Search results for: decision making style

Authors: Sajjad Farahani, Wilkistar Otieno, Xiaohang Yue

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This paper develops a model for the optimal disposition decision for product returns in a remanufacturing system with limited recoverable inventory capacity. In this model, a constant demand is satisfied by remanufacturing returned products which are up to the minimum required quality grade. The quality grade of returned products is uncertain and remanufacturing cost increases as the quality level decreases, and remanufacturer wishes to determine which returned product to accept to be remanufactured for reselling, and any unaccepted returns may be salvaged at a value that increases with their quality level. Accepted returns can be stocked for remanufacturing upon demand requests, but incur a holding cost. A Markov decision problem is formulated in order to evaluate various performance measures for this system and obtain the optimal remanufacturing policy. A detailed numerical study reveals that our approach to the disposition problem outperforms the current industrial practice ignoring quality grade of returned products. In addition, we identify conditions under which this improvement is the highest.

Keywords: green supply chain management, matrix geometric method, production recovery, reverse supply chains

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Authors: Linh Tong

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This paper aims to contribute to the understanding of key education reforms in Vietnam from 1945 to 2017, which reflects an evolution of socio-political needs of the Socialist Republic of Vietnam throughout this period. It explores the contextual conditions, motivations and ambitions influencing the formation of the education reforms in Vietnam. It also looks, from an applied practical perspective, at the influence of politics on education policy-making. The research methodology includes a content analysis of curriculum designs proposed by the Ministry of Education and Training, relevant resolutions and executive orders passed by the National Assembly and the Prime Minister, as well as interviews with experts and key stakeholders. The results point to a particular configuration of factors which have been inspiring the shape and substance of these reforms and which have most certainly influenced their implementation. This configuration evolves from the immediate needs to erase illiteracy and cultivate socialist economic model at the beginning of Vietnam’s independence in 1945-1975, to a renewed urge to adopt market-oriented economy in 1986 and cautiously communicate with the outside world until 2000s, and to currently a demonstrated desire to fully integrate into the global economy and tackle with rising concerns about national security (the South China Sea Dispute), environmental sustainability, construction of a knowledge economy, and a rule-of-law society. Overall, the paper attempts to map Vietnam’s socio-political needs with the changing sets of goals and expected outcomes in teaching and learning methodologies and practices as introduced in Vietnamese key education reforms.

Keywords: curriculum development, knowledge society, national security, politics of education policy-making, Vietnam's education reforms

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Authors: Rafael Alvarenga, Hubert Herbaux, Laurent Linguet

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The uncertainty concerning the power production of intermittent renewable energy is one of the main barriers to the integration of such assets into the power grid. Efforts have thus been made to develop methods to quantify this uncertainty, allowing producers to ensure more reliable and profitable engagements related to their future power delivery. Even though a diversity of probabilistic approaches was proposed in the literature giving promising results, the added value of adopting such methods for scheduling intermittent power plants is still unclear. In this study, the profits obtained by a decision-making model used to optimally schedule an existing PV power plant connected to batteries are compared when the model is fed with deterministic and probabilistic forecasts generated with two of the most recent methods proposed in the literature. Moreover, deterministic forecasts with different accuracy levels were used in the experiments, testing the utility and the capability of probabilistic methods of modeling the progressively increasing uncertainty. Even though probabilistic approaches are unquestionably developed in the recent literature, the results obtained through a study case show that deterministic forecasts still provide the best performance if accurate, ensuring a gain of 14% on final profits compared to the average performance of probabilistic models conditioned to the same forecasts. When the accuracy of deterministic forecasts progressively decreases, probabilistic approaches start to become competitive options until they completely outperform deterministic forecasts when these are very inaccurate, generating 73% more profits in the case considered compared to the deterministic approach.

Keywords: PV power forecasting, uncertainty quantification, optimal scheduling, power systems

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Authors: Emine Zeytinli

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Ottoman carpets were depicted in Renaissance painting while they were exported commercially. The carpets were highly demanded and used by the middle and upper classes of Western European countries. The motifs, designs, patterns, and ornamentation of these carpets were decorative objects of luxury for Western European residences as well as paintings. Oriental carpets found their way into European market already from the medieval times to the present century. They were considered as luxury items first, however, demanded by middle classes in Europe and North America within the nineteenth century. This century brought unprecedented changes in production and consumption in the world. Expanding industries created quick urbanization, changed the city life and new types of goods dominated the entire century. Increases in income allowed Europeans to spend on luxury items, consumers taste changed in number of ways including furniture and decoration. Use of a carpet in the orient lifestyle often considered as an art object with Western aesthetic sensibility. A carpet with an oriental character, an essential part of home decoration, was highly appreciated for floor, table covering and wall hanging. Turkish carpets with distinctive classical style, patterns, and colours were changed for the tastes of European consumers. This paper attempts to analyse how the taste and preferences of European and American consumers increased their buying of oriental objects namely carpets. The production of local hand woven carpet industry developed, carpet factories were set up and special weaving schools were opened in some major waving centres, and carpet weaving became one of the main manufacturing and export commodity of the empire. All of these attempts increased the reputation and market share in international market. The industry flourished, commercially operated carpet looms, sales revenues and export increased unprecedentedly. British and Ottoman archival documents, parliamentary papers and travel notes were used to analysed above mention effect on how the foreign demand changed designs of carpets and the business itself, how the production in households moved to the commercial premises and a flourished the industry.

Keywords: consumption patterns, carpet weaving, ottoman oriental carpets, commercialisation

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Authors: Titik Wahyuningsih

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The folktale to discuss is Ande-Ande Lumut, a story that is believed to be a history of two kingdoms in East Java, Indonesia. The title refers to the main male character in the story. This research is a library research which aims to know the patriarchal values in Indonesia. The data for the research is the song in the story that is actually the conversation between Ande-Ande Lumut and a mom who adopts him. It is told in the lines that many beautiful girls come to propose Ande-Ande Lumut but he does not want to accept them and keeps on staying in his upstairs room. Finally, he says yes for Klething Kuning to whom his mom describes as a girl with ugly face. Ande-Ande Lumut's decision is taken as Klething Kuning is the only girl who doesn't let Yuyu Kangkang help her. Yuyu Kangkang is described as a very big crab that helps the girls to cross the river but ask them to kiss him. Through the lense of feminist approach, Ande-Ande Lumut shows the men’s preference and dominance to make final decision over women. Even though the girls are actively propose their future husband, but they do it without giving any requirements. Meanwhile, the future husband chooses a girl with a criterion that no male has ever touched her, although the male is a crab.

Keywords: future spouse, Indonesian folktale, acceptance, patriarchal

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Authors: Bingquan Shen

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Convolutional Neural Networks (CNN) have demonstrated their effectiveness in synthesizing 3D views of object instances at various viewpoints. Given the problem where one have limited viewpoints of a particular object for classification, we present a pose normalization architecture to transform the object to existing viewpoints in the training dataset before classification to yield better classification performance. We have demonstrated that this Pose Normalization Network (PNN) can capture the style of the target object and is able to re-render it to a desired viewpoint. Moreover, we have shown that the PNN improves the classification result for the 3D chairs dataset and ShapeNet airplanes dataset when given only images at limited viewpoint, as compared to a CNN baseline.

Keywords: convolutional neural networks, object classification, pose normalization, viewpoint invariant

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Authors: Catalina Albornoz, Giacomo Barbieri

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Crop monitoring has shown to reduce vulnerability to spreading plagues and pathologies in crops. Remote sensing with Unmanned Aerial Vehicles (UAVs) has made crop monitoring more precise, cost-efficient and accessible. Nowadays, remote monitoring involves calculating maps of vegetation indices by using different software that takes either Truecolor (RGB) or multispectral images as an input. These maps are then used to segment the crop into management zones. Finally, knowing the spectral signature of a crop (the reflected radiation as a function of wavelength) can be used as an input for decision-making and crop characterization. The calculation of vegetation indices using software such as Pix4D has high precision for monoculture plantations. However, this paper shows that using this software on mixed crops may lead to errors resulting in an incorrect segmentation of the field. Within this work, authors propose to filter all the elements different from the main crop before the calculation of vegetation indices and the spectral signature. A filter based on the Sobel method for border detection is used for filtering a coffee crop. Results show that segmentation into management zones changes with respect to the traditional situation in which a filter is not applied. In particular, it is shown how the values of the spectral signature change in up to 17% per spectral band. Future work will quantify the benefits of filtering through the comparison between in situ measurements and the calculated vegetation indices obtained through remote sensing.

Keywords: coffee, filtering, mixed crop, precision agriculture, remote sensing, spectral signature

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Authors: M. Aghili, S. Tabarestani, C. Freytes, M. Shojaie, M. Cabrerizo, A. Barreto, N. Rishe, R. E. Curiel, D. Loewenstein, R. Duara, M. Adjouadi

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A major challenge in medical studies, especially those that are longitudinal, is the problem of missing measurements which hinders the effective application of many machine learning algorithms. Furthermore, recent Alzheimer's Disease studies have focused on the delineation of Early Mild Cognitive Impairment (EMCI) and Late Mild Cognitive Impairment (LMCI) from cognitively normal controls (CN) which is essential for developing effective and early treatment methods. To address the aforementioned challenges, this paper explores the potential of using the eXtreme Gradient Boosting (XGBoost) algorithm in handling missing values in multiclass classification. We seek a generalized classification scheme where all prodromal stages of the disease are considered simultaneously in the classification and decision-making processes. Given the large number of subjects (1631) included in this study and in the presence of almost 28% missing values, we investigated the performance of XGBoost on the classification of the four classes of AD, NC, EMCI, and LMCI. Using 10-fold cross validation technique, XGBoost is shown to outperform other state-of-the-art classification algorithms by 3% in terms of accuracy and F-score. Our model achieved an accuracy of 80.52%, a precision of 80.62% and recall of 80.51%, supporting the more natural and promising multiclass classification.

Keywords: eXtreme gradient boosting, missing data, Alzheimer disease, early mild cognitive impairment, late mild cognitive impair, multiclass classification, ADNI, support vector machine, random forest

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Authors: Feng Li, Qian Peng

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The mass start speed skating (MSSS) is a new event for the 2018 PyeongChang Winter Olympics and will be an official race for the 2022 Beijing Winter Olympics. Considering that the event rankings were based on points gained on laps, it is worthwhile to investigate the pacing behavior on each lap that directly influences the ranking of the race. The aim of this study was to detect the pacing behavior and performance on MSSS regarding skaters’ level (SL), competition stage (semi-final/final) (CS) and gender (G). All the men's and women's races in the World Cup and World Championships were analyzed in the 2018-2019 and 2019-2020 seasons. As a result, a total of 601 skaters from 36 games were observed. ANOVA for repeated measures was applied to compare the pacing behavior on each lap, and the three-way ANOVA for repeated measures was used to identify the influence of SL, CS, and G on pacing behavior and total time spent. In general, the results showed that the pacing behavior from fast to slow were cluster 1—laps 4, 8, 12, 15, 16, cluster 2—laps 5, 9, 13, 14, cluster 3—laps 3, 6, 7, 10, 11, and cluster 4—laps 1 and 2 (p=0.000). For CS, the total time spent in the final was less than the semi-final (p=0.000). For SL, top-level skaters spent less total time than the middle-level and low-level (p≤0.002), while there was no significant difference between the middle-level and low-level (p=0.214). For G, the men’s skaters spent less total time than women on all laps (p≤0.048). This study could help to coach staff better understand the pacing behavior regarding SL, CS, and G, further providing references concerning promoting the pacing strategy and decision making before and during the race.

Keywords: performance analysis, pacing strategy, winning strategy, winter Olympics

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Authors: Srinivas Peri, Siva Abhishek Sirivella, Tejaswini Kallakuri, Uzair Ahmad

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Smart cities and intelligent transportation systems rely heavily on effective traffic management and infrastructure planning. This research tackles the data scarcity challenge by generating realistically synthetic traffic data from the PeMS-Bay dataset, enhancing predictive modeling accuracy and reliability. Advanced techniques like TimeGAN and GaussianCopula are utilized to create synthetic data that mimics the statistical and structural characteristics of real-world traffic. The future integration of Spatial-Temporal Generative Adversarial Networks (ST-GAN) is anticipated to capture both spatial and temporal correlations, further improving data quality and realism. Each synthetic data generation model's performance is evaluated against real-world data to identify the most effective models for accurately replicating traffic patterns. Long Short-Term Memory (LSTM) networks are employed to model and predict complex temporal dependencies within traffic patterns. This holistic approach aims to identify areas with low vehicle counts, reveal underlying traffic issues, and guide targeted infrastructure interventions. By combining GAN-based synthetic data generation with LSTM-based traffic modeling, this study facilitates data-driven decision-making that improves urban mobility, safety, and the overall efficiency of city planning initiatives.

Keywords: GAN, long short-term memory (LSTM), synthetic data generation, traffic management

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Authors: Yenie Norambuena

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Analogical reasoning is a cognitive process that consists of structured comparisons of mental representations and scheme construction. Because of its heuristic function, it is ubiquitous in cognition and could play an important role in language development. The use of analogies is expressed early in children and this behavior is also reflected in language, suggesting a possible way to understand the complex links between thought and language. The current research examines factors of verbal and non-verbal reasoning that should be taken into consideration in the study of language development for their relations and predictive value. The study was conducted with 48 Chilean preschoolers (Spanish speakers) from 4 to 6-year-old. We assessed children’s verbal analogical reasoning, non-verbal analogical reasoning and linguistics skills (Listening Comprehension, Phonemic awareness, Alphabetic principle, Syllabification, Lexical repetition and Lexical decision). The results evidenced significant correlations between analogical reasoning factors and linguistic skills and they can predict linguistic performance mainly on oral comprehension, lexical decision and phonological skills. These findings suggest a fundamental interrelationship between analogical reasoning and linguistic performance on children’s and points to the need to consider this cognitive process in comprehensive theories of children's language development.

Keywords: verbal analogical reasoning, non-verbal analogical reasoning, linguistic skills, language development

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Authors: Justin Woulfe

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Siloed logistics and supply chain management systems throughout the Department of Defense (DOD) has led to disparate approaches to modeling and simulation (M&S), a lack of understanding of how one system impacts the whole, and issues with “optimal” solutions that are good for one organization but have dramatic negative impacts on another. Many different systems have evolved to try to understand and account for uncertainty and try to reduce the consequences of the unknown. As the DoD undertakes expansive digital transformation initiatives, there is an opportunity to fuse and leverage traditionally disparate data into a centrally hosted source of truth. With a streamlined process incorporating machine learning (ML) and artificial intelligence (AI), advanced M&S will enable informed decisions guiding program success via optimized operational readiness and improved mission success. One of the current challenges is to leverage the terabytes of data generated by monitored systems to provide actionable information for all levels of users. The implementation of a cloud-based application analyzing data transactions, learning and predicting future states from current and past states in real-time, and communicating those anticipated states is an appropriate solution for the purposes of reduced latency and improved confidence in decisions. Decisions made from an ML and AI application combined with advanced optimization algorithms will improve the mission success and performance of systems, which will improve the overall cost and effectiveness of any program. The Systecon team constructs and employs model-based simulations, cutting across traditional silos of data, aggregating maintenance, and supply data, incorporating sensor information, and applying optimization and simulation methods to an as-maintained digital twin with the ability to aggregate results across a system’s lifecycle and across logical and operational groupings of systems. This coupling of data throughout the enterprise enables tactical, operational, and strategic decision support, detachable and deployable logistics services, and configuration-based automated distribution of digital technical and product data to enhance supply and logistics operations. As a complete solution, this approach significantly reduces program risk by allowing flexible configuration of data, data relationships, business process workflows, and early test and evaluation, especially budget trade-off analyses. A true capability to tie resources (dollars) to weapon system readiness in alignment with the real-world scenarios a warfighter may experience has been an objective yet to be realized to date. By developing and solidifying an organic capability to directly relate dollars to readiness and to inform the digital twin, the decision-maker is now empowered through valuable insight and traceability. This type of educated decision-making provides an advantage over the adversaries who struggle with maintaining system readiness at an affordable cost. The M&S capability developed allows program managers to independently evaluate system design and support decisions by quantifying their impact on operational availability and operations and support cost resulting in the ability to simultaneously optimize readiness and cost. This will allow the stakeholders to make data-driven decisions when trading cost and readiness throughout the life of the program. Finally, sponsors are available to validate product deliverables with efficiency and much higher accuracy than in previous years.

Keywords: artificial intelligence, digital transformation, machine learning, predictive analytics

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Authors: Kavita Goel, Donald Winchester

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In a world of information overload and work complexities, academics often struggle to create an online instructional environment enabling efficient and effective student learning. Research has established that students’ learning styles are different, some learn faster when taught using audio and visual methods. Attributes like prior knowledge and mental effort affect their learning. ‘Cognitive load theory’, opines learners have limited processing capacity. Cognitive load depends on the learner’s prior knowledge, the complexity of content and tasks, and instructional environment. Hence, the proper allocation of cognitive resources is critical for students’ learning. Consequently, a lecturer needs to understand the limits and strengths of the human learning processes, various learning styles of students, and accommodate these requirements while designing online assessments. As acknowledged in the cognitive load theory literature, visual and auditory explanations of worked examples potentially lead to a reduction of cognitive load (effort) and increased facilitation of learning when compared to conventional sequential text problem solving. This will help learner to utilize both subcomponents of their working memory. Instructional design changes were introduced at the case site for the delivery of the postgraduate business subjects. To make effective use of auditory and visual modalities, video recorded lectures, and key concept webinars were delivered to students. Videos were prepared to free up student limited working memory from irrelevant mental effort as all elements in a visual screening can be viewed simultaneously, processed quickly, and facilitates greater psychological processing efficiency. Most case study students in the postgraduate programs are adults, working full-time at higher management levels, and studying part-time. Their learning style and needs are different from other tertiary students. The purpose of the audio and visual interventions was to lower the students cognitive load and provide an online environment supportive to their efficient learning. These changes were expected to impact the student’s learning experience, their academic performance and retention favourably. This paper posits that these changes to instruction design facilitates students to integrate new knowledge into their long-term memory. A mixed methods case study methodology was used in this investigation. Primary data were collected from interviews and survey(s) of students and academics. Secondary data were collected from the organisation’s databases and reports. Some evidence was found that the academic performance of students does improve when new instructional design changes are introduced although not statistically significant. However, the overall grade distribution of student’s academic performance has changed and skewed higher which shows deeper understanding of the content. It was identified from feedback received from students that recorded webinars served as better learning aids than material with text alone, especially with more complex content. The recorded webinars on the subject content and assessments provides flexibility to students to access this material any time from repositories, many times, and this enhances students learning style. Visual and audio information enters student’s working memory more effectively. Also as each assessment included the application of the concepts, conceptual knowledge interacted with the pre-existing schema in the long-term memory and lowered student’s cognitive load.

Keywords: cognitive load theory, learning style, instructional environment, working memory

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Authors: Adejoke Omowumi Adediran

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The commitment of African states to liberalise civil aviation in Africa through the implementation of the Yamoussoukro Decision of 1999 was reiterated in 2015 at the African Union Assembly meeting. A declaration was made by African Heads of government at the meeting to ensure the immediate implementation of the decision towards the establishment of a Single African Air Transport Market (SAATM) by 2017. A SAATM will imply among others, a removal of all commercial restrictions for African airlines in Africa; access to any route in Africa by African airlines without any required permit or authorisation; and a common set of regulations for airlines in African member states. As the envisioned 2017 date for launching the SAATM could not be met, a new date of January 2018 has been set. The lack of political will by African States, however, remains a prominent challenge to the realisation of the SAATM. As at June 2017, only twenty-one states had signed the commitment to actualise the decision creating the SAATM. In actualisation of the SAATM, a regulatory framework has been established to efficiently manage the new African airline industry, and regulatory texts have been adopted as part of the legal regime. This legal regime is to regulate both interstate and domestic operations. Airlines in Nigeria are currently faced with certain challenges which ultimately affect their effectiveness and passengers as well do not enjoy utmost customer satisfaction with services rendered by the airlines. Although Nigeria has demonstrated support for the SAATM since 2015, as Nigeria alongside ten other states, signed the initial commitment, whether or not SAATM will eventually be beneficial to airlines and passengers has become an issue in the light of the challenges of the Nigerian airline industry. Remarkably, the benefit of the SAATM is to a large extent ultimately determined by its legal framework. Using doctrinal research, this paper examines the legal implications of the SAATM on airlines and passengers in Nigeria. This paper analyses the legal framework of SAATM and juxtaposes this with the particular issues affecting airlines and passengers in Nigeria such as financial difficulties on the part of airlines and consumer protection as regards passengers. Among others, it can be asserted that the legal regime affords an opportunity for business expansion and creates a fair environment for competition. This is beneficial not only to the airlines but to passengers as well. In addition, in the interest of passengers, consumer rights are prescribed, and the regulations also cater for situations where airlines interrupt their services, as losses arising from these situations will be mitigated. There is indeed no doubt that the SAATM will be of great utility to both airlines and passengers in Nigeria.

Keywords: airlines, civil aviation, competition, consumer protection, passengers, single African air transport market, yamoussoukro decision

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Authors: Aistis ZekevicIus

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Biopolitical theory, which was first developed by Michel Foucault, takes into consideration the administration of life by implying a style of government based on the regulation of populations as its subject. The intensification of the #BlackLivesMatter movement and popular outcries against racial discrimination in the US health system have prompted us to reconsider the relationship between biopolitics and race in the face of the COVID-19 pandemic. Based on works by Foucault, Achille Mbembe and Nicholas Mirzoeff that transcend the boundaries of poststructuralism, critical theory and postcolonial studies, the paper suggests that the global pandemic has highlighted new aspects of the interplay between biopower and race by encouraging the search for scapegoats, deepening the structural racial inequality, and thus producing necropolitical regimes of exclusion.

Keywords: biopolitics, biopower, necropolitics, pandemic, race

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Authors: Anda-veronica Gherman, Dimitrios Varthalitis

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Neurosurgery, like many surgical specialties, undoubtedly exhibits a significant gender gap, particularly in leadership positions. While increasing women representation in neurosurgery is important, it is crucial to increase their presence in leadership positions. Across the globe and Europe there are concerning trends of only 4% of all neurosurgical departments being chaired by women. This study aims to explore the situation regarding gender disparities in leadership in the United Kingdom and to identify possible contributing factors as well as discussing future strategies to bridge this gap. Methods: A literature review was conducted utilising PubMed as main database with search keywords including ‘female neurosurgeon’, ‘women neurosurgeon’, ‘gender disparity’, ‘leadership’ and ‘UK’. Additionally, a manual search of all neurosurgical departments in the UK was performed to identify the current female department leads and training director leads. Results: The literature search identified a paucity of literature addressing specifically leadership in female neurosurgeons within the UK, with very few published papers specifically on this topic. Despite more than half of medical students in the UK being female, only a small proportion pursue a surgical career, with neurosurgery being one of the least represented specialties. Only 27% of trainee neurosurgeons are female, and numbers are even lower at a consultant level, where women represent just 8%.Findings from published studies indicated that only 6.6% of leadership positions in neurosurgery are occupied by women in the UK. Furthermore, our manual searches across UK neurosurgical departments revealed that around 5% of department lead positions are currently held by women. While this figure is slightly higher than the European average of 4%, it remains lower compared to figures of 10% in other North-West European countries. The situation is slightly more positive looking at the training directors, with 15% being female. Discussion: The findings of this study highlight a significant gender disparity in leadership positions within neurosurgery in the UK, which may have important implications, perpetuating the lack of diversity on the decision-making process, limiting the career advancement opportunities of women and depriving the neurosurgical field from the voices, opinions and talents of women. With women representing half of the population, there is an undeniable need for more female leaders at the policy-making level. There are many barriers that can contribute to these numbers, including bias, stereotypes, lack of mentorship and work-like balance. A few solutions to overcome these barriers can be training programs addressing bias and impostor syndrome, leadership workshops tailored for female needs, better workplace policies, increased in formal mentorship and increasing the visibility of women in neurosurgery leadership positions through media, speaking opportunities, conferences, awards etc. And lastly, more research efforts should focus on the leadership and mentorship of women in neurosurgery, with an increased number of published papers discussing these issues.

Keywords: female neurosurgeons, female leadership, female mentorship, gender disparities

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Authors: Jiwan Kumar, Pooja, Sandeep Negi, Anjum Rouf, Amit Kumar, Naveen Lakra

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In modern times where, health issues are increasing day by day, breast cancer is also one of them, which is very crucial and really important to find in the early stages. Doctors can use this model in order to tell their patients whether a cancer is not harmful (benign) or harmful (malignant). We have used the knowledge of machine learning in order to produce the model. we have used algorithms like Logistic Regression, Random forest, support Vector Classifier, Bayesian Network and Radial Basis Function. We tried to use the data of crucial parts and show them the results in pictures in order to make it easier for doctors. By doing this, we're making ML better at finding breast cancer, which can lead to saving more lives and better health care.

Keywords: Bayesian network, radial basis function, ensemble learning, understandable, data making better, random forest, logistic regression, breast cancer

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Authors: Rhisan Mae Enriquez-Morales

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The primary concern of this study is to answer the question: How does the Philippine government formulate its foreign policy with respect to its territorial claims over areas in the West Philippine Sea after the Scarborough standoff in April 2012? Specifically, the study seeks to provide understanding on the political process in the formulation of foreign policy relating to the Philippine claims in the West Philippine Sea after the 2012 Scarborough Standoff, by looking into the relationship of bureaucracies and how it influences the decision-making process. Secondly, this study aims to determine the long and short term foreign policies of the Philippines with respect to its territorial claims over the West Philippine Sea. Lastly, this study seeks to determine the implication of Philippine foreign policy in settling the West Philippine Sea dispute on the country’s national security. The Bureaucratic Politics Model (BPM) in Foreign Policy Analysis (FPA) is the framework utilized in this study, which focuses primarily on the relationship of bureaucracies in the formulation of foreign policy and how these agencies influence the process of foreign policy formulation. The findings of this study reveal that: first, the Philippines foreign policy in the West Philippine Sea continues to develop to address current developments in the WPS. Second, as the government requires demilitarization there is a shift from traditional to non-traditional security approach. This shift caused inconvenience from the defense sector particularly the Navy thinking that they are being deprived of their traditional roles. Lastly, the Philippine government’s greater emphasis on internal security operation implies the need to reassess its security concerns and look into territorial security.

Keywords: bureaucratic politics model, foreign policy analysis, security, West Philippine sea

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Authors: Zahra Ahmadi, Fazlollah Adibnia

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Scheduling of tasks and the optimal allocation of resources in the cloud are based on the dynamic nature of tasks and the heterogeneity of resources. Applications that are based on the scientific workflow are among the most widely used applications in this field, which are characterized by high processing power and storage capacity. In order to increase their efficiency, it is necessary to plan the tasks properly and select the best virtual machine in the cloud. The goals of the system are effective factors in scheduling tasks and resource selection, which depend on various criteria such as time, cost, current workload and processing power. Multi-criteria decision-making methods are a good choice in this field. In this research, a new method of work planning and resource allocation in a heterogeneous environment based on the modified AHP algorithm is proposed. In this method, the scheduling of input tasks is based on two criteria of execution time and size. Resource allocation is also a combination of the AHP algorithm and the first-input method of the first client. Resource prioritization is done with the criteria of main memory size, processor speed and bandwidth. What is considered in this system to modify the AHP algorithm Linear Max-Min and Linear Max normalization methods are the best choice for the mentioned algorithm, which have a great impact on the ranking. The simulation results show a decrease in the average response time, return time and execution time of input tasks in the proposed method compared to similar methods (basic methods).

Keywords: hierarchical analytical process, work prioritization, normalization, heterogeneous resource allocation, scientific workflow

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Authors: Thuy Hanh Nguyen Thi

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By the method of deep reading and text analysis, this article will analyze the use and creation of dialects as a way of demonstrating Yan Lianke's creative stance. This article indicates that this is the writer’s narrative strategy in a fight against aphasia, a language disorder of Chinese people and culture, demonstrating a sense of return to folklore and marks his own linguistic style. In terms of verbal text, the dialect in the Yan Lianke’s novels manifested through the use of words, sentences and dialects. There are two types of dialects that exist in Yan Lianke’s novels: the current dialect system and the particular dialect system of Pa Lau world created by the writer himself in order to enrich the vocabulary of Han Chinese.

Keywords: Yan Lianke , aphasia, dialect, Pa Lou world

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Authors: Zohreh Mohammadi, Jan Kaspar, Peter Lohmander, Robert Marusak, Harald Vacik, Ljusk Ola Eriksson

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Storms are important damaging agents in European forest ecosystems. In the latest decades, significant economic losses in European forestry occurred due to storms. This study investigates the problem of optimal harvest planning when forest stands risk to be felled by storms. One of the most applicable mathematical methods which are being used to optimize forest management is stochastic dynamic programming (SDP). This method belongs to the adaptive optimization class. Sequential decisions, such as harvest decisions, can be optimized based on sequential information about events that cannot be perfectly predicted, such as the future storms and the future states of wind protection from other forest stands. In this paper, stochastic dynamic programming is used to maximize the expected present value of the profits from an area consisting of several forest stands. The region of analysis is the Czech Republic. The harvest decisions, in a particular time period, should be simultaneously taken in all neighbor stands. The reason is that different stands protect each other from possible winds. The optimal harvest age of a particular stand is a function of wind speed and different wind protection effects. The optimal harvest age often decreases with wind speed, but it cannot be determined for one stand at a time. When we consider a particular stand, this stand also protects other stands. Furthermore, the particular stand is protected by neighbor stands. In some forest stands, it may even be rational to increase the harvest age under the influence of stronger winds, in order to protect more valuable stands in the neighborhood. It is important to integrate wind risk in forestry decision-making.

Keywords: Czech republic, forest stands, stochastic dynamic programming, wind risk

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Authors: James M. Wakiru, Liliane Pintelon, Peter Muchiri, Stanley Mburu

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Maintenance performance measurement (MPM) represents an integrated aspect that considers both operational and maintenance related aspects while evaluating the effectiveness and efficiency of maintenance to ensure assets are working as they should. Three salient issues require to be addressed for an asset-intensive organization to employ an MPM-based framework to optimize maintenance. Firstly, the organization should establish important perfomance metric(s), in this case the maintenance objective(s), which they will be focuss on. The second issue entails aligning the maintenance objective(s) with maintenance optimization. This is achieved by deriving maintenance performance indicators that subsequently form an objective function for the optimization program. Lastly, the objective function is employed in an optimization program to derive maintenance decision support. In this study, we develop a framework that initially identifies the crucial maintenance performance measures, and employs them to derive maintenance decision support. The proposed framework is demonstrated in a case study of a geothermal drilling rig, where the objective function is evaluated utilizing a simulation-based model whose parameters are derived from empirical maintenance data. Availability, reliability and maintenance inventory are depicted as essential objectives requiring further attention. A simulation model is developed mimicking a drilling rig operations and maintenance where the sub-systems are modelled undergoing imperfect maintenance, corrective (CM) and preventive (PM), with the total cost as the primary performance measurement. Moreover, three maintenance spare inventory policies are considered; classical (retaining stocks for a contractual period), vendor-managed inventory with consignment stock and periodic monitoring order-to-stock (s, S) policy. Optimization results infer that the adoption of (s, S) inventory policy, increased PM interval and reduced reliance of CM actions offers improved availability and total costs reduction.

Keywords: maintenance, vendor-managed, decision support, performance, optimization

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Authors: Derejaw Lake Melie, Alemu Kumlachew Tegegne

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The increasing prevalence of social media in Ethiopia has exacerbated societal challenges by fostering the proliferation of negative emotional posts and comments. Illicit use of social media has further exacerbated divisions among the population. Addressing these issues through manual identification and aggregation of emotions from millions of users for swift decision-making poses significant challenges, particularly given the rapid growth of Amharic language usage on social platforms. Consequently, there is a critical need to develop an intelligent system capable of automatically detecting and categorizing negative emotional content into social, religious, and political categories while also filtering out toxic online content. This paper aims to leverage sentiment analysis techniques to achieve automatic detection and filtering of negative emotional content from Amharic social media texts, employing a comparative study of deep learning algorithms. The study utilized a dataset comprising 29,962 comments collected from social media platforms using comment exporter software. Data pre-processing techniques were applied to enhance data quality, followed by the implementation of deep learning methods for training, testing, and evaluation. The results showed that CNN, GRU, LSTM, and Bi-LSTM classification models achieved accuracies of 83%, 50%, 84%, and 86%, respectively. Among these models, Bi-LSTM demonstrated the highest accuracy of 86% in the experiment.

Keywords: negative emotion, emotion detection, social media filtering sentiment analysis, deep learning.

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Authors: Busari Lasisi

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Literary works are often devalued to mere fictions and are left with no essence and contributions to history. The sub-structured rational delineating literary works from history is anchored on the aesthetic and flowery expressions that are therein embedded for artistic enrichment. This does not distance a literary work (from whichever genres it is drawn) reflecting the socio-economic, cultural and political cum religious perspectives of a given people and society. This is the very reason justifying the veracity that a writer does not anchor his writing outside of his society. He writes mirroring (his or a given society’s) events, places and duration of consciousness thereby making history evident. In the light of this reality, literary works are not just seen as fictions, imaginative and unrealistic pieces; for they are never unconnected to history. Thus, making authors of literary works historians and their works engrafted useful historical documents. Using the works of Ahmadou Korouma, a renown Ivorian writer, the praxis of this paper therefore in New Historicism approach postulates that literary works are underlying unexplored historic materials, and literature a jumelle to history.

Keywords: literature, history, New Historicism, authors

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Authors: Rattanakorn Phadungthin, Juthathip Haema

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This research presents an assessment of the criticality of the substation's power transformer using the Entropy Weight method to enable more effective maintenance planning. Typically, transformers fail due to heat, electricity, chemical reactions, mechanical stress, and extreme climatic conditions. Effective monitoring of the insulating oil is critical to prevent transformer failure. However, finding appropriate weights for dissolved gases is a major difficulty due to the lack of a defined baseline and the requirement for subjective expert opinion. To decrease expert prejudice and subjectivity, the Entropy Weight method is used to optimise the weightings of eleven key dissolved gases. The algorithm to assess the criticality operates through five steps: create a decision matrix, normalise the decision matrix, compute the entropy, calculate the weight, and calculate the criticality score. This study not only optimises gas weighing but also greatly minimises the need for expert judgment in transformer maintenance. It is expected to improve the efficiency and reliability of power transformers so failures and related economic costs are minimized. Furthermore, maintenance schemes and ranking are accomplished appropriately when the assessment of criticality is reached.

Keywords: criticality assessment, dissolved gas, maintenance scheme, power transformer

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Authors: Rui Lança, Elisa Silva, Fátima Farinha, Miguel José Oliveira, Manuel Duarte Pinheiro, Cátia Miguel

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Tourism has a great potential to bring up progress across the Sustainable Development Goals (SDGs). If well managed and monitored, the tourism sector can create quality jobs, reduce poorness and offer incentives for environmental preservation, helping on the transition towards more inclusive and resilient economies. However, without proper safeguards and investments, expansion of the tourism market will increase pressure on biodiversity and the ecosystems on which the livelihoods of local communities depend. Competitivity is a key dimension in tourism, sustainable tourism challenge new dimensions to competitivity, namely environmental, social, institutional and economic achieve a medium and long-term competitivity. It is undoubtedly clear on the tourism sector, the importance of the region sustainability in the current touristic destinations offer. The basis of a tourism region prosperity will depend on /of it. The OBSERVE project intends to be an instrument for monitoring and evaluating the sustainability levels of the Algarve region. Its main objective is to provide environmental, economic, social-cultural and institutional indicators to support the decision-making process for a sustainable growth of the region. The project´s main goal is a digital portal with the most relevant indicators to allow evaluating and communicating the performance of the region in a sustainable growth perspective. This paper presents the OBSERVE project and highlights the potential contribution to a broad perspective of competitivity and its contribution for different stakeholders and the touristic value chain. Limitations and opportunities are also discussed.

Keywords: sustainable tourism, competitivity, OBSERVE project, Algarve region

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Authors: Gu Pang, Bartosz Gebka

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We forecast the demand of total container throughput at the Indonesia’s largest seaport, Tanjung Priok Port. We propose four univariate forecasting models, including SARIMA, the additive Seasonal Holt-Winters, the multiplicative Seasonal Holt-Winters and the Vector Error Correction Model. Our aim is to provide insights into whether forecasting the total container throughput obtained by historical aggregated port throughput time series is superior to the forecasts of the total throughput obtained by summing up the best individual terminal forecasts. We test the monthly port/individual terminal container throughput time series between 2003 and 2013. The performance of forecasting models is evaluated based on Mean Absolute Error and Root Mean Squared Error. Our results show that the multiplicative Seasonal Holt-Winters model produces the most accurate forecasts of total container throughput, whereas SARIMA generates the worst in-sample model fit. The Vector Error Correction Model provides the best model fits and forecasts for individual terminals. Our results report that the total container throughput forecasts based on modelling the total throughput time series are consistently better than those obtained by combining those forecasts generated by terminal-specific models. The forecasts of total throughput until the end of 2018 provide an essential insight into the strategic decision-making on the expansion of port's capacity and construction of new container terminals at Tanjung Priok Port.

Keywords: SARIMA, Seasonal Holt-Winters, Vector Error Correction Model, container throughput

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Authors: jihen ayadi sellami

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The rapid evolution of the global financial landscape has led to increased attention to corporate tax policies and the need to understand the factors that influence their tax behavior. In order to mitigate any residual loss for shareholders resulting from tax aggressiveness and resolve the agency problem, appropriate systems that separate the function of management from that of controlling are needed. In this context of growing concerns to limit aggressive corporate taxation practices through governance, this study discusses. Its aims is to examine the influence of six key characteristics of the board of directors (board size, diligence, CEO duality, presence of audit committees, gender diversity and independence of directors), given a governance mechanism, on the tax decisions of non-financial corporations in the United State. In fact, using a sample of 90 non-financial US firms from S&P 500 over a period of 4 years going from 2014 to 2017, the results based on a multivariate linear regression highlight significant associations between these characteristics and corporate tax policy. Notably, larger board, gender diversity, diligence and increased director independence appear to play an important role in reducing aggressive taxation. While duality has a positive and significant correlation with tax aggressiveness, that can be explained by the fact that the manager did properly exploit his specific position within the company. These findings contribute to a deeper understanding of how board characteristics can influence corporate tax management, providing avenues for more effective corporate governance and more responsible tax decision-making

Keywords: tax aggressiveness, board of directors, board size, CEO duality, audit committees, gender diversity, director independence, diligence, corporate governance, united states

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Authors: Praveen Chandramenon, Andrew Gascoyne, Fideline Tchuenbou-Magaia

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Aquaponics offers a simple conclusive solution to the food and environmental crisis of the world. This approach combines the idea of Aquaculture (growing fish) to Hydroponics (growing vegetables and plants in a soilless method). Smart Aquaponics explores the use of smart technology including artificial intelligence and IoT, to assist farmers with better decision making and online monitoring and control of the system. Identification of different growth stages of Swiss Chard plants and predicting its harvest time is found to be important in Aquaponic yield management. This paper brings out the comparative analysis of a standard Aquaponics with a Vermiponics (Aquaponics with worms), which was grown in the controlled environment, by implementing IoT and deep learning-based growth stage segregation and harvest time prediction of Swiss Chards before and after applying an optimal freshwater replenishment. Data collection, Growth stage classification and Harvest Time prediction has been performed with and without water replenishment. The paper discusses the experimental design, IoT and sensor communication with architecture, data collection process, image segmentation, various regression and classification models and error estimation used in the project. The paper concludes with the results comparison, including best models that performs growth stage segregation and harvest time prediction of the Aquaponic and Vermiponic testbed with and without freshwater replenishment.

Keywords: aquaponics, deep learning, internet of things, vermiponics

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Authors: Wan-Yu Liu, Tiffany April Lin, Yu-Chieh Tang, Yi-Lin Wang, Chieh-Hui Li

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As the national income in Taiwan has been raised, the life style of the public has also been changed, so that the tourism industry gradually moves from a service industry to an experience economy. The Lihpaoland is one of the most popular theme parks in Taiwan. However, the related works on performance of service quality of the park have been lacking since its re-operation in 2012. Therefore, this study investigates the quality of software/hardware facilities and services of the Lihpaoland, and aims to achieve the following three goals: 1) analyzing how various sample data of tourists leads to different results for service quality of LihpaoLand; 2) analyzing how tourists respond to the service tangibility, service reliability, service responsiveness, service guarantee, and service empathy of LihpaoLand; 3) according to the theoretical and empirical results, proposing how to improve the overall facilities and services of LihpaoLand, and hoping to provide suggestions to the LihpaoLand or other related businesses to make decision. The survey was conducted on the tourists to the LihpaoLand using convenience sampling, and 400 questionnaires were collected successfully. Analysis results show that tourists paid much attention to maintenance of amusement facilities and safety of the park, and were satisfied with them, which are great advantages of the park. However, transportation around the LihpaoLand was inadequate, and the price of the Fullon hotel (which is the hotel closest to the LihpaoLand) were not accepted by tourists – more promotion events are recommended. Additionally, the shows are not diversified, and should be improved with the highest priority. Tourists did not pay attention to service personnel’s clothing and the ticket price, but they were not satisfied with them. Hence, this study recommends to design more distinctive costumes and conduct ticket promotions. Accordingly, the suggestions made in this study for LihpaoLand are stated as follows: 1) Diversified amusement facilities should be provided to satisfy the needs at different ages. 2) Cheep but tasty catering and more distinctive souvenirs should be offered. 3) Diversified propaganda schemes should be strengthened to increase number of tourists. 4) Quality and professional of the service staff should be enhanced to acquire public praise and tourists revisiting. 5) Ticket promotions in peak seasons, low seasons, and special events should be conducted. 6) Proper traffic flows should be planned and combined with technologies to reduce waiting time of tourists. 7) The features of theme landscape in LihpaoLand should be strengthened to increase willingness of the tourists with special preferences to visit the park. 8) Ticket discounts or premier points card promotions should be adopted to reward the tourists with high loyalty.

Keywords: service quality, customers’ satisfaction, theme park, Taiwan

Procedia PDF Downloads 473