Search results for: multiple template matching

Authors: Adel Sabry Eesa, Adnan Mohsin Abdulazeez Brifcani, Zeynep Orman

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This paper presents a new meta-heuristic bio-inspired optimization algorithm which is called Cuttlefish Algorithm (CFA). The algorithm mimics the mechanism of color changing behavior of the cuttlefish to solve numerical global optimization problems. The colors and patterns of the cuttlefish are produced by reflected light from three different layers of cells. The proposed algorithm considers mainly two processes: reflection and visibility. Reflection process simulates light reflection mechanism used by these layers, while visibility process simulates visibility of matching patterns of the cuttlefish. To show the effectiveness of the algorithm, it is tested with some other popular bio-inspired optimization algorithms such as Genetic Algorithms (GA), Particle Swarm Optimization (PSO) and Bees Algorithm (BA) that have been previously proposed in the literature. Simulations and obtained results indicate that the proposed CFA is superior when compared with these algorithms.

Keywords: Cuttlefish Algorithm, bio-inspired algorithms, optimization, global optimization problems

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Authors: Adam Beaunoyer, Cory Beaunoyer, Mohammed Elmorsy, Hanan Saleh

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This research aims to create a reinforcement learning agent capable of racing in challenging simulated environments with a low collision count. We present a reinforcement learning agent that can navigate challenging tracks using both a Deep Q-Network (DQN) and a Soft Actor-Critic (SAC) method. A challenging track includes curves, jumps, and varying road widths throughout. Using open-source code on Github, the environment used in this research is based on the 1995 racing game WipeOut. The proposed reinforcement learning agent can navigate challenging tracks rapidly while maintaining low racing completion time and collision count. The results show that the SAC model outperforms the DQN model by a large margin. We also propose an alternative multiple-car model that can navigate the track without colliding with other vehicles on the track. The SAC model is the basis for the multiple-car model, where it can complete the laps quicker than the single-car model but has a higher collision rate with the track wall.

Keywords: reinforcement learning, soft actor-critic, deep q-network, self-driving cars, artificial intelligence, gaming

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Authors: Anika Chebrolu

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Mono-targeted drugs can be of limited efficacy against complex diseases. Recently, multi-target drug design has been approached as a promising tool to fight against these challenging diseases. However, the scope of current computational approaches for multi-target drug design is limited. DeepLig presents a de-novo drug discovery platform that uses reinforcement learning to generate and optimize novel, potent, and multitargeted drug candidates against protein targets. DeepLig’s model consists of two networks in interplay: a generative network and a predictive network. The generative network, a Stack- Augmented Recurrent Neural Network, utilizes a stack memory unit to remember and recognize molecular patterns when generating novel ligands from scratch. The generative network passes each newly created ligand to the predictive network, which then uses multiple Graph Attention Networks simultaneously to forecast the average binding affinity of the generated ligand towards multiple target proteins. With each iteration, given feedback from the predictive network, the generative network learns to optimize itself to create molecules with a higher average binding affinity towards multiple proteins. DeepLig was evaluated based on its ability to generate multi-target ligands against two distinct proteins, multi-target ligands against three distinct proteins, and multi-target ligands against two distinct binding pockets on the same protein. With each test case, DeepLig was able to create a library of valid, synthetically accessible, and novel molecules with optimal and equipotent binding energies. We propose that DeepLig provides an effective approach to design multi-targeted drug therapies that can potentially show higher success rates during in-vitro trials.

Keywords: drug design, multitargeticity, de-novo, reinforcement learning

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Authors: Lien Fa Lin, Yung Ming Li, Hsin Chen Hsieh

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The rise of smartphones brings new concept So-Lo-Mo (social-local-mobile) in mobile commerce area in recent years. However, current So-Lo-Mo services only focus on individual users but not a group of users, and the development of group commerce is not enough to satisfy the demand of real-time group buying and less to think about the social relationship between customers. In this research, we integrate mobile intelligence with group commerce and consider customers' preference, real-time context, and social influence as components in the mechanism. With the support of this mechanism, customers are able to gather near customers with the same potential purchase willingness through mobile devices when he/she wants to purchase products or services to have a real-time group-buying. By matching the demand and supply of mobile group-buying market, this research improves the business value of mobile commerce and group commerce further.

Keywords: group formation, group commerce, mobile commerce, So-Lo-Mo, social influence

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Authors: Razieh Esfandiarisedgh

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Community architecture has been proposed as an alternative approach in architecture, with three political, sociological, and psychological approaches. In community architecture, the psychological approach, as the only approach related to community design, has an important index called a sense of community. Changes in today's modern society, such as the shrinking of families, cause a decrease in the sense of community and unwillingness of people. It has become a residential complex to be present in public spaces. This issue can be increased by creating motivation with the help of design for the presence and participation of people in public spaces and taking advantage of the facilities and quality of these spaces. This research used the qualitative research method, studied and collected information, and used observation and interviews in the selected sample. Through targeted sampling and matching it with the extracted design table, it was concluded that climate and culture are known as two important factors in the collective view of housing in Hamedan.

Keywords: community architecture, sense of community, environmental psychology, architecture

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Authors: Gabriele Borg, Alexei Debono, Charlie Abela

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There on a constant rise in the availability of high volumes of data gathered from multiple sources, resulting in an abundance of unprocessed information that can be used to monitor patterns and trends in user behaviour. Similarly, year after year, Malta is also constantly experiencing ongoing population growth and an increase in mobilization demand. This research takes advantage of data which is continuously being sourced and converting it into useful information related to the traffic problem on the Maltese roads. The scope of this paper is to provide a methodology to create a custom dataset (MalTra - Malta Traffic) compiled from multiple participants from various locations across the island to identify the most common routes taken to expose the main areas of activity. This use of big data is seen being used in various technologies and is referred to as ITSs (Intelligent Transportation Systems), which has been concluded that there is significant potential in utilising such sources of data on a nationwide scale. Furthermore, a series of traffic prediction graph neural network models are conducted to compare MalTra to large-scale traffic datasets.

Keywords: graph neural networks, traffic management, big data, mobile data patterns

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Authors: Xiaodi Su, Xin Ting Zheng, Laura Sutarlie, Nur Asinah binte Mohamed Salleh, Yong Yu

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Wound healing is a dynamic process with multiple phases. Rapid profiling and quantitative characterization of inflammation and infection remain challenging. We have developed paper-like battery-free multiplexed sensors for holistic wound assessment via quantitative detection of multiple inflammation and infection markers. In one of the designs, the sensor patch consists of a wax-printed paper panel with five colorimetric sensor channels arranged in a pattern resembling a five-petaled flower (denoted as a ‘Petal’ sensor). The five sensors are for temperature, pH, trimethylamine, uric acid, and moisture. The sensor patch is sandwiched between a top transparent silicone layer and a bottom adhesive wound contact layer. In the second design, a palm-like-shaped paper strip is fabricated by a paper-cutter printer (denoted as ‘Palm’ sensor). This sensor strip carries five sensor regions connected by a stem sampling entrance that enables rapid colorimetric detection of multiple bacteria metabolites (aldehyde, lactate, moisture, trimethylamine, tryptophan) from wound exudate. For both the “\’ Petal’ and ‘Palm’ sensors, color images can be captured by a mobile phone. According to the color changes, one can quantify the concentration of the biomarkers and then determine wound healing status and identify/quantify bacterial species in infected wounds. The ‘Petal’ and ‘Palm’ sensors are validated with in-situ animal and ex-situ skin wound models, respectively. These sensors have the potential for integration with wound dressing to allow early warning of adverse events without frequent removal of the plasters. Such in-situ and early detection of non-healing condition can trigger immediate clinical intervention to facilitate wound care management.

Keywords: wound infection, colorimetric sensor, paper fluidic sensor, wound care

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Authors: S. Fazi, B. Behdani

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The inland transportation of maritime containers entails the use of different modalities whose capacity is typically booked in advance. Containers may miss their scheduled departure time at a terminal for several reasons, such as delays, change of transport modes, multiple bookings pending. In those cases, it may be difficult for transport service providers to find last minute containers to fill the vacant capacity. Similarly to other industries, overbooking could potentially limit these drawbacks at the cost of a lower service level in case of actual excess of capacity in overbooked rides. However, the presence of multiple modalities may provide the required flexibility in rescheduling and limit the dissatisfaction of the shippers in case of containers in overbooking. This flexibility is known with the term 'synchromodality'. In this paper, we evaluate via discrete event simulation the application of overbooking. Results show that in certain conditions overbooking can significantly increase profit and utilization of high-capacity means of transport, such as barges and trains. On the other hand, in case of high penalty costs and limited no-show, overbooking may lead to an excessive use of expensive trucks.

Keywords: discrete event simulation, flexibility, inland shipping, multimodality, overbooking

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Authors: Pawan Kumar Mishra, Ganesh Singh Bisht

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Super-resolution is the technique that is being used in computer vision to construct high-resolution images from a single low-resolution image. It is used to increase the frequency component, recover the lost details and removing the down sampling and noises that caused by camera during image acquisition process. High-resolution images or videos are desired part of all image processing tasks and its analysis in most of digital imaging application. The target behind super-resolution is to combine non-repetition information inside single or multiple low-resolution frames to generate a high-resolution image. Many methods have been proposed where multiple images are used as low-resolution images of same scene with different variation in transformation. This is called multi-image super resolution. And another family of methods is single image super-resolution that tries to learn redundancy that presents in image and reconstruction the lost information from a single low-resolution image. Use of deep learning is one of state of art method at present for solving reconstruction high-resolution image. In this research, we proposed Deep Denoising Super Resolution (DDSR) that is a deep neural network for effectively reconstruct the high-resolution image from low-resolution image.

Keywords: resolution, deep-learning, neural network, de-blurring

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Authors: Nickolas A. Stewart, Donald C. Hefelfinger, Garrett V. Brittain, Timothy C. Frommeyer, Adrienne Stolfi

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Orthopedic surgery is currently considered one of the most competitive specialties that medical students can apply to for residency training. As evidenced by increasing United States Medical Licensing Examination (USMLE) scores, overall grades, and publication, presentation, and abstract numbers, this specialty is getting increasingly competitive. The recent change of USMLE Step 1 scores to pass/fail has resulted in additional challenges for medical students planning to apply for orthopedic residency. Until now, these scores have been a tool used by residency programs to screen applicants as an initial factor to determine the strength of their application. With USMLE STEP 1 converting to a pass/fail grading criterion, the question remains as to what will take its place on the ERAS application. The primary objective of this study is to determine the trends in the number of research projects, abstracts, presentations, and publications among orthopedic residency applicants. Secondly, this study seeks to determine if there is a relationship between the number of research projects, abstracts, presentations, and publications, and match rates. The researchers utilized the National Resident Matching Program's Charting Outcomes in the Match between 2007 and 2022 to identify mean publications and research project numbers by allopathic and osteopathic US orthopedic surgery senior applicants. A paired t test was performed between the mean number of publications and research projects by matched and unmatched applicants. Additionally, simple linear regressions within matched and unmatched applicants were used to determine the association between year and number of abstracts, presentations, and publications, and a number of research projects. For determining whether the increase in the number of abstracts, presentations, and publications, and a number of research projects is significantly different between matched and unmatched applicants, an analysis of covariance is used with an interaction term added to the model, which represents the test for the difference between the slopes of each group. The data shows that from 2007 to 2022, the average number of research publications increased from 3 to 16.5 for matched orthopedic surgery applicants. The paired t-test had a significant p-value of 0.006 for the number of research publications between matched and unmatched applicants. In conclusion, the average number of publications for orthopedic surgery applicants has significantly increased for matched and unmatched applicants from 2007 to 2022. Moreover, this increase has accelerated in recent years, as evidenced by an increase of only 1.5 publications from 2007 to 2001 versus 5.0 publications from 2018 to 2022. The number of abstracts, presentations, and publications is a significant factor regarding an applicant's likelihood to successfully match into an orthopedic residency program. With USMLE Step 1 being converted to pass/fail, the researchers expect students and program directors will place increased importance on additional factors that can help them stand out. This study demonstrates that research will be a primary component in stratifying future orthopedic surgery applicants. In addition, this suggests the average number of research publications will continue to accelerate. Further study is required to determine whether this growth is sustainable.

Keywords: publications, orthopedic surgery, research, residency applications

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Authors: Drissi Mokhtaria, Chouaih Abdelkader, Fodil Hamzaoui

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Our work is about a comparison of experimental and theoretical results of the electron charge density distribution and the electrostatic potential around the M-Nitrophenol Molecule (m-NPH) kwon for its interesting physical characteristics. The molecular experimental results have been obtained from a high-resolution X-ray diffraction study. Theoretical investigations were performed under the Gaussian program using the Density Functional Theory at B3LYP level of theory at 6-31G*. The multipolar model of Hansen and Coppens was used for the experimental electron charge density distribution around the molecule, while we used the DFT methods for the theoretical calculations. The electron charge density obtained in both methods allowed us to find out the different molecular properties such us the electrostatic potential and the dipole moment which were finally subject to a comparison leading to an outcome of a good matching results obtained in both methods.

Keywords: electron charge density, m-nitrophenol, nonlinear optical compound, electrostatic potential, optimized geometric

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Authors: Laura Frydel

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Chloroxylenol is a common ingredient in disinfectants. Due to the use of this compound in large amounts, it is more and more often detected in rivers, sewage, and also in human body fluids. In recent years, there have been concerns about the potentially harmful effects of chloroxylenol on human health and the environment. This paper presents the synthesis, a brief characterization and the use of a halloysite-carbon adsorbent for the removal of chloroxylenol from water. The template in the halloysite-carbon adsorbent was acid treated bleached halloysite, and the carbon precursor was cellulose dissolved in zinc (II) chloride, which was dissolved in 37% hydrochloric acid. The FTIR spectra before and after the adsorption process allowed to determine the presence of functional groups, bonds in the halloysite-carbon composite, and the binding mechanism of the adsorbent and adsorbate. The morphology of the bleached halloysite sample and the sample of the halloysite-carbon adsorbent were characterized by scanning electron microscopy (SEM) with surface analysis by X-ray dispersion spectrometry (EDS). The specific surface area, total pore volume and mesopore and micropore volume were determined using the ASAP 2020 volumetric adsorption analyzer. Total carbon and total organic carbon were determined for the halloysite-carbon adsorbent. The halloysite-carbon adsorbent was used to remove chloroxylenol from water. The degree of removal of chloroxylenol from water using the halloysite-carbon adsorbent was about 90%. Adsorption studies show that the halloysite-carbon composite can be used as an effective adsorbent for removing chloroxylenol from water.

Keywords: adsorption, cellulose, chloroxylenol, halloysite

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Authors: Rohit Kothari, Muneer Mohamed, Vivekanandh K., Sunmeet Sandhu, Preema Sinha, Anuj Bhatnagar

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Introduction: Hyper-IgE syndrome is a rare primary immunodeficiency syndrome characterised by triad of severe atopic dermatitis, recurrent pulmonary infections, and recurrent staphylococcal skin infections. The diagnosis requires a high degree of suspicion, typical clinical features, and not mere rise in serum-IgE levels, which may be seen in multiple conditions. Genetic studies are not always possible in a resource poor setting. This study highlights various presentations of Hyper-IgE syndrome in skin of color children. Case-series: Our study had six children of Hyper-IgE syndrome aged twomonths to tenyears. All had onset in first ten months of life except one with a late-onset at two years. All had recurrent eczematoid rash, which responded poorly to conventional treatment, secondary infection, multiple episodes of hospitalisation for pulmonary infection, and raised serum IgE levels. One case had occasional vesicles, bullae, and crusted plaques over both the extremities. Genetic study was possible in only one of them who was found to have pathogenic homozygous deletions of exon-15 to 18 in DOCK8 gene following which he underwent bone marrow transplant (BMT), however, succumbed to lower respiratory tract infection two months after BMT and rest of them received multiple courses of antibiotics, oral/ topical steroids, and cyclosporine intermittently with variable response. Discussion: Our study highlights various characteristics, presentation, and management of this rare syndrome in children. Knowledge of these manifestations in skin of color will facilitate early identification and contribute to optimal care of the patients as representative data on the same is limited in literature.

Keywords: absolute eosinophil count, atopic dermatitis, eczematous rash, hyper-immunoglobulin E syndrome, pulmonary infection, serum IgE, skin of color

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Authors: Tshipeng Mwenze, Charles Okujeni, Abdi Siad, Russel Bailie, Dirk Frei, Marcelene Voigt, Petrus Le Roux

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The Platreef is a platinum group element (PGE) deposit in the northern limb of the Bushveld Igneous Complex (BIC) which was emplaced as a series of mafic and ultramafic sills between the Main Zone (MZ) and the country rocks. The PGE mineralisation in the Platreef is hosted in different rock types, and its distribution and style vary with depth and along strike. This study contributes towards understanding the processes involved in the genesis of the Platreef. Twenty-four Platreef (2 harzburgites, 4 olivine pyroxenites, 17 feldspathic pyroxenites and 1 gabbronorite) and few MZ (1 gabbronorite and 1 leucogabbronorite) quarter core samples were collected from four drill cores (e.g., TN754, TN200, SS339, and OY482) and analysed for whole-rock Sr-Nd isotope data. The results show positive ɛNd values (+3.53 to +7.51) for harzburgites suggesting their parental magmas derived from the depleted Mantle. The remaining Platreef rocks have negative ɛNd values (-2.91 to -22.88) and show significant variations in Sr-Nd isotopic compositions. The first group of Platreef samples has relatively high isotopic compositions (ɛNd= -2.91 to -5.68; ⁸⁷Sr/⁸⁶Sri= 0.709177 - 0.711998). The second group of Platreef samples has Sr ratios (⁸⁷Sr/⁸⁶Sri= 0.709816-0.712106) overlapping with samples of the first group but slightly lower ɛNd values (-7.44 to -8.39). Lastly, the third group of Platreef samples has low ɛNd values (-10.82 to -14.32) and low Sr ratios (⁸⁷Sr/⁸⁶Sri= 0.707545-0.710042) than those from samples of the two Platreef groups mentioned above. There is, however, a Platreef sample with ɛNd value (-5.26) in range with the Platreef samples of the first group, but its Sr ratio (0.707281) is the lowest even when compared to samples of the third Platreef group. There are also five other Platreef samples which have either anomalous ɛNd or Sr ratios which make it difficult to assess their isotopic compositions relative to other samples. These isotopic variations for the Platreef samples indicate both multiple sources and multiple magma chambers where varying crustal contamination styles have operated during the evolution of these magmas prior their emplacements into the Platreef setting as sills. Furthermore, the MZ rocks have different Sr-Nd isotopic compositions (For OY482 gabbronorite [ɛNd= +0.65; ⁸⁷Sr/⁸⁶Sri= 0.711746]; for TN754 leucogabbronorite [ɛNd= -7.44; ⁸⁷Sr/⁸⁶Sri= 0.709322]) which do not only indicate different MZ magma chambers, but also different magmas from those of the Platreef. Although the Platreef is still considered a single stratigraphic unit in the northern limb of the BIC, its genesis involved multiple magmatic processes which evolved independently from each other.

Keywords: crustal contamination styles, magma chambers, magma sources, multiple sills emplacement

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Authors: Ian Walmsley

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The aim of this paper is to explore the influence of time and temporality on the experience of coming off heroin in prison. The presentation draws on qualitative data collected during a small-scale pilot study of the role of self-care in the process of coming off drugs in prison. Time and temporality emerged as a key theme in the interview transcripts. Drug dependent prisoners experience of time in prison has not been recognized in the research literature. Instead, the literature on prison time typically views prisoners as a homogenous group or tends to focus on the influence of aging and gender on prison time. Furthermore, there is a tendency in the literature on prison drug treatment and recovery to conceptualize drug dependent prisoners as passive recipients of prison healthcare, rather than active agents. In building on these gaps, this paper argues that drug dependent prisoners experience multiple temporalities which involve an interaction between the body-times of the drug dependent prisoner and the economy of time in prison. One consequence of this interaction is the feeling that they are doing, at this point in their prison sentence, double prison time. The second part of the argument is that time and temporality were a means through which they governed their withdrawing bodies. In addition, this paper will comment on the challenges of prison research in England.

Keywords: heroin withdrawal, time and temporality, prison, body

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Authors: Yi Shan Cheng, Ya Hui Yeh, Hsiao Wen Hsu

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Delirium is an acute state of confusion, which is mainly the result of the interaction of many factors, including: age>65 years, comorbidity, cognitive function and visual/auditory impairment, dehydration, pain, sleep disorder, pipeline retention, general anesthesia and major surgery… etc. Researches show the prevalence of delirium in hospitalized elderly patients over 50%. If it doesn't improve in time, may cause cognitive decline or impairment, not only prolong the length of hospital stay but also increase mortality. Some studies have shown that multiple nonpharmacological interventions are the most effective and common strategies, which are reorientation, early mobility, promoting sleep and nutritional support (including water intake), could improve or prevent delirium in the hospitalized elderly. In Taiwan, only one research to compare the delirium incidence of the older patients who have received orthopedic surgery between multi-nonpharmacological interventions and general routine care. Therefore, the purpose of this study is to address the prevention or improvement of delirium incidence density in medical hospitalized elderly, provide clinical nurses as a reference for clinical implementation, and develop follow-up related research. This study is a quasi-experimental design using purposive sampling. Samples are from two wards: the geriatric ward and the general medicine ward at a medical center in central Taiwan. The sample size estimated at least 100, and then the data will be collected through a self-administered structured questionnaire, including: demographic and professional evaluation items. Case recruiting from 5/13/2023. The research results will be analyzed by SPSS for Windows 22.0 software, including descriptive statistics and inferential statistics: logistic regression、Generalized Estimating Equation(GEE)、multivariate analysis of variance(MANOVA).

Keywords: multiple nonpharmacological interventions, hospitalized elderly, delirium incidence, delirium

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Authors: Mahshid Hatamzad, Geanette Polanco

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Improving the performance of Winter Road Maintenance (WRM) can increase the traffic safety and reduce the cost as well as environmental impacts. This study evaluates the efficiency of WRM technique, named salting, in the Arctic area by using Data Envelopment Analysis (DEA), which is a non-parametric linear method to measure the efficiencies of decision-making units (DMUs) based on handling multiple inputs and multiple outputs at the same time that their associated weights are not known. Here, roads are considered as DMUs for which the efficiency must be determined. The three input variables considered are traffic flow, road area and WRM cost. In addition, the two output variables included are level of safety in the roads and environment impacts resulted from WRM, which is also considered as an uncontrollable factor in the second scenario. The results show the performance of DMUs from the most efficient WRM to the inefficient/least efficient one and this information provides decision makers with technical support and the required suggested improvements for inefficient WRM, in order to achieve a cost-effective WRM and a safe road transportation during wintertime in the Arctic areas.

Keywords: environmental impacts, DEA, risk and safety, WRM

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Authors: Sanela Slavkovic, Congor Nad, Spela Golubovic

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Background: The diagnosis of multiple sclerosis (MS) is a major life challenge and has repercussions on all aspects of the daily functioning of those attained by it – personal activities, social participation, and quality of life. Regular follow-up of only the neurological status is not informative enough so that it could provide data on the sort of support and rehabilitation that is required. Objective: The aim of this study was to establish the current level of functioning of persons attained by MS and the factors that influence it. Methods: The study was conducted in Serbia, on a sample of 108 persons with relapse-remitting form of MS, aged 20 to 53 (mean 39.86 years; SD 8.20 years). All participants were fully ambulatory. Methods applied in the study include Expanded Disability Status Scale-EDSS and World Health Organization Disability Assessment Schedule, WHODAS 2.0 (36-item version, self-administered). Results: Participants were found to experience the most problems in the domains of Participation, Mobility, Life activities and Cognition. The least difficulties were found in the domain of Self-care. Symptom duration was the only control variable with a significant partial contribution to the prediction of the WHODAS scale score (β=0.30, p < 0.05). The total EDSS score correlated with the total WHODAS 2.0 score (r=0.34, p=0.00). Statistically significant differences in the domain of EDSS 0-5.5 were found within categories (0-1.5; 2-3.5; 4-5.5). The more pronounced a participant’s EDSS score was, although not indicative of large changes in the neurological status, the more apparent the changes in the functional domain, i.e. in all areas covered by WHODAS 2.0. Pyramidal (β=0.34, p < 0.05) and Bowel and bladder (β=0.24, p < 0.05) functional systems were found to have a significant partial contribution to the prediction of the WHODAS score. Conclusion: Measuring functioning and disability is important in the follow-up of persons suffering from MS in order to plan rehabilitation and define areas in which additional support is needed.

Keywords: disability, functionality, multiple sclerosis, rehabilitation

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Authors: Gopalasingam Daisan

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Landmines and ammunitions have been creating a significant threat to the people and animals, after the war, the landmines remain in the land and it plays a vital role in civilian’s security. Especially the Children are at the highest risk because they are curious. After all, an unexploded bomb can look like a tempting toy to an inquisitive child. The initial step of designing the UAS (Unmanned Aircraft Systems) for landmine detection is to choose an appropriate and effective sensor to locate the landmines and other unexploded ammunitions. The sensor weight and other components related to the sensor supporting device’s weight are taken as a payload weight. The mission requirement is to find the landmines in a particular area by making a proper path that will cover all the vicinity in the desired area. The weight estimation of the UAV (Unmanned Aerial Vehicle) can be estimated by various techniques discovered previously with good accuracy at the first phase of the design. The next crucial part of the design is to calculate the power requirement and the wing loading calculations. The matching plot techniques are used to determine the thrust-to-weight ratio, and this technique makes this process not only easiest but also precisely. The wing loading can be calculated easily from the stall equation. After these calculations, the wing area is determined from the wing loading equation and the required power is calculated from the thrust to weight ratio calculations. According to the power requirement, an appropriate engine can be selected from the available engine from the market. And the wing geometric parameter is chosen based on the conceptual sketch. The important steps in the wing design to choose proper aerofoil and which will ensure to create sufficient lift coefficient to satisfy the requirements. The next component is the tail; the tail area and other related parameters can be estimated or calculated to counteract the effect of the wing pitching moment. As the vertical tail design depends on many parameters, the initial sizing only can be done in this phase. The fuselage is another major component, which is selected based on the slenderness ratio, and also the shape is determined on the sensor size to fit it under the fuselage. The landing gear is one of the important components which is selected based on the controllability and stability requirements. The minimum and maximum wheel track and wheelbase can be determined based on the crosswind and overturn angle requirements. The minor components of the landing gear design and estimation are not the focus of this project. Another important task is to calculate the weight of the major components and it is going to be estimated using empirical relations and also the mass is added to each such component. The CG and moment of inertia are also determined to each component separately. The sensitivity of the weight calculation is taken into consideration to avoid extra material requirements and also reduce the cost of the design. Finally, the aircraft performance is calculated, especially the V-n (velocity and load factor) diagram for different flight conditions such as not disturbed and with gust velocity.

Keywords: landmine, UAS, matching plot, optimization

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Authors: Sirapop Nuannimnoi, Hojjat Baghban, Ching-Yao Huang

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Over the past decade, with the fast development of blockchain technology since the birth of Bitcoin, there has been a massive increase in the usage of Cryptocurrencies. Cryptocurrencies are not seen as an investment opportunity due to the market’s erratic behavior and high price volatility. With the recent success of deep reinforcement learning (DRL), portfolio management can be modeled and automated. In this paper, we propose a novel DRL-based multi-agent system to automatically make proper trading decisions on multiple cryptocurrencies and gain profits in the highly volatile cryptocurrency market. We also extend this multi-agent system with horizontal federated transfer learning for better adapting to the inclusion of new cryptocurrencies in our portfolio; therefore, we can, through the concept of diversification, maximize our profits and minimize the trading risks. Experimental results through multiple simulation scenarios reveal that this proposed algorithmic trading system can offer three promising key advantages over other systems, including maximized profits, minimized risks, and adaptability.

Keywords: cryptocurrency portfolio management, algorithmic trading, federated learning, multi-agent reinforcement learning

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Authors: Sangeeta Palekar, Neeraj Rangwani, Akash Poddar, Jayu Kalambe

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The bio-medical analysis is an indispensable procedure for identifying health-related diseases like diabetes. Monitoring the glucose level in our body regularly helps us identify hyperglycemia and hypoglycemia, which can cause severe medical problems like nerve damage or kidney diseases. This paper presents a method for predicting the glucose concentration in blood samples using image processing and machine learning algorithms. The glucose solution is prepared by the glucose oxidase (GOD) and peroxidase (POD) method. An experimental database is generated based on the colorimetric technique. The image of the glucose solution is captured by the raspberry pi camera and analyzed using image processing by extracting the RGB, HSV, LUX color space values. Regression algorithms like multiple linear regression, decision tree, RandomForest, and XGBoost were used to predict the unknown glucose concentration. The multiple linear regression algorithm predicts the results with 97% accuracy. The image processing and machine learning-based approach reduce the hardware complexities of existing platforms.

Keywords: artificial intelligence glucose detection, glucose oxidase, peroxidase, image processing, machine learning

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Authors: Madiha Liaqat, Rehan Ahmed Khan, Shahid Kamal

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Multiple imputation with delta adjustment is a versatile and transparent technique for addressing univariate missing data in the presence of various missing mechanisms. This approach allows for the exploration of sensitivity to the missing-at-random (MAR) assumption. In this review, we outline the delta-adjustment procedure and illustrate its application for assessing the sensitivity to deviations from the MAR assumption. By examining diverse missingness scenarios and conducting sensitivity analyses, we gain valuable insights into the implications of missing data on our analyses, enhancing the reliability of our study's conclusions. In our study, we focused on assessing logPSA, a continuous biomarker in incomplete prostate cancer data, to examine the robustness of conclusions against plausible departures from the MAR assumption. We introduced several approaches for conducting sensitivity analyses, illustrating their application within the pattern mixture model (PMM) under the delta adjustment framework. This proposed approach effectively handles missing data, particularly loss to follow-up.

Keywords: loss to follow-up, incomplete response, multiple imputation, sensitivity analysis, prostate cancer

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Authors: M. I. Youssef, A. E. Emam, M. Abd Elghany

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This paper evaluates the bit error rate (BER) performance of MIMO-OFDM communication system. MIMO system uses multiple transmitting and receiving antennas with different coding techniques to either enhance the transmission diversity or spatial multiplexing gain. Utilizing alamouti algorithm were the same information transmitted over multiple antennas at different time intervals and then collected again at the receivers to minimize the probability of error, combat fading and thus improve the received signal to noise ratio. While utilizing V-BLAST algorithm, the transmitted signals are divided into different transmitting channels and transferred over the channel to be received by different receiving antennas to increase the transmitted data rate and achieve higher throughput. The paper provides a study of different diversity gain coding schemes and spatial multiplexing coding for MIMO systems. A comparison of various channels' estimation and equalization techniques are given. The simulation is implemented using MATLAB, and the results had shown the performance of transmission models under different channel environments.

Keywords: MIMO communication, BER, space codes, channels, alamouti, V-BLAST

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Authors: B. E. Narkhede, S. K. Mahajan, B. T. Patil, R. D. Raut

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Information quality is a major performance measure for an Enterprise Resource Planning (ERP) system of any firm. This study identifies various critical success factors of information quality. The effect of various critical success factors like project management, reengineering efforts and interdepartmental communications on information quality is analyzed using a multiple regression model. Here quantitative data are collected from respondents from various firms through structured questionnaire for assessment of the information quality, project management, reengineering efforts and interdepartmental communications. The validity and reliability of the data are ensured using techniques like factor analysis, computing of Cronbach’s alpha. This study gives relative importance of each of the critical success factors. The findings suggest that among the various factors influencing information quality careful reengineering efforts are the most influencing factor. This paper gives clear insight to managers and practitioners regarding the relative importance of critical success factors influencing information quality so that they can formulate a strategy at the beginning of ERP system implementation.

Keywords: Enterprise Resource Planning (ERP), information systems (IS), multiple regression, information quality

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Authors: John Hawkins, Graham Burton

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Contextual targeting is a common strategy for advertising that places marketing messages in media locations that are expected to be aligned with the target audience. There are multiple major challenges to contextual targeting: the ideal categorisation scheme needs to be known, as well as the most appropriate subsections of that scheme for a given campaign or creative. In addition, the campaign reach is typically limited when targeting becomes narrow, so a balance must be struck between requirements. Finally, refinement of the process is limited by the use of evaluation methods that are either rapid but non-specific (click through rates), or reliable but slow and costly (conversions or brand recall studies). In this study we evaluate the use of attention measurement as a technique for understanding the performance of targeting on the basis of specific contextual topics. We perform the analysis using a large scale dataset of impressions categorised using the iAB V2.0 taxonomy. We evaluate multiple levels of the categorisation hierarchy, using categories at different positions within an initial creative specific ranking. The results illustrate that measuring attention time is an affective signal for the performance of a specific creative within a specific context. Performance is sustained across a ranking of categories from one period to another.

Keywords: contextual targeting, digital advertising, attention measurement, marketing performance

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Authors: Abderrahmane Khiat, Moussa Benaissa

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Due to the increasing number of sources of information available on the web and their distribution and heterogeneity, ontology alignment became a very important and inevitable problem to ensure semantic interoperability. Instance-based ontology alignment is based on the comparison of the extensions of concepts; and represents a very promising technique to find semantic correspondences between entities of different ontologies. In practice, two situations may arise: ontologies that share many common instances and ontologies that share few or do not share common instances. In this paper, we describe an approach to manage the latter case. This approach exploits the reasoning on ontologies in order to create a corpus of common instances. We show that it is theoretically powerful because it is based on description logics and very useful in practice. We present the experimental results obtained by running our approach on ontologies of OAEI 2012 benchmark test. The results show the performance of our approach.

Keywords: description logic inference, instance-based ontology alignment, semantic interoperability, semantic web

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Authors: Alexiou Dimitra, Fragkaki Maria

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The objective of the paper is the study of geographic, economic and educational variables and their contribution to determine the position of each member-state among the EU-28 countries based on the values of seven variables as given by Eurostat. The Data Analysis methods of Multiple Factorial Correspondence Analysis (MFCA) Principal Component Analysis and Factor Analysis have been used. The cross tabulation tables of data consist of the values of seven variables for the 28 countries for 2014. The data are manipulated using the CHIC Analysis V 1.1 software package. The results of this program using MFCA and Ascending Hierarchical Classification are given in arithmetic and graphical form. For comparison reasons with the same data the Factor procedure of Statistical package IBM SPSS 20 has been used. The numerical and graphical results presented with tables and graphs, demonstrate the agreement between the two methods. The most important result is the study of the relation between the 28 countries and the position of each country in groups or clouds, which are formed according to the values of the corresponding variables.

Keywords: Multiple Factorial Correspondence Analysis, Principal Component Analysis, Factor Analysis, E.U.-28 countries, Statistical package IBM SPSS 20, CHIC Analysis V 1.1 Software, Eurostat.eu Statistics

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Authors: Betsy Hannam

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Background: Staff nurses felt overwhelmed to learn new skills or complete competencies during their shift. Med/Surg units need to provide dedicated, uninterrupted time to complete training and mandatory competencies and practice skills. Purpose: To improve nurse satisfaction and competence by creating a Skills Day with uninterrupted time to complete competencies, brush up on skills, and evaluate skills learned through pre- and post-tests. Methods: The USL and CNL interviewed nurses to obtain input regarding skills needing reinforcement and included mandatory competencies relevant to Med/Surg to create the Skills Day agenda. Content experts from multiple disciplines were invited to educate staff to help address knowledge gaps. To increase attendance, multiple class days were offered. Results: 2018 Skills Day was held for an inpatient unit with 95% participation (n=35 out of 37RNs). The average pretest score, comprised of content questions from topics discussed, was 57%, and post test scoresaveraged 80%. 94% of test scores improved or remained the same. RNs were given an evaluation at the end of the day, where100% of staff noted Skills Day as beneficial, and 97% requested to repeat next year. Another Med/Surg unit asked to join Skills Day in 2019. In 2019, with 89% participation (n=57 out 64 RNs), the average pretest score was 68%, and the average post test score was 85%. 97% of scores improved or remained the same. 98% reported the class as beneficial, and 96% requested to repeat next year. Skills Day 2020-2022 on hold due to COVID. Looking forward to Skills Day 2023. Conclusion: Skills Day allows nurses to maintain competencies and improve knowledge in areas of interest without the stress of a patient assignment. Having unit leaders organize Skills Day, with the involvement of content experts from multiple disciplines, showed to be a successful and innovative team approach to support professional development.

Keywords: education, competency, skills day, medical/surgical

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Authors: Rohini Hariharan, Yazhini R, Bhamidipati Naga Shrikarti

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In the realm of cricket, particularly within the context of the Indian Premier League (IPL), the ability to predict team scores accurately holds significant importance for both cricket enthusiasts and stakeholders alike. This paper presents a comprehensive study on IPL score prediction utilizing various machine learning algorithms, including Support Vector Machines (SVM), XGBoost, Multiple Regression, Linear Regression, K-nearest neighbors (KNN), and Random Forest. Through meticulous data preprocessing, feature engineering, and model selection, we aimed to develop a robust predictive framework capable of forecasting team scores with high precision. Our experimentation involved the analysis of historical IPL match data encompassing diverse match and player statistics. Leveraging this data, we employed state-of-the-art machine learning techniques to train and evaluate the performance of each model. Notably, Multiple Regression emerged as the top-performing algorithm, achieving an impressive accuracy of 77.19% and a precision of 54.05% (within a threshold of +/- 10 runs). This research contributes to the advancement of sports analytics by demonstrating the efficacy of machine learning in predicting IPL team scores. The findings underscore the potential of advanced predictive modeling techniques to provide valuable insights for cricket enthusiasts, team management, and betting agencies. Additionally, this study serves as a benchmark for future research endeavors aimed at enhancing the accuracy and interpretability of IPL score prediction models.

Keywords: indian premier league (IPL), cricket, score prediction, machine learning, support vector machines (SVM), xgboost, multiple regression, linear regression, k-nearest neighbors (KNN), random forest, sports analytics

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Authors: Ghazal Faraj, András Micsik

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Shapes Constraint Language (SHACL), a World Wide Web Consortium (W3C) language, provides well-defined shapes and RDF graphs, named "shape graphs". These shape graphs validate other resource description framework (RDF) graphs which are called "data graphs". The structural features of SHACL permit generating a variety of conditions to evaluate string matching patterns, value type, and other constraints. Moreover, the framework of SHACL supports high-level validation by expressing more complex conditions in languages such as SPARQL protocol and RDF Query Language (SPARQL). SHACL includes two parts: SHACL Core and SHACL-SPARQL. SHACL Core includes all shapes that cover the most frequent constraint components. While SHACL-SPARQL is an extension that allows SHACL to express more complex customized constraints. Validating the efficacy of dataset mapping is an essential component of reconciled data mechanisms, as the enhancement of different datasets linking is a sustainable process. The conventional validation methods are the semantic reasoner and SPARQL queries. The former checks formalization errors and data type inconsistency, while the latter validates the data contradiction. After executing SPARQL queries, the retrieved information needs to be checked manually by an expert. However, this methodology is time-consuming and inaccurate as it does not test the mapping model comprehensively. Therefore, there is a serious need to expose a new methodology that covers the entire validation aspects for linking and mapping diverse datasets. Our goal is to conduct a new approach to achieve optimal validation outcomes. The first step towards this goal is implementing SHACL to validate the mapping between the International Committee for Documentation (CIDOC) conceptual reference model (CRM) and one of its ontologies. To initiate this project successfully, a thorough understanding of both source and target ontologies was required. Subsequently, the proper environment to run SHACL and its shape graphs were determined. As a case study, we performed SHACL over a CIDOC-CRM dataset after running a Pellet reasoner via the Protégé program. The applied validation falls under multiple categories: a) data type validation which constrains whether the source data is mapped to the correct data type. For instance, checking whether a birthdate is assigned to xsd:datetime and linked to Person entity via crm:P82a_begin_of_the_begin property. b) Data integrity validation which detects inconsistent data. For instance, inspecting whether a person's birthdate occurred before any of the linked event creation dates. The expected results of our work are: 1) highlighting validation techniques and categories, 2) selecting the most suitable techniques for those various categories of validation tasks. The next plan is to establish a comprehensive validation model and generate SHACL shapes automatically.

Keywords: SHACL, CIDOC-CRM, SPARQL, validation of ontology mapping

Procedia PDF Downloads 236