Search results for: distributed algorithms

Authors: Krishna Mohan Bathula, Fatou Bintou Loucoubar, FNU Kaleemunnisa, Christelle Scharff, Mark Anthony De Castro

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Voice recognition algorithms such as automatic speech recognition and text-to-speech systems with African languages can play an important role in bridging the digital divide of Artificial Intelligence in Africa, contributing to the establishment of a fully inclusive information society. This paper proposes a Deep Learning model that can classify the user responses as inputs for an interactive voice response system. A dataset with Wolof language words ‘yes’ and ‘no’ is collected as audio recordings. A two stage Data Augmentation approach is adopted for enhancing the dataset size required by the deep neural network. Data preprocessing and feature engineering with Mel-Frequency Cepstral Coefficients are implemented. Convolutional Neural Networks (CNNs) have proven to be very powerful in image classification and are promising for audio processing when sounds are transformed into spectra. For performing voice response classification, the recordings are transformed into sound frequency feature spectra and then applied image classification methodology using a deep CNN model. The inference model of this trained and reusable Wolof voice response recognition system can be integrated with many applications associated with both web and mobile platforms.

Keywords: automatic speech recognition, interactive voice response, voice response recognition, wolof word classification

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Authors: Fakhrullah Sarwari, Hiroko Ono

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Kabul city has suffered a lot in 40 years of conflict of civil war and “The war on terror”. After the invasion of Afghanistan by the United States of America and its allies in 2001, the Taliban was removed from operational power, but The Taliban and other terrorist groups remained in remote areas of the country, they started suicide attacks and bombings. Hence to protect from these attacks officials surrounded their office buildings and houses with concrete blast walls. It gives a bad landscape to the city and creates traffic congestions. Our research contains; questionnaire, reviewing Kabul Municipality documents and literature review. Questionnaires were distributed to Kabul citizens to find out how people feel by seeing the T-Walls on Kabul streets? And what problems they face with T-Walls. “The T-Walls pull down commission” of Kabul Municipality documents were reviewed to find out what caused the failure of this commission. A literature review has been done to compare Kabul with Washington D.C on how they designed the city against terrorism threat without turning the cities into lock down. Bogota city of Columbia urban happiness movement is reviewed and compared with Kabul. The finding of research revealed that citizens of Kabul want security but not at the expense of public realm and creating the architecture of fear. It also indicates that increasing the T-walls do not give secure feeling but instead; it increases terror, hatred and affect people’s optimism. At the end, a series of recommendation is suggested on the issue.

Keywords: anti-terror design, Kabul, T-Walls, urban happiness

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Authors: Radovan Bures, Madgalena Streckova, Maria Faberova, Pavel Kurek

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Composite material based on Fe3Si micro-particles and Mn-Zn nano-ferrite was prepared using powder metallurgy technology. The sol-gel followed by autocombustion process was used for synthesis of Mn0.8Zn0.2Fe2O4 ferrite. 3 wt.% of mechanically milled ferrite was mixed with Fe3Si powder alloy. Mixed micro-nano powder system was homogenized by the Resonant Acoustic Mixing using ResodynLabRAM Mixer. This non-invasive homogenization technique was used to preserve spherical morphology of Fe3Si powder particles. Uniaxial cold pressing in the closed die at pressure 600 MPa was applied to obtain a compact sample. Microwave sintering of green compact was realized at 800°C, 20 minutes, in air. Density of the powders and composite was measured by Hepycnometry. Impulse excitation method was used to measure elastic properties of sintered composite. Mechanical properties were evaluated by measurement of transverse rupture strength (TRS) and Vickers hardness (HV). Resistivity was measured by 4 point probe method. Ferrite phase distribution in volume of the composite was documented by metallographic analysis. It has been found that nano-ferrite particle distributed among micro- particles of Fe3Si powder alloy led to high relative density (~93%) and suitable mechanical properties (TRS >100 MPa, HV ~1GPa, E-modulus ~140 GPa) of the composite. High electric resistivity (R~6.7 ohm.cm) of prepared composite indicate their potential application as soft magnetic material at medium and high frequencies.

Keywords: micro- and nano-composite, soft magnetic materials, microwave sintering, mechanical and electric properties

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Authors: Julius Onyancha, Valentina Plekhanova

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One of the significant issues facing web users is the amount of noise in web data which hinders the process of finding useful information in relation to their dynamic interests. Current research works consider noise as any data that does not form part of the main web page and propose noise web data reduction tools which mainly focus on eliminating noise in relation to the content and layout of web data. This paper argues that not all data that form part of the main web page is of a user interest and not all noise data is actually noise to a given user. Therefore, learning of noise web data allocated to the user requests ensures not only reduction of noisiness level in a web user profile, but also a decrease in the loss of useful information hence improves the quality of a web user profile. Noise Web Data Learning (NWDL) tool/algorithm capable of learning noise web data in web user profile is proposed. The proposed work considers elimination of noise data in relation to dynamic user interest. In order to validate the performance of the proposed work, an experimental design setup is presented. The results obtained are compared with the current algorithms applied in noise web data reduction process. The experimental results show that the proposed work considers the dynamic change of user interest prior to elimination of noise data. The proposed work contributes towards improving the quality of a web user profile by reducing the amount of useful information eliminated as noise.

Keywords: web log data, web user profile, user interest, noise web data learning, machine learning

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Authors: Ferdinando Montemari, Antonio Vitale, Nicola Genito, Luca Garbarino, Urbano Tancredi, Domenico Accardo, Michele Grassi, Giancarmine Fasano, Anna Elena Tirri

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The development of guidance, navigation and control algorithms and avionic procedures requires the disposability of suitable analysis and verification tools, such as simulation environments, which support the design process and allow detecting potential problems prior to the flight test, in order to make new technologies available at reduced cost, time and risk. This paper presents a simulation environment for avionic software development and qualification, especially aimed at equipment for general aviation aircrafts and unmanned aerial systems. The simulation environment includes models for short and medium-range radio-navigation aids, flight assistance systems, and ground control stations. All the software modules are able to simulate the modeled systems both in fast-time and real-time tests, and were implemented following component oriented modeling techniques and requirement based approach. The paper describes the specific models features, the architectures of the implemented software systems and its validation process. Performed validation tests highlighted the capability of the simulation environment to guarantee in real-time the required functionalities and performance of the simulated avionics systems, as well as to reproduce the interaction between these systems, thus permitting a realistic and reliable simulation of a complete mission scenario.

Keywords: ADS-B, avionics, NAVAIDs, real-time simulation, TCAS, UAS ground control station

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Authors: Aneta Nitsch-Osuch, Katarzyna Zycinska, Ewa Gyrczuk, Agnieszka Topczewska-Cabanek, Kazimierz Wardyn

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Introduction: Influenza is an important clinical and epidemiological problem and should be considered as a possible nosocomial infection. The aim of the study was to determine the influenza vaccine coverage rates among Polish nurses and to find out drivers and barriers for influenza vaccination among this group of health care workers (HCWs). Material and methods: The self- fulfilled survey with 26 questions about the knowledge, perception, and influenza coverage rates was distributed among 461 nurses. Results: Only 15% of nurses were vaccinated against influenza in the consecutive seasons. The majority (75%) of the regularly vaccinated nurses were ambulatory careworkers. The difference between the number of vaccinated hospitals and ambulatory care nurses was statistically significant (p < 0.05). The main motivating factors for an influenza vaccination were: a fear of the illness and its complications (97%) and a free of charge vaccine available at the workplace (87%). Ambulatory care nurses more often declared that they were vaccinated mainly to protect themselves while hospital care nurses more often declared the will to protect their patients, these differences in the perception and attitudes to an influenza vaccination among hospital and ambulatory care nurses were statistically significant (p < 0.05). The main barriers for an influenza vaccination among the nursing staff were: a lack of reimbursement of the vaccine (95%), a lack of insufficient knowledge about the effectiveness, and safety of the influenza vaccine (54%). The ambulatory care nurses more often found influenza vaccination as the ethical duty compared to hospital care nurses (p < 0.05). Conclusions: The influenza vaccine coverage rates among the Polish nurses are low and must be improved in the future. More educational activities dedicated to HCWs may result in the increased awareness of influenza vaccination benefits for both medical professionals and patients.

Keywords: influenza, vaccination, nurses, ambulatory careworkers

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Authors: Xingxing Peng

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With the development of society, there are more and more online communication scenarios such as teleconference and online education. In the process of conference communication, the quality of voice communication is a very important part, and noise may cause the communication effect of participants to be greatly reduced. Therefore, voice noise reduction has an important impact on scenarios such as voice calls. This research focuses on the key technologies of the sound transmission process. The purpose is to maintain the audio quality to the maximum so that the listener can hear clearer and smoother sound. Firstly, to solve the problem that the traditional speech enhancement algorithm is not ideal when dealing with non-stationary noise, an adaptive speech noise reduction algorithm is studied in this paper. Traditional noise estimation methods are mainly used to deal with stationary noise. In this chapter, we study the spectral characteristics of different noise types, especially the characteristics of non-stationary Burst noise, and design a noise estimator module to deal with non-stationary noise. Noise features are extracted from non-speech segments, and the noise estimation module is adjusted in real time according to different noise characteristics. This adaptive algorithm can enhance speech according to different noise characteristics, improve the performance of traditional algorithms to deal with non-stationary noise, so as to achieve better enhancement effect. The experimental results show that the algorithm proposed in this chapter is effective and can better adapt to different types of noise, so as to obtain better speech enhancement effect.

Keywords: speech noise reduction, speech enhancement, self-adaptation, Wiener filter algorithm

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Authors: Mohamed Ahmed Khalil

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The number of deep well anode ground beds (GBs) have been retrieved due to unoperated anode chains. New identical magnetite anode chains (MAC) have been installed at Raslanuf complex impressed current Cathodic protection (ICCP) system, distributed at different plants (Utility, ethylene and polyethylene). All problems associated with retrieving and installation of MACs have been discussed, rectified and presented. All GB-associated severely corroded wellhead casings were well maintained and/or replaced by new fabricated and modified ones. The main cause of the wellhead casing's severe internal corrosion was discussed and the conducted remedy action to overcome future corrosion problems is presented. All GB-connected anode junction boxes (AJBs) and shunts were closely inspected, maintained and necessary replacement and/or modifications were carried out on shunts. All damaged GB concrete foundations (CF) have been inspected and completely replaced. All GB-associated Transformer-Rectifiers Units (TRU) were subjected to thorough inspection and necessary maintenance was performed on each individual TRU. After completion of all MACs and TRU maintenance activities, each cathodic protection station (CPS) has been re-operated, alternative current (AC), direct current (DC), voltage and structure to soil potential (S/P) measurements have been conducted, recorded and all obtained test results are presented. DC current outputs have been adjusted and DC current outputs of each MAC have been recorded for each GB AJB.

Keywords: magnetite anodes, deep well, ground beds, cathodic protection, transformer rectifier, impressed current, junction boxes

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Authors: Adel. M. Sharaf, Foad H. Gandoman

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Nowadays, Photovoltaic-PV Farms/ Parks and large PV-Smart Grid Interface Schemes are emerging and commonly utilized in Renewable Energy distributed generation. However, PV-hybrid-Dc-Ac Schemes using interface power electronic converters usually has negative impact on power quality and stabilization of modern electrical network under load excursions and network fault conditions in smart grid. Consequently, robust FACTS based interface schemes are required to ensure efficient energy utilization and stabilization of bus voltages as well as limiting switching/fault onrush current condition. FACTS devices are also used in smart grid-Battery Interface and Storage Schemes with PV-Battery Storage hybrid systems as an elegant alternative to renewable energy utilization with backup battery storage for electric utility energy and demand side management to provide needed energy and power capacity under heavy load conditions. The paper presents a robust interface PV-Li-Ion Battery Storage Interface Scheme for Distribution/Utilization Low Voltage Interface using FACTS stabilization enhancement and dynamic maximum PV power tracking controllers. Digital simulation and validation of the proposed scheme is done using MATLAB/Simulink software environment for Low Voltage- Distribution/Utilization system feeding a hybrid Linear-Motorized inrush and nonlinear type loads from a DC-AC Interface VSC-6-pulse Inverter Fed from the PV Park/Farm with a back-up Li-Ion Storage Battery.

Keywords: AC FACTS, smart grid, stabilization, PV-battery storage, Switched Filter-Compensation (SFC)

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Authors: Michelle Odudu

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Ghana and Nigeria are known for their high levels of Occupational Fraud in public offices. The governments of both countries have emphasised their commitment to reducing the losses caused to the state by pledging their allegiance to the counter-fraud agencies to help tackle Occupational Fraud. Yet it seems that the prosecution of such cases is ineffective as high-profile fraudsters can operate with immunity and their cases remain unprosecuted. This research project was based on in-depth examinations of 50 occupational fraud cases involving high-profile individuals in both countries. In doing so, it established the characteristics of those who were prosecuted; the extent to which prosecutions were effectively managed; the barriers to effective prosecutions; and the similarities or differences between the occurrences in both countries. The aim of the project is to examine the practice of and barriers to prosecution of large-scale occupational fraud of those in senior public positions in Ghana and Nigeria. The study drew on the experiences of stakeholders such as defence and prosecution barristers, academics, and fraud analysts via semi-structured interviews and questionnaires. 13 interviews were conducted in Ghana and in Nigeria, where respondents were recruited using a snowball approach. Questionnaires were physically distributed: 20 of the staff at EOCO and 10 to NGO staff in Ghana; 6 and 5 came back, respectively. The empirical data collected suggests that there is no lack of will on the agencies’ part to at least commence proceedings. However, various impediments hamper a successful completion of prosecution. Challenges were more evident in Nigeria, where agencies are less effective at retrieving stolen assets and changing social norms. This is further compounded by several cultural and political factors, which create limitations leaving many cases ‘still pending’.

Keywords: comparative, prosecution, punishment, international, whitecollar, fraud

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Authors: Sergey Kopysov, Nikita Nedozhogin, Leonid Tonkov

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The article presents a parallel iterative solver for large sparse linear systems which can be used on a heterogeneous platform. Traditionally, the problem of solving linear systems does not scale well on multi-CPU/multi-GPUs clusters. For example, most of the attempts to implement the classical conjugate gradient method were at best counted in the same amount of time as the problem was enlarged. The paper proposes the pipelined variant of the conjugate gradient method (PCG), a formulation that is potentially better suited for hybrid CPU/GPU computing since it requires only one synchronization point per one iteration instead of two for standard CG. The standard and pipelined CG methods need the vector entries generated by the current GPU and other GPUs for matrix-vector products. So the communication between GPUs becomes a major performance bottleneck on multi GPU cluster. The article presents an approach to minimize the communications between parallel parts of algorithms. Additionally, computation and communication can be overlapped to reduce the impact of data exchange. Using the pipelined version of the CG method with one synchronization point, the possibility of asynchronous calculations and communications, load balancing between the CPU and GPU for solving the large linear systems allows for scalability. The algorithm is implemented with the combined use of technologies: MPI, OpenMP, and CUDA. We show that almost optimum speed up on 8-CPU/2GPU may be reached (relatively to a one GPU execution). The parallelized solver achieves a speedup of up to 5.49 times on 16 NVIDIA Tesla GPUs, as compared to one GPU.

Keywords: conjugate gradient, GPU, parallel programming, pipelined algorithm

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Authors: Thampi Rawther, Jack Cecire, Andrew Sutherland

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Introduction: CT facilitates the diagnosis of potentially life-threatening injuries and facilitates early management. There is evidence that reduced CT acquisition time reduces mortality and length of hospital stay. Currently, there are variable recommendations for ideal timing. Indeed, the NHS standard contract for a major trauma service and STAG both recommend immediate access to CT within a maximum time of 60min and appropriate reporting within 60min of the scan. At Coffs Harbour Health Campus (CHHC), a CT radiographer is on site between 8am-11pm. Aim: To investigate the average time to CT at CHHC and assess for any significant relationship between time to CT and injury severity score (ISS) or time of triage. Method: All major trauma calls between Jan 2021-Oct 2021 were audited (N=87). Patients were excluded if they went from ED to the theatre. Time to CT is defined as the time between triage to the timestamp on the first CT image. Median and interquartile range was used as a measure of central tendency as the data was not normally distributed, and Chi-square test was used to determine association. Results: The median time to CT is 51.5min (IQR 40-74). We found no relationship between time to CT and ISS (P=0.18) and time of triage to time to CT (P=0.35). We compared this to other centres such as John Hunter Hospital and Gold Coast Hospital. We found that the median CT acquisition times were 76min (IQR 52-115) and 43min, respectively. Conclusion: This shows an avenue for improvement given 35% of CT’s were >30min. Furthermore, being proactive and aware of time to CT as an important factor to trauma management can be another avenue for improvement. Based on this, we will re-audit in 12-24months to assess if any improvement has been made.

Keywords: imaging, rural surgery, trauma surgery, improvement

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Authors: Jailan Mohamed El Demerdash

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Millennials are a distinctive and dominant consumer group whose behavior, preferences and purchase decisions are broadly explored but not fully understood yet. Making up the largest market segment in the world, and in Egypt, they have the power to reinvent the hospitality industry and contribute to forming prospective demand for green hotels by showing willingness to adopting their environmental-friendly practices. The current study aims to enhance better understanding of Millennials' perception about sustainable initiatives and to increase the prediction power of their intentions regarding green hotel practices in Egypt. In doing so, the study is exploring the relation among different factors; Millennials' environmental awareness, their acceptance of green practices and their willingness to pay more for them. Millennials' profile, their preferences and environmental decision-making process are brought under light to stimulate actions of hospitality decision-makers and hoteliers. Bearing in mind that responsible consumerism is depending on understanding the different influences on consumption. The study questionnaire was composed of four sections and it was distributed to random Egyptian travelers' blogs and Facebook groups, with approximately 8000 members. Analysis of variance test (ANOVA) was used to examine the study variables. The findings indicated that Millennials' environmental awareness will not be a significant factor in their acceptance of hotel green practices, as well as, their willingness to pay more for them. However, Millennials' acceptance of the level of hotel green practices will have an impact on their willingness to pay more. Millennials were found to have a noticeable level of environmental awareness but lack commitment to tolerating hotel green practices and their associated high prices.

Keywords: millennials, environment, awareness, paying more

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Authors: Isaac Owusu Asante, Yushi Jiang, Hailin Tao

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Live streaming marketing, the new electronic commerce element, became an optional marketing channel following the COVID-19 pandemic. Many sellers have leveraged the features presented by live streaming to increase sales. Studies on live streaming have focused on gaming and consumers’ loyalty to brands through live streaming, using interview questionnaires. This study, however, was conducted to measure real-time observable interactions between consumers and sellers. Based on the affordance theory, this study conceptualized constructs representing the interactive features and examined how they drive consumers’ purchase willingness during live streaming sessions using 1238 datasets from Amazon Live, following the manual observation of transaction records. Using structural equation modeling, the ordinary least square regression suggests that live viewers, new followers, live chats, and likes positively affect purchase willingness. The Sobel and Monte Carlo tests show that new followers, live chats, and likes significantly mediate the relationship between live viewers and purchase willingness. The study introduces a new way of measuring interactions in live streaming commerce and proposes a way to manually gather data on consumer behaviors in live streaming platforms when the application programming interface (API) of such platforms does not support data mining algorithms.

Keywords: livestreaming marketing, live chats, live viewers, likes, new followers, purchase willingness

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Authors: D. S. Malatesh, K. J. Ananda, C. Ansar Kamran, K. Ganesh Udupa

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Dirofilaria repens is a mosquito-borne filarioid nematode of dogs and other carnivores and accidentally affects humans. D. repens is reported in many countries, including India. Subcutaneous dirofilariosis caused by D. repens is a zoonotic disease, widely distributed throughout Europe, Asia, and Africa, with higher prevalence reported in dogs from Sri Lanka (30-60%), Iran (61%) and Italy (21-25%). Dirofilariasis in dogs was diagnosed by detection of microfilariae in blood. Identification of different Dirofilaria species was done by using molecular methods like polymerase chain reaction (PCR). Even though many researchers reported molecular evidence of D. repens across India, to our best knowledge there is no data available on molecular diagnosis of D. repens in dogs and its zoonotic implication in Karnataka state a southern state in India. The aim of the present study was to identify the Dirofilaria species occurring in dogs from Karnataka, India. Out of 310 samples screened for the presence of microfilariae using traditional diagnostic methods, 99 (31.93%) were positive for the presence of microfilariae. Based on the morphometry, the microfilariae were identified as D. repens. For confirmation of species, the samples were subjected to PCR using pan filarial primers (DIDR-F1, DIDR-R1) for amplification of internal transcribed spacer region 2 (ITS2) of the ribosomal DNA. The PCR product of 484 base pairs on agarose gel was indicative of D. repens. Hence, a single PCR reaction using pan filarial primers can be used to differentiate filarial species found in dogs. The present study confirms that dirofilarial species occurring in dogs from Karnataka is D. repens and further sequencing studies are needed for genotypic characterization of D. repens.

Keywords: Dirofilaria repens, molecular characterization, polymerase chain reaction, Karnataka, India

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Authors: Linyu Wang, Mingjun Zhu, Jianhong Xiang, Hanyu Jiang

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Aiming at the problem that the spectral efficiency of hybrid precoding (HP) is too low in the current millimeter wave (mmWave) massive multiple input multiple output (MIMO) system, this paper proposes a digital joint equivalent channel hybrid precoding algorithm, which is based on the introduction of digital encoding matrix iteration. First, the objective function is expanded to obtain the relation equation, and the pseudo-inverse iterative function of the analog encoder is derived by using the pseudo-inverse method, which solves the problem of greatly increasing the amount of computation caused by the lack of rank of the digital encoding matrix and reduces the overall complexity of hybrid precoding. Secondly, the analog coding matrix and the millimeter-wave sparse channel matrix are combined into an equivalent channel, and then the equivalent channel is subjected to Singular Value Decomposition (SVD) to obtain a digital coding matrix, and then the derived pseudo-inverse iterative function is used to iteratively regenerate the simulated encoding matrix. The simulation results show that the proposed algorithm improves the system spectral efficiency by 10~20%compared with other algorithms and the stability is also improved.

Keywords: mmWave, massive MIMO, hybrid precoding, singular value decompositing, equivalent channel

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Authors: Keshav Raj Acharya, Thakur Silwal, Neelam C. Poudyal

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Buffer zones, the peripheral areas around the national parks and wildlife reserves, are available for the purpose of benefitting the local inhabitants by providing forest products for subsistence needs of basic forest products outside the protected areas. The forest area within the buffer zone has been managed as a buffer zone community forest (BZCF) for the last 25 years after the approval of the buffer zone management regulation 1996. With a case study of select BZCF in Bardia National Park, this study aims to analyze whether the benefit provided by BZCF is equally available to poor users among other socioeconomic classes of the users. The findings are based on the analysis of cross-sectional data involving household surveys (n=305) and key informants’ interviews (n=10) as well as office records available at different 5 buffer zone community forest user groups offices. Results indicate that despite the provisions of subsidized rates for poor; poor households were more deprived due to higher forest products price particularly, the timber price in buffer zone. Evidence also indicate that due to the increased forest coverage, the incidence of wildlife damage has also increased and impacted the poor more due to lack of land ownership as well as limited alternatives. Clear community forest management guidelines with equitable benefit sharing and compensatory mechanisms to the users of poor socioeconomic class have been identified as a solution to increase the benefit to poor users in BZCFUGs.

Keywords: crop depredation, forest products, users, wellbeing ranking

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Authors: Dongju Kim, Youngjoo Suh, Hyojin Kim, Gyeongyeong Kim

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Acoustic Emission Testing (AET) is widely used to test the structural integrity of an operational hydrogen storage container, and clustering algorithms are frequently used in pattern recognition methods to interpret AET results. However, the interpretation of AET results can vary from user to user as the tuning of the relevant parameters relies on the user's experience and knowledge of AET. Therefore, it is necessary to use a deep learning model to identify patterns in acoustic emission (AE) signal data that can be used to classify defects instead. In this paper, a deep learning-based model for classifying the types of defects in hydrogen storage tanks, using AE sensor waveforms, is proposed. As hydrogen storage tanks are commonly constructed using carbon fiber reinforced polymer composite (CFRP), a defect classification dataset is collected through a tensile test on a specimen of CFRP with an AE sensor attached. The performance of the classification model, using one-dimensional convolutional neural network (1-D CNN) and synthetic minority oversampling technique (SMOTE) data augmentation, achieved 91.09% accuracy for each defect. It is expected that the deep learning classification model in this paper, used with AET, will help in evaluating the operational safety of hydrogen storage containers.

Keywords: acoustic emission testing, carbon fiber reinforced polymer composite, one-dimensional convolutional neural network, smote data augmentation

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Authors: Nathalie Piquemal, Faiçal Zellama, Bathélemy Bolivar, Leyla Sall

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Canada defines itself as a country of immigration and a multicultural nation, ideologically, politically and programmatically (in terms of its integration practices). As such, principles of hospitality may seem, at first glance, incontestable, given the convergence of the views of the majority of Canadian politicians on the need to welcome, each year, a significant number of immigrants and to offer them the hospitality that facilitates their transition to Canadian citizenship. However, immigrants are welcomed in a Canadian societal context in which power and resources are unevenly distributed, resulting in complex social relationships between hosts and newcomers. Qualitative data obtained from newcomers in Winnipeg, Manitoba, Canada, focuses on experiences of hospitality, with special attention to host-guest social and power dynamics, contested policies on foreign credential and micro spaces of belongingin a multicultural context. The act of welcoming a newcomer is inherently shaped by both macropolitical structures and everyday relational practices that can lead to experiences of belonging, marginalisation, empowerment and/or disempowerment depending on economic agenda, humanitarian and humanistic orientations. We first explore the extent to which immigrants experience hospitality in relation to unequal distribution of power and resources as well as cultural discontinuities. We then examine ways in which immigrants have been able to find sanctuaries of hospitality within their own ethnocultural communities. Finally, we discuss the complexity of hospitality in a multicultural context and offer critical insights on host factors that may produce, develop and nurture hospitable environments.

Keywords: migration, hospitality, diversity, culture, race

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Authors: David Percy

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Landslides are a geologic phenomenon that affects a large number of inhabited places and are constantly being monitored and studied for the prediction of future occurrences. Reconstructability analysis (RA) is a methodology for extracting informative models from large volumes of data that work exclusively with discrete data. While RA has been used in medical applications and social science extensively, we are introducing it to the spatial sciences through applications like landslide prediction. Since RA works exclusively with discrete data, such as soil classification or bedrock type, working with continuous data, such as porosity, requires that these data are binned for inclusion in the model. RA constructs models of the data which pick out the most informative elements, independent variables (IVs), from each layer that predict the dependent variable (DV), landslide occurrence. Each layer included in the model retains its classification data as a primary encoding of the data. Unlike other machine learning algorithms that force the data into one-hot encoding type of schemes, RA works directly with the data as it is encoded, with the exception of continuous data, which must be binned. The usual physical and derived layers are included in the model, and testing our results against other published methodologies, such as neural networks, yields accuracy that is similar but with the advantage of a completely transparent model. The results of an RA session with a data set are a report on every combination of variables and their probability of landslide events occurring. In this way, every combination of informative state combinations can be examined.

Keywords: reconstructability analysis, machine learning, landslides, raster analysis

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Authors: Chen Zhang, Nan Chen

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High-dimensional process monitoring becomes increasingly important in many application domains, where usually the process distribution is unknown and much more complicated than the normal distribution, and the between-stream correlation can not be neglected. However, since the process dimension is generally much bigger than the reference sample size, most traditional nonparametric multivariate control charts fail in high-dimensional cases due to the curse of dimensionality. Furthermore, when the process goes out of control, the influenced variables are quite sparse compared with the whole dimension, which increases the detection difficulty. Targeting at these issues, this paper proposes a new nonparametric monitoring scheme for high-dimensional processes. This scheme first projects the high-dimensional process into several subprocesses using random projections for dimension reduction. Then, for every subprocess with the dimension much smaller than the reference sample size, a local nonparametric control chart is constructed based on the spatial rank test to detect changes in this subprocess. Finally, the results of all the local charts are fused together for decision. Furthermore, after an out-of-control (OC) alarm is triggered, a diagnostic framework is proposed. using the square-root LASSO. Numerical studies demonstrate that the chart has satisfactory detection power for sparse OC changes and robust performance for non-normally distributed data, The diagnostic framework is also effective to identify truly changed variables. Finally, a real-data example is presented to demonstrate the application of the proposed method.

Keywords: random projection, high-dimensional process control, spatial rank, sequential change detection

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Authors: Sushish Baral, Pratibha Joshi, Yaman Maharjan

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Glaucoma is a chronic eye condition that causes vision loss that is irreversible. Early detection and treatment are critical to prevent vision loss because it can be asymptomatic. For the identification of glaucoma, multiple deep learning algorithms are used. Transformer-based architectures, which use the self-attention mechanism to encode long-range dependencies and acquire extremely expressive representations, have recently become popular. Convolutional architectures, on the other hand, lack knowledge of long-range dependencies in the image due to their intrinsic inductive biases. The aforementioned statements inspire this thesis to look at transformer-based solutions and investigate the viability of adopting transformer-based network designs for glaucoma detection. Using retinal fundus images of the optic nerve head to develop a viable algorithm to assess the severity of glaucoma necessitates a large number of well-curated images. Initially, data is generated by augmenting ocular pictures. After that, the ocular images are pre-processed to make them ready for further processing. The system is trained using pre-processed images, and it classifies the input images as normal or glaucoma based on the features retrieved during training. The Vision Transformer (ViT) architecture is well suited to this situation, as it allows the self-attention mechanism to utilise structural modeling. Extensive experiments are run on the common dataset, and the results are thoroughly validated and visualized.

Keywords: glaucoma, vision transformer, convolutional architectures, retinal fundus images, self-attention, deep learning

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Authors: Hafsa Qabool, Rashna Sukhia, Mubassar Fida

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Introduction: Success of orthodontic mechanotherapy is highly dependent upon patient cooperation and compliance throughout the duration of treatment. This study was conducted to assess the cooperation and compliance of adult orthodontic patients during the leveling and alignment, space closure/molar correction, and finishing stages of tooth movement. Materials and Methods: Patient cooperation and compliance among three stages of orthodontic treatment were assessed using the Orthodontic Patient Cooperation Scale (OPCS) and Clinical Compliance Evaluation (CCE) form. A sample size of 38 was calculated for each stage of treatment; therefore, 114 subjects were included in the study. Shapiro-Wilk test identified that the data were normally distributed. One way ANOVA was used to evaluate the percentage cooperation and compliance among the three stages. Pair-wise comparisons between the three stages were performed using Post-hoc Tukey. Results: Statistically significant difference was seen for scores of patient compliance using CCE (p = 0.01); however, the results of the OPCS showed a non-significant difference for patient cooperation (p = 0.16) among the three stages of treatment. Post-hoc analysis showed significant differences (p = 0.01) in patient cooperation and compliance between space closure and the finishing stage. Highly significant (p < 0.001) decline in oral hygiene was found with the progression of orthodontic treatment. Conclusions: Improvement in the cooperation and compliance levels for adult orthodontic patients was observed during space closure & molar correction stage, which then showed a decline as treatment progressed. Oral hygiene was progressively compromised as orthodontic treatment progressed.

Keywords: patient compliance, adult orthodontics, orthodontic motivation, orthodontic patient adherence

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Authors: Renukswamy Chikkamath, Vishvapalsinhji Ramsinh Parmar, Christoph Hewel, Markus Endres

Abstract:

Given a patent document, identifying distinct semantic annotations is an interesting research aspect. Text annotation helps the patent practitioners such as examiners and patent attorneys to quickly identify the key arguments of any invention, successively providing a timely marking of a patent text. In the process of manual patent analysis, to attain better readability, recognising the semantic information by marking paragraphs is in practice. This semantic annotation process is laborious and time-consuming. To alleviate such a problem, we proposed a dataset to train machine learning algorithms to automate the highlighting process. The contributions of this work are: i) we developed a multi-class dataset of size 150k samples by traversing USPTO patents over a decade, ii) articulated statistics and distributions of data using imperative exploratory data analysis, iii) baseline Machine Learning models are developed to utilize the dataset to address patent paragraph highlighting task, and iv) future path to extend this work using Deep Learning and domain-specific pre-trained language models to develop a tool to highlight is provided. This work assists patent practitioners in highlighting semantic information automatically and aids in creating a sustainable and efficient patent analysis using the aptitude of machine learning.

Keywords: machine learning, patents, patent sentiment analysis, patent information retrieval

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Authors: Anuradha Chug, Sunali Gandhi

Abstract:

Regression Testing is a very expensive and time-consuming process carried out to ensure the validity of modified software. Due to the availability of insufficient resources to re-execute all the test cases in time constrained environment, efforts are going on to generate test data automatically without human efforts. Many search based techniques have been proposed to generate efficient, effective as well as optimized test data, so that the overall cost of the software testing can be minimized. The generated test data should be able to uncover all potential lapses that exist in the software or product. Inspired from the natural behavior of bat for searching her food sources, current study employed a meta-heuristic, search-based bat algorithm for optimizing the test data on the basis certain parameters without compromising their effectiveness. Mathematical functions are also applied that can effectively filter out the redundant test data. As many as 50 Java programs are used to check the effectiveness of proposed test data generation and it has been found that 86% saving in testing efforts can be achieved using bat algorithm while covering 100% of the software code for testing. Bat algorithm was found to be more efficient in terms of simplicity and flexibility when the results were compared with another nature inspired algorithms such as Firefly Algorithm (FA), Hill Climbing Algorithm (HC) and Ant Colony Optimization (ACO). The output of this study would be useful to testers as they can achieve 100% path coverage for testing with minimum number of test cases.

Keywords: regression testing, test case selection, test case prioritization, genetic algorithm, bat algorithm

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Authors: Apityan Tume, Sefater Gbashi, Stephanie Dillon

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The present study was aimed at investigating the weaning knowledge, attitudes and practices among mothers with infants in Benue and Zamfara States, Nigeria. A questionnaire consisting of both open and close-ended questions was adopted for this study. Socio-demographic information was captured with this questionnaire. This is in addition to capturing data linked to respondents' knowledge and attitude to weaning and the weaning practice itself. 400 questionnaires were distributed, and 329 was filled and returned. Results obtained from the study revealed that the mother's location; mother's level of education, her occupation, her age; and her level of education would have an effect on her practice of and attitude to weaning. Essentially, mothers based in Zamfara who were full-time housewives, tailor or trader were more likely to start weaning at a later age than mothers who have the same occupation but based in Benue. Furthermore, the results revealed mothers who were uneducated and based in Zamfara were more likely to wean their babies later than the uneducated mothers, mothers educated to primary, secondary or tertiary level based in Benue. It was also evident that mothers who were low-income earners and based in Zamfara were more likely to start weaning later than the low, average or high-income earning mothers in Benue. Moreover, younger mothers in Zamfara age 20-25 years were more likely to start weaning later than mothers of other age groups. In overall, mothers had a good understanding of best practices with regards to weaning. Notwithstanding, mothers could be further encouraged and educated about the advantages of exclusive breastfeeding and complementary feeding practices as well as the benefits therein to enhance the healthy growth of the children.

Keywords: breastfeeding, complementary feeding, socio-demographic, weaning

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Authors: Georgiana Onicescu, Yuqian Shen

Abstract:

Due to the complex nature of geo-referenced data, multicollinearity of the risk factors in public health spatial studies is a commonly encountered issue, which leads to low parameter estimation accuracy because it inflates the variance in the regression analysis. To address this issue, we proposed a two-stage variable selection method by extending the least absolute shrinkage and selection operator (Lasso) to the Bayesian spatial setting, investigating the impact of risk factors to health outcomes. Specifically, in stage I, we performed the variable selection using Bayesian Lasso and several other variable selection approaches. Then, in stage II, we performed the model selection with only the selected variables from stage I and compared again the methods. To evaluate the performance of the two-stage variable selection methods, we conducted a simulation study with different distributions for the risk factors, using geo-referenced count data as the outcome and Michigan as the research region. We considered the cases when all candidate risk factors are independently normally distributed, or follow a multivariate normal distribution with different correlation levels. Two other Bayesian variable selection methods, Binary indicator, and the combination of Binary indicator and Lasso were considered and compared as alternative methods. The simulation results indicated that the proposed two-stage Bayesian Lasso variable selection method has the best performance for both independent and dependent cases considered. When compared with the one-stage approach, and the other two alternative methods, the two-stage Bayesian Lasso approach provides the highest estimation accuracy in all scenarios considered.

Keywords: Lasso, Bayesian analysis, spatial analysis, variable selection

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Authors: Maxime A. J. Kuitche, Ruxandra M. Botez, Arthur Guillemin

Abstract:

As the Unmanned Aerial Systems have found diverse utilities in both military and civil aviation, the necessity to obtain an accurate aerodynamic model has shown an enormous growth of interest. Recent modeling techniques are procedures using optimization algorithms and statistics that require many flight tests and are therefore extremely demanding in terms of costs. This paper presents a procedure to estimate the aerodynamic behavior of an unmanned aerial system from a numerical approach using computational fluid dynamic analysis. The study was performed using an unstructured mesh obtained from a grid convergence analysis at a Mach number of 0.14, and at an angle of attack of 0°. The flow around the aircraft was described using a standard k-ω turbulence model. Thus, the Reynold Averaged Navier-Stokes (RANS) equations were solved using ANSYS FLUENT software. The method was applied on the UAS-S45 designed and manufactured by Hydra Technologies in Mexico. The lift, the drag, and the pitching moment coefficients were obtained at different angles of attack for several flight conditions defined in terms of altitudes and Mach numbers. The results obtained from the Computational Fluid Dynamics analysis were compared with the results obtained by using the DATCOM semi-empirical procedure. This comparison has indicated that our approach is highly accurate and that the aerodynamic model obtained could be useful to estimate the flight dynamics of the UAS-S45.

Keywords: aerodynamic modelling, CFD Analysis, ANSYS FLUENT, UAS-S45

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Authors: Xiaohan Gao, Kai Liu

Abstract:

As essential components of urban areas, primary and secondary schools are extensively distributed throughout various regions of the city. During times of urban disturbances, these schools become direct carriers of complex disruptions. Therefore, fostering resilient schools becomes a pivotal driving force to promote high-quality urban development and a cornerstone of sustainable school growth. This paper adopts the theory of spatial resilience and focuses on primary and secondary schools in Chinese cities as the research subject. The study first explores the potential disturbance risks faced by schools and delves into the origin and concept of spatial resilience in the educational context. Subsequently, the paper conducts a meta-analysis to characterize the spatial resilience of primary and secondary schools and devises a spatial resilience planning mechanism. Drawing insights from exemplary cases both domestically and internationally, the research formulates spatial and planning resilience strategies for primary and secondary schools to cope with perturbations. These strategies encompass creating an overall layout that integrates harmoniously with nature, promoting organic growth in the planning structure, fostering ecological balance in the landscape system, and enabling dynamic adaptation in architectural spaces. By cultivating the capacity for "resistance-adaptation-transformation," these approaches support sustainable development within the school space. The ultimate goal of this project is to establish a cohesive and harmonious layout that advances the sustainable development of primary and secondary schools while contributing to the overall resilience of urban areas.

Keywords: complex disruption, primary and secondary schools, spatial resilience, sustainable development

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Authors: Gian Carlo F. Maliwat, Stephanie F. Velasquez, Janice A. Ragaza

Abstract:

A 50-day growth trial was conducted to evaluate the efficacy of Chlorella vulgaris (Beijerinck) as an ingredient in the diets of giant freshwater prawn Macrobrachium rosenbergii (De Man) postlarvae (PL30). Immune response (total haemocyte count and prophenoloxidase activity) was also assessed by subjecting postlarvae to a challenge test against Aeromonas hydrophila (Chester) for 14 days. Isonitrogenous and iso-lipidic test diets were prepared using a fishmeal-based-positive control diet (D0) and four basal diets with inclusion levels of 2% (D2), 4% (D4), 6% (D6) and 8% (D8) C. vulgaris. Postlarvae of M. rosenbergii were randomly stocked (mean initial body weight of 0.19 ± 0.02 g) in 30-L tanks in three replicates per dietary treatment for evaluation of growth performance. Another set of postlarvae (mean initial body weight of 1.25 ± 0.02 g) was randomly distributed in 95-L tanks in three replicates per dietary treatment for the assessment of immune response. Results showed that specific growth rate was significantly higher (P < 0.05) in postlarvae fed D4 and D6. Variations in values for carcass protein, lipid, moisture, and ash were also evident. Postlarvae fed diets with Chlorella showed increased prophenol oxidase activity and total haemocyte counts. Moreover, the survival rate after challenge with A. hydrophila was significantly increased (P < 0.05). Inclusion of C. vulgaris in diets enhanced immune response and resistance of M. rosenbergii postlarvae against A. hydrophila infection.

Keywords: Chlorella vulgaris, haemocyte count, Macrobrachium rosenbergii, prophenoloxidase activity

Procedia PDF Downloads 131