days of vaccinating, as represented by the VAI, was largely a product of the governing body’s effectiveness and economic status, as well as overall excellence in health practises.

Keywords: data mining, Pearson correlation, COVID-19, vaccination rates and hesitancy

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Authors: Md. Arifur Rahman, Ummya Salma, Nazrul Islam

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Private sector banking business is one of the leading businesses of Bangladesh as it is profitable and directly attached with the economic development of the country. Training has got very high importance in this sector for increasing the performance of the banks. It has a long term impact on a number of aspects of the bank employees and their performances. It is an investment of the organization that is permanent in nature. Study shows that there are positive relationships between training and the employee commitment, job retention, job satisfaction and company performance. Training is also concerned with promotion, compensation, work-life policies, career development, task and contextual performance of the employees. As such, this paper aims at identifying the impact of training on employee commitment, job retention, job satisfaction and the performance of the private sector banks in Bangladesh. Both primary and secondary data were used to conduct the study. Data were collected from the bank officers who were trained in their banks. Both descriptive and inferential statistics were used to analyze the data. Descriptive statistics were used to describe the present situation of the banks and their employees. Inferential statistics were used to identify the factors and their significance concerned with training. Results show that there is a significant relationship between the performance and the training of the employees. It also shows that the training can motivate employees and encourage them to work hard. However, this study did not find any relationship between the commitment of the employees and the training. This study suggests that for increasing the performance of the banks, training is a must which is to be given deliberately for improving the specific skills of the bank employees.

Keywords: training, promotion, compensation, work-life policies

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Authors: Rupali, Anil Kumar Jaiswal

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Cloud computing can be defined as one of the prominent technologies that lets a user change, configure and access the services online. it can be said that this is a prototype of computing that helps in saving cost and time of a user practically the use of cloud computing can be found in various fields like education, health, banking etc.  Cloud computing is an internet dependent technology thus it is the major responsibility of Cloud Service Providers(CSPs) to care of data stored by user at data centers. Scheduling in cloud computing environment plays a vital role as to achieve maximum utilization and user satisfaction cloud providers need to schedule resources effectively.  Job scheduling for cloud computing is analyzed in the following work. To complete, recreate the task calculation, and conveyed scheduling methods CloudSim3.0.3 is utilized. This research work discusses the job scheduling for circulated processing condition also by exploring on this issue we find it works with minimum time and less cost. In this work two load balancing techniques have been employed: ‘Throttled stack adjustment policy’ and ‘Active VM load balancing policy’ with two brokerage services ‘Advanced Response Time’ and ‘Reconfigure Dynamically’ to evaluate the VM_Cost, DC_Cost, Response Time, and Data Processing Time. The proposed techniques are compared with Round Robin scheduling policy.

Keywords: physical machines, virtual machines, support for repetition, self-healing, highly scalable programming model

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Authors: Anupama Purohit

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The paper is a study on the inevitable cross-linguistic effect found in the early multilingual learners. The cross-linguistic behaviour like code-mixing, code-switching, foreign accent, literal translation, redundancy and syntactic manipulation effected due to other languages on the English language output of a non-native pre-school child are discussed here. A case study method is adopted in this paper to support the claim of the title. A simultaneously tetra lingual pre-school child’s (within 1;3 to 4;0) language behaviour is analysed here. The sample output data of the child is gathered from the diary entries maintained by her family, regular observations and video recordings done since her birth. She is getting the input of her mother tongue, Sambalpuri, from her grandparents only; Hindi, the local language from her play-school and the neighbourhood; English only from her mother and occasional visit of other family friends; Odia only during the reading of the Odia story book. The child is exposed to code-mixing of all the languages throughout her childhood. But code-mixing, literal translation, redundancy and duplication were absent in her initial stage of multilingual acquisition. As the child was more proficient in English in comparison to her other first languages and had never heard code-mixing in English language; it was expected from her input pattern of English (one parent, English language) that she would maintain purity in her use of English while talking to the English language interlocutor. But with gradual increase in the language proficiency in each of the languages of the child, her handling of the multiple codes becomes deft cross-linguistically. It can be deduced from the case study that after attaining certain milestone proficiency in each language, the child’s linguistic faculty can operate at a metalinguistic level. The functional use of each morpheme, their arrangement in words and in the sentences, the supra segmental features, lexical-semantic mapping, culture specific use of a language and the pragmatic skills converge to give a typical childlike multilingual output in an intelligible manner to the multilingual people (with the same set of languages in combination). The result is appealing because for expressing the same ideas which the child used to speak (may be with grammatically wrong expressions) in one language, gradually, she starts showing cross-linguistic effect in her expressions. So the paper pleads for the separatist view from the very beginning of the holophrastic phase (as the child expresses in addressee-specific language); but development of a metalinguistic ability that helps the child in communicating in a sophisticated way according to the linguistic status of the addressee is unique to the multilingual child. This metalinguistic ability is independent of the mode if input of a multilingual child.

Keywords: code-mixing, cross-linguistic effect, early multilingualism, literal translation

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Authors: Belgherbi Aicha, Hadjidj Ismahen, Bessaid Abdelhafid

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The interpretation of medical images benefits from anatomical and physiological priors to optimize computer- aided diagnosis applications. Segmentation of liver and liver lesion is regarded as a major primary step in computer aided diagnosis of liver diseases. Precise liver segmentation in abdominal CT images is one of the most important steps for the computer-aided diagnosis of liver pathology. In this papers, a semi- automated method for medical image data is presented for the liver and liver lesion segmentation data using mathematical morphology. Our algorithm is currency in two parts. In the first, we seek to determine the region of interest by applying the morphological filters to extract the liver. The second step consists to detect the liver lesion. In this task; we proposed a new method developed for the semi-automatic segmentation of the liver and hepatic lesions. Our proposed method is based on the anatomical information and mathematical morphology tools used in the image processing field. At first, we try to improve the quality of the original image and image gradient by applying the spatial filter followed by the morphological filters. The second step consists to calculate the internal and external markers of the liver and hepatic lesions. Thereafter we proceed to the liver and hepatic lesions segmentation by the watershed transform controlled by markers. The validation of the developed algorithm is done using several images. Obtained results show the good performances of our proposed algorithm

Keywords: anisotropic diffusion filter, CT images, hepatic lesion segmentation, Liver segmentation, morphological filter, the watershed algorithm

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Authors: Abimbola Olaotan Akinsete

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Government all over the world has intensified efforts in making internet and resource centres readily available in public institutions and centres for the advancement of humanity and working processes. Information and communication resource centre will not only help in the reduction of task that are presumed to be herculean. This centres influenced the working rate and productivity of both staffs and students and its benefit. The utilization of the internet and information resource centre will not only speed up service delivery, working time and efficiency of the system. Information and Communication Technology plays significant roles in presenting equalization strategy for developing university community and improving educational service delivery. This equalization will not only advance, accelerate and ensure results are accessed electronically, ensuring the transfer and confirmation of students’ academic records and their results in the world without physically available to request for these services. This study seeks to make Critical Appraisal of Adekunle Ajasin University Policy on Internet Resource Centre in Service Delivery Adekunle Ajasin University, Akungba-Akoko, Ondo State. The study employ descriptive survey design method in identifying hindrances of the non-utilization of technology in the service delivery in the university. Findings revealed that the adoption of internet and resource centre in the Exams and Records unit of the University shall help in delivering more in students’ records/results processing.

Keywords: internet, resource, centre, policy and service delivery

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Authors: Farhad Salek, Shahaboddin Resalati

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The production of electric vehicles (EVs) featuring lithium-ion battery technology has substantially escalated over the past decade, demonstrating a steady and persistent upward trajectory. The imminent retirement of electric vehicle (EV) batteries after approximately eight years underscores the critical need for their redirection towards recycling, a task complicated by the current inadequacy of recycling infrastructures globally. A potential solution for such concerns involves extending the operational lifespan of electric vehicle (EV) batteries through their utilization in stationary energy storage systems during secondary applications. Such adoptions, however, require addressing the safety concerns associated with batteries’ knee points and thermal runaways. This paper develops an accurate mathematical model representative of the second-life battery packs from a cell-to-pack scale using an equivalent circuit model (ECM) methodology. Neural network algorithms are employed to forecast the degradation parameters based on the EV batteries' aging history to develop a degradation model. The degradation model is integrated with the ECM to reflect the impacts of the cycle aging mechanism on battery parameters during operation. The developed model is tested under real-life load profiles to evaluate the life span of the batteries in various operating conditions. The methodology and the algorithms introduced in this paper can be considered the basis for Battery Management System (BMS) design and techno-economic analysis of such technologies.

Keywords: second life battery, electric vehicles, degradation, neural network

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Authors: Rohini Hariharan, Yazhini R., Blessy Maria Mathew

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The task of detecting email spam is a very important one in the era of digital technology that needs effective ways of curbing unwanted messages. This paper presents an approach aimed at making email spam categorization algorithms transparent, reliable and more trustworthy by incorporating Local Interpretable Model-agnostic Explanations (LIME). Our technique assists in providing interpretable explanations for specific classifications of emails to help users understand the decision-making process by the model. In this study, we developed a complete pipeline that incorporates LIME into the spam classification framework and allows creating simplified, interpretable models tailored to individual emails. LIME identifies influential terms, pointing out key elements that drive classification results, thus reducing opacity inherent in conventional machine learning models. Additionally, we suggest a visualization scheme for displaying keywords that will improve understanding of categorization decisions by users. We test our method on a diverse email dataset and compare its performance with various baseline models, such as Gaussian Naive Bayes, Multinomial Naive Bayes, Bernoulli Naive Bayes, Support Vector Classifier, K-Nearest Neighbors, Decision Tree, and Logistic Regression. Our testing results show that our model surpasses all other models, achieving an accuracy of 96.59% and a precision of 99.12%.

Keywords: text classification, LIME (local interpretable model-agnostic explanations), stemming, tokenization, logistic regression.

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Authors: Allaeddine Athmani, Abdelhacine Gouasmia, Tiago Ferreira, Romeu Vicente

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Seismic vulnerability assessment of masonry buildings is a fundamental issue even for moderate to low seismic hazard regions. This fact is even more important when dealing with old structures such as those located in Annaba city (Algeria), which the majority of dates back to the French colonial era from 1830. This category of buildings is in high risk due to their highly degradation state, heterogeneous materials and intrusive modifications to structural and non-structural elements. Furthermore, they are usually shelter a dense population, which is exposed to such risk. In order to undertake a suitable seismic risk mitigation strategies and reinforcement process for such structures, it is essential to estimate their seismic resistance capacity at a large scale. In this sense, two seismic vulnerability index methods and damage estimation have been adapted and applied to a pilot-scale building area located in the moderate seismic hazard region of Annaba city: The first one based on the EMS-98 building typologies, and the second one derived from the Italian GNDT approach. To perform this task, the authors took the advantage of an existing data survey previously performed for other purposes. The results obtained from the application of the two methods were integrated and compared using a geographic information system tool (GIS), with the ultimate goal of supporting the city council of Annaba for the implementation of risk mitigation and emergency planning strategies.

Keywords: Annaba city, EMS98 concept, GNDT method, old city center, seismic vulnerability index, unreinforced masonry buildings

Procedia PDF Downloads 617