Search results for: decision processing

Authors: D. Shakirah Shukor, A. Aminudin, Hashim U. A., Waziralilah N. Fathiah, T. Vikneshvaran

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The interior noise control is essential to be explored due to the interior acoustic analysis is significant in the systems such as automobiles, aircraft, air-handling system and diesel engine exhausts system. In this research, experimental work was undertaken for canceling an active noise in the rectangular enclosure. The rectangular enclosure was fabricated with multiple speakers and microphones inside the enclosure. A software program using digital signal processing is implemented to evaluate the proposed method. Experimental work was conducted to obtain the acoustic behavior and characteristics of the rectangular enclosure and noise cancellation based on active noise control in low-frequency range. Noise is generated by using multispeaker inside the enclosure and microphones are used for noise measurements. The technique for noise cancellation relies on the principle of destructive interference between two sound fields in the rectangular enclosure. One field is generated by the original or primary sound source, the other by a secondary sound source set up to interfere with, and cancel, that unwanted primary sound. At the end of this research, the result of output noise before and after cancellation are presented and discussed. On the basis of the findings presented in this research, an active noise cancellation in the rectangular enclosure is worth exploring in order to improve the noise control technologies.

Keywords: active noise control, digital signal processing, noise cancellation, rectangular enclosure

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Authors: Jailan Mohamed El Demerdash

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

Keywords: millennials, environment, awareness, paying more

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Authors: Anirudh Lahiri

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Neuromorphic computing, inspired by the intricate operations of biological neural networks, offers a revolutionary approach to overcoming the limitations of traditional computing architectures. This research proposes the integration of spintronics with neuromorphic systems, aiming to enhance computational performance, scalability, and energy efficiency. Traditional computing systems, based on the Von Neumann architecture, struggle with scalability and efficiency due to the segregation of memory and processing functions. In contrast, the human brain exemplifies high efficiency and adaptability, processing vast amounts of information with minimal energy consumption. This project explores the use of spintronics, which utilizes the electron's spin rather than its charge, to create more energy-efficient computing systems. Spintronic devices, such as magnetic tunnel junctions (MTJs) manipulated through spin-transfer torque (STT) and spin-orbit torque (SOT), offer a promising pathway to reducing power consumption and enhancing the speed of data processing. The integration of these devices within a neuromorphic framework aims to replicate the efficiency and adaptability of biological systems. The research is structured into three phases: an exhaustive literature review to build a theoretical foundation, laboratory experiments to test and optimize the theoretical models, and iterative refinements based on experimental results to finalize the system. The initial phase focuses on understanding the current state of neuromorphic and spintronic technologies. The second phase involves practical experimentation with spintronic devices and the development of neuromorphic systems that mimic synaptic plasticity and other biological processes. The final phase focuses on refining the systems based on feedback from the testing phase and preparing the findings for publication. The expected contributions of this research are twofold. Firstly, it aims to significantly reduce the energy consumption of computational systems while maintaining or increasing processing speed, addressing a critical need in the field of computing. Secondly, it seeks to enhance the learning capabilities of neuromorphic systems, allowing them to adapt more dynamically to changing environmental inputs, thus better mimicking the human brain's functionality. The integration of spintronics with neuromorphic computing could revolutionize how computational systems are designed, making them more efficient, faster, and more adaptable. This research aligns with the ongoing pursuit of energy-efficient and scalable computing solutions, marking a significant step forward in the field of computational technology.

Keywords: material science, biological engineering, mechanical engineering, neuromorphic computing, spintronics, energy efficiency, computational scalability, synaptic plasticity.

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Authors: Shima Nabinejad, Holger Schüttrumpf

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Farmers who are living in flood-prone areas such as coasts are exposed to storm surges increased due to climate change. Crop cultivation is the most important economic activity of farmers, and in the time of flooding, agricultural lands are subject to inundation. Additionally, overflow saline water causes more severe damage outcomes than riverine flooding. Agricultural crops are more vulnerable to salinity than other land uses for which the economic damages may continue for a number of years even after flooding and affect farmers’ decision-making for the following year. Therefore, it is essential to assess what extent the agricultural areas are flooded and how much the associated flood damage to each individual farmer is. To address these questions, we integrated farmers’ decision-making at farm-scale with flood risk management. The integrated model includes identification of hazard scenarios, failure analysis of structural measures, derivation of hydraulic parameters for the inundated areas and analysis of the economic damages experienced by each farmer. The present study has two aims; firstly, it attempts to investigate the flooded cropland and potential crop damages for the whole area. Secondly, it compares them among farmers’ field for three flood scenarios, which differ in breach locations of the flood protection structure. To achieve its goal, the spatial distribution of fields and cultivated crops of farmers were fed into the flood risk model, and a 100-year storm surge hydrograph was selected as the flood event. The study area was Pellworm Island that is located in the German Wadden Sea National Park and surrounded by North Sea. Due to high salt content in seawater of North Sea, crops cultivated in the agricultural areas of Pellworm Island are 100% destroyed by storm surges which were taken into account in developing of depth-damage curve for analysis of consequences. As a result, inundated croplands and economic damages to crops were estimated in the whole Island which was further compared for six selected farmers under three flood scenarios. The results demonstrate the significance and the flexibility of the proposed model in flood risk assessment of flood-prone areas by integrating flood risk management and decision-making.

Keywords: crop damages, flood risk analysis, individual farmer, inundated cropland, Pellworm Island, storm surges

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Authors: K. Shaji Mon, P. R. John Sreenidhi

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In deep-submicrometer CMOS processes, time-domain resolution of a digital signal is becoming higher than voltage resolution of analog signals. This claim is nowadays pushing toward a new circuit design paradigm in which the traditional analog signal processing is expected to be progressively substituted by the processing of times in the digital domain. Within this novel paradigm, digitally controlled delay lines (DCDL) should play the role of digital-to-analog converters in traditional, analog-intensive, circuits. Digital delay locked loops are highly prevalent in integrated systems.The proposed paper addresses the glitches present in delay circuits along with area,power dissipation and signal integrity.The digitally controlled delay lines(DCDL) under study have been designed in a 90 nm CMOS technology 6 layer metal Copper Strained SiGe Low K Dielectric. Simulation and synthesis results show that the novel circuits exhibit no glitches for dual output coarse DCDL with less power dissipation and consumes less area compared to the glitch free NAND based DCDL.

Keywords: glitch free, NAND-based DCDL, CMOS, deep-submicrometer

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Authors: Lingfei Sun

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Based on the significant value of ecology, the strategic planning for ecological civilization construction was mentioned in the 17th and 18th National Congress of the Communist Party of China. How to generate economic value based on the environmental capacity is not only an economic decision but also a political decision to make. Boshan took the full use of “Ecology” and transformed it as an inexhaustible green resource to benefit people, reflecting the sustainable value of new agriculture development mode. The Strawberry Harvest Festival and Blueberry Harvest Festival hosted approximately 96,000 and 54,000 leisure tourists respectively in 2014. For the Kiwi Harvest Festival in August 2014, in average, it attracted about 4600 tourists per day, which generated daily kiwi sales of 50,000 lbs and 3 million RMB (About 476,000 USD) of daily revenue. The purpose of this study is to elaborate the modes of agritourism development, by analyzing the cases in rural area of Boshan, China. Interviews with the local government officers were applied to discover operation mode of agritourism operation. The financial data was used to demonstrate the strength of government policy and improvement of the income of rural people. The result indicated that there are mainly three types of modes: the Intensive Mode, the Model Mode and the Mixed Mode, supported by case study respectively. With the boom of tourism, the development of agritourism in Boshan relies on the agriculture encouraging policy of China and the effort of local government; meanwhile, large scale of cultivation and the product differentiation are the crucial elements for the success of rural agritourism projects.

Keywords: agriculture, agritourism, economy, rural area development

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Authors: Dong Seop Lee, Byung Sik Kim

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In the 4th Industrial Revolution, various intelligent technologies have been developed in many fields. These artificial intelligence technologies are applied in various services, including disaster management. Disaster information management does not just support disaster work, but it is also the foundation of smart disaster management. Furthermore, it gets historical disaster information using artificial intelligence technology. Disaster information is one of important elements of entire disaster cycle. Disaster information management refers to the act of managing and processing electronic data about disaster cycle from its’ occurrence to progress, response, and plan. However, information about status control, response, recovery from natural and social disaster events, etc. is mainly managed in the structured and unstructured form of reports. Those exist as handouts or hard-copies of reports. Such unstructured form of data is often lost or destroyed due to inefficient management. It is necessary to manage unstructured data for disaster information. In this paper, the Optical Character Recognition approach is used to convert handout, hard-copies, images or reports, which is printed or generated by scanners, etc. into electronic documents. Following that, the converted disaster data is organized into the disaster code system as disaster information. Those data are stored in the disaster database system. Gathering and creating disaster information based on Optical Character Recognition for unstructured data is important element as realm of the smart disaster management. In this paper, Korean characters were improved to over 90% character recognition rate by using upgraded OCR. In the case of character recognition, the recognition rate depends on the fonts, size, and special symbols of character. We improved it through the machine learning algorithm. These converted structured data is managed in a standardized disaster information form connected with the disaster code system. The disaster code system is covered that the structured information is stored and retrieve on entire disaster cycle such as historical disaster progress, damages, response, and recovery. The expected effect of this research will be able to apply it to smart disaster management and decision making by combining artificial intelligence technologies and historical big data.

Keywords: disaster information management, unstructured data, optical character recognition, machine learning

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Authors: Hassan S. Hussein, Rania A. Abul Seoud, Amr M. Refaat

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Scientific researchers face in the analysis of very large data sets that is increasing noticeable rate in today’s and tomorrow’s technologies. Hadoop and Spark are types of software that developed frameworks. Hadoop framework is suitable for many Different hardware platforms. In this research, a scientific Linux cluster for Big Data analysis (SLBD) is presented. SLBD runs open source software with large computational capacity and high performance cluster infrastructure. SLBD composed of one cluster contains identical, commodity-grade computers interconnected via a small LAN. SLBD consists of a fast switch and Gigabit-Ethernet card which connect four (nodes). Cloudera Manager is used to configure and manage an Apache Hadoop stack. Hadoop is a framework allows storing and processing big data across the cluster by using MapReduce algorithm. MapReduce algorithm divides the task into smaller tasks which to be assigned to the network nodes. Algorithm then collects the results and form the final result dataset. SLBD clustering system allows fast and efficient processing of large amount of data resulting from different applications. SLBD also provides high performance, high throughput, high availability, expandability and cluster scalability.

Keywords: big data platforms, cloudera manager, Hadoop, MapReduce

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Authors: Rajesh Chakraborty, Dibyendu Das, Rabindra Nath Barman, Uttam Kumar Mandal

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In the present study, the objective is to find out the most effective zone causing soil erosion in the Gumati river basin located in the state of Tripura, a north eastern state of India using analytical hierarchy process (AHP) and multi-objective optimization on the basis of ratio analysis (MOORA).The watershed is segmented into 20 zones based on Area. The watershed is considered by pointing the maximum elevation from sea lever from Google earth. The soil erosion is determined using the universal soil loss equation. The different independent variables of soil loss equation bear different weightage for different soil zones. And therefore, to find the weightage factor for all the variables of soil loss equation like rainfall runoff erosivity index, soil erodibility factor etc, analytical hierarchy process (AHP) is used. And thereafter, multi-objective optimization on the basis of ratio analysis (MOORA) approach is used to select the most effective zone causing soil erosion. The MCDM technique concludes that the maximum soil erosion is occurring in the zone 14.

Keywords: soil erosion, analytic hierarchy process (AHP), multi criteria decision making (MCDM), universal soil loss equation (USLE), multi-objective optimization on the basis of ratio analysis (MOORA)

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Authors: Chia-Jung Chen, Yu-Chi Hsu, June-Dong Lin, Kun-Chen Chan, Chieh-Tien Wang, Li-Ching Wu, Chung-Feng Liu

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With fast advances in healthcare technology, various total laboratory automation (TLA) processes have been proposed. However, adopting TLA needs quite high funding. This study explores an early adoption experience by Taiwan’s large-scale hospital group, the Chimei Hospital Group (CMG), which owns three branch hospitals (Yongkang, Liouying and Chiali, in order by service scale), based on the five stages of Everett Rogers’ Diffusion Decision Process. 1.Knowledge stage: Over the years, two weaknesses exists in laboratory department of CMG: 1) only a few examination categories (e.g., sugar testing and HbA1c) can now be completed and reported within a day during an outpatient clinical visit; 2) the Yongkang Hospital laboratory space is dispersed across three buildings, resulting in duplicated investment in analysis instruments and inconvenient artificial specimen transportation. Thus, the senior management of the department raised a crucial question, was it time to process the redesign of the laboratory department? 2.Persuasion stage: At the end of 2013, Yongkang Hospital’s new building and restructuring project created a great opportunity for the redesign of the laboratory department. However, not all laboratory colleagues had the consensus for change. Thus, the top managers arranged a series of benchmark visits to stimulate colleagues into being aware of and accepting TLA. Later, the director of the department proposed a formal report to the top management of CMG with the results of the benchmark visits, preliminary feasibility analysis, potential benefits and so on. 3.Decision stage: This TLA suggestion was well-supported by the top management of CMG and, finally, they made a decision to carry out the project with an instrument-leasing strategy. After the announcement of a request for proposal and several vendor briefings, CMG confirmed their laboratory automation architecture and finally completed the contracts. At the same time, a cross-department project team was formed and the laboratory department assigned a section leader to the National Taiwan University Hospital for one month of relevant training. 4.Implementation stage: During the implementation, the project team called for regular meetings to review the results of the operations and to offer an immediate response to the adjustment. The main project tasks included: 1) completion of the preparatory work for beginning the automation procedures; 2) ensuring information security and privacy protection; 3) formulating automated examination process protocols; 4) evaluating the performance of new instruments and the instrument connectivity; 5)ensuring good integration with hospital information systems (HIS)/laboratory information systems (LIS); and 6) ensuring continued compliance with ISO 15189 certification. 5.Confirmation stage: In short, the core process changes include: 1) cancellation of signature seals on the specimen tubes; 2) transfer of daily examination reports to a data warehouse; 3) routine pre-admission blood drawing and formal inpatient morning blood drawing can be incorporated into an automatically-prepared tube mechanism. The study summarizes below the continuous improvement orientations: (1) Flexible reference range set-up for new instruments in LIS. (2) Restructure of the specimen category. (3) Continuous review and improvements to the examination process. (4) Whether installing the tube (specimen) delivery tracks need further evaluation.

Keywords: innovation decision process, total laboratory automation, health care

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Authors: Pasan Dunuwila, V. H. L. Rodrigo, Naohiro Goto

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Sri Lanka is one the largest natural rubber (NR) producers of the world, where the NR industry is a major foreign exchange earner. Among the locally manufactured NR products, concentrated latex (CL) and ribbed smoked sheets (RSS) hold a significant position. Furthermore, these products become the foundation for many products utilized by the people all over the world (e.g. gloves, condoms, tires, etc.). Processing of CL and RSS costs a significant amount of material, energy, and workforce. With this background, both manufacturing lines have immensely challenged by waste, low productivity, lack of cost efficiency, rising cost of production, and many environmental issues. To face the above challenges, the adaptation of sustainable manufacturing measures that use less energy, water, materials, and produce less waste is imperative. However, these sectors lack comprehensive studies that shed light on such measures and thoroughly discuss their improvement potentials from both environmental and economic points of view. Therefore, based on a study of three CL and three RSS mills in Sri Lanka, this study deploys sustainable manufacturing techniques and tools to uncover the underlying potentials to improve performances in CL and RSS processing sectors. This study is comprised of three steps: 1. quantification of average material waste, economic losses, and greenhouse gas (GHG) emissions via material flow analysis (MFA), material flow cost accounting (MFCA), and life cycle assessment (LCA) in each manufacturing process, 2. identification of improvement options with the help of Pareto and What-if analyses, field interviews, and the existing literature; and 3. validation of the identified improvement options via the re-execution of MFA, MFCA, and LCA. With the help of this methodology, the economic and environmental hotspots, and the degrees of improvement in both systems could be identified. Results highlighted that each process could be improved to have less waste, monetary losses, manufacturing costs, and GHG emissions. Conclusively, study`s methodology and findings are believed to be beneficial for assuring the sustainable growth not only in Sri Lankan NR processing sector itself but also in NR or any other industry rooted in other developing countries.

Keywords: concentrated latex, natural rubber, ribbed smoked sheets, Sri Lanka

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Authors: Wynne De Jong, Robert Miller, Ross Riggs

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Beyond the number of nurses providing care for patients, having nurses with the right skills, experience and education is essential to ensure the best possible outcomes for patients. Research studies continue to link nurse staffing and skill mix with nurse-sensitive patient outcomes; numerous studies clearly show that superior patient outcomes are associated with higher levels of regulated staff. Due to the limited number of tools and processes available to assist nurse leaders with staffing models of care, nurse leaders are constantly faced with the ongoing challenge to ensure their staffing models of care best suit their patient population. In 2009, several hospitals in Ontario, Canada participated in a research study to develop and evaluate an RN/RPN utilization toolkit. The purpose of this study was to develop and evaluate a toolkit for Registered Nurses/Registered Practical Nurses Staff mix decision-making based on the College of Nurses of Ontario, Canada practice standards for the utilization of RNs and RPNs. This paper will highlight how an organization has further developed the Patient Care Needs Assessment (PCNA) questionnaire, a major component of the toolkit. Moreover, it will demonstrate how it has utilized the information from PCNA to clearly identify patient and family care needs, thus providing evidence-based results to assist leaders with matching the best staffing skill mix to their patients.

Keywords: nurse staffing models of care, skill mix, nursing health human resources, patient safety

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Authors: Ellie Abdi

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This paper is a review of a recently published book (April 2018) by Dr. Ellie Abdi. The book divides into two sections of 1) leadership in school organization and 2) pragmatic research in physical education professional development. The first part of the book explores school organizational development in terms of 1) communication development, 2) community development, and 3) decision making development. It concludes to acknowledge that decision making is the heart of educational management. This is while communication and community are essential to the development of the school organization. The role of a leader in a professional learning community (PLC) is acknowledged with the organizational development plan and moves onto 5 overall objectives of a professional development plan. It clarifies that professional learning community (PLC) benefits both students and professionals in education. Furthermore, professional development needs to be involved in opportunities to value diversity and foundations of learning, in addition to search for veteran teachers who offer a rich combination of experience and perspective. School educational platform in terms of teacher training in physical education is discussed in the second part. The book reviews that well-designed programs are powerful and constructive ways to identify the strength and weaknesses of teachers. Post-positivism, constructivism, advocacy/participatory, and pragmatism in teacher education are also disclosed. The book specifically unfolds pragmatic research in professional development of physical education. It provides researchers, doctoral, and masters level students with defined examples. In summary, the book shows how appropriate it is when many different traditions are displayed in a pragmatic way, following the stages of research from development to dissemination.

Keywords: leadership, physical education, pragmatic, professional development

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Authors: Ju-Hong Lee, Ching-Wei Liao

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Adaptive beamforming using antenna array of sensors is useful in the process of adaptively detecting and preserving the presence of the desired signal while suppressing the interference and the background noise. For conventional adaptive array beamforming, we require a prior information of either the impinging direction or the waveform of the desired signal to adapt the weights. The adaptive weights of an antenna array beamformer under a steered-beam constraint are calculated by minimizing the output power of the beamformer subject to the constraint that forces the beamformer to make a constant response in the steering direction. Hence, the performance of the beamformer is very sensitive to the accuracy of the steering operation. In the literature, it is well known that the performance of an adaptive beamformer will be deteriorated by any steering angle error encountered in many practical applications, e.g., the wireless communication systems with massive antennas deployed at the base station and user equipment. Hence, developing effective signal processing techniques to deal with the problem due to steering angle error for array beamforming systems has become an important research work. In this paper, we present an effective signal processing technique for constructing an adaptive beamformer against the steering angle error. The proposed array beamformer adaptively estimates the actual direction of the desired signal by using the presumed steering vector and the received array data snapshots. Based on the presumed steering vector and a preset angle range for steering mismatch tolerance, we first create a matrix related to the direction vector of signal sources. Two projection matrices are generated from the matrix. The projection matrix associated with the desired signal information and the received array data are utilized to iteratively estimate the actual direction vector of the desired signal. The estimated direction vector of the desired signal is then used for appropriately finding the quiescent weight vector. The other projection matrix is set to be the signal blocking matrix required for performing adaptive beamforming. Accordingly, the proposed beamformer consists of adaptive quiescent weights and partially adaptive weights. Several computer simulation examples are provided for evaluating and comparing the proposed technique with the existing robust techniques.

Keywords: adaptive beamforming, robustness, signal blocking, steering angle error

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Authors: Tibah Alharbi, Renzo Cordina, David Power

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Relatively few studies have explored how venture capitalist investors (VCs) make investment decisions and the information they rely on when taking an equity stake in an investee company. In addition, little is known about how much investors monitor start-ups after the decision to invest has been made. The VC scene in the US or European context is understood better than that of developing countries such as those in the Middle East. Although some differences among VC investors have been identified, the reasons behind such differences have not been fully explored – especially in a country such as Saudi Arabia. Therefore, this research seeks to understand the impact of external factors on the VC investor’ behaviour. The unique cultural and legal environments in the Kingdom of Saudi Arabia, the growing VC sector in the country, and the increasing importance attached to start-ups under the Saudi Government’s Vision 2030 program make such an investigation timely. Ascertaining the perceptions of VC investors in such a context will provide a deeper understanding of the determinants of VC investment in a novel setting. Using semi-structured interviews with over 20 participants, the research explores the structure of VC funds, the cycle of the VC investment in a start-up from the sourcing of deals, the screening and evaluation of such deals, the closing of such deals, and finally, the monitoring of such investments before the decision to exit such deals at the appropriate time. The results show some similarities to the VC model, which characterizes such investment in the US and Europe, but several differences emerge given the unique cultural and legal settings within the Kingdom. The results provide an in-depth understanding of the VC investors’ mindset relative to the existing studies in the literature.

Keywords: exit, monitoring, start-ups, venture capital

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Authors: Aicha Majda, Abdelhamid El Hassani

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Lung CT image segmentation is a prerequisite in lung CT image analysis. Most of the conventional methods need a post-processing to deal with the abnormal lung CT scans such as lung nodules or other lesions. The simplest similarity measure in the standard Graph Cuts Algorithm consists of directly comparing the pixel values of the two neighboring regions, which is not accurate because this kind of metrics is extremely sensitive to minor transformations such as noise or other artifacts problems. In this work, we propose an improved version of the standard graph cuts algorithm based on the Patch-Based similarity metric. The boundary penalty term in the graph cut algorithm is defined Based on Patch-Based similarity measurement instead of the simple intensity measurement in the standard method. The weights between each pixel and its neighboring pixels are Based on the obtained new term. The graph is then created using theses weights between its nodes. Finally, the segmentation is completed with the minimum cut/Max-Flow algorithm. Experimental results show that the proposed method is very accurate and efficient, and can directly provide explicit lung regions without any post-processing operations compared to the standard method.

Keywords: graph cuts, lung CT scan, lung parenchyma segmentation, patch-based similarity metric

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Authors: Rebecca Hafner, David Elmes, Daniel Read

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The current research applies decision-making theory to the problem of increasing uptake of energy efficient technologies in the market place, where uptake is currently slower than one might predict following rational choice models. We apply the alignable/non-alignable features effect and explore the impact of varying information structure on the consumers’ preference for standard versus energy efficient technologies. In two studies we present participants with a choice between similar (boiler vs. boiler) vs. dissimilar (boiler vs. heat pump) technologies, described by a list of alignable and non-alignable attributes. In study One there is a preference for alignability when options are similar; an effect mediated by an increased tendency to infer missing information is the same. No effects of alignability on preference are found when options differ. One explanation for this split-shift in attentional focus is a change in construal levels potentially induced by the added consideration of environmental concern. Study two was designed to explore the interplay between alignability and construal level in greater detail. We manipulated construal level via a thought prime task prior to taking part in the same heating systems choice task, and find that there is a general preference for non-alignability, regardless of option type. We draw theoretical and applied implications for the type of information structure best suited for the promotion of energy efficient technologies.

Keywords: alignability effects, decision making, energy-efficient technologies, sustainable behaviour change

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Authors: Simiksha Balkissoon, Jerome Andrew, Bruce Sithole

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Approximately half of the wood processed in the Forestry, Timber, Pulp and Paper (FTPP) sector is accumulated as waste. The concept of a “green economy” encourages industries to employ revolutionary, transformative technologies to eliminate waste generation by exploring the development of new value chains. The transition towards an almost paperless world driven by the rise of digital media has resulted in a decline in traditional paper markets, prompting the FTTP sector to reposition itself and expand its product offerings by unlocking the potential of value-adding opportunities from renewable resources such as wood to generate revenue and mitigate its environmental impact. The production of valuable products from wood waste such as sawdust has been extensively explored in recent years. Wood components such as lignin, cellulose and hemicelluloses, which can be extracted selectively by chemical processing, are suitable candidates for producing numerous high-value products. In this study, a novel approach to produce high-value cellulose products, such as dissolving wood pulp (DWP), from sawdust was developed. DWP is a high purity cellulose product used in several applications such as pharmaceutical, textile, food, paint and coatings industries. The proposed approach demonstrates the potential to eliminate several complex processing stages, such as pulping and bleaching, which are associated with traditional commercial processes to produce high purity cellulose products such as DWP, making it less chemically energy and water-intensive. The developed process followed the path of experimentally designed lab tests evaluating typical processing conditions such as residence time, chemical concentrations, liquid-to-solid ratios and temperature, followed by the application of suitable purification steps. Characterization of the product from the initial stage was conducted using commercially available DWP grades as reference materials. The chemical characteristics of the products thus far have shown similar properties to commercial products, making the proposed process a promising and viable option for the production of DWP from sawdust.

Keywords: biomass, cellulose, chemical treatment, dissolving wood pulp

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Authors: S. Sharan Sundar, Nithish B. Moudhgalya, Nidhi Bhandari, Vineeth Vijayaraghavan

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Automatic Short Answer Grading is one of the prime applications of artificial intelligence in education. Several approaches involving the utilization of selective handcrafted features, graphical matching techniques, concept identification and mapping, complex deep frameworks, sentence embeddings, etc. have been explored over the years. However, keeping in mind the real-world application of the task, these solutions present a slight overhead in terms of computations and resources in achieving high performances. In this work, a simple and effective solution making use of elemental features based on statistical, linguistic properties, and word-based similarity measures in conjunction with tree-based classifiers and regressors is proposed. The results for classification tasks show improvements ranging from 1%-30%, while the regression task shows a stark improvement of 35%. The authors attribute these improvements to the addition of multiple similarity scores to provide ensemble of scoring criteria to the models. The authors also believe the work could reinstate that classical natural language processing techniques and simple machine learning models can be used to achieve high results for short answer grading.

Keywords: artificial intelligence, intelligent systems, natural language processing, text mining

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Authors: Rohini Hariharan, Yazhini R., Blessy Maria Mathew

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The task of detecting email spam is a very important one in the era of digital technology that needs effective ways of curbing unwanted messages. This paper presents an approach aimed at making email spam categorization algorithms transparent, reliable and more trustworthy by incorporating Local Interpretable Model-agnostic Explanations (LIME). Our technique assists in providing interpretable explanations for specific classifications of emails to help users understand the decision-making process by the model. In this study, we developed a complete pipeline that incorporates LIME into the spam classification framework and allows creating simplified, interpretable models tailored to individual emails. LIME identifies influential terms, pointing out key elements that drive classification results, thus reducing opacity inherent in conventional machine learning models. Additionally, we suggest a visualization scheme for displaying keywords that will improve understanding of categorization decisions by users. We test our method on a diverse email dataset and compare its performance with various baseline models, such as Gaussian Naive Bayes, Multinomial Naive Bayes, Bernoulli Naive Bayes, Support Vector Classifier, K-Nearest Neighbors, Decision Tree, and Logistic Regression. Our testing results show that our model surpasses all other models, achieving an accuracy of 96.59% and a precision of 99.12%.

Keywords: text classification, LIME (local interpretable model-agnostic explanations), stemming, tokenization, logistic regression.

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Authors: Ana Fouto

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Background: Continuing Personal Development (CPD) enables professionals to keep up to date with the professional requirements, broadening their knowledge and expertise. However, much of the research explores the registered professionals’ experiences and the factors that influence their choice of engaging, despite the unregistered staff providing the majority of the direct patient care. Aim: To explore the Nursing/Midwifery Assistants’ (NAs) perception of the concept of CPD, as well as explore the factors that influence the NAs to engage (or not) with CPD experiences. Methodology: This pilot study used a qualitative approach through a case study, where a semi-structured interview was applied to three NAs to explore the factors that influence the decision-making of process of CPD engagement. Thematic analysis was used to analyse their answers and interpret patterns and associations. Findings: All the participants agreed that CPD is important and relevant to their practice and personal lives. Five main categories were identified: NAs’ scope of practice, the impact of CPD; decision-making process; challenges; changes required. Although similar findings to the registered nurses were identified, the lack of CPD regulation for NAs and the rapid evolution of their role make the CPD engagement more problematic. Conclusion: Engagement with CPD is influenced by a wide range of professional (organisational and national) and personal factors. NAs perceive lack of management support at different stages of the CPD activities as a main influence. Organisations should be more flexible in the recruitment, offer of CPD choices, content, delivery, and contractual arrangements of NAs, which may increase engagement.

Keywords: nursing assistants, engagement, factors, pilot, continuing professional development (CPD)

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Authors: Asma Salman, Brian Gabbitas, Peng Cao, Deliang Zhang

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The performance of a coating is strongly dependent upon its microstructure, which in turn is dependent on the characteristics of the feedstock powder. This study involves the evaluation and performance of a titanium-based composite coating produced by the HVOF (high-velocity oxygen fuel) spraying method. The feedstock for making the composite coating was produced using high energy mechanical milling of TiO2 and Al powders followed by a combustion reaction. The characteristics of the feedstock powder were improved by treating it with an organic binder. Two types of coatings were produced using treated and untreated feedstock powders. The microstructures and characteristics of both types of coatings were studied, and their thermal shock resistance was accessed by dipping into molten aluminum. The results of this study showed that feedstock treatment did not have a significant effect on the microstructure of the coatings. However, it did affect the uniformity, thickness and surface roughness of the coating on the steel substrate. A coating produced by an untreated feedstock showed better thermal shock resistance in molten aluminum compared with the one produced by PVA (polyvinyl alcohol) treatment.

Keywords: coating, feedstock, powder processing, thermal shock resistance, thermally spraying

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Authors: Taylan Kabbani, Ekrem Duman

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The design of adaptive systems that take advantage of financial markets while reducing the risk can bring more stagnant wealth into the global market. However, most efforts made to generate successful deals in trading financial assets rely on Supervised Learning (SL), which suffered from various limitations. Deep Reinforcement Learning (DRL) offers to solve these drawbacks of SL approaches by combining the financial assets price "prediction" step and the "allocation" step of the portfolio in one unified process to produce fully autonomous systems capable of interacting with its environment to make optimal decisions through trial and error. In this paper, a continuous action space approach is adopted to give the trading agent the ability to gradually adjust the portfolio's positions with each time step (dynamically re-allocate investments), resulting in better agent-environment interaction and faster convergence of the learning process. In addition, the approach supports the managing of a portfolio with several assets instead of a single one. This work represents a novel DRL model to generate profitable trades in the stock market, effectively overcoming the limitations of supervised learning approaches. We formulate the trading problem, or what is referred to as The Agent Environment as Partially observed Markov Decision Process (POMDP) model, considering the constraints imposed by the stock market, such as liquidity and transaction costs. More specifically, we design an environment that simulates the real-world trading process by augmenting the state representation with ten different technical indicators and sentiment analysis of news articles for each stock. We then solve the formulated POMDP problem using the Twin Delayed Deep Deterministic Policy Gradient (TD3) algorithm, which can learn policies in high-dimensional and continuous action spaces like those typically found in the stock market environment. From the point of view of stock market forecasting and the intelligent decision-making mechanism, this paper demonstrates the superiority of deep reinforcement learning in financial markets over other types of machine learning such as supervised learning and proves its credibility and advantages of strategic decision-making.

Keywords: the stock market, deep reinforcement learning, MDP, twin delayed deep deterministic policy gradient, sentiment analysis, technical indicators, autonomous agent

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Authors: Ching-Hsu Huang, Yao-Ling Liu

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Over the past several decades there has been considerable research on the topics of entrepreneurship education and nascent entrepreneurial competence. The purpose of this study is to explore the nascent entrepreneurial competence within entrepreneurship education via the use of three studies. It will be a three-phrases longitudinal study and the effective plan will combine the qualitative and quantitative mixed research methodology in order to understand the issues of nascent entrepreneurship and entrepreneurial competence in hospitality industry in Taiwan. In study one, the systematic literature reviews and twelve nascent entrepreneurs who graduated from hospitality management department will be conducted simultaneously to construct the nascent entrepreneurial competence indicators. Nine subjects who are from industry, government, and academia will be the decision makers in terms of forming the systematic nascent entrepreneurial competence indicators. The relative importance of indicators to each decision maker will be synthesized and compared using the Analytic Hierarchy Process method. According to the results of study one, this study will develop the teaching module of nascent hospitality entrepreneurship. It will include the objectives, context, content, audiences, assessment, pedagogy and outcomes. Based on the results of the second study, the quasi-experiment will be conducted in third study to explore the influence of nascent hospitality entrepreneurship teaching module on learners’ learning effectiveness. The nascent hospitality entrepreneurship education program and entrepreneurial competence will be promoted all around the hospitality industry and vocational universities. At the end, the implication for designing the nascent hospitality entrepreneurship teaching module and training programs will be suggested for the nascent entrepreneurship education. All of the proposed hypotheses will be examined and major finding, implication, discussion, and recommendations will be provided for the government and education administration in hospitality field.

Keywords: entrepreneurial competence, hospitality entrepreneurship, nascent entrepreneurial, training in hospitality entrepreneurship

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Authors: Carla Idely Palencia-Aguilar

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Mining in Colombia represents around 2% of the GDP (USD 8 billion in 2018), with main productions represented by coal, nickel, gold, silver, emeralds, iron, limestone, gypsum, among others. Sand and Gravel had been decreasing its participation of the GDP with a reduction of 33.2 million m3 in 2015, to 27.4 in 2016, 22.7 in 2017 and 15.8 in 2018, with a consumption of approximately 3 tons/inhabitant. However, with the new government policies it is expected to increase in the following years. Mining causes temporary environmental impacts, once restoration and rehabilitation takes place, social, environmental and economic benefits are higher than the initial state. A way to demonstrate how the mining interventions had contributed to improve the characteristics of the region after sand and gravel mining, the NDVI (Normalized Difference Vegetation Index) from MODIS and ASTER were employed. The histograms show not only increments of vegetation in the area (8 times higher), but also topographies similar to the ones before the intervention, according to the application for sustainable development selected: either agriculture, forestry, cattle raising, artificial wetlands or do nothing. The decision was based upon a Multicriteria analysis for optimal land use, with three main variables: geostatistics, evapotranspiration and groundwater characteristics. The use of remote sensing, meteorological stations, piezometers, sunphotometers, geoelectric analysis among others; provide the information required for the multicriteria decision. For cattle raising and agricultural applications (where various crops were implemented), conservation of products were tested by means of nanotechnology. The results showed a duration of 2 years with no chemicals added for preservation and concentration of vitamins of the tested products.

Keywords: ASTER, Geostatistics, MODIS, Multicriteria

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Authors: Ravi Kumar

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Agricultural wastes are the non-product outcomes of agricultural processing whose monetary value is less as compared to its collection cost, transportation, and processing. When such agricultural waste is not properly disposed of, it may damage the natural environment and cause detrimental pollution in the atmosphere. Agricultural development and intensive farming methods usually result in wastes that remarkably affect the rural environments in particular and the global environment in general. Agricultural waste has toxicity latent to human beings, animals, and plants through various indirect and direct outlets. The present paper explores the various activities that result in agricultural waste and the routes that can utilize the agricultural waste in a manageable manner to reduce its adverse impact on the environment. Presently, the agricultural waste management system for ecological agriculture and sustainable development has emerged as a crucial issue for policymakers. There is an urgent need to consider agricultural wastes as prospective resources rather than undesirable in order to avoid the transmission and contamination of water, land, and air resources. Waste management includes the disposal and treatment of waste with a view to eliminate threats of waste by modifying the waste to condense the microbial load. The study concludes that proper waste utilization and management will facilitate the purification and development of the ecosystem and provide feasible biofuel resources. This proper utilization and management of these wastes for agricultural production may reduce their accumulation and further reduce environmental pollution by improving environmental health.

Keywords: agricultural waste, utilization, management, environment, health

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Authors: William Adzawla

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In the midst of climate variability and change, the role of gender in ensuring food production remains vital. Therefore, this study analysed the gendered productivity among maize farmers, and the effects of climate change and variability as well as livelihood diversification on gendered productivity gap. This involved a total of 619 farmers selected through a multistage sampling procedure. The data was analysed using Oaxaca Blinder decomposition model. From the result, there is a significant productivity gap of 58.8% and 44.8% between male and female heads, and between male heads and female spouses, respectively. About 87.47% and 98.08% of the variations in gendered productivity were explained by resource endowment. While livelihood diversification significantly influenced gendered productivity through endowment and coefficient effect, climate variables significantly affect productivity gap through only coefficient effects. The study concluded that there is a substantial gendered productivity gap among farmers and this is particularly due to differences in endowment. Generally, there is a high potential of reducing gendered productivity gaps through the provision of equal diversification opportunities and reducing females’ vulnerability to climate change. Among the livelihood activities, off-farm activities such as agro-processing and shea butter processing should be promoted. Similarly, the adoption of on-farm adaptation strategies should be promoted among the farmers.

Keywords: climate change and variability, gender, livelihood diversification, oaxaca-blinder decomposition, productivity gap

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Authors: Soha A. Bahanshal, Vaibhav Verdhan, Bayong Kim

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Graduation rates at six-year colleges are becoming a more essential indicator for incoming fresh students and for university rankings. Predicting student graduation is extremely beneficial to schools and has a huge potential for targeted intervention. It is important for educational institutions since it enables the development of strategic plans that will assist or improve students' performance in achieving their degrees on time (GOT). A first step and a helping hand in extracting useful information from these data and gaining insights into the prediction of students' progress and performance is offered by machine learning techniques. Data analysis and visualization techniques are applied to understand and interpret the data. The data used for the analysis contains students who have graduated in 6 years in the academic year 2017-2018 for science majors. This analysis can be used to predict the graduation of students in the next academic year. Different Predictive modelings such as logistic regression, decision trees, support vector machines, Random Forest, Naïve Bayes, and KNeighborsClassifier are applied to predict whether a student will graduate. These classifiers were evaluated with k folds of 5. The performance of these classifiers was compared based on accuracy measurement. The results indicated that Ensemble Classifier achieves better accuracy, about 91.12%. This GOT prediction model would hopefully be useful to university administration and academics in developing measures for assisting and boosting students' academic performance and ensuring they graduate on time.

Keywords: prediction, decision trees, machine learning, support vector machine, ensemble model, student graduation, GOT graduate on time

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Authors: Cyrillus Prabandana

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Supply chain optimisation requires multiple factors as consideration or constraints. These factors are including but not limited to demand forecasting, raw material fulfilment, production capacity, inventory level, facilities locations, transportation means, and manpower availability. By knowing all manageable factors involved and assuming the uncertainty with pre-defined percentage factors, an integrated supply chain model could be developed to manage various business scenarios. This paper analyse the utilisation of geographical point of view to develop an integrated supply chain network model to optimise the distribution of finished product appropriately according to forecasted demand and available supply. The supply chain optimisation model shows that small change in one supply chain constraint is possible to largely impact other constraints, and the new information from the model should be able to support the decision making process. The model was focused on three areas, i.e. raw material fulfilment, production capacity and finished products transportation. To validate the model suitability, it was implemented in a project aimed to optimise the concrete supply chain in a mining location. The high level of operations complexity and involvement of multiple stakeholders in the concrete supply chain is believed to be sufficient to give the illustration of the larger scope. The implementation of this geographical supply chain network modeling resulted an optimised concrete supply chain from raw material fulfilment until finished products distribution to each customer, which indicated by lower percentage of missed concrete order fulfilment to customer.

Keywords: decision making, geographical supply chain modeling, supply chain optimisation, supply chain

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Authors: Markus Remm, Sebastian Dienert

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Vehicle interiors are not only changing in terms of design and functionality but also due to new driving situations in which, for example, autonomous operating modes are possible. Flexible seating positions are changing the requirements for passive safety system behavior and location in the interior of a vehicle. With fully autonomous driving, the driver can, for example, leave the position behind the steering wheel and take a seated position facing backward. Since autonomous and non-autonomous vehicles will share the same road network for the foreseeable future, accidents cannot be avoided, which makes the use of passive safety systems indispensable. With JENOPTIK-VOTAN® A technology, the trend towards flexible predetermined airbag weakening lines is enabled. With the help of laser beams, the predetermined weakening lines are introduced from the backside of the components so that they are absolutely invisible. This machining process is sensor-controlled and guarantees that a small residual wall thickness remains for the best quality and reliability for airbag weakening lines. Due to the wide processing range of the laser, the processing of almost all materials is possible. A CO₂ laser is used for many plastics, natural fiber materials, foams, foils and material composites. A femtosecond laser is used for natural materials and textiles that are very heat-sensitive. This laser type has extremely short laser pulses with very high energy densities. Supported by a high-precision and fast movement of the laser beam by a laser scanner system, the so-called cold ablation is enabled to predetermine weakening lines layer by layer until the desired residual wall thickness remains. In that way, for example, genuine leather can be processed in a material-friendly and process-reliable manner without design implications to the components A-Side. Passive safety in the vehicle is increased through the interaction of modern airbag technology and high-precision laser airbag weakening. The JENOPTIK-VOTAN® A product family has been representing this for more than 25 years and is pointing the way to the future with new and innovative technologies.

Keywords: design freedom, interior material processing, laser technology, passive safety

Procedia PDF Downloads 121