Search results for: pivot language translation approach

Authors: Amit Kumar Jain, Surbhi Jain

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The urban freight in a city constitutes 10 to 18 per cent of all city road traffic, and 40 per cent of air pollution and noise emissions, are directly related to commercial transport. The policy measures implemented by urban planners have sought to restrict rather than assist goods-vehicle operations. This approach has temporarily controlled the urban transport demand during peak hours of traffic but has not effectively solved transport congestion. The solution discussed in the paper envisages the development of a comprehensive network of Urban Freight Stations (UFS) connected through underground conveyor belts in the city in line with baggage segregation and distribution in any of the major airports. The transportation of freight shall be done in standard size containers/cars through rail borne carts. The freight can be despatched or received from any of the UFS. Once freight is booked for a destination from any of the UFS, it would be stuffed in the container and digitally tagged for the destination. The container would reach the destination UFS through a network of rail borne carts. The container would be de-stuffed at the destination UFS and sent for further delivery, or the consignee may be asked to collect the consignment from urban freight station. The obvious benefits would be decongestion of roads, reduction in air and noise pollution, saving in manpower used for freight transportation.

Keywords: congestion, urban freight, intelligent transport system, pollution

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Authors: S. Bader, N. Essoukri Ben Amara

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With the technological development and rise of virtual worlds, these spaces are becoming more and more attractive for cybercriminals, hidden behind avatars and fictitious identities. Since access to these spaces is not restricted or controlled, some impostors take advantage of gaining unauthorized access and practicing cyber criminality. This paper proposes an identity management approach for securing access to virtual worlds. The major purpose of the suggested solution is to install a strong security mechanism to protect virtual identities represented by avatars. Thus, only legitimate users, through their corresponding avatars, are allowed to access the platform resources. Access is controlled by integrating an authentication process based on biometrics. In the request process for registration, a user fingerprint is enrolled and then encrypted into a watermark utilizing a cancelable and non-invertible algorithm for its protection. After a user personalizes their representative character, the biometric mark is embedded into the avatar through a watermarking procedure. The authenticity of the avatar identity is verified when it requests authorization for access. We have evaluated the proposed approach on a dataset of avatars from various virtual worlds, and we have registered promising performance results in terms of authentication accuracy, acceptation and rejection rates.

Keywords: identity management, security, biometrics authentication and authorization, avatar, virtual world

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Authors: Amir Tosson, Mohammad Reza, Christian Gutt

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Metadata stands at the forefront of advancing data management practices within research communities, with particular significance in the realms of neutron and photon scattering. This paper introduces a groundbreaking approach—dynamic metadata schema—within the context of X-ray Photon Correlation Spectroscopy (XPCS). XPCS, a potent technique unravelling nanoscale dynamic processes, serves as an illustrative use case to demonstrate how dynamic metadata can revolutionize data acquisition, sharing, and analysis workflows. This paper explores the challenges encountered by the neutron and photon communities in navigating intricate data landscapes and highlights the prowess of dynamic metadata in addressing these hurdles. Our proposed approach empowers researchers to tailor metadata definitions to the evolving demands of experiments, thereby facilitating streamlined data integration, traceability, and collaborative exploration. Through tangible examples from the XPCS domain, we showcase how embracing dynamic metadata standards bestows advantages, enhancing data reproducibility, interoperability, and the diffusion of knowledge. Ultimately, this paper underscores the transformative potential of dynamic metadata, heralding a paradigm shift in data management within the neutron and photon research communities.

Keywords: metadata, FAIR, data analysis, XPCS, IoT

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Authors: Amal Abdelhadi

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Consumer misbehavior evaluation can be markedly different based on a number of variables and different from one environment to another. Using three aberrant consumer behavior (ACB) scenarios (shoplifting, stealing from hotel rooms and software piracy) this study aimed to explore Libyan seller and consumers of ACB. Materials were collected by using a multi-method approach was employed (qualitative and quantitative approaches) in two fieldwork phases. In the phase stage, a qualitative data were collected from 26 Libyan sellers’ by face-to-face interviews. In the second stage, a consumer survey was used to collect quantitative data from 679 Libyan consumers. This study found that the consumer’s and seller’s evaluation of ACB are not always consistent. Further, ACB evaluations differed based on the form of ACB. Furthermore, the study found that not all consumer behaviors that were considered as bad behavior in other countries have the same evaluation in Libya; for example, software piracy. Therefore this study suggested a newly developed classification of ACB based on marketers’ and consumers’ views. This classification provides 9 ACB types within two dimensions (marketers’ and consumers’ views) and three degrees of behavior evaluation (good, acceptable and misbehavior).

Keywords: aberrant consumer behavior, Libya, multi-method approach, planned behavior theory

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Authors: Mortez Alijani, Anas Osman

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Recently, the popularity of Unmanned aerial vehicles (UAVs) has skyrocketed amidst the unprecedented events and the global pandemic, as they play a key role in both the security and health sectors, through surveillance, taking test samples, transportation of crucial goods and spreading awareness among civilians. However, the process of designing and producing such aerial robots is suppressed by the internal and external constraints that pose serious challenges. Landing is one of the key operations during flight, especially, the autonomous landing of UAVs on a moving platform is a scientifically complex engineering problem. Typically having a successful automatic landing of UAV on a moving platform requires accurate localization of landing, fast trajectory planning, and robust control planning. To achieve these goals, the information about the autonomous landing process such as the intersection point, the position of platform/UAV and inclination angle are more necessary. In this study, the mathematical approach to this problem in the X-Y axis based on the inclination angle and position of UAV in the landing process have been presented. The experimental results depict the accurate position of the UAV, intersection between UAV and moving platform and inclination angle in the landing process, allowing prediction of the intersection point.

Keywords: autonomous landing, inclination angle, unmanned aerial vehicles, moving platform, X-Y axis, intersection point

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Authors: Emmy Pratiwi, Ketut B. Artana, A. A. B. Dinariyana

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Madura Strait is considered as one of the busiest shipping channels in Indonesia. High vessel traffic density in Madura Strait gives serious threat due to navigational safety in this area, i.e. ship collision. This study is necessary as an attempt to enhance the safety of marine traffic. Fuzzy inference system (FIS) is proposed to calculate risk collision of ships. Collision risk is evaluated based on ship domain, Distance to Closest Point of Approach (DCPA), and Time to Closest Point of Approach (TCPA). Data were collected by utilizing Automatic Identification System (AIS). This study considers several ships’ domain models to give the characteristic of marine traffic in the waterways. Each encounter in the ship domain is analyzed to obtain the level of collision risk. Risk level of ships, as the result in this study, can be used as guidance to avoid the accident, providing brief description about safety traffic in Madura Strait and improving the navigational safety in the area.

Keywords: automatic identification system, collision risk, DCPA, fuzzy inference system, TCPA

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Authors: Aedah Abd Rahman

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Software organisations may implement single or multiple frameworks in order to remain competitive. There are wide selection of generic Software Process Improvement (SPI) frameworks, best practices and standards implemented with different focuses and goals. Issues and difficulties emerge in the SPI practices from the context of software development and IT Service Management (ITSM). This research looks into the integration of multiple frameworks from the perspective of software development and ITSM. The research question of this study is how to define steps of methodology to solve the multi model software process improvement problem. The objective of this study is to define the research approach and methodologies to produce a more integrated and efficient Multi Model Process Improvement (MMPI) solution. A multi-step methodology is used which contains the case study, framework mapping and Delphi study. The research outcome has proven the usefulness and appropriateness of the proposed framework in SPI and quality practice in Malaysian software industries. This mixed method research approach is used to tackle problems from every angle in the context of software development and services. This methodology is used to facilitate the implementation and management of multi model environment of SPI frameworks in multiple domains.

Keywords: Delphi study, methodology, multi model software process improvement, service management

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Authors: Loris Franchi, Daniele Calvi, Sabrina Corpino

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This paper presents a design methodology in which stakeholders are assisted with the exploration of a so-called negotiation space, aiming to the maximization of both group social welfare and single stakeholder’s perceived utility. The outcome results in less design iterations needed for design convergence while obtaining a higher solution effectiveness. During the early stage of a space project, not only the knowledge about the system but also the decision outcomes often are unknown. The scenario is exacerbated by the fact that decisions taken in this stage imply delayed costs associated with them. Hence, it is necessary to have a clear definition of the problem under analysis, especially in the initial definition. This can be obtained thanks to a robust generation and exploration of design alternatives. This process must consider that design usually involves various individuals, who take decisions affecting one another. An effective coordination among these decision-makers is critical. Finding mutual agreement solution will reduce the iterations involved in the design process. To handle this scenario, the paper proposes a design methodology which, aims to speed-up the process of pushing the mission’s concept maturity level. This push up is obtained thanks to a guided negotiation space exploration, which involves autonomously exploration and optimization of trade opportunities among stakeholders via Artificial Intelligence algorithms. The negotiation space is generated via a multidisciplinary collaborative optimization method, infused by game theory and multi-attribute utility theory. In particular, game theory is able to model the negotiation process to reach the equilibria among stakeholder needs. Because of the huge dimension of the negotiation space, a collaborative optimization framework with evolutionary algorithm has been integrated in order to guide the game process to efficiently and rapidly searching for the Pareto equilibria among stakeholders. At last, the concept of utility constituted the mechanism to bridge the language barrier between experts of different backgrounds and differing needs, using the elicited and modeled needs to evaluate a multitude of alternatives. To highlight the benefits of the proposed methodology, the paper presents the design of a CubeSat mission for the observation of lunar radiation environment. The derived solution results able to balance all stakeholders needs and guaranteeing the effectiveness of the selection mission concept thanks to its robustness in valuable changeability. The benefits provided by the proposed design methodology are highlighted, and further development proposed.

Keywords: concurrent engineering, artificial intelligence, negotiation in engineering design, multidisciplinary optimization

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Authors: Sehrish Qayyum

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Active military power contention is shifting to economic and cyberwar to retain hegemony. Attuned Pestle analysis confirms that structural stress of hegemon’s power loss drives a containment approach towards caging actions. Ongoing diplomatic, asymmetric, proxy and direct wars are increasing stress hegemon’s power retention due to tangled military and economic alliances. It creates the condition of catalepsy with defective reflexive control which affects the core warfare operations. When one’s own power is doubted it gives power to one’s own doubt to ruin all planning either done with superlative cost-benefit analysis. Strategically calculated estimation of Hegemon’s power game since the early WWI to WWII, WWII-to Cold War and then to the current era in three chronological periods exposits that Thucydides’s trap became the reason for war broke out. Thirst for power is the demise of imagination and cooperation for better sense to prevail instead it drives ashes to dust. Pestle analysis is a wide array of evaluation from political and economic to legal dimensions of the state matters. It helps to develop the Pacification and Security Policy Plan (PSPP) to avoid hegemon’s structural stress of power loss in fact, in turn, creates an alliance with maximum amicable outputs. PSPP may serve to regulate and pause the hurricane of power clashes. PSPP along with a strategic work plan is based on Pestle analysis to deal with any conceivable war condition and approach for saving international peace. Getting tangled into self-imposed epistemic dilemmas results in regret that becomes the only option of performance. It is a generic application of probability tests to find the best possible options and conditions to develop PSPP for any adversity possible so far. Innovation in expertise begets innovation in planning and action-plan to serve as a rheostat approach to deal with any plausible power clash.

Keywords: alliance, hegemon, pestle analysis, pacification and security policy plan, security

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Authors: Abe Degale D., Cheng Jian

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When creating automated video surveillance systems, violent action recognition is crucial. In recent years, hand-crafted feature detectors have been the primary method for achieving violence detection, such as the recognition of fighting activity. Researchers have also looked into learning-based representational models. On benchmark datasets created especially for the detection of violent sequences in sports and movies, these methods produced good accuracy results. The Hockey dataset's videos with surveillance camera motion present challenges for these algorithms for learning discriminating features. Image recognition and human activity detection challenges have shown success with deep representation-based methods. For the purpose of detecting violent images and identifying aggressive human behaviours, this research suggested a deep representation-based model using the transfer learning idea. The results show that the suggested approach outperforms state-of-the-art accuracy levels by learning the most discriminating features, attaining 99.34% and 99.98% accuracy levels on the Hockey and Movies datasets, respectively.

Keywords: violence detection, faster RCNN, transfer learning and, surveillance video

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Authors: Anandita Mitro, Archana Srivastava, Bidisha Banerjee

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The present paper attempts to analyze the hierarchy and interconnection of factors responsible for the uptake of BCG vaccination in India. The study uses National Family Health Survey (NFHS-5) data which was conducted during 2019-21. The univariate logistic regression method is used to understand the univariate effects while the interconnection effects have been studied using the Categorical Inference Tree (CIT) which is a non-parametric Machine Learning (ML) model. The hierarchy of the factors is further established using Conditional Inference Forest which is an extension of the CIT approach. The results suggest that BCG vaccination coverage was influenced more by system-level factors and awareness than education or socio-economic status. Factors such as place of delivery, antenatal care, and postnatal care were crucial, with variations based on delivery location. Region-specific differences were also observed which could be explained by the factors. Awareness of the disease was less impactful along with the factor of wealth and urban or rural residence, although awareness did appear to substitute for inadequate ANC. Thus, from the policy point of view, it is revealed that certain subpopulations have less prevalence of vaccination which implies that there is a need for population-specific policy action to achieve a hundred percent coverage.

Keywords: vaccination, NFHS, machine learning, public health

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Authors: Vishal Das, Tianyi Mao, Zhicheng Geng, Carmen Flores, Diego Pelloso, Fang Wang

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Accurate sell-in and sell-out forecasting is a ubiquitous problem in the retail industry. It is an important element of any demand planning activity. As a global food and beverage company, Nestlé has hundreds of products in each geographical location that they operate in. Each product has its sell-in and sell-out time series data, which are forecasted on a weekly and monthly scale for demand and financial planning. To address this challenge, Nestlé Chilein collaboration with Amazon Machine Learning Solutions Labhas developed their in-house solution of using machine learning models for forecasting. Similar products are combined together such that there is one model for each product category. In this way, the models learn from a larger set of data, and there are fewer models to maintain. The solution is scalable to all product categories and is developed to be flexible enough to include any new product or eliminate any existing product in a product category based on requirements. We show how we can use the machine learning development environment on Amazon Web Services (AWS) to explore a set of forecasting models and create business intelligence dashboards that can be used with the existing demand planning tools in Nestlé. We explored recent deep learning networks (DNN), which show promising results for a variety of time series forecasting problems. Specifically, we used a DeepAR autoregressive model that can group similar time series together and provide robust predictions. To further enhance the accuracy of the predictions and include domain-specific knowledge, we designed an ensemble approach using DeepAR and XGBoost regression model. As part of the ensemble approach, we interlinked the sell-out and sell-in information to ensure that a future sell-out influences the current sell-in predictions. Our approach outperforms the benchmark statistical models by more than 50%. The machine learning (ML) pipeline implemented in the cloud is currently being extended for other product categories and is getting adopted by other geomarkets.

Keywords: sell-in and sell-out forecasting, demand planning, DeepAR, retail, ensemble machine learning, time-series

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Authors: Diana López-Soto, Soumaya Yacout, Francisco Ángel-Bello

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Although inventories are considered as stocks of money sitting on shelve, they are needed in order to secure a constant and continuous production. Therefore, companies need to have control over the amount of inventory in order to find the balance between excessive and shortage of inventory. The classification of items according to certain criteria such as the price, the usage rate and the lead time before arrival allows any company to concentrate its investment in inventory according to certain ranking or priority of items. This makes the decision making process for inventory management easier and more justifiable. The purpose of this paper is to present a new approach for the classification of new items based on the already existing criteria. This approach is called the Logical Analysis of Data (LAD). It is used in this paper to assist the process of ABC items classification based on multiple criteria. LAD is a data mining technique based on Boolean theory that is used for pattern recognition. This technique has been tested in medicine, industry, credit risk analysis, and engineering with remarkable results. An application on ABC inventory classification is presented for the first time, and the results are compared with those obtained when using the well-known AHP technique and the ANN technique. The results show that LAD presented very good classification accuracy.

Keywords: ABC multi-criteria inventory classification, inventory management, multi-class LAD model, multi-criteria classification

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Authors: E. Hegazy, S. Anderberg, J. Krook

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The city's wire-based infrastructure systems (WBIS) are responsible for the delivery of electricity, telecommunications, sanitation, drainage, and district heating and are a necessity for sustainable modern urban life. Maintaining the functionality of these structures involves high costs and, brings disturbance to the local community and effects on the environment. One key reason for this is that the cables and pipes are placed under streets, making system parts easily worn and their service lifetime reduced, and all maintenance and renewal rely on recurrent needs for excavation. In Sweden, a significant part of wire-based infrastructure is already outdated and will need to be replaced in the coming decades. The replacement of these systems will entail massive costs as well as important traffic disruption and environmental disturbance. However, this challenge may also open a unique opportunity to introduce new, more sustainable technologies and management practices. The transformation of WBIS management for long-term sustainability and meeting maintenance and renewal needs does not have a comprehensive approach. However, a systemic approach may inform WBIS management. This approach considers both technical and non-technical aspects, as well as time-related factors. Nevertheless, there is limited systemic knowledge of how different factors influence current management practices. The aim of this study is to address this knowledge gap and contribute to the understanding of what factors influence the current practice of WBIS management. A case study approach is used to identify current management practices, the underlying factors that influence them, and their implications for sustainability outcomes. The case study is based on both quantitative data on the local system and data from interviews and workshops with local practitioners and other stakeholders. Linköping was selected as a case since it provided good accessibility to the water administration and relevant data for analyzing water infrastructure management strategies. It is a sufficiently important city in Sweden to be able to identify challenges, which, to some extent, are common to all Swedish cities. By uncovering current practices and what is influencing Linköping, knowledge gaps and uncertainties related to sustainability consequences were highlighted. The findings show that goals, priorities, and policies controlling management are short-termed, and decisions on maintenance and renewal are often restricted to finding solutions to the most urgent issues. Sustainability transformation in the infrastructure area will not be possible through individual efforts without coordinated technical, organizational, business, and regulatory changes.

Keywords: case study, infrastructure, management, practice, Sweden

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Authors: Ayliana Dharmawan, Heng Yong Sheng, Zhang Xiaojin, Tan Thai Lian

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To facilitate discharge planning, doctors are presently required to assign an Estimated Discharge Date (EDD) for each patient admitted to the hospital. This assignment of the EDD is largely based on the doctor’s judgment. This can be difficult for cases which are complex or relatively new to the doctor. It is hypothesized that a data-driven approach would be able to facilitate the doctors to make accurate estimations of the discharge date. Making use of routinely collected data on inpatient discharges between January 2013 and May 2016, a predictive model was developed using machine learning techniques to predict the Length of Stay (and hence the EDD) of inpatients, at the point of admission. The predictive performance of the model was compared to that of the clinicians using accuracy measures. Overall, the best performing model was found to be able to predict EDD with an accuracy improvement in Average Squared Error (ASE) by -38% as compared to the first EDD determined by the present method. It was found that important predictors of the EDD include the provisional diagnosis code, patient’s age, attending doctor at admission, medical specialty at admission, accommodation type, and the mean length of stay of the patient in the past year. The predictive model can be used as a tool to accurately predict the EDD.

Keywords: inpatient, estimated discharge date, EDD, prediction, data-driven

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Authors: Emily Schulz

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The ancient Vedic culture from India had traditions that supported all aspects of health, including psychological health, and are relevant in the current era. These traditions have been compiled by Professor Dr. Purna, a rare Himalayan Master, into the Purna Health Management System (PHMS). The PHMS is a unique, holistic, and integrated approach to health management. It is comprised of four key factors: Health, Fitness, and Nutrition (HF&N), Life Balance (Stress Management) (LB-SM), Spiritual Growth and Development (SG&D); and Living in Harmony with the Natural Environment (LHWNE). The purpose of the PHMS is to give people the tools to take responsibility for managing their own holistic health and wellbeing. A study using a cross-sectional mixed-methods anonymous online survey was conducted during 2017-2018. Adult students of Professor Dr. Purna were invited to participate through announcements made at various events He held throughout the globe. Follow-up emails were sent with consenting language for interested parties and provided them with a link to the survey. Participation in the study was completely voluntary and no incentives were given to respond to the survey. The overall aim of the study was to investigate the effectiveness of implementation of the PHMS on practitioners' emotional balance. However, given the holistic nature of the PHMS, survey questions also inquired about participants’ physical health, stress level, ability to manage stress, and wellbeing using Likert scales. The survey also included some open-ended questions to gain an understanding of the participants’ experiences with the PHMS relative to their emotional balance. In total, 52 people out of 253 potential respondents participated in the study. Data were analyzed using nonparametric Spearman’s Rho correlation coefficient (rs) since the data were not on a normal distribution. Statistical significance was set at p < .05. Results of the study suggested that there are moderate to strong statistically significant relationships (p < .001) between participants' frequent implementation of each of the four key factors of the PHMS and self-reported mental/emotional health (HF&N rs = 0.42; LB-SM rs = 0.54; SG&D rs = 0.49; LHWNE rs = 0.45) Results also demonstrated statistically significant relationships (p < .001) between participants' frequent implementation of each of the four key factors of the PHMS and their self-reported ability to manage stress (HF&N rs = 0.44; LB-SM rs = 0.55; SG&D rs = 0.39; LHWNE rs = 0.55). Additionally, those who reported experiencing better physical health also reported better mental/emotional health (rs = 0.49, p < .001) and better ability to manage stress (rs = 0.46, p < .001). The findings of this study suggest that wisdom from the ancient Vedic culture may be useful for those working in the field of psychology and related fields who would like to assist clients in calming their mind and emotions and managing their stress levels.

Keywords: balanced emotions, balanced mind, stress management, Vedic philosophy

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Authors: Chet Narayan Acharya, Prachyakorn Chaiyakot, Nuttaya Yuangyai

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This research explores the symbiotic relationship between community based tourism (CBT) businesses and sustainable livelihood practices in the context of Amaltari Tharu Village Community, Nepal. Through a thorough investigation, it analyzes the impact of community-based tourism entrepreneurship on local livelihoods. Using an interpretive case study approach, it assesses how tourism activities affect the sociocultural, economic, and environmental dimensions of the community. The research highlights the sustainable livelihood practices embraced by local communities, guided by the principles of sustainable development. The study employs a triangulation approach to analyze data, aiming to generate insights that contribute to a KICK START discourse on sustainable tourism in the region, shedding light on the connection between community-based tourism entrepreneurship and local livelihood practices. The findings are expected to offer valuable discourse among academic researchers and suggestions for sustainable tourism management strategies in similar global contexts.

Keywords: community based tourism businesses, sustainable livelihood, Nepal, sociocultural and environmental impact, kick start discourse

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Authors: Mourad Makhlouf Sabrina Bouriche, Zoubir Benmaamar, Didier Villemin

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Background: The increasing presence of electromagnetic interference (EMI) requires the development of effective protection solutions. Intelligent textiles offer a promising approach due to their wear ability and the possibility of integration into everyday clothing. In this study, the use of graphene and polyaniline for EMI shielding on cotton fabrics was examined. Methods: In this study, the continuous dyeing of recycled graphite-derived graphene and polyaniline was examined. Bottom-reforming technology was adopted to improve adhesion and achieve uniform distribution of conductive material on the fiber surface. The effect of material weight ratio on fabric performance and X-band EMI shielding effectiveness (SE) was evaluated. Significant Findings: The dyed cotton fabrics incorporating graphene, polyaniline, and their combination exhibited improved conductivity. Notably, these fabrics achieved EMI SE values ranging from 9 to 16 dB within the X-band frequency range (8-9 GHz). These findings demonstrate the potential of this approach for developing intelligent textiles with effective EMI shielding capabilities. Additionally, the utilization of recycled materials contributes to a more sustainable shielding solution.

Keywords: Intelligent textiles, graphene, polyaniline, electromagnetic shielding, conductivity, recycling

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Authors: Hanan Alshaher, Jinsheng Xu

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Large amounts of data on the web can provide valuable information. For example, product reviews help business owners measure customer satisfaction. Sentiment analysis classifies texts into two polarities: positive and negative. This paper examines movie reviews and tweets using a new term weighting scheme, called one-over-sigma (1/sigma), on benchmark datasets for sentiment classification. The proposed method aims to improve the performance of sentiment classification. The results show that 1/sigma is more accurate than the popular term weighting schemes. In order to verify if the entropy reflects the discriminating power of terms, we report a comparison of entropy values for different term weighting schemes.

Keywords: 1/sigma, natural language processing, sentiment analysis, term weighting scheme, text classification

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Authors: Genziana Lay

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A wide majority of older children and adolescents who enter the juvenile court system present with an array of problematic symptoms and behaviors including anxiety, depression, aggressive acting out, detachment, and substance abuse. Attachment theory offers a framework for understanding normative and pathological functioning, which during development is influenced by emotional, social and cognitive elements. There is clear evidence that children and adolescents with the highest risk of developing adaptation problems present an insecure attachment profile. Most offending minors have experienced dysfunctional family relationships as well as social and/or economic deprivation. Their maladaptive attachment develops not only through their relationship with caregivers but with the environment at large. Activation of their faulty attachment system leads them to feel emotionally overwhelmed and engage in destructive behaviors and decision-making. A psycho-social intervention with this population conceptualized as attachment therapy is a multi-faceted, practical approach that has shown excellent results in terms of increased psychological well-being and drastically reduced rates of re-offense/ destructive behavior. Through several; components including psychotherapy, monitoring, volunteering, meditation and socialization, the program focuses on seven dimensions: self-efficacy, responsibility, empathy/reparation, autonomy/security, containment/structure, insight building, and relational health. This paper presents the program and illustrates how the framework of attachment theory practically applied to psycho-social intervention has great therapeutic and social reparation potential. Preliminary evidence drawn from the Sassari Juvenile Court is very promising; this paper will illustrate these results and propose an even more comprehensive, applicable approach to psycho-social reparative intervention that leads to greater psychological health and reduced recidivism in the child and adolescent population.

Keywords: attachment, child, adolescent, crime, juvenile, psychosocial

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Authors: Gabor Kiraly

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Wine regions are based on equilibria between climate, soil, grape varieties, and farming expertise that define the special character and quality of local vine farming and wine production. Changes in climate conditions may increase risk of destabilizing this equilibrium. Adaptation decisions, including adjusting practices, processes and capitals in response to climate change stresses – may reduce this risk. However, farmers’ adaptive behavior are subject to a wide range of factors and forces such as links between climate change implications and production, farm - scale adaptive capacity and other external forces that might hinder them to make efficient response to climate change challenges. This paper will aim to study climate change adaptation practices and strategies of grape growers in a way of applying a complex and holistic approach involving theories, methods and tools both from environmental and social sciences. It will introduce the field of adaptation studies as an evidence - based discourse by presenting an overview of examples from wine regions where adaptation studies have already reached an advanced stage. This will serve as a theoretical background for a preliminary research with the aim to examine the feasibility and applicability of such a research approach in the Hungarian context.

Keywords: climate change, adaptation, viticulture, Hungary

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Authors: Samaneh Nadali, Masrah Azrifah Azmi Murad, Nurfadhlina Mohammad Sharef

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Sarcasm is a common phenomenon in social media which is a nuanced form of language for stating the opposite of what is implied. Due to the intentional ambiguity, analysis of sarcasm is a difficult task not only for a machine but even for a human. Although sarcasm detection has an important effect on sentiment, it is usually ignored in social media analysis because sarcasm analysis is too complicated. While there is a few systems exist which can detect sarcasm, almost no work has been carried out on a study and the review of the existing work in this area. This survey presents a nearly full image of sarcasm detection techniques and the related fields with brief details. The main contributions of this paper include the illustration of the recent trend of research in the sarcasm analysis and we highlight the gaps and propose a new framework that can be explored.

Keywords: sarcasm detection, sentiment analysis, social media, sarcasm analysis

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Authors: Phillip F. Kanokanga, Patrick W. Mamimine, Molline Mwando, Charity Mapingure

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Tourism development tends to follow anchor attractions, including cities and their surroundings, while marginalizing small towns and their environs. This is even though the small towns and their hinterlands can also offer competitive tourism products. The Zimbabwe Tourism Authority (ZTA) gathers visitor statistics of major tourist destinations only thereby sidelining the density of tourist traffic that either passes through or visits the small towns in the country. Unless this problem is addressed, the tourism potential of small towns and their hinterlands will not be fully tapped for economic development. Using qualitative research methodology, this study investigated the opportunities for tourism development in the Greater Chinhoyi Area. The study revealed that the Greater Chinhoyi area had potential for heritage tourism, village tourism, urban tourism, educational tourism, dark tourism, recreational tourism, agrotourism, and nature tourism. There is a need to link the various tourism resources in the Greater Chinhoyi area to anchor attractions in dominant resorts, then develop and present the tourism products in transit towns as ‘appetisers’ or ‘appetisser attractions’ before one gets to the main destination.

Keywords: anchor attractions, appetisers, heritage tourism, agrotourism, small towns, tourism corridor, systems approach, hidden treasures

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Authors: Liz van Acker, Linda Colley

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Political processes are shaped by the gendered culture of parliaments. This paper examines how the institution of parliament has been affected by the changing number of women in politics. In order to understand how and why gender change occurs, the paper employs a feminist historical institutionalism approach. It argues that while it is difficult to change the gendered nature of political institutions, it is possible, from a gender perspective, to understand the processes of change both formally and informally. Increasing women’s representation has been a slow process which has not occurred without political struggles. A broadly defined ‘feminist historical institutionalism’ has critiqued existing approaches to institutions and combined historical institutional analysis with tools of gender to enhance our understanding of institutional processes and change. The paper examines the gendered rules, norms, and practices that influence institutional design choices and processes. Institutions such as Parliament often are able to adjust to women’s entry and absorb them without too much interruption. Exploring the hidden aspects to informal institutions involves identifying unspoken and accepted norms that may guide decision-making – exposing and questioning the gender status quo. This paper examines the representation of women in the Queensland Parliament, Australia. It places the Queensland experience in historical context, as well as in the national and international context. The study is interesting, given that its gender representation has rocketed from one of the worst performing states in 2012 to one of the best performing in 2015 with further improvements in 2017. The state currently has a re-elected female Premier, a female Deputy Premier and a female-dominated cabinet – in fact, Queensland was the first ministry in Australia to have a majority of women in its Cabinet. However, it is unnecessary to dig far below these headlines to see that this is uncharacteristic of its history: progress towards this current position has been slow and patchy. The paper finds that matters such as the glass ceiling and the use of quotas explain women’s recent success in Queensland politics.

Keywords: feminist historical institutional approach, glass ceiling, quotas, women’s participation in politics

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Authors: Belgherbi Aicha, Hadjidj Ismahen, Bessaid Abdelhafid

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Accurate Liver, spleen and kidneys segmentation in abdominal CT images is one of the most important steps for computer aided abdominal organs pathology diagnosis. In this paper, we have proposed a new semi-automatic algorithm for Liver, spleen and kidneys area extraction in abdominal CT images. Our proposed method is based on hierarchical segmentation and watershed algorithm. In our approach, a powerful technique has been designed to suppress over-segmentation based on mosaic image and on the computation of the watershed transform. The algorithm is currency in two parts. In the first, we seek to improve the quality of the gradient-mosaic image. In this step, we propose a method for improving the gradient-mosaic image by applying the anisotropic diffusion filter followed by the morphological filters. Thereafter we proceed to the hierarchical segmentation of the liver, spleen and kidney. To validate the segmentation technique proposed, we have tested it on several images. Our segmentation approach is evaluated by comparing our results with the manual segmentation performed by an expert. The experimental results are described in the last part of this work.

Keywords: anisotropic diffusion filter, CT images, morphological filter, mosaic image, multi-abdominal organ segmentation, mosaic image, the watershed algorithm

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Authors: Diana Cunha, Catarina Coelho, Ana Paula Relvas, Elisabeth Kastenholz

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Context: Tourist experiences have a recognized impact on the well-being of individuals. However, studies on the specific benefits of tourist experiences for families are scattered across different disciplines. This study aims to systematically review the literature to synthesize the evidence on the benefits of tourist experiences for families. Research Aim: The main objective is to systematize the evidence in the literature regarding the benefits of tourist experiences for families. Methodology: A systematic literature review was conducted using Nvivo, analyzing 33 scientific studies obtained from various databases. The search terms used were "family"/ "couple" and "tourist experience". The studies included quantitative, qualitative, mixed methods, and literature reviews. All works prior to the year 2000 were excluded, and the search was restricted to full text. A language filter was also used, considering articles in Portuguese, English, and Spanish. For NVivo analysis, information was coded based on both deductive and inductive perspectives. To minimize the subjectivity of the selection and coding process, two of the authors discussed the process and agreed on criteria that would make the coding more objective. Once the coding process in NVivo was completed, the data relating to the identification/characterization of the works were exported to the Statistical Package for the Social Sciences (SPPS), to characterize the sample. Findings: The results highlight that tourist experiences have several benefits for family systems, including the strengthening of family and marital bonds, the creation of family memories, and overall well-being and life satisfaction. These benefits contribute to both immediate relationship quality improvement and long-term family identity construction and transgenerational transmission. Theoretical Importance: This study emphasizes the systemic nature of the effects and relationships within family systems. It also shows that no harm was reported within these experiences, with only some challenges related to positive outcomes. Data Collection and Analysis Procedures: The study collected data from 33 scientific studies published predominantly after 2013. The data were analyzed using Nvivo, employing a systematic review approach. Question Addressed: The study addresses the question of the benefits of tourist experiences for families and how these experiences contribute to family functioning and individual well-being. Conclusion: Tourist experiences provide opportunities for families to enhance their interpersonal relationships and create lasting memories. The findings suggest that formal interventions based on evidence could further enhance the potential benefits of these experiences and be a valuable preventive tool in therapeutic interventions.

Keywords: family systems, individual and family well-being, marital satisfaction, tourist experiences

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Authors: Tianqi Wu, Min Wang

Abstract:

Research investigating language acquisition from a constructionist perspective has demonstrated that language is learned as constructions at various linguistic levels, which is related to factors of frequency, semantic prototypicality, and form-meaning contingency. However, previous research on construction learning tended to focus on clause-level constructions such as verb argument constructions but few attempts were made to study morpheme-level constructions such as the progressive construction, which is regarded as a source of acquisition problems for English learners from diverse L1 backgrounds, especially for those whose L1 do not have an equivalent construction such as German and Chinese. To trace the developmental trajectory of Chinese EFL learners’ use of the progressive with respect to verb frequency, verb-progressive contingency, and verbal prototypicality and generality, a learner corpus consisting of three sub-corpora representing three different English proficiency levels was extracted from the Chinese Learners of English Corpora (CLEC). As the reference point, a native speakers’ corpus extracted from the Louvain Corpus of Native English Essays was also established. All the texts were annotated with C7 tagset by part-of-speech tagging software. After annotation all valid progressive hits were retrieved with AntConc 3.4.3 followed by a manual check. Frequency-related data showed that from the lowest to the highest proficiency level, (1) the type token ratio increased steadily from 23.5% to 35.6%, getting closer to 36.4% in the native speakers’ corpus, indicating a wider use of verbs in the progressive; (2) the normalized entropy value rose from 0.776 to 0.876, working towards the target score of 0.886 in native speakers’ corpus, revealing that upper-intermediate learners exhibited a more even distribution and more productive use of verbs in the progressive; (3) activity verbs (i.e., verbs with prototypical progressive meanings like running and singing) dropped from 59% to 34% but non-prototypical verbs such as state verbs (e.g., being and living) and achievement verbs (e.g., dying and finishing) were increasingly used in the progressive. Apart from raw frequency analyses, collostructional analyses were conducted to quantify verb-progressive contingency and to determine what verbs were distinctively associated with the progressive construction. Results were in line with raw frequency findings, which showed that contingency between the progressive and non-prototypical verbs represented by light verbs (e.g., going, doing, making, and coming) increased as English proficiency proceeded. These findings altogether suggested that beginning Chinese EFL learners were less productive in using the progressive construction: they were constrained by a small set of verbs which had concrete and typical progressive meanings (e.g., the activity verbs). But with English proficiency increasing, their use of the progressive began to spread to marginal members such as the light verbs.

Keywords: Construction learning, Corpus-based, Progressives, Prototype

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Authors: Rodrigo Buaiz Boabaid

Abstract:

Tokenization of Blue Bonds is an emerging Green Finance tool that has the potential to scale Blue Carbon Projects to fight climate change. This innovative solution has a huge potential to democratize the green finance market and catalyze innovations in the climate change finance sector. Switzerland has emerged as a leader in the Green Finance space and is well-positioned to drive the adoption of Tokenization of Blue & Green Bonds. This unique approach has the potential to unlock new sources of capital and enable global investors to participate in the financing of sustainable blue carbon projects. By leveraging the power of blockchain technology, Tokenization of Blue Bonds can provide greater transparency, efficiency, and security in the investment process, while also reducing transaction costs. Investments are in line with the highest regulations and designed according to the stringent legal framework and compliance standards set by Switzerland. The potential benefits of Tokenization of Blue Bonds are significant and could transform the way that sustainable projects are financed. By unlocking new sources of capital, this approach has the potential to accelerate the deployment of Blue Carbon projects and create new opportunities for investors to participate in the fight against climate change.

Keywords: blue carbon, blue bonds, green finance, Tokenization, blockchain solutions

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Authors: Armin Amindari

Abstract:

Hermetic compressors are used widely for refrigeration, heat pump, and air conditioning applications. With the improvement of energy conservation and environmental protection requirements, inverter compressors that operates at different speeds have become increasingly attractive in the industry. Although speed change capability is more efficient, passing through resonant frequencies may lead to excessive vibrations. In this work, an integrated vibration control approach based on flexible multi-body dynamics theory is used for optimizing the vibration amplitudes of the compressor at different operating speeds. To examine the compressor vibrations, all the forces and moments exerted on the cylinder block were clarified and minimized using balancers attached to the upper and lower ends of the motor rotor and crankshaft. The vibration response of the system was simulated using Motionview™ software. In addition, mass-spring optimization was adopted to shift the resonant frequencies out of the operating speeds. The modal shapes of the system were studied using Optistruct™ solver. Using this approach, the vibrations were reduced up to 56% through dynamic simulations. The results were in high agreement with various experimental test data. In addition, the vibration resonance problem observed at low speeds was solved by shifting the resonant frequencies through optimization studies.

Keywords: vibration, MBD, compressor, hermetic

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Authors: Dilusha Rajapakse

Abstract:

The main reason for printing coloured imageries, pattern or motif onto textiles is to enhance the visual appearance of the surface so that the final textile product would get the required attention from potential customers. Such colours and patterns are permanently applied onto the textiles using conventional static colourants, and we expect such decorations to be last for the entire lifecycle of the textile product. The focus of this research presentation is to discuss the ability to integrate multicolour and pattern changing aesthetics onto textiles with the application of water based photochromic colourants. By adopting a research through design approach, a number of iterative flatbed screen printing experiments were conducted to explore the process of printing water based photochromic colours on textile surfaces. The research resulted in several technical parameters that have to be considered during the process of screen printing. Moreover, a modified printing technique that could be used to apply decorative photographic imagery onto textile with multicolour changing effects was also identified. A number of product applications for such dynamic printed textiles were revealed, and appropriate visual evidence was referred to justify the finding.

Keywords: dynamic aesthetics, multicolour changing textiles, non-emissive colours, printed textile design

Procedia PDF Downloads 375