Search results for: improved sparrow search algorithm

Authors: Frederica Pettit

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Contribution: Stakeholder involvement in CSR is essential to ensuring pro-environmental attitudes and behaviours across multi-stakeholder groups. Despite increased awareness of the benefits surrounding a collaborative approach to sustainability communications, its success is limited by difficulties engaging with active online conversations with stakeholder groups. Whilst previous research defines the effectiveness of sustainability communications; this paper contributes to knowledge through the development of a theoretical framework that explores the processes to achieving pro-environmental attitudes and behaviours in stakeholder groups. The research will also consider social media as an opportunity to communicate CSR information to all stakeholder groups. Approach: A systematic review was chosen to investigate the effectiveness of the types of sustainability communications used in the hospitality and tourism industries. The systematic review was completed using Web of Science and Scopus using the search terms “sustainab* communicat*” “effective or effectiveness,” and “hospitality or tourism,” limiting the results to peer-reviewed research. 133 abstracts were initially read, with articles being excluded for irrelevance, duplicated articles, non-empirical studies, and language. A total of 45 papers were included as part of the systematic review. 5 propositions were created based on the results of the systematic review, helping to develop a theoretical framework of the processes needed for companies to encourage pro-environmental behaviours across multi-stakeholder groups. Results: The theoretical framework developed in the paper determined the processes necessary for companies to achieve pro-environmental behaviours in stakeholders. The processes to achieving pro-environmental attitudes and behaviours are stakeholder-focused, identifying the need for communications to be specific to their targeted audience. Collaborative communications that enable stakeholders to engage with CSR information and provide feedback lead to a higher awareness of CSR shared visions and pro-environmental attitudes and behaviours. These processes should also aim to improve their relationships with stakeholders through transparency of CSR, CSR strategies that match stakeholder values and ethics whilst prioritizing sustainability as part of their job role. Alternatively, companies can prioritize pro-environmental behaviours using choice editing by mainstreaming sustainability as the only option. In recent years, there has been extensive research on social media as a viable source of sustainability communications, with benefits including direct interactions with stakeholders, the ability to enforce the authenticity of CSR activities and encouragement of pro-environmental behaviours. Despite this, there are challenges to implementing CSR, including difficulties controlling stakeholder criticisms, negative stakeholder influences and comments left on social media platforms. Conclusion: A lack of engagement with CSR information is a reoccurring reason for preventing pro-environmental attitudes and behaviours across stakeholder groups. Traditional CSR strategies contribute to this due to their inability to engage with their intended audience. Hospitality and tourism companies are improving stakeholder relationships through collaborative processes which reduce single-use plastic consumption. A collaborative approach to communications can lead to stakeholder satisfaction, leading to changes in attitudes and behaviours. Different sources of communications are accessed by different stakeholder groups, identifying the need for targeted sustainability messaging, creating benefits such as direct interactions with stakeholders, the ability to enforce the authenticity of CSR activities, and encouraging engagement with sustainability information.

Keywords: hospitality, pro-environmental attitudes and behaviours, sustainability communication, social media

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Authors: Madia Safdar, G. Amjad Hussain, Mashhood Ahmad

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One of the major challenges of today’s era is peak demand which causes stress on the transmission lines and also raises the cost of energy generation and ultimately higher electricity bills to the end users, and it was used to be managed by the supply side management. However, nowadays this has been withdrawn because of existence of potential in the demand side management (DSM) having its economic and- environmental advantages. DSM in domestic load can play a vital role in reducing the peak load demand on the network provides a significant cost saving. In this paper the potential of demand response (DR) in reducing the peak load demands and electricity bills to the electric users is elaborated. For this purpose the domestic appliances are modeled in MATLAB Simulink and controlled by a module called energy management controller. The devices are categorized into controllable and uncontrollable loads and are operated according to real-time tariff pricing pattern instead of fixed time pricing or variable pricing. Energy management controller decides the switching instants of the controllable appliances based on the results from optimization algorithms. In GAMS software, the MILP (mixed integer linear programming) algorithm is used for optimization. In different cases, different constraints are used for optimization, considering the comforts, needs and priorities of the end users. Results are compared and the savings in electricity bills are discussed in this paper considering real time pricing and fixed tariff pricing, which exhibits the existence of potential to reduce electricity bills and peak loads in demand side management. It is seen that using real time pricing tariff instead of fixed tariff pricing helps to save in the electricity bills. Moreover the simulation results of the proposed energy management system show that the gained power savings lie in high range. It is anticipated that the result of this research will prove to be highly effective to the utility companies as well as in the improvement of domestic DR.

Keywords: controllable and uncontrollable domestic loads, demand response, demand side management, optimization, MILP (mixed integer linear programming)

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Authors: Ahmet Karagoz, Irfan Karagoz

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Synthetic Aperture Radar (SAR) is a radar mechanism that can be integrated into manned and unmanned aerial vehicles to create high-resolution images in all weather conditions, regardless of day and night. In this study, SAR images of military vehicles with different azimuth and descent angles are pre-processed at the first stage. The main purpose here is to reduce the high speckle noise found in SAR images. For this, the Wiener adaptive filter, the mean filter, and the median filters are used to reduce the amount of speckle noise in the images without causing loss of data. During the image segmentation phase, pixel values are ordered so that the target vehicle region is separated from other regions containing unnecessary information. The target image is parsed with the brightest 20% pixel value of 255 and the other pixel values of 0. In addition, by using appropriate parameters of statistical region merging algorithm, segmentation comparison is performed. In the step of feature extraction, the feature vectors belonging to the vehicles are obtained by using Gabor filters with different orientation, frequency and angle values. A number of Gabor filters are created by changing the orientation, frequency and angle parameters of the Gabor filters to extract important features of the images that form the distinctive parts. Finally, images are classified by sparse representation method. In the study, l₁ norm analysis of sparse representation is used. A joint database of the feature vectors generated by the target images of military vehicle types is obtained side by side and this database is transformed into the matrix form. In order to classify the vehicles in a similar way, the test images of each vehicle is converted to the vector form and l₁ norm analysis of the sparse representation method is applied through the existing database matrix form. As a result, correct recognition has been performed by matching the target images of military vehicles with the test images by means of the sparse representation method. 97% classification success of SAR images of different military vehicle types is obtained.

Keywords: automatic target recognition, sparse representation, image classification, SAR images

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Authors: Bilal Ahmed, Muhammad Rafiq, Choongjae Im

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The tech-savvy Gen Z's transfer towards interactive concept learning is hammering the demand for online collaborative learning environments, renovating conventional education approaches. The authors propose a novel approach to enhance learning outcomes to improve retention in 3D interactive education by connecting virtual reality (VR) and non-VR devices in the classroom and distance learning. The study evaluates students' experiences with VR interconnectivity devices in human anatomy lectures using real-time 3D interactive data visualization. Utilizing the renowned "Guo & Pooles Inventory" and the "Flow for Presence Questionnaires," it used an experimental research design with a control and experimental group to assess this novel connecting strategy's effectiveness and significant potential for in-person and online educational settings during the sessions. The experimental group's interactions, engagement levels, and usability experiences were assessed using the "Guo & Pooles Inventory" and "Flow for Presence Questionnaires," which measure their sense of presence, engagement, and immersion throughout the learning process using a 5-point Likert scale. At the end of the sessions, we used the "Perceived Usability Scale" to find our proposed system's overall efficiency, effectiveness, and satisfaction. By comparing both groups, the students in the experimental group used the integrated VR environment and VR to non-VR devices, and their sense of presence and attentiveness was significantly improved, allowing for increased engagement by giving students diverse technological access. Furthermore, learners' flow states demonstrated increased absorption and focus levels, improving information retention and Perceived Usability. The findings of this study can help educational institutions optimize their technology-enhanced teaching methods for traditional classroom settings as well as distance-based learning, where building a sense of connection among remote learners is critical. This study will give significant insights into educational technology and its ongoing progress by analyzing engagement, interactivity, usability, satisfaction, and presence.

Keywords: interactive learning environments, human-computer interaction, virtual reality, computer- supported collaborative learning

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Authors: B. Rusyidi

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Since 2009, the Indonesian Ministry of Social Affairs has been implementing Program Kesejahteraan Sosial Anak (PKSA) (Child Welfare Program) a conditional cash transfer program that targets neglected children, children with disabilities, street children, children in conflict with the law, and children in need of special protection, all from poor households. PKSA integrates three elements: Transfer of cash, care and social services through social workers, and institutional childcare assistance. This qualitative study analyzed the roles and the challenges of social workers in implementing PKSA and lays out recommendations to inform policy changes. Data were collected in late 2014 from national and local government and non-government child welfare agencies, social workers, and childcare institution representatives through interviews and Focused Group Discussions (FGDs). Field work took place in six districts in the provinces of Jakarta, Central Java and South Sulawesi. The study found that the social workers’ role was significant in facilitating cash transfer, providing education and guidance, and linking children and families to basic social services. This improved utilization of basic social services enhanced children and families’ behaviors and contributed to the well being of the children. However, only a small number of childcare institutions have social workers, leaving many children and families without care and social service linkages, depriving them of rehabilitative components to help them regain their social functions. Some social workers reported their struggles with heavy workloads, lack of professional competencies and training, limited job security, and inadequate professional acknowledgment from other professions. Parts of those challenges were due to the centralized nature of the program and the lack of shared vision and commitment about the child protection system among related government agencies both at the national and local levels. The study highlights the necessity to implement an integrated child protection system, decentralize the PKSA program, and increase the number, competence, case management, and management and monitoring of social workers. The most recent progress of the program and its impacts on social workers are also discussed.

Keywords: child protection, conditional cash transfer, program decentralization, social worker, working conditions

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Authors: Shoaib Rasool

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Migration trends in rural areas of Gujrat are increasing day by day among illiterate people as they consider it as a source of attraction and charm of destination. It affects the life standard both positive and negative way to their families left behind in the context of poverty, socio-economic status and life standards. It also promotes material items and as well as social indicators of living, housing conditions, schooling of their children’s, health seeking behavior and to some extent their family environment. The nature of the present study is to analyze socio-economic conditions regarding life standard of emigrant families left behind in rural areas of Gujrat district, Pakistan. A survey design was used on 150 families selected from rural areas of Gujrat districts through purposive sampling technique. A well-structured questionnaire was administered by the researcher to explore the nature of the study and for further data collection process. The measurement tool was pretested on 20 families to check the workability and reliability before the actual data collection. Statistical tests were applied to draw results and conclusion. The preliminary findings of the study show that emigration has left deep social-economic impacts on life standards of rural families left behind in Gujrat. They improved their life status and living standard through remittances. Emigration is one of the major sources of development of economy of household and it also alleviate poverty at house household level as well as community and country level. The rationale behind migration varies individually and geographically. There are popular considered attractions in Pakistan includes securing high status, improvement in health condition, coping other, getting married then to acquire nationality, using the unfair means, opting educational visas etc. Emigrants are not only sending remittances but also returning with newly acquired skills and valuable knowledge to their country of origin because emigrants learn new methods of living and working. There are also women migrants who experience social downward mobility by engaging in jobs that are beneath their educational qualifications.

Keywords: emigration, life standard, families, left behind, rural area, Gujrat

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Authors: Akane Uda, Ami Tabata, Mi An, Misa Komaki, Ryotaro Ito, Mayumi Inoue, Takehiro Sasai, Yusuke Kusano, Toshihiro Kato

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Early intervention is recommended for children with autism spectrum disorder (ASD), and an increasing number of children have received support and intervention before school age in recent years. In this study, we systematically reviewed preschool interventions focused on repetitive behaviors observed in children with ASD, which are often observed at younger ages. Inclusion criteria were as follows : (1) Child of preschool status (age ≤ 7 years) with a diagnosis of ASD (including autism, Asperger's, and pervasive developmental disorder) or a parent (caregiver) with a preschool child with ASD, (2) Physician-confirmed diagnosis of ASD (autism, Asperger's, and pervasive developmental disorder), (3) Interventional studies for repetitive behaviors, (4) Original articles published within the past 10 years (2012 or later), (5) Written in English and Japanese. Exclusion criteria were as follows: (1) Systematic reviews or meta-analyses, (2) Conference reports or books. We carefully scrutinized databases to remove duplicate references and used a two-step screening process to select papers. The primary screening included close scrutiny of titles and abstracts to exclude articles that did not meet the eligibility criteria. During the secondary screening, we carefully read the complete text to assess eligibility, which was double-checked by six members at the laboratory. Disagreements were resolved through consensus-based discussion. Our search yielded 304 papers, of which nine were included in the study. The level of evidence was as follows: three randomized controlled trials (level 2), four pre-post studies (level 4b), and two case reports (level 5). Seven articles selected for this study described the effectiveness of interventions. Interventions for repetitive behaviors in preschool children with ASD were categorized as five interventions that directly involved the child and four educational programs for caregivers and parents. Studies that directly intervened with children used early intensive intervention based on applied behavior analysis (Early Start Denver Model, Early Intensive Behavioral Intervention, and the Picture Exchange Communication System) and individualized education based on sensory integration. Educational interventions for caregivers included two methods; (a) education regarding combined methods and practices of applied behavior analysis in addition to classification and coping methods for repetitive behaviors, and (b) education regarding evaluation methods and practices based on children’s developmental milestones in play. With regard to the neurophysiological basis of repetitive behaviors, environmental factors are implicated as possible contributors. We assumed that applied behavior analysis was shown to be effective in reducing repetitive behaviors because analysis focused on the interaction between the individual and the environment. Additionally, with regard to educational interventions for caregivers, the intervention was shown to promote behavioral change in children based on the caregivers' understanding of the classification of repetitive behaviors and the children’s developmental milestones in play and adjustment of the person-environment context led to a reduction in repetitive behaviors.

Keywords: autism spectrum disorder, early intervention, repetitive behaviors, systematic review

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Authors: Dávid Kovács, Bálint Csanády, Dániel Boros, Iván Ivkovic, Lóránt Nagy, Dalma Tóth-Lakits, László Márkus, András Lukács

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The modeling practice of financial instruments has seen significant change over the last decade due to the recognition of time-dependent and stochastically changing correlations among the market prices or the prices and market characteristics. To represent this phenomenon, the Stochastic Correlation Process (SCP) has come to the fore in the joint modeling of prices, offering a more nuanced description of their interdependence. This approach has allowed for the attainment of realistic tail dependencies, highlighting that prices tend to synchronize more during intense or volatile trading periods, resulting in stronger correlations. Evidence in statistical literature suggests that, similarly to the volatility, the SCP of certain stock prices follows rough paths, which can be described using fractional differential equations. However, estimating parameters for these equations often involves complex and computation-intensive algorithms, creating a necessity for alternative solutions. In this regard, the Fractional Ornstein-Uhlenbeck (fOU) process from the family of fractional processes offers a promising path. We can effectively describe the rough SCP by utilizing certain transformations of the fOU. We employed neural networks to understand the behavior of these processes. We had to develop a fast algorithm to generate a valid and suitably large sample from the appropriate process to train the network. With an extensive training set, the neural network can estimate the process parameters accurately and efficiently. Although the initial focus was the fOU, the resulting model displayed broader applicability, thus paving the way for further investigation of other processes in the realm of financial mathematics. The utility of SCP extends beyond its immediate application. It also serves as a springboard for a deeper exploration of fractional processes and for extending existing models that use ordinary Wiener processes to fractional scenarios. In essence, deploying both SCP and fractional processes in financial models provides new, more accurate ways to depict market dynamics.

Keywords: fractional Ornstein-Uhlenbeck process, fractional stochastic processes, Heston model, neural networks, stochastic correlation, stochastic differential equations, stochastic volatility

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Authors: Yuliya Y. Gavrichenko

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Background and Aims: The increasing bacterial resistance to almost all the available antibiotics has encouraged scientists to search for alternative sources of antibacterial agents. Nowadays, it is known that many plant secondary metabolites have diverse biological activity. These compounds can be potentially active against human bacterial and viral infections. Extended research has been carried out to explore the use of the luminescent bacterial test as a rapid, accurate and inexpensive method to assess the antibacterial properties and to predict the biological activity spectra for plant origin substances. Method: Botanical material of fifteen species was collected from their natural and cultural habitats on the Crimean peninsula. The aqueous extracts of following plants were tested: Robinia pseudoacacia L., Sideritis comosa, Cotinus coggygria Scop., Thymus serpyllum L., Juglans regia L., Securigera varia L., Achillea millefolium L., Phlomis taurica, Corylus avellana L., Sambucus nigra L., Helichrysum arenarium L., Glycyrrhiza glabra L., Elytrigia repens L., Echium vulgare L., Conium maculatum L. The test was carried out using luminous strains of marine bacteria Photobacterium leiognathi, which was isolated from the Sea of Azov as well as four Escherichia coli MG1655 recombinant strains harbouring Vibrio fischeri luxCDABE genes. Results: The bactericidal capacity of plant extracts showed significant differences in the study. Cotinus coggygria, Phlomis taurica, Juglans regia L. proved to be the most toxic to P. leiognathi. (EC50 = 0.33 g dried plant/l). Glycyrrhiza glabra L., Robinia pseudoacacia L., Sideritis comosa and Helichrysum arenarium L. had moderate inhibitory effects (EC50 = 3.3 g dried plant/l). The rest of the aqueous extracts have decreased the luminescence of no more than 50% at the lowest concentration (16.5 g dried plant/l). Antibacterial activity of herbal extracts against constitutively luminescent E. coli MG1655 (pXen7-lux) strain was observed at approximately the same level as for P. leiognathi. Cotinus coggygria and Conium maculatum L. extracts have increased light emission in the mutant E. coli MG1655 (pFabA-lux) strain which is associated with cell membranes damage. Sideritis comosa, Phlomis taurica, Juglans regia induced SOS response in E. coli (pColD-lux) strain. Glycyrrhiza glabra L. induced protein damage response in E. coli MG1655 (pIbpA-lux) strain. Conclusion: The received results have shown that the plants’ extracts had nonspecific antimicrobial effects against both E. coli (pXen7-lux) and P. leiognathi biosensors. Mutagenic, cytotoxic and protein damage effects have been observed. In general, the bioluminescent inhibition test result correlated with the traditional use of screened plants. It leads to the following conclusion that whole-cell luminescent biosensors could be the indicator of overall plants antibacterial capacity. The results of the investigation have shown a possibility of bioluminescent method in medicine and pharmacy as an approach to research the antibacterial properties of medicinal plants.

Keywords: antibacterial property, bioluminescence, medicinal plants, whole-cell biosensors

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Authors: Teresa Kupczyk, Selçuk Mustafa Özcan, Joanna Kubicka

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The paper presents results of specialist literature analysis concerning learning outcomes and student satisfaction as a factor of the necessary change in the Polish higher education system. The objective of the empirical research was to determine students’ assessment of learning outcomes, satisfaction of their expectations, as well as their satisfaction with lectures and practical classes held in the traditional form, e-learning and video-conference. The assessment concerned effectiveness of time spent at classes, usefulness of the delivered knowledge, instructors’ preparation and teaching skills, application of tools, studies curriculum, its adaptation to students’ needs and labour market, as well as studying conditions. Self-assessment of learning outcomes was confronted with assessment by lecturers. The indirect objective of the research was also to identify how students assessed their activity and commitment in acquisition of knowledge and their discipline in achieving education goals. It was analysed how the studies held affected the students’ willingness to improve their skills and assessment of their perspectives at the labour market. To capture the changes underway, the research was held at the beginning, during and after completion of the studies. The study group included 86 students of two editions of full-time studies majoring in Management and specialising in “Mega-event organisation”. The studies were held within the EU-funded project entitled “Responding to challenges of new markets – innovative managerial education”. The results obtained were analysed statistically. Average results and standard deviations were calculated. In order to describe differences between the studied variables present during the process of studies, as well as considering the respondents’ gender, t-Student test for independent samples was performed with the IBM SPSS Statistics 21.0 software package. Correlations between variables were identified by calculation of Pearson and Spearman correlation coefficients. Research results suggest necessity to introduce some changes in the teaching system applied at Polish higher education institutions, not only considering the obtained outcomes, but also impact on students’ willingness to improve their qualifications constantly, improved self-assessment among students and their opportunities at the labour market.

Keywords: higher education, learning outcomes, students, change

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Authors: Camille Van Eupen, Dirk Maes, Marc Herremans, Kristijn R. R. Swinnen, Ben Somers, Stijn Luca

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In ecology, species distribution models are commonly implemented to study species-environment relationships. These models increasingly rely on opportunistic citizen science data when high-quality species records collected through standardized recording protocols are unavailable. While these opportunistic data are abundant, uncertainty is usually high, e.g., due to observer effects or a lack of metadata. Data quality filtering is often used to reduce these types of uncertainty in an attempt to increase the value of studies relying on opportunistic data. However, filtering should not be performed blindly. In this study, recommendations are built for data quality filtering of opportunistic species occurrence data that are used as input for species distribution models. Using an extensive database of 5.7 million citizen science records from 255 species in Flanders, the impact on model performance was quantified by applying three data quality filters, and these results were linked to species traits. More specifically, presence records were filtered based on record attributes that provide information on the observation process or post-entry data validation, and changes in the area under the receiver operating characteristic (AUC), sensitivity, and specificity were analyzed using the Maxent algorithm with and without filtering. Controlling for sample size enabled us to study the combined impact of data quality filtering, i.e., the simultaneous impact of an increase in data quality and a decrease in sample size. Further, the variation among species in their response to data quality filtering was explored by clustering species based on four traits often related to data quality: commonness, popularity, difficulty, and body size. Findings show that model performance is affected by i) the quality of the filtered data, ii) the proportional reduction in sample size caused by filtering and the remaining absolute sample size, and iii) a species ‘quality profile’, resulting from a species classification based on the four traits related to data quality. The findings resulted in recommendations on when and how to filter volunteer generated and opportunistically collected data. This study confirms that correctly processed citizen science data can make a valuable contribution to ecological research and species conservation.

Keywords: citizen science, data quality filtering, species distribution models, trait profiles

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Authors: Hossein Aghababaei, Sergio Vitale, Giampaolo Ferraioli

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In the context of Synthetic Aperture Radar (SAR) data, polarization is an important source of information for Earth's surface monitoring. SAR Systems are often considered to transmit only one polarization. This constraint leads to either single or dual polarimetric SAR imaging modalities. Single polarimetric systems operate with a fixed single polarization of both transmitted and received electromagnetic (EM) waves, resulting in a single acquisition channel. Dual polarimetric systems, on the other hand, transmit in one fixed polarization and receive in two orthogonal polarizations, resulting in two acquisition channels. Dual polarimetric systems are obviously more informative than single polarimetric systems and are increasingly being used for a variety of remote sensing applications. In dual polarimetric systems, the choice of polarizations for the transmitter and the receiver is open. The choice of circular transmit polarization and coherent dual linear receive polarizations forms a special dual polarimetric system called hybrid polarimetry, which brings the properties of rotational invariance to geometrical orientations of features in the scene and optimizes the design of the radar in terms of reliability, mass, and power constraints. The complete characterization of target scattering, however, requires fully polarimetric data, which can be acquired with systems that transmit two orthogonal polarizations. This adds further complexity to data acquisition and shortens the coverage area or swath of fully polarimetric images compared to the swath of dual or hybrid polarimetric images. The search for solutions to augment dual polarimetric data to full polarimetric data will therefore take advantage of full characterization and exploitation of the backscattered field over a wider coverage with less system complexity. Several methods for reconstructing fully polarimetric images using hybrid polarimetric data can be found in the literature. Although the improvements achieved by the newly investigated and experimented reconstruction techniques are undeniable, the existing methods are, however, mostly based upon model assumptions (especially the assumption of reflectance symmetry), which may limit their reliability and applicability to vegetation and forest scenarios. To overcome the problems of these techniques, this paper proposes a new framework for reconstructing fully polarimetric information from hybrid polarimetric data. The framework uses Deep Learning solutions to augment hybrid polarimetric data without relying on model assumptions. A convolutional neural network (CNN) with a specific architecture and loss function is defined for this augmentation problem by focusing on different scattering properties of the polarimetric data. In particular, the method controls the CNN training process with respect to several characteristic features of polarimetric images defined by the combination of different terms in the cost or loss function. The proposed method is experimentally validated with real data sets and compared with a well-known and standard approach from the literature. From the experiments, the reconstruction performance of the proposed framework is superior to conventional reconstruction methods. The pseudo fully polarimetric data reconstructed by the proposed method also agree well with the actual fully polarimetric images acquired by radar systems, confirming the reliability and efficiency of the proposed method.

Keywords: SAR image, polarimetric SAR image, convolutional neural network, deep learnig, deep neural network

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Authors: Tekalign Gidi Kure

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This paper addresses a range of serious problems involving higher education in Ethiopia. In spite of increased enrollment in higher education, educational quality is deteriorating afterwards. Thus, this paper tried to assess the role of social infrastructure in education for economic development of the country and examined major critical problems in higher education of Ethiopia such as higher education finance, curriculum development, and instructor’s career development. Primarily the paper discusses the fundamental contributions of social infrastructure in higher education to economic development; namely development of human capital, improved health, life expectancy, increased productivity, and personal saving, then, the paper examines critically higher education in three regimes of Ethiopia (Emperor Regime, Derg Regime and EPDRF/current government). Thus, four main questions were raised during this research: "What are the antecedents of Ethiopia Higher Education System under three regimes?", " what are the current and emerging higher educational needs in Ethiopia economic development?", " what are the role of private sector in addressing the gaps in the higher education of the country and its adverse effect on quality issues? ", and "what improvements are needed in higher education system of Ethiopia?". Documents from Ministry of Education in Ethiopia, National Statistical Abstracts, and Reports from the World Bank and other recognized institutions were used in addition to recent empirical researches conducted in the country. In doing so, care had been taken to reduce prejudiced reports by involving different reports from multiple sources. The paper concludes that during emperor system higher education enrollment was among the very lowest in the world, therefore, the skilled human resource available to guide development were little, but the cost was very high. During the Derg regime where an ideological change in the system penetrated into higher education resulted with the lack of a large amount of resources to support higher education; the war inside and outside the country diverts resources from the sector. The main purpose of this paper is not only to discuss the problems and issues of higher education in the past, but it also investigates the influence that the current expansion of higher education has on the finance, staff, and other resources for the quality of education. The paper concludes that higher education in Ethiopia are financed by government, outdated curriculum and lagging behind the standard regarding qualified staff. Finally, it provided inevitable solutions if the country wants to gain well record in quality of education as well.

Keywords: social infrastructure, higher education, ethiopia, education quality

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Authors: F. DelGaudio, H. Gill

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Resuscitation during cardiopulmonary (CPA) arrest is dynamic, high stress, high acuity situation, which can easily lead to communication breakdown, and errors. The care of these high acuity patients has also been shown to increase physiologic stress and task saturation of providers, which can negatively impact the care being provided. These difficulties are further complicated during a global pandemic and pose a significant safety risk to bedside providers. Nurse-led codes are a relatively new concept that may be a potential solution for alleviating some of these difficulties. An experienced nurse who has completed advanced cardiac life support (ACLS), and additional training, assumed the responsibility of directing the mechanics of the appropriate ACLS algorithm. This was done in conjunction with a physician who also acted as a physician leader. The additional nurse-led code training included a multi-disciplinary in situ simulation of a CPA on a suspected COVID-19 patient. During the CPA, the nurse leader’s responsibilities include: ensuring adequate compression depth and rate, minimizing interruptions in chest compressions, the timing of rhythm/pulse checks, and appropriate medication administration. In addition, the nurse leader also functions as a last line safety check for appropriate personal protective equipment and limiting exposure of staff. The use of nurse-led codes for CPA has shown to decrease the cognitive overload and task saturation for the physician, as well as limiting the number of staff being exposed to a potentially infectious patient. The real-world application has allowed physicians to perform and oversee high-risk procedures such as intubation, line placement, and point of care ultrasound, without sacrificing the integrity of the resuscitation. Nurse-led codes have also given the physician the bandwidth to review pertinent medical history, advanced directives, determine reversible causes, and have the end of life conversations with family. While there is a paucity of research on the effectiveness of nurse-led codes, there are many potentially significant benefits. In addition to its value during a pandemic, it may also be beneficial during complex circumstances such as extracorporeal cardiopulmonary resuscitation.

Keywords: cardiopulmonary arrest, COVID-19, nurse-led code, task saturation

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Authors: Emilia K. H. Danielsson-Waters, Malaz Elsaddig, Kevin Jones

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Unfortunately, it is well known that neonatal deaths are a common and potentially preventable occurrence across the world. Neonatal resuscitation is a simple and inexpensive intervention that can effectively reduce this rate, and can be taught and implemented globally. This project is a follow-on from one in 2012, which found that neonatal resuscitation simulation was valuable for education, but would be better improved by being delivered by local staff. Methods: This study involved auditing the neonatal admission and death records within a rural Ugandan hospital, alongside implementing a Train-The-Trainer teaching scheme to teach Neonatal Resuscitation. One local doctor was trained for simulating neonatal resuscitation, whom subsequently taught an additional 14 staff members in one-afternoon session. Participants were asked to complete questionnaires to assess their knowledge and confidence pre- and post-simulation, and a survey to identify barriers and drivers to simulation. Results: The results found that the neonatal mortality rate in this hospital was 25% between July 2016- July 2017, with birth asphyxia, prematurity and sepsis being the most common causes. Barriers to simulation that were identified predominantly included a lack of time, facilities and opportunity, yet all members stated simulation was beneficial for improving skills and confidence. The simulation session received incredibly positive qualitative feedback, and also a 0.58-point increase in knowledge (p=0.197) and 0.73-point increase in confidence (0.079). Conclusion: This research shows that it is possible to create a teaching scheme in a rural hospital, however, many barriers are in place for its sustainability, and a larger sample size with a more sensitive scale is required to achieve statistical significance. This is undeniably important, because teaching neonatal resuscitation can have a direct impact on neonatal mortality. Subsequently, recommendations include that efforts should be put in place to create a sustainable training scheme, for example, by employing a resuscitation officer. Moreover, neonatal resuscitation teaching should be conducted more frequently in hospitals, and conducted in a wider geographical context, including within the community, in order to achieve its full effect.

Keywords: neonatal resuscitation, sustainable medical education, train-the-trainer, Uganda

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Authors: Mohammad Abdul Momin, Sekender Ali, Mahbubul Alam, Rafiquel Islam, Mohammad Mizanul Haque Kazal

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Improved high-yielding modern rice varieties can reduce hunger and food insecurity in Bangladesh. However, lower adoption and higher adoption gap of modern rice varieties are the main concerns of rice researchers, extension specialists, and legislators. This study attempts to determine the adoption status and adoption gap of 10 selected BRRI-released Boro rice varieties to assess some selected socio-economic characteristics of the rice farmers and to explore the contribution of the selected socio-economic characteristics of farmers to their adoption gap of selected BRRI-released Boro varieties. Necessary data were collected from 03 September to 31 December 2021 using a well-structured pre-tested interview schedule from 371 randomly selected farmers covering 12 agricultural blocks of four Upazilas under Cumilla, Mymensingh, Tangail, and Bogura districts. The study revealed that most (73.05%) of the rice farmers had high adoption and low adoption gap; 23.72% had moderate adoption and adoption gap; and the rest 3.23% of respondents’ farmers had low adoption and high adoption gap of BRRI-released Boro rice varieties. Overall adoption and adoption gap of BRRI-released Bororice varieties were 77.02% and 22.98%, respectively. Based on the descending order of the Adoption Index, BRRI dhan29 ranked 1st, followed by BRRI dhan28. The adoption indices of these two top-ranked varieties were 38.84 and 30.43, respectively, which were much higher than others. Third to ninth ranked varieties were BRRI dhan58, BRRI dhan89, BRRI dhan88, BRRI dhan50, BRRI dhan74, BRRI dhan81, and BRRI dhan63. Reverse-ranked orders were observed based on the descending order of the Adoption Gap Index (AGI). Stepwise multiple regression analysis indicated that ‘knowledge on BRRI-released Boro rice varieties’, ‘extension contacts’, ‘rice farming profitability’, ‘rice farming experience’, and ‘satisfaction on BRRI-releasedBoro rice varieties’ of the farmers had a significant negative contribution to their adoption gap, i.e., positive contribution to their adoption of BRRI-released Boro rice varieties. The study concluded that policy interventions should be taken to improve farmers’ knowledge of BRRI-releasedBoro rice varieties by increasing extension contact to all the lower and higher experienced farmers to make them profitable and satisfied to increase adoption and decrease the adoption gap of BRRI-released Boro rice varieties. These issues also urge policy interventions for the rethinking of current dissemination tactics to ensure the widespread adoption of newly released modern Boro rice varieties at the farm level.

Keywords: adoption, adoption gap, Boro, rice, BRRI, Bangladesh

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Authors: Christine Eickhoff, Alyssa Adams, Alaine Duncan

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The Washington, DC Veterans Affairs Medical Center (DC VAMC) offers complementary and integrative health (CIH) services such as acupuncture, yoga, meditation, and nutrition education through a coordinated outpatient clinic. The primary population utilizing CIH services are veterans with chronic pain. Acupuncture is one of the most popular of the CIH services available at the DC VAMC. As interest and availability grows, it is important to measure health outcomes associated with CIH service utilization. The purpose of this study was to investigate pain and mental health outcomes for veterans with chronic pain enrolled in individual acupuncture services in the DC VAMC. Veterans at the DC VAMC with self-identified chronic pain and no prior acupuncture experience were recruited for the study (n=70). Veterans were referred for services by a medical provider and completed baseline assessments at the program orientation prior to participating in any CIH services. Veterans received four individual, full-body acupuncture appointments within four weeks of study enrollment. After the first month, participants were scheduled for six appointments that occurred every two weeks and then eight more sessions that were scheduled one month apart. Follow-up assessments were administered at 2, 4, 6, 8, and 12 months. The findings reported will include completed time points at two and four months. Measures include a demographics survey, the Measure Yourself Medical Outcome Profile-2 (MYMOP-2), The Beck Depression Inventory (BDI-II), the Defense Veterans Pain Rating Scale (DVPRS), and the Pain Disability Questionnaire (PDQ). In this sample, 67% identified a pain condition as their primary health concern. Between baseline and two-month follow-up, there were significant improvements in participants’ primary health concern (MYMOP-2 p=0.010), general wellbeing (MYMOP-2 p=0.011), and a significant decrease in the use of medication (MYMOP-2 p<0.000). Between 2 and 4-month follow-up, pain disability (PDQ p=0.035), pain rating (DVPRS p=0.027), and depression (BDI-II p=0.003) significantly improved. Preliminary findings indicate that individual acupuncture therapy can be effective at improving health outcomes, well-being, and decreasing medication use in U.S. military veterans with chronic pain. Findings also suggest that individual acupuncture therapy can improve pain ratings, pain disability, and depression in veterans with chronic pain.

Keywords: acupuncture, chronic pain, depression, integrative health, medication use, military, pain, veterans, wellbeing

Procedia PDF Downloads 256

Authors: Fazıl Gökgöz, Fahrettin Filiz

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Load forecasting has become crucial in recent years and become popular in forecasting area. Many different power forecasting models have been tried out for this purpose. Electricity load forecasting is necessary for energy policies, healthy and reliable grid systems. Effective power forecasting of renewable energy load leads the decision makers to minimize the costs of electric utilities and power plants. Forecasting tools are required that can be used to predict how much renewable energy can be utilized. The purpose of this study is to explore the effectiveness of LSTM-based neural networks for estimating renewable energy loads. In this study, we present models for predicting renewable energy loads based on deep neural networks, especially the Long Term Memory (LSTM) algorithms. Deep learning allows multiple layers of models to learn representation of data. LSTM algorithms are able to store information for long periods of time. Deep learning models have recently been used to forecast the renewable energy sources such as predicting wind and solar energy power. Historical load and weather information represent the most important variables for the inputs within the power forecasting models. The dataset contained power consumption measurements are gathered between January 2016 and December 2017 with one-hour resolution. Models use publicly available data from the Turkish Renewable Energy Resources Support Mechanism. Forecasting studies have been carried out with these data via deep neural networks approach including LSTM technique for Turkish electricity markets. 432 different models are created by changing layers cell count and dropout. The adaptive moment estimation (ADAM) algorithm is used for training as a gradient-based optimizer instead of SGD (stochastic gradient). ADAM performed better than SGD in terms of faster convergence and lower error rates. Models performance is compared according to MAE (Mean Absolute Error) and MSE (Mean Squared Error). Best five MAE results out of 432 tested models are 0.66, 0.74, 0.85 and 1.09. The forecasting performance of the proposed LSTM models gives successful results compared to literature searches.

Keywords: deep learning, long short term memory, energy, renewable energy load forecasting

Procedia PDF Downloads 266

Authors: K. L. Pan, M. B. Chang

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Nitrogen oxides (NOₓ) is regarded as one of the most important air pollutants. It not only causes adverse environmental effects but also harms human lungs and respiratory system. As a post-combustion treatment, selective catalytic reduction (SCR) possess the highest NO removal efficiency ( ≥ 85%), which is considered as the most effective technique for removing NO from gas streams. However, injection of reducing agent such as NH₃ is requested, and it is costly and may cause secondary pollution. Reduction of NO with carbon monoxide (CO) as reducing agent has been previously investigated. In this process, the key step involves the NO adsorption and dissociation. Also, the high performance mainly relies on the amounts of oxygen vacancy on catalyst surface and redox ability of catalyst, because oxygen vacancy can activate the N-O bond to promote its dissociation. Additionally, perfect redox ability can promote the adsorption of NO and oxidation of CO. Typically, noble metals such as iridium (Ir), platinum (Pt), and palladium (Pd) are used as catalyst for the reduction of NO with CO; however, high cost has limited their applications. Recently, transition metal oxides have been investigated for the reduction of NO with CO, especially CuₓOy, CoₓOy, Fe₂O₃, and MnOₓ are considered as effective catalysts. However, deactivation is inevitable as oxygen (O₂) exists in the gas streams because active sites (oxygen vacancies) of catalyst are occupied by O₂. In this study, Cu-Ce-Fe-Co is prepared and supported on activated carbon by impregnation method to form 10% wt. Cu-Ce-Fe-Co/activated carbon catalyst. Generally, addition of activated carbon on catalyst can bring several advantages: (1) NO can be effectively adsorbed by interaction between catalyst and activated carbon, resulting in the improvement of NO removal, (2) direct NO decomposition may be achieved over carbon associated with catalyst, and (3) reduction of NO could be enhanced by a reducing agent over carbon-supported catalyst. Therefore, 10% wt. Cu-Ce-Fe-Co/activated carbon may have better performance for reduction of NO with CO. Experimental results indicate that NO conversion achieved with 10% wt. Cu-Ce-Fe-Co/activated carbon reaches 83% at 150°C with 300 ppm NO and 10,000 ppm CO. As temperature is further increased to 200°C, 100% NO conversion could be achieved, implying that 10% wt. Cu-Ce-Fe-Co/activated carbon prepared has good activity for the reduction of NO with CO. In order to investigate the effect of O₂ on reduction of NO with CO, 1-5% O₂ are introduced into the system. The results indicate that NO conversions still maintain at ≥ 90% with 1-5% O₂ conditions at 200°C. It is worth noting that effect of O₂ on reduction of NO with CO could be significantly improved as carbon is used as support. It is inferred that carbon support can react with O₂ to produce CO₂ as O₂ exists in the gas streams. Overall, 10% wt. Cu-Ce-Fe-Co/activated carbon is demonstrated with good potential for reduction of NO with CO, and possible mechanisms will be elucidated in this paper.

Keywords: nitrogen oxides (NOₓ), carbon monoxide (CO), reduction of NO with CO, carbon material, catalysis

Procedia PDF Downloads 256

Authors: Tesfaye Mengistu

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Power cables play a crucial role in the transmission and distribution of electrical energy. As the electricity generation, transmission, distribution, and storage systems become smarter, there is a growing emphasis on incorporating intelligent approaches to ensure the reliability of power cables. Various types of electrical cables are employed for transmitting and distributing electrical energy, with cross-linked polyethylene (XLPE) cables being widely utilized due to their exceptional electrical and mechanical properties. However, insulation defects can occur in XLPE cables due to subpar manufacturing techniques during production and cable joint installation. To address this issue, experts have proposed different methods for monitoring XLPE cables. Some suggest the use of interdigital capacitive (IDC) technology for online monitoring, while others propose employing continuous wave (CW) terahertz (THz) imaging systems to detect internal defects in XLPE plates used for power cable insulation. In this study, we have developed models that employ a custom dataset collected locally to classify the physical safety status of individual power cables. Our models aim to replace physical inspections with computer vision and image processing techniques to classify defective power cables from non-defective ones. The implementation of our project utilized the Python programming language along with the TensorFlow package and a convolutional neural network (CNN). The CNN-based algorithm was specifically chosen for power cable defect classification. The results of our project demonstrate the effectiveness of CNNs in accurately classifying power cable defects. We recommend the utilization of similar or additional datasets to further enhance and refine our models. Additionally, we believe that our models could be used to develop methodologies for detecting power cable defects from live video feeds. We firmly believe that our work makes a significant contribution to the field of power cable inspection and maintenance. Our models offer a more efficient and cost-effective approach to detecting power cable defects, thereby improving the reliability and safety of power grids.

Keywords: artificial intelligence, computer vision, defect detection, convolutional neural net

Procedia PDF Downloads 112

Authors: Adebisi A. Sunday, Babajide Adeokin

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The persistent rise in farm theft in rural region of Nigeria is attributed to the lack of adequate and effective policing in the regions; thus, this brought about the inevitable introduction of native charms on farmlands as a means of fortification of harvests against theft in Ayetoro community. The use of charm by farmers as security on farmlands is a traditional crime control mechanism that is largely based on unwritten laws which greatly influenced the lives of people, and their attitudes toward the society. This research presents a qualitative sociological study on how native charms are deployed by farmers for protection against theft. The study investigated the various types of charms that are employed as security measures among farmers in Ayetoro community and the rationale behind the use of these mechanisms as farm security. The study utilized qualitative method to gather data in the research process. Under the qualitative method, in-depth interview method was adopted to generate a robust and detailed data from the respondents. Also the data generated were analysed qualitatively using thematic content analysis and simple description which was preceded by transcription of data from the recorder. It was revealed that amidst numerous charms known, two major charms are used on farmlands as a measure of social control in Ayetoro community, Ogun state South West Nigeria. Furthermore, the result of this study showed that, the desire for safekeeping of harvest from pilferers and the heavy punishments dispense on offenders by native charms are the reasons why farmers deploy charms on their farms. In addition, findings revealed that the adoption of these charms for protection has improved yields among farmers in the community because the safety of harvest has been made possible by virtue of the presence of various charms in the farm lands. Therefore, based on the findings of this study, it is recommended that such measures should be recognized in mainstream social control mechanisms in the fight against crime in Nigeria and the rest of the world. Lastly, native charms could be installed in all social and cooperate organisation and position of authority to prevent theft of valuables and things hold with utmost importance.

Keywords: Ayetoro, farm theft, mechanism, native charms, Pilferer

Procedia PDF Downloads 145

Authors: Zhan Panpan, Lu Lan, Sun Yong, He Xiongwen, Yan Dong, Gu Ming

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The function of the two spacecraft docking network, the communication and control of a docking target with various spacecrafts is realized in the space lab data management system. In order to solve the problem of the complex data communication mode between the space lab and various spaceships, and the problem of software reuse caused by non-standard protocol, a data management software system supporting rendezvous and docking with various spaceships has been designed. The software system is based on CCSDS Spcecraft Onboard Interface Service(SOIS). It consists of Software Driver Layer, Middleware Layer and Appliaction Layer. The Software Driver Layer hides the various device interfaces using the uniform device driver framework. The Middleware Layer is divided into three lays, including transfer layer, application support layer and system business layer. The communication of space lab plaform bus and the docking bus is realized in transfer layer. Application support layer provides the inter tasks communitaion and the function of unified time management for the software system. The data management software functions are realized in system business layer, which contains telemetry management service, telecontrol management service, flight status management service, rendezvous and docking management service and so on. The Appliaction Layer accomplishes the space lab data management system defined tasks using the standard interface supplied by the Middleware Layer. On the basis of layered architecture, rendezvous and docking tasks and the rendezvous and docking management service are independent in the software system. The rendezvous and docking tasks will be activated and executed according to the different spaceships. In this way, the communication management functions in the independent flight mode, the combination mode of the manned spaceship and the combination mode of the cargo spaceship are achieved separately. The software architecture designed standard appliction interface for the services in each layer. Different requirements of the space lab can be supported by the use of standard services per layer, and the scalability and flexibility of the data management software can be effectively improved. It can also dynamically expand the number and adapt to the protocol of visiting spaceships. The software system has been applied in the data management subsystem of the space lab, and has been verified in the flight of the space lab. The research results of this paper can provide the basis for the design of the data manage system in the future space station.

Keywords: space lab, rendezvous and docking, data management, software system

Procedia PDF Downloads 368

Authors: Veronica S. Moertini, Vinsensius Kevin, Gede Karya

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Customer reviews have been collected by many kinds of e-commerce websites selling products, services, hotel rooms, tickets and so on. Each website collects its own customer reviews. The reviews can be crawled, collected from those websites and stored as big data. Text analysis techniques can be used to analyze that data to produce summarized information, such as customer opinions. Then, these opinions can be published by independent service provider websites and used to help customers in choosing the most suitable products or services. As the opinions are analyzed from big data of reviews originated from many websites, it is expected that the results are more trusted and accurate. Indonesian customers write reviews in Indonesian language, which comes with its own structures and uniqueness. We found that most of the reviews are expressed with “daily language”, which is informal, do not follow the correct grammar, have many abbreviations and slangs or non-formal words. Hadoop is an emerging platform aimed for storing and analyzing big data in distributed systems. A Hadoop cluster consists of master and slave nodes/computers operated in a network. Hadoop comes with distributed file system (HDFS) and MapReduce framework for supporting parallel computation. However, MapReduce has weakness (i.e. inefficient) for iterative computations, specifically, the cost of reading/writing data (I/O cost) is high. Given this fact, we conclude that MapReduce function is best adapted for “one-pass” computation. In this research, we develop an efficient technique for extracting or mining opinions from big data of Indonesian reviews, which is based on MapReduce with one-pass computation. In designing the algorithm, we avoid iterative computation and instead adopt a “look up table” technique. The stages of the proposed technique are: (1) Crawling the data reviews from websites; (2) cleaning and finding root words from the raw reviews; (3) computing the frequency of the meaningful opinion words; (4) analyzing customers sentiments towards defined objects. The experiments for evaluating the performance of the technique were conducted on a Hadoop cluster with 14 slave nodes. The results show that the proposed technique (stage 2 to 4) discovers useful opinions, is capable of processing big data efficiently and scalable.

Keywords: big data analysis, Hadoop MapReduce, analyzing text data, mining Indonesian reviews

Procedia PDF Downloads 201

Authors: Hossein Aghababaei, Sergio Vitale, Giampaolo ferraioli

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In the context of Synthetic Aperture Radar (SAR) data, polarization is an important source of information for Earth's surface monitoring . SAR Systems are often considered to transmit only one polarization. This constraint leads to either single or dual polarimetric SAR imaging modalities. Single polarimetric systems operate with a fixed single polarization of both transmitted and received electromagnetic (EM) waves, resulting in a single acquisition channel. Dual polarimetric systems, on the other hand, transmit in one fixed polarization and receive in two orthogonal polarizations, resulting in two acquisition channels. Dual polarimetric systems are obviously more informative than single polarimetric systems and are increasingly being used for a variety of remote sensing applications. In dual polarimetric systems, the choice of polarizations for the transmitter and the receiver is open. The choice of circular transmit polarization and coherent dual linear receive polarizations forms a special dual polarimetric system called hybrid polarimetry, which brings the properties of rotational invariance to geometrical orientations of features in the scene and optimizes the design of the radar in terms of reliability, mass, and power constraints. The complete characterization of target scattering, however, requires fully polarimetric data, which can be acquired with systems that transmit two orthogonal polarizations. This adds further complexity to data acquisition and shortens the coverage area or swath of fully polarimetric images compared to the swath of dual or hybrid polarimetric images. The search for solutions to augment dual polarimetric data to full polarimetric data will therefore take advantage of full characterization and exploitation of the backscattered field over a wider coverage with less system complexity. Several methods for reconstructing fully polarimetric images using hybrid polarimetric data can be found in the literature. Although the improvements achieved by the newly investigated and experimented reconstruction techniques are undeniable, the existing methods are, however, mostly based upon model assumptions (especially the assumption of reflectance symmetry), which may limit their reliability and applicability to vegetation and forest scenarios. To overcome the problems of these techniques, this paper proposes a new framework for reconstructing fully polarimetric information from hybrid polarimetric data. The framework uses Deep Learning solutions to augment hybrid polarimetric data without relying on model assumptions. A convolutional neural network (CNN) with a specific architecture and loss function is defined for this augmentation problem by focusing on different scattering properties of the polarimetric data. In particular, the method controls the CNN training process with respect to several characteristic features of polarimetric images defined by the combination of different terms in the cost or loss function. The proposed method is experimentally validated with real data sets and compared with a well-known and standard approach from the literature. From the experiments, the reconstruction performance of the proposed framework is superior to conventional reconstruction methods. The pseudo fully polarimetric data reconstructed by the proposed method also agree well with the actual fully polarimetric images acquired by radar systems, confirming the reliability and efficiency of the proposed method.

Keywords: SAR image, deep learning, convolutional neural network, deep neural network, SAR polarimetry

Procedia PDF Downloads 90

Authors: Valdir Moura, Raniele dos Anjos de Souza, Fernando Gomes de Souza, Jose Vagner da Silva, Jerry Adriani Johann

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The estimate of the Area of Land to be planted with annual crops and its stratification by the municipality are important variables in crop forecast. Nowadays in Brazil, these information’s are obtained by the Brazilian Institute of Geography and Statistics (IBGE) and published under the report Assessment of the Agricultural Production. Due to the high cloud cover in the main crop growing season (October to March) it is difficult to acquire good orbital images. Thus, one alternative is to work with remote sensing data from dates before the crop growing season. This work presents the use of multitemporal Landsat data gathered on July and September (before the summer growing season) in order to estimate the area of land to be planted with summer crops in an area of São Paulo State, Brazil. Geographic Information Systems (GIS) and digital image processing techniques were applied for the treatment of the available data. Supervised and non-supervised classifications were used for data in digital number and reflectance formats and the multitemporal Normalized Difference Vegetation Index (NDVI) images. The objective was to discriminate the tracts with higher probability to become planted with summer crops. Classification accuracies were evaluated using a sampling system developed basically for this study region. The estimated areas were corrected using the error matrix derived from these evaluations. The classification techniques presented an excellent level according to the kappa index. The proportion of crops stratified by municipalities was derived by a field work during the crop growing season. These proportion coefficients were applied onto the area of land to be planted with summer crops (derived from Landsat data). Thus, it was possible to derive the area of each summer crop by the municipality. The discrepancies between official statistics and our results were attributed to the sampling and the stratification procedures. Nevertheless, this methodology can be improved in order to provide good crop area estimates using remote sensing data, despite the cloud cover during the growing season.

Keywords: area intended for summer culture, estimated area planted, agriculture, Landsat, planting schedule

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Authors: A. Georgiou, A. Galani, A. Karatza, G. E. Bampasidis

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Augmented Reality (AR) technology has become integral in enhancing visitor experiences at Greece's UNESCO World Heritage Sites. This research meticulously investigates various facets of AR applications/games associated with these revered sites. The cultural heritage represents the identity of each nation in the world. Technology can breathe life into this identity. Through Augmented Reality (AR), individuals can travel back in time, visit places they cannot access in real life, discover the history of these places, and live unique experiences. The study examines the objectives and intended goals behind the development and deployment of each augmented reality application/game pertaining to the UNESCO World Heritage Sites in Greece. It thoroughly analyzes the scenarios presented within these AR games/applications, examining how historical narratives, interactive elements, and cultural context are incorporated to engage users. Furthermore, the research identifies and assesses the technological platforms utilized for the development and implementation of these AR experiences, encompassing mobile devices, AR headsets, or specific software frameworks. It classifies and examines the types of augmented reality employed within these applications/games, including marker-based, markerless, location-based, or immersive AR experiences. Evaluation of the benefits accrued by visitors engaging with these AR applications/games, such as enhanced learning experiences, improved cultural understanding, and heightened engagement with the heritage sites, forms a crucial aspect of this study. Additionally, the research scrutinizes potential drawbacks or limitations associated with the AR applications/games, considering technological barriers, user accessibility issues, or constraints affecting user experience. By thoroughly investigating these pivotal aspects, this research aims to provide a comprehensive overview and analysis of the landscape of augmented reality applications/games linked to the UNESCO World Heritage Sites in Greece. The findings seek to contribute nuanced insights into the effectiveness, challenges, and opportunities associated with leveraging AR technology for heritage site preservation, visitor engagement, and cultural enrichment.

Keywords: augmented reality, AR applications, UNESCO sites, cultural heritage, Greece, visitor engagement, historical narratives

Procedia PDF Downloads 64

Authors: Neslihan Demirci, Serdar Durdağı

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Mycobacterium tuberculosis is still a worldwide disease-causing agent that, according to WHO, led to the death of 1.5 million people from tuberculosis (TB) in 2020. The bacteria reside in macrophages located specifically in the lung. There is a known quadruple drug therapy regimen for TB consisting of isoniazid (INH), rifampin (RIF), pyrazinamide (PZA), and ethambutol (EMB). Over the past 60 years, there have been great contributions to treatment options, such as recently approved delamanid (OPC67683) and bedaquiline (TMC207/R207910), targeting mycolic acid and ATP synthesis, respectively. Also, there are natural compounds that can block the tryptophanyl-tRNA synthetase (TrpRS) enzyme, chuangxinmycin, and indolmycin. Yet, already the drug resistance is reported for those agents. In this study, the newly released TrpRS enzyme structure is investigated for potential inhibitor drugs from already synthesized molecules to help the treatment of resistant cases and to propose an alternative drug for the quadruple drug therapy of tuberculosis. Maestro, Schrodinger is used for docking and molecular dynamic simulations. In-house library containing ~8000 compounds among FDA-approved indole-containing compounds, a total of 57 obtained from the ChemBL were used for both ATP and tryptophan binding pocket docking. Best of indole-containing 57 compounds were subjected to hit expansion and compared later with virtual screening workflow (VSW) results. After docking, VSW was done. Glide-XP docking algorithm was chosen. When compared, VSW alone performed better than the hit expansion module. Best scored compounds were kept for ten ns molecular dynamic simulations by Desmond. Further, 100 ns molecular dynamic simulation was performed for elected molecules according to Z-score. The top three MMGBSA-scored compounds were subjected to steered molecular dynamic (SMD) simulations by Gromacs. While SMD simulations are still being conducted, ponesimod (for multiple sclerosis), vilanterol (β₂ adrenoreceptor agonist), and silodosin (for benign prostatic hyperplasia) were found to have a significant affinity for tuberculosis TrpRS, which is the propulsive force for the urge to expand the research with in vitro studies. Interestingly, top-scored ponesimod has been reported to have a side effect that makes the patient prone to upper respiratory tract infections.

Keywords: drug repurposing, molecular dynamics, tryptophanyl-tRNA synthetase, tuberculosis

Procedia PDF Downloads 123

Authors: Mayur Kudale, Priyanka Panchal

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Cellulose fibres, mainly cotton, are the most important kind of fibre used for manufacturing shirting fabric. However, to overcome its main disadvantage, that is it gets wrinkled after washing, is to use special kind of finish which is resin finish. This finish provides a resistance against shrinkage along with improved wet and dry wrinkle recovery to cellulosic textiles. The Durable Press (DP) finish uses a mechanism of cross-linking with polymers or resin to inhibit the easy movement of the cellulose chains. The purpose of these experimentations on the weave is to observe and compare the variations in properties after DP finish without adverse effect on strength of the fabric. In this work, we have prepared three types of fabric weaves viz. Plain, Twill and Sateen with their construction parameters intact. To get the projected results, this work uses three types of variables viz. concentration of Resin, Temperature and Time. Resultant of these variables is only change in weave or construction on DP finish which further opens the possibilities of improvement of DP either of mentioned weaves. The combined effect of such various parametric resin finish methodology will give the best method to improve the DP. However, the DP finish can cause a side effect of reduction in elasticity and flexibility of cellulosic fibres. The natural cellulose could loss abrasion resistance along with tear and tensile strength by applying DP finish. In this work, it is taken care that the tear strength of fabric will not drop below certain limit otherwise the fabric will tear down easily. In this work, it is found that there is a significant drop in tearing and tensile strength with the improvement of DP finish. Later on, it is also found that the twill weave has more percentage drop in tearing strength as compared to plain and sateen weave. There is major kind of observations obtained after this work. First, the mixing of cotton should be done properly to achieve the higher DP rating in plain weave. Second, the careful combination of warp, weft and fabric construction must be decided to avoid the high drop in tear and tensile strength in a twill weave. Third, the sateen weave has a good sheen and DP rating hence it can be used in shirting of gents and ladies dress materials. This concludes that to achieve higher DP ratings, use plain weave construction than twill and sateen because it has the lowest tear and tensile strength drop.

Keywords: concentration of resin, cross-linking, durable press (DP) finish, sheen, tear and tensile strength, weave

Procedia PDF Downloads 301

Authors: Susan Ka Yee Chow

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Effective clinical preceptorship is affecting students’ competence and fostering their growth in applying theoretical knowledge and skills in clinical settings. Any difference between the expected and actual learning experience will reduce nursing students’ interest in clinical practices and having a negative consequence with their clinical performance. This cross-sectional study is an attempt to compare the differences between preferred and actual preceptorship experience of final year nursing students, and to examine the relationship between the actual preceptorship experience and perceived clinical competence of the students in a tertiary institution. Participants of the study were final year bachelor nursing students of a self-financing tertiary institution in Hong Kong. The instruments used to measure the effectiveness of clinical preceptorship was developed by the participating institution. The scale consisted of five items in a 5-point likert scale. The questions including goals development, critical thinking, learning objectives, asking questions and providing feedback to students. The “Clinical Competence Questionnaire” by Liou & Cheng (2014) was used to examine students’ perceived clinical competences. The scale consisted of 47 items categorized into four domains, namely nursing professional behaviours; skill competence: general performance; skill competence: core nursing skills and skill competence: advanced nursing skills. There were 193 questionnaires returned with a response rate of 89%. The paired t-test was used to compare the differences between preferred and actual preceptorship experiences of students. The results showed significant differences (p<0.001) for the five questions. The mean for the preferred scores is higher than the actual scores resulting statistically significance. The maximum mean difference was accepted goal and the highest mean different was giving feedback. The Pearson Correlation Coefficient was used to examine the relationship. The results showed moderate correlations between nursing professional behaviours with asking questions and providing feedback. Providing useful feedback to students is having moderate correlations with all domains of the Clinical Competence Questionnaire (r=0.269 – 0.345). It is concluded that nursing students do not have a positive perception of the clinical preceptorship. Their perceptions are significantly different from their expected preceptorship. If students were given more opportunities to ask questions in a pedagogical atmosphere, their perceived clinical competence and learning outcomes could be improved as a result.

Keywords: clinical preceptor, clinical competence, clinical practicum, nursing students

Procedia PDF Downloads 127

Authors: Qiong Li

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This study investigated in which condition computer-mediated communication (CMC) could promote pragmatic development. The focal feature included four Chinese sentence final particles (SFPs), a, ya, ba, and ne. They occur frequently in Chinese, and function as mitigators to soften the tone of speech. However, L2 acquisition of SFPs is difficult, suggesting the necessity of additional exposure to or explicit instruction on Chinese SFPs. This study follows this line and aims to explore two research questions: (1) Is CMC combined with data-driven instruction more effective than CMC alone in promoting L2 Chinese learners’ SFP use? (2) How does L2 Chinese learners’ SFP use change over time, as compared to the production of native Chinese speakers? The study involved 19 intermediate-level learners of Chinese enrolled at a private American university. They were randomly assigned to two groups: (1) the control group (N = 10), which was exposed to SFPs through CMC alone, (2) the treatment group (N = 9), which was exposed to SFPs via CMC and data-driven instruction. Learners interacted with native speakers on given topics through text-based CMC over Skype. Both groups went through six 30-minute CMC sessions on a weekly basis, with a one-week interval after the first two CMC sessions and a two-week interval after the second two CMC sessions (nine weeks in total). The treatment group additionally received a data-driven instruction after the first two sessions. Data analysis focused on three indices: token frequency, type frequency, and acceptability of SFP use. Token frequency was operationalized as the raw occurrence of SFPs per clause. Type frequency was the range of SFPs. Acceptability was rated by two native speakers using a rating rubric. The results showed that the treatment group made noticeable progress over time on the three indices. The production of SFPs approximated the native-like level. In contrast, the control group only slightly improved on token frequency. Only certain SFPs (a and ya) reached the native-like use. Potential explanations for the group differences were discussed in two aspects: the property of Chinese SFPs and the role of CMC and data-driven instruction. Though CMC provided the learners with opportunities to notice and observe SFP use, as a feature with low saliency, SFPs were not easily noticed in input. Data-driven instruction in the treatment group directed the learners’ attention to these particles, which facilitated the development.

Keywords: computer-mediated communication, data-driven instruction, pragmatic development, second language Chinese, sentence final particles

Procedia PDF Downloads 418