Search results for: fields extraction from administrative documents

Authors: Chux Gervase Iwu, Simon Nsengimane

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Entrepreneurship stimulates socio-economic development in many countries, if not all. It creates jobs and decreases unemployment and inequality. There are other benefits that are accruable from entrepreneurship, namely the empowerment of women and the promotion of better livelihoods. Innovation has become a weapon for business competition, growth, and sustainability. Paradoxically, it is a threat to businesses because products can be duplicated; new products may decrease the market share of existing ones or delete them from the market. This creates a constant competitive environment that calls for updates, innovation, and the invention of new products and services. Thus, the importance of higher education in instilling a good entrepreneurial mindset in students has become even more critical. It can be argued that the business environment is under enormous pressure from several factors, including the fourth industrial revolution, which calls for the adoption and use of information and communication technology, which is the catalyst for many innovations and organisational changes. Therefore, it is crucial that higher education students are equipped with relevant knowledge and skills to respond effectively to the needs of the business environment and create a vibrant entrepreneurship ecosystem. In South Africa, entrepreneurship education or some form of it has been a privilege for economic and management fields of study, leaving behind other fields. Entrepreneurship should not be limited to business faculties but rather extended to other fields of study. This is perhaps the reason for low levels of entrepreneurship uptake among South African graduates if they are compared with the graduates in other countries. There may be other reasons for the low entrepreneurship uptake. Some of these have been documented in extant literature to include (1) not enough time was spent teaching entrepreneurship in the business faculties, (2) the skills components in the curricula are insufficient, and (3) the overall attitudes/mindsets necessary to establish and run sustainable enterprises seem absent. Therefore, four important areas are recognised as crucial for the effective implementation of entrepreneurship education: policy, private sector engagement, curriculum development, and teacher development. The purpose of this research is to better comprehend the views, aspirations, and expectations of students and faculty members to design an entrepreneurial teaching model for higher education institutions. A qualitative method will be used to conduct a purposive interview with undergraduate and graduate students in select higher institutions. Members of faculty will also be included in the sample as well as, where possible, two or more government personnel responsible for higher education policy development. At present, interpretative analysis is proposed for the analysis of the interviews with the support of Atlas Ti. It is hoped that an entrepreneurship education model in the South African context is realised through this study.

Keywords: entrepreneurship education, higher education institution, graduate unemployment, curriculum development

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Authors: Kayode A. Olaniyi, Olabanji F. Omotoye, Adeola A. Ogunleye

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The skew detection and correction form an important part of digital document analysis. This is because uncompensated skew can deteriorate document features and can complicate further document image processing steps. Efficient text document analysis and digitization can rarely be achieved when a document is skewed even at a small angle. Once the documents have been digitized through the scanning system and binarization also achieved, document skew correction is required before further image analysis. Research efforts have been put in this area with algorithms developed to eliminate document skew. Skew angle correction algorithms can be compared based on performance criteria. Most important performance criteria are accuracy of skew angle detection, range of skew angle for detection, speed of processing the image, computational complexity and consequently memory space used. The standard Hough Transform has successfully been implemented for text documentation skew angle estimation application. However, the standard Hough Transform algorithm level of accuracy depends largely on how much fine the step size for the angle used. This consequently consumes more time and memory space for increase accuracy and, especially where number of pixels is considerable large. Whenever the Hough transform is used, there is always a tradeoff between accuracy and speed. So a more efficient solution is needed that optimizes space as well as time. In this paper, an improved Hough transform (HT) technique that optimizes space as well as time to robustly detect document skew is presented. The modified algorithm of Hough Transform presents solution to the contradiction between the memory space, running time and accuracy. Our algorithm starts with the first step of angle estimation accurate up to zero decimal place using the standard Hough Transform algorithm achieving minimal running time and space but lacks relative accuracy. Then to increase accuracy, suppose estimated angle found using the basic Hough algorithm is x degree, we then run again basic algorithm from range between ±x degrees with accuracy of one decimal place. Same process is iterated till level of desired accuracy is achieved. The procedure of our skew estimation and correction algorithm of text images is implemented using MATLAB. The memory space estimation and process time are also tabulated with skew angle assumption of within 00 and 450. The simulation results which is demonstrated in Matlab show the high performance of our algorithms with less computational time and memory space used in detecting document skew for a variety of documents with different levels of complexity.

Keywords: hough-transform, skew-detection, skew-angle, skew-correction, text-document

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Authors: Obaid AlOtaibi, Salman Hussain

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Wind energy is associated with many geographical factors including wind speed, climate change, surface topography, environmental impacts, and several economic factors, most notably the advancement of wind technology and energy prices. It is the fastest-growing and least economically expensive method for generating electricity. Wind energy generation is directly related to the characteristics of spatial wind. Therefore, the feasibility study for the wind energy conversion system is based on the value of the energy obtained relative to the initial investment and the cost of operation and maintenance. In Kuwait, wind energy is an appropriate choice as a source of energy generation. It can be used in groundwater extraction in agricultural areas such as Al-Abdali in the north and Al-Wafra in the south, or in fresh and brackish groundwater fields or remote and isolated locations such as border areas and projects away from conventional power electricity services, to take advantage of alternative energy, reduce pollutants, and reduce energy production costs. The study covers the State of Kuwait with an exception of metropolitan area. Climatic data were attained through the readings of eight distributed monitoring stations affiliated with Kuwait Institute for Scientific Research (KISR). The data were used to assess the daily, monthly, quarterly, and annual available wind energy accessible for utilization. The researchers applied the Suitability Model to analyze the study by using the ArcGIS program. It is a model of spatial analysis that compares more than one location based on grading weights to choose the most suitable one. The study criteria are: the average annual wind speed, land use, topography of land, distance from the main road networks, urban areas. According to the previous criteria, the four proposed locations to establish wind farm projects are selected based on the weights of the degree of suitability (excellent, good, average, and poor). The percentage of areas that represents the most suitable locations with an excellent rank (4) is 8% of Kuwait’s area. It is relatively distributed as follows: Al-Shqaya, Al-Dabdeba, Al-Salmi (5.22%), Al-Abdali (1.22%), Umm al-Hayman (0.70%), North Wafra and Al-Shaqeeq (0.86%). The study recommends to decision-makers to consider the proposed location (No.1), (Al-Shqaya, Al-Dabdaba, and Al-Salmi) as the most suitable location for future development of wind farms in Kuwait, this location is economically feasible.

Keywords: Kuwait, renewable energy, spatial analysis, wind energy

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Authors: Simona Maria Ignat

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Bullying online has been an increasing studied topic during the last years. Different approaches, psychological, linguistic, or computational, have been applied. To our best knowledge, a definition and a set of characteristics of phenomenon agreed internationally as a common framework are still waiting for answers. Thus, the objectives of this paper are the identification of bullying utterances on Twitter and their algorithms. This research paper is focused on the identification of words or groups of words, categorized as “utterances”, with bullying effect, from Twitter platform, extracted on a set of legislative criteria. This set is the result of analysis followed by synthesis of law documents on bullying(online) from United States of America, European Union, and Ireland. The outcome is a linguistic corpus with approximatively 10,000 entries. The methods applied to the first objective have been the following. The discourse analysis has been applied in identification of keywords with bullying effect in texts from Google search engine, Images link. Transcription and anonymization have been applied on texts grouped in CL1 (Corpus linguistics 1). The keywords search method and the legislative criteria have been used for identifying bullying utterances from Twitter. The texts with at least 30 representations on Twitter have been grouped. They form the second corpus linguistics, Bullying utterances from Twitter (CL2). The entries have been identified by using the legislative criteria on the the BoW method principle. The BoW is a method of extracting words or group of words with same meaning in any context. The methods applied for reaching the second objective is the conversion of parts of speech to alphabetical and numerical symbols and writing the bullying utterances as algorithms. The converted form of parts of speech has been chosen on the criterion of relevance within bullying message. The inductive reasoning approach has been applied in sampling and identifying the algorithms. The results are groups with interchangeable elements. The outcomes convey two aspects of bullying: the form and the content or meaning. The form conveys the intentional intimidation against somebody, expressed at the level of texts by grammatical and lexical marks. This outcome has applicability in the forensic linguistics for establishing the intentionality of an action. Another outcome of form is a complex of graphemic variations essential in detecting harmful texts online. This research enriches the lexicon already known on the topic. The second aspect, the content, revealed the topics like threat, harassment, assault, or suicide. They are subcategories of a broader harmful content which is a constant concern for task forces and legislators at national and international levels. These topic – outcomes of the dataset are a valuable source of detection. The analysis of content revealed algorithms and lexicons which could be applied to other harmful contents. A third outcome of content are the conveyances of Stylistics, which is a rich source of discourse analysis of social media platforms. In conclusion, this corpus linguistics is structured on legislative criteria and could be used in various fields.

Keywords: corpus linguistics, cyberbullying, legislation, natural language processing, twitter

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Authors: V. Veeraprathap, G. S. Harish, G. Narendra Kumar

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Uncontrolled growth of abnormal cells in the lung in the form of tumor can be either benign (non-cancerous) or malignant (cancerous). Patients with Lung Cancer (LC) have an average of five years life span expectancy provided diagnosis, detection and prediction, which reduces many treatment options to risk of invasive surgery increasing survival rate. Computed Tomography (CT), Positron Emission Tomography (PET), and Magnetic Resonance Imaging (MRI) for earlier detection of cancer are common. Gaussian filter along with median filter used for smoothing and noise removal, Histogram Equalization (HE) for image enhancement gives the best results without inviting further opinions. Lung cavities are extracted and the background portion other than two lung cavities is completely removed with right and left lungs segmented separately. Region properties measurements area, perimeter, diameter, centroid and eccentricity measured for the tumor segmented image, while texture is characterized by Gray-Level Co-occurrence Matrix (GLCM) functions, feature extraction provides Region of Interest (ROI) given as input to classifier. Two levels of classifications, K-Nearest Neighbor (KNN) is used for determining patient condition as normal or abnormal, while Artificial Neural Networks (ANN) is used for identifying the cancer stage is employed. Discrete Wavelet Transform (DWT) algorithm is used for the main feature extraction leading to best efficiency. The developed technology finds encouraging results for real time information and on line detection for future research.

Keywords: artificial neural networks, ANN, discrete wavelet transform, DWT, gray-level co-occurrence matrix, GLCM, k-nearest neighbor, KNN, region of interest, ROI

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Authors: Sangita Pokhrel, Nalinda Somasiri, Rebecca Jeyavadhanam, Swathi Ganesan

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Tourism is a booming industry with huge future potential for global wealth and employment. There are countless data generated over social media sites every day, creating numerous opportunities to bring more insights to decision-makers. The integration of Big Data Technology into the tourism industry will allow companies to conclude where their customers have been and what they like. This information can then be used by businesses, such as those in charge of managing visitor centers or hotels, etc., and the tourist can get a clear idea of places before visiting. The technical perspective of natural language is processed by analysing the sentiment features of online reviews from tourists, and we then supply an enhanced long short-term memory (LSTM) framework for sentiment feature extraction of travel reviews. We have constructed a web review database using a crawler and web scraping technique for experimental validation to evaluate the effectiveness of our methodology. The text form of sentences was first classified through Vader and Roberta model to get the polarity of the reviews. In this paper, we have conducted study methods for feature extraction, such as Count Vectorization and TFIDF Vectorization, and implemented Convolutional Neural Network (CNN) classifier algorithm for the sentiment analysis to decide the tourist’s attitude towards the destinations is positive, negative, or simply neutral based on the review text that they posted online. The results demonstrated that from the CNN algorithm, after pre-processing and cleaning the dataset, we received an accuracy of 96.12% for the positive and negative sentiment analysis.

Keywords: counter vectorization, convolutional neural network, crawler, data technology, long short-term memory, web scraping, sentiment analysis

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Authors: Aitana Alonso-Nogueira, Helia Estévez-Fernández, Isaías García

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This paper presents an approach to reduce some of its current flaws in the requirements phase inside the software development process. It takes the software requirements of an application, makes a conceptual modeling about it and formalizes it within JSON documents. This formal model is lodged in a NoSQL database which is document-oriented, that is, MongoDB, because of its advantages in flexibility and efficiency. In addition, this paper underlines the contributions of the detailed approach and shows some applications and benefits for the future work in the field of automatic code generation using model-driven engineering tools.

Keywords: conceptual modelling, JSON, NoSQL databases, requirements engineering, software development

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Authors: P. Karthick, K. Mahesh

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Video has become an increasingly significant component of our digital everyday contact. With the advancement of greater contents and shows of the resolution, its significant volume poses serious obstacles to the objective of receiving, distributing, compressing, and revealing video content of high quality. In this paper, we propose the primary beginning to complete a deep video compression model that jointly upgrades all video compression components. The video compression method involves splitting the video into frames, comparing the images using convolutional neural networks (CNN) to remove duplicates, repeating the single image instead of the duplicate images by recognizing and detecting minute changes using generative adversarial network (GAN) and recorded with long short-term memory (LSTM). Instead of the complete image, the small changes generated using GAN are substituted, which helps in frame level compression. Pixel wise comparison is performed using K-nearest neighbours (KNN) over the frame, clustered with K-means, and singular value decomposition (SVD) is applied for each and every frame in the video for all three color channels [Red, Green, Blue] to decrease the dimension of the utility matrix [R, G, B] by extracting its latent factors. Video frames are packed with parameters with the aid of a codec and converted to video format, and the results are compared with the original video. Repeated experiments on several videos with different sizes, duration, frames per second (FPS), and quality results demonstrate a significant resampling rate. On average, the result produced had approximately a 10% deviation in quality and more than 50% in size when compared with the original video.

Keywords: video compression, K-means clustering, convolutional neural network, generative adversarial network, singular value decomposition, pixel visualization, stochastic gradient descent, frame per second extraction, RGB channel extraction, self-detection and deciding system

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Authors: I. Fahi, T. Remmal, F. El Kamel, B. Ayoub

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The treatment of the geotechnical database of the region of Casablanca was difficult to achieve due to the heterogeneity of the nomenclature of the lithological formations composing its soil. It appears necessary to harmonize the nomenclature of the facies and to produce cartographic documents useful for construction projects and studies before any investment program. To achieve this, more than 600 surveys made by the Public Laboratory for Testing and Studies (LPEE) in the agglomeration of Casablanca, were studied. Moreover, some local observations were made in different places of the metropolis. Each survey was the subject of a sheet containing lithological succession, macro and microscopic description of petrographic facies with photographic illustration, as well as measurements of geomechanical tests. In addition, an X-ray diffraction analysis was made in order to characterize the surficial formations of the region.

Keywords: Casablanca, guidebook, petrography, soil

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Authors: Hozaifa Zaki, Ghada Soliman

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In this paper, we introduced an approach for Automated Dotted Arabic Expiration Date Extraction using Optimized Convolutional Autoencoder (ARABEX) with bidirectional LSTM. This approach is used for translating the Arabic dot-matrix expiration dates into their corresponding filled-in dates. A custom lightweight Convolutional Recurrent Neural Network (CRNN) model is then employed to extract the expiration dates. Due to the lack of available dataset images for the Arabic dot-matrix expiration date, we generated synthetic images by creating an Arabic dot-matrix True Type Font (TTF) matrix to address this limitation. Our model was trained on a realistic synthetic dataset of 3287 images, covering the period from 2019 to 2027, represented in the format of yyyy/mm/dd. We then trained our custom CRNN model using the generated synthetic images to assess the performance of our model (ARABEX) by extracting expiration dates from the translated images. Our proposed approach achieved an accuracy of 99.4% on the test dataset of 658 images, while also achieving a Structural Similarity Index (SSIM) of 0.46 for image translation on our dataset. The ARABEX approach demonstrates its ability to be applied to various downstream learning tasks, including image translation and reconstruction. Moreover, this pipeline (ARABEX+CRNN) can be seamlessly integrated into automated sorting systems to extract expiry dates and sort products accordingly during the manufacturing stage. By eliminating the need for manual entry of expiration dates, which can be time-consuming and inefficient for merchants, our approach offers significant results in terms of efficiency and accuracy for Arabic dot-matrix expiration date recognition.

Keywords: computer vision, deep learning, image processing, character recognition

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Authors: Siddhartha Khare, Nitish Kr Boro, Omm Animesh Mishra

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Recent food price hikes may signal the end of an era of predictable global grain crop plenty due to climate change, population expansion, and dietary changes. Food consumption will treble in 20 years, requiring enormous production expenditures. Climate and the atmosphere changed owing to rainfall and seasonal cycles in the past decade. India's tropical agricultural relies on evapotranspiration and monsoons. In places with limited resources, the global environmental change affects agricultural productivity and farmers' capacity to adjust to changing moisture patterns. Motivated by these difficulties, satellite remote sensing might be combined with near-surface imaging data (smartphones, UAVs, and PhenoCams) to enable phenological monitoring and fast evaluations of field-level consequences of extreme weather events on smallholder agriculture output. To accomplish this technique, we must digitally map all communities agricultural boundaries and crop kinds. With the improvement of satellite remote sensing technologies, a geo-referenced database may be created for rural Indian agriculture fields. Using AI, we can design digital agricultural solutions for individual farms. Main objective is to Geo-enable each farm along with their seasonal crop information by combining Artificial Intelligence (AI) with satellite and near-surface data and then prepare long term crop monitoring through in-depth field analysis and scanning of fields with satellite derived vegetation indices. We developed an AI based algorithm to understand the timelapse based growth of vegetation using PhenoCam or Smartphone based images. We developed an android platform where user can collect images of their fields based on the android application. These images will be sent to our local server, and then further AI based processing will be done at our server. We are creating digital boundaries of individual farms and connecting these farms with our smart phone application to collect information about farmers and their crops in each season. We are extracting satellite-based information for each farm from Google earth engine APIs and merging this data with our data of tested crops from our app according to their farm’s locations and create a database which will provide the data of quality of crops from their location.

Keywords: artificial intelligence, satellite remote sensing, crop monitoring, android and web application

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Authors: Kusay Wheib, Alexandra Krvchenko

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Crop yields are influenced by many factors, including natural ones, such as soil and environmental characteristics of the agricultural land, as well as manmade ones, such as management applications. One of the factors that frequently affect crop yields in undulating Midwest landscapes is topography, which controls the movement of water and nutrients necessary for plant life. The main objective of this study is to examine how field topography influences performance of different management practices in undulated terrain of southwest Michigan. A total of 26 agricultural fields, ranging in size from 1.1 to 7.4 ha, from the Scale-Up at Kellogg Biological Station were included in the study. The two studied factors were crop species with three levels, i.e., corn (Zea mays L.) soybean (Glycine max L.), and wheat (Triticum aestivum L.), and management practice with three levels, i.e., conventional, low input, and organic managements. They were compared under three contrasting topographical settings, namely, summit (includes summits and shoulders), slope (includes backslopes), and depression (includes footslope and toeslope). Yield data of years 2007 through 2012 was processed, cleaned, and filtered, average yield then was calculated for each field, topographic setting, and year. Topography parameters, including terrain, slope, curvature, flow direction and wetness index were computed under ArcGIS environment for each topographic class of each field to seek their effects on yield. Results showed that topographical depressions produced greatest yields in most studied fields, while managements with chemical inputs, both low input and conventional, resulted in higher yields than the organic management.

Keywords: sustainable agriculture, precision agriculture, topography, yield

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Authors: Getaneh Berie Tarekegn

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A precise localization system is crucial for many artificial intelligence Internet of Things (AI-IoT) applications in the era of smart cities. Their applications include traffic monitoring, emergency alarming, environmental monitoring, location-based advertising, intelligent transportation, and smart health care. The most common method for providing continuous positioning services in outdoor environments is by using a global navigation satellite system (GNSS). Due to nonline-of-sight, multipath, and weather conditions, GNSS systems do not perform well in dense urban, urban, and suburban areas.This paper proposes a generative AI-based positioning scheme for large-scale wireless settings using fingerprinting techniques. In this article, we presented a semi-supervised deep convolutional generative adversarial network (S-DCGAN)-based radio map construction method for real-time device localization. It also employed a reliable signal fingerprint feature extraction method with t-distributed stochastic neighbor embedding (t-SNE), which extracts dominant features while eliminating noise from hybrid WLAN and long-term evolution (LTE) fingerprints. The proposed scheme reduced the workload of site surveying required to build the fingerprint database by up to 78.5% and significantly improved positioning accuracy. The results show that the average positioning error of GAILoc is less than 0.39 m, and more than 90% of the errors are less than 0.82 m. According to numerical results, SRCLoc improves positioning performance and reduces radio map construction costs significantly compared to traditional methods.

Keywords: location-aware services, feature extraction technique, generative adversarial network, long short-term memory, support vector machine

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Authors: Vahid Bairami Rad

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The intelligentization of agricultural fields can control the temperature, humidity, and variables affecting the growth of agricultural products online and on a mobile phone or computer. Smarting agricultural fields and gardens is one of the best and best ways to optimize agricultural equipment and has a 100 percent direct effect on the growth of plants and agricultural products and farms. Smart farms are the topic that we are going to discuss today, the Internet of Things and artificial intelligence. Agriculture is becoming smarter every day. From large industrial operations to individuals growing organic produce locally, technology is at the forefront of reducing costs, improving results and ensuring optimal delivery to market. A key element to having a smart agriculture is the use of useful data. Modern farmers have more tools to collect intelligent data than in previous years. Data related to soil chemistry also allows people to make informed decisions about fertilizing farmland. Moisture meter sensors and accurate irrigation controllers have made the irrigation processes to be optimized and at the same time reduce the cost of water consumption. Drones can apply pesticides precisely on the desired point. Automated harvesting machines navigate crop fields based on position and capacity sensors. The list goes on. Almost any process related to agriculture can use sensors that collect data to optimize existing processes and make informed decisions. The Internet of Things (IoT) is at the center of this great transformation. Internet of Things hardware has grown and developed rapidly to provide low-cost sensors for people's needs. These sensors are embedded in IoT devices with a battery and can be evaluated over the years and have access to a low-power and cost-effective mobile network. IoT device management platforms have also evolved rapidly and can now be used securely and manage existing devices at scale. IoT cloud services also provide a set of application enablement services that can be easily used by developers and allow them to build application business logic. Focus on yourself. These development processes have created powerful and new applications in the field of Internet of Things, and these programs can be used in various industries such as agriculture and building smart farms. But the question is, what makes today's farms truly smart farms? Let us put this question in another way. When will the technologies associated with smart farms reach the point where the range of intelligence they provide can exceed the intelligence of experienced and professional farmers?

Keywords: food security, IoT automation, wireless communication, hybrid lifestyle, arduino Uno

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Authors: Vaishakh Medikeri

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Energy in this Universe is the most basic characteristic of every particle. Since the birth of life on this planet, there has been a quest undertaken by the living beings to analyze, understand and harness the precious natural facts of the nature. In this quest, one of the greatest undertaken is the process of harnessing the naturally available energy. Scientists around the globe have discovered many ways to harness the freely available energy. But even today we speak of “Power Crisis”. Nikola Tesla once said “Nature has stored up in this universe infinite energy”. Energy is everywhere around us in unlimited quantities; all of it waiting to be harnessed by us. Here in this paper a method has been proposed to harness earth's electric field and transmit the stored electric energy using strong magnetic fields and electric fields. In this paper a new technique has been proposed to harness earth's electric field which is everywhere around the world in infinite quantities. Near the surface of the earth there is an electric field of about 120V/m. This electric field is used to charge a capacitor with high capacitance. Later the energy stored is allowed to pass through a device which converts the DC stored into AC. The AC so produced is then passed through a step down transformer to magnify the incoming current. Later the current passes through the RLC circuit. Later the current can be transmitted wirelessly using the principle of resonant inductive coupling. The proposed apparatus can be placed in most of the required places and any circuit tuned to the frequency of the transmitted current can receive the energy. The new source of renewable energy is of great importance if implemented since the apparatus is not costly and can be situated in most of the required places. And also the receiver which receives the transmitted energy is just an RLC circuit tuned to the resonant frequency of the transmitted energy. By using the proposed apparatus the energy losses can be reduced to a very large extent.

Keywords: capacitor, inductive resonant coupling, RLC circuit, transmission of electricity

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Authors: H. Emari, Z. Emari

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The purpose of this paper is to design an applied framework for strategic thinking which can be applied in all managerial levels and all types of organizational environments. No special applied frame has been presented for this thinking. This paper presents a theoretical framework for the thinking type of a manager by making a historical research and studying the scientific documents about thinking of a strategist. In the new theoretical framework it has been tried to suggest the best type of thinking for a strategist after analyzing the environment of his decisions. So, in this framework, the traditional viewpoint about strategic thinking, which has considered it as a special type of right-brain thinking against other types of right-brain thinking and suggested it for a strategist, was put aside and suggests that the strategist should use a suitable type of thinking under different conditions.

Keywords: strategic thinking, systemic thinking, lateral thinking, intuitive thinking, hybrid thinking

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Authors: Hien Thi Dieu Truong, Mahmoud Al-Sarayreh, Pullanagari Reddy, Marlon M. Reis, Richard Archer

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New Zealand mānuka honey is a honeybee product derived mainly from Leptospermum scoparium nectar. The potent antibacterial activity of mānuka honey derives principally from methylglyoxal (MGO), in addition to the hydrogen peroxide and other lesser activities present in all honey. MGO is formed from dihydroxyacetone (DHA) unique to L. scoparium nectar. Mānuka honey also has an idiosyncratic phenolic profile that is useful as a chemical maker. Authentic mānuka honey is highly valuable, but almost all honey is formed from natural mixtures of nectars harvested by a hive over a time period. Once diluted by other nectars, mānuka honey irrevocably loses value. We aimed to apply hyperspectral imaging to honey frames before bulk extraction to minimise the dilution of genuine mānuka by other honey and ensure authenticity at the source. This technology is non-destructive and suitable for an industrial setting. Chemometrics using linear Partial Least Squares (PLS) and Support Vector Machine (SVM) showed limited efficacy in interpreting chemical footprints due to large non-linear relationships between predictor and predictand in a large sample set, likely due to honey quality variability across geographic regions. Therefore, an advanced modelling approach, one-dimensional convolutional neural networks (1D-CNN), was investigated for analysing hyperspectral data for extraction of biochemical information from honey. The 1D-CNN model showed superior prediction of honey quality (R² = 0.73, RMSE = 2.346, RPD= 2.56) to PLS (R² = 0.66, RMSE = 2.607, RPD= 1.91) and SVM (R² = 0.67, RMSE = 2.559, RPD=1.98). Classification of mono-floral manuka honey from multi-floral and non-manuka honey exceeded 90% accuracy for all models tried. Overall, this study reveals the potential of HSI and deep learning modelling for automating the evaluation of honey quality in frames.

Keywords: mānuka honey, quality, purity, potency, deep learning, 1D-CNN, chemometrics

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Authors: Wei Bin Shen

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Lou Gang world irrigation project heritage in Taihu Lake is a systematic irrigation project integrating water conservancy, ecology and culture. Through the methods of historical documents and field investigation, this paper deeply analyzes the formation history, connotation and value of Lou Gang polder system: Lou Gang heritage, describes in detail the relationship between Lou Gang polder system in Taihu Lake and the development and evolution of Huzhou City, and initially explores the protection and Utilization Strategies of Lou Gang water conservancy cultural heritage resources in Taihu Lake from the current situation.

Keywords: Lou Gang, protection strategy, urban evolution, waterconservancyculturalheritage

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Authors: Alfredo Fortunato, Carmelofrancesco Origlia, Sara Laurita, Rossella Nicoletti

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In the post-pandemic recovery phase of tourism, the role of a Destination Management Organization (DMO) as a coordinated management system of all the elements that make up a destination (attractions, access, marketing, human resources, brand, pricing, etc.) is also becoming relevant for local territories. The objective of a DMO is to maximize the visitor's perception of value and quality while ensuring the competitiveness and sustainability of the destination, as well as the long-term preservation of its natural and cultural assets, and to catalyze benefits for the local economy and residents. In carrying out the multiple functions to which it is called, the DMO can leverage a collective intelligence that comes from the ability to pool information, explicit and tacit knowledge, and relationships of the various stakeholders: policymakers, public managers and officials, entrepreneurs in the tourism supply chain, researchers, data journalists, schools, associations and committees, citizens, etc. The DMO potentially has at its disposal large volumes of data and many of them at low cost, that need to be properly processed to produce value. Based on these assumptions, the paper presents a conceptual framework for building an information system to support the DMO in the intelligent management of a tourist destination tested in an area of southern Italy. The approach adopted is data-informed and consists of four phases: (1) formulation of the knowledge problem (analysis of policy documents and industry reports; focus groups and co-design with stakeholders; definition of information needs and key questions); (2) research and metadatation of relevant sources (reconnaissance of official sources, administrative archives and internal DMO sources); (3) gap analysis and identification of unconventional information sources (evaluation of traditional sources with respect to the level of consistency with information needs, the freshness of information and granularity of data; enrichment of the information base by identifying and studying web sources such as Wikipedia, Google Trends, Booking.com, Tripadvisor, websites of accommodation facilities and online newspapers); (4) definition of the set of indicators and construction of the information base (specific definition of indicators and procedures for data acquisition, transformation, and analysis). The framework derived consists of 6 thematic areas (accommodation supply, cultural heritage, flows, value, sustainability, and enabling factors), each of which is divided into three domains that gather a specific information need to be represented by a scheme of questions to be answered through the analysis of available indicators. The framework is characterized by a high degree of flexibility in the European context, given that it can be customized for each destination by adapting the part related to internal sources. Application to the case study led to the creation of a decision support system that allows: •integration of data from heterogeneous sources, including through the execution of automated web crawling procedures for data ingestion of social and web information; •reading and interpretation of data and metadata through guided navigation paths in the key of digital story-telling; •implementation of complex analysis capabilities through the use of data mining algorithms such as for the prediction of tourist flows.

Keywords: collective intelligence, data framework, destination management, smart tourism

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Authors: Pavol Tanuska, Pavel Vazan, Michal Kebisek, Dominika Jurovata

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The paper gives the results of the project that was oriented on the usage of knowledge discoveries from production systems for needs of the hierarchical process control. One of the main project goals was the proposal of knowledge discovery model for process control. Specifics data mining methods and techniques was used for defined problems of the process control. The gained knowledge was used on the real production system, thus, the proposed solution has been verified. The paper documents how it is possible to apply new discovery knowledge to be used in the real hierarchical process control. There are specified the opportunities for application of the proposed knowledge discovery model for hierarchical process control.

Keywords: hierarchical process control, knowledge discovery from databases, neural network, process control

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Authors: Benjamin Kuester, Bjoern Eilert, Malte Stonis, Ludger Overmeyer

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Increasing quality requirements make reliable and effective quality management indispensable. This includes the complaint handling in which the 8D method is widely used. The 8D report as a written documentation of the 8D method is one of the key quality documents as it internally secures the quality standards and acts as a communication medium to the customer. In practice, however, the 8D report is mostly faulty and of poor quality. There is no quality control of 8D reports today. This paper describes the use of natural language processing for the automated evaluation of 8D reports. Based on semantic analysis and text-mining algorithms the presented system is able to uncover content and formal quality deficiencies and thus increases the quality of the complaint processing in the long term.

Keywords: 8D report, complaint management, evaluation system, text-mining

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Authors: K. M. Anusha, R. Krishnaveni

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Crossing a decade of the 21st century, the paradigm of human resources can be seen evolving with the strategic gene induced into it. There seems to be a radical shift descending as the corporate sector calls on its HR team to become strategic rather than administrative. This transferal eventually requires the metrics employed by these HR teams not to be just operationally reactive but to be aligned to an evidence-based strategic thinking. Realizing the growing need for a prescriptive metric model for effective HR analytics, this study has designed a conceptual framework for a basic metric model that can assist IT-HRM professionals to transition to a practice of evidence-based decision-making to enhance organizational performance.

Keywords: metric model, evidence based HR, HR analytics, strategic HR practices, IT sector

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Authors: Małgorzata Cichy

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Afghanistan is a source of conflicts that affect security on a global scale. The role of ethnic and national determinants in the peacebuilding process in this country remains an extremely important factor in this respect. Research methods include literature and data analysis (scientific literature, documents of governmental and non-governmental organizations, statistical data and media reports), institutional and legal analysis, as well as decision-making method. The main objective of the research is a comprehensive answer to the question of how ethnic and national factors affect the process of building peace in Afghanistan after 2021 and what impact it has on international security.

Keywords: Afghanistan, pashtuns, peace, taliban

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Authors: S. Malarvizhi, S. Sadiq Basha, M. Santhosh Kumar, K. Saravanan, R. Sasikumar, R. Satheesh

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This is a web based application which provides authorization for the vehicle identification and registration. It also provides mutual authentication between the police and users in order to avoid misusage. The QR code generation in this application overcomes the difficulty in the manual registration of the vehicle documents. This generated QR code is placed in the number plates of the vehicles. The QR code is scanned using the QR Reader installed in the smart devices. The police officials can check the vehicle details and file cases on accidents, theft and traffic rules violations using QR code. In addition to vehicle insurance payments and renewals, the renewal alert is sent to the vehicle owner about payment deadline. The non-permitted vehicles can be blocked in the next check-post by sending the alert messages.

Keywords: QR code, QR reader, registration, authentication, idenfication

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Authors: Yu Guang

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“Case studies are the preferred strategy when ‘how’ or ‘why’ questions are being posed.” Therefore, the study is based on two cases: strategy performed in JingNan War and by NIKE. The two samples are chosen as they are of comparability. Data are gathered and PEST and SWOT are used as analysis models to examine their strategic employment in order that the answer to brilliant strategies in variety is found. The niche strategy has been used in the past and present, in the battle fields and business. The homogeneity among diversity is the skill of performing strategies.

Keywords: challenger, homogeneity, managing diversity, niche strategy

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Authors: Dan Li, Hao Tang

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The idea of “public bonds” was foreign to Qing China because it went against the traditional political ideology that supported that the emperor had absolute ownership over the nation. When a new fiscal crisis emerged out of the First Sino-Japanese War in 1894, the Qing rulers had no better option than to issue domestic bonds. This article documents the processes of issuance, distribution, and reimbursement for a total of three bonds issued by the Qing. These processes reveal how a well-established Western fiscal instrument could be extremely awkward and difficult to implant in China—a culturally, politically, and institutionally different society. Our paper sheds light on why Qing China failed to rise as a modern fiscal state.

Keywords: public bond, Qing China, fiscal crisis, fiscal state, the first Sino-Japanese war

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Authors: Babulayi B. Wilson

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Unsatisfactory performance of construction projects in both the industrialised and developing countries indicate that there could be several defects in construction projects phases. Notwithstanding the fact that some project defects are often conceived at the initiation phase of construction projects, insufficient knowledge of contract procedures has been identified as one of the major sources of construction disputes. Contract procedures are a set of rules that outlines the primary obligations and liabilities of parties involved in the implementation of a construction project. Engineering professional bodies often codify contract procedures into standard forms of contract such as the Institution of Civil Engineers (ICE, UK) and Association of Consulting Engineers (ACE, UK) and keep them under constant review by updating any clause to reflect any change in case law or relevant piece of legislation. Even so, it is the responsibility of a professional body or conditions of contract draftsperson to introduce contract-specific clauses that may be necessary for business efficacy but not covered in the chosen standard conditions of contract. In Botswana, the use of clients’ drafted and/or un-adapted for environment of use international forms of contract in conjunction with client-drafted pricing schedules is common. The product of the latter often impact negatively upon contractors’ claims and payments, in that, tender rates and prices can only be deemed to be sufficient if the chosen conditions of contract compliment the pricing schedule (use of standardised procurement documents). In addition, client drafted and the use of borrowed forms of contract such as FIDIC often conflict with domicile law resulting in costly disputes on the part of the client. It is upon the preceding text that the object of the research is to measure the level of knowledge of contract procedures amongst key stakeholders in the Botswana construction industry by requesting a representative sample from the industry and academia to respond to tutorial questions prepared from two commonly used forms of contract for civil works, that is, FIDIC (International Form of Contract) and ICE (UK). The questions were prepared under the following captions: (a) preparation of tender documents (b) obligations of the parties (c) contract administration; and (d) claims, variations, and valuation of variations. After ascertaining that the level of knowledge of contract procedures is insufficient among most practitioners in the Botswana construction industry, major procurement entities, and engineering institutions of learning; a guide to drafting a condition of a construction contract was developed and then validated through seminars and workshops. In the present, the effectiveness of the guide is not yet measured but feedback from seminars and workshops conducted indicates an appreciation of the guide by the majority of major construction industry stakeholders.

Keywords: contract procedures, conditions of contract, professional practice, construction law, forms of contract

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Authors: Kenichi Hirota, Jifeng Wang, Sadao Araki, Koji Endo, Hideki Yamamoto

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Zirconium (Zr) has been used as a fuel cladding tube for nuclear reactors, because of the excellent corrosion resistance and the low adsorptive material for neutron. Generally speaking, the natural resource of Zr is often containing Hf that has similar properties. The content of Hf in the Zr resources is about 2~4 wt%. In the industrial use, the content of Hf in Zr resources should be lower than the 100 ppm. However, the separation of Zr and Hf is not so easy, because of similar chemical and physical properties such as melting point, boiling point and things. Solvent extraction method has been applied for the separation of Zr and Hf from Zr natural resources. This method can separate Hf with high efficiency (Hf < 100ppm), however, it needs much amount of organic solvents for solvent extraction and the cost of its disposal treatment is high. Therefore, we attached attention for the fractional crystallization. This separation method depends on the solubility difference of Zr and Hf in the solvent. In this work, hexafluorozirconate (hafnate) (K2Zr(Hf)F6) was used as model compound. Solubility of K2ZrF6 in water showed lower than that of K2HfF6. By repeating of this treatment, it is possible to purify Zr, practically. In this case, 16-18 times of recrystallization stages were needed for its high purification. The improvement of the crystallization process was carried out in this work. Water, hydrofluoric acid (HF) and hydrofluoric acid (HF) +hydrochloric acid (HCl) mixture were chosen as solvent for dissolution of Zr and Hf. In the experiment, 10g of K2ZrF6 was added to each solvent of 100mL. Each solution was heated for 1 hour at 353K. After 1h of this operation, they were cooled down till 293K, and were held for 5 hours at 273K. Concentration of Zr or Hf was measured using ICP analysis. It was found that Hf was separated from Zr-Hf mixed compound with high efficiency, when HF-HCl solution was used for solvent of crystallization. From the comparison of the particle size of each crystal by SEM, it was confirmed that the particle diameter of the crystal showed smaller size with decreasing of Hf content. This paper concerned with purification of Zr from Zr-Hf mixture using crystallization method.

Keywords: crystallization, zirconium, hafnium, separation

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Authors: Handan Alan, Ulkü Baykal

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Personal characteristics help people understand and recognize both themselves and other people. They are also known to have direct effects on managerial behaviors. Managers’ personalities indicate how they think, perceive reality and relate to others, and affect their decision-making and problem-solving methods. This descriptive study aims to determine the personal characteristics of nurse managers and the personal and professional factors that affect them since sufficient data does not exist on personal characteristics despite the focus on the leadership and managerial characteristics in nursing. The study population consisted of nurses working in administrative positions at hospitals affiliated with the public hospitals union, research and practice hospitals affiliated with universities and private hospitals in cities in the Marmara Region. The study sample consisted of nurse managers working in the hospitals that permitted conducting the study (excluding private branch hospitals). The data were collected after obtaining the approval of the Clinical Research Ethics Committee of Çanakkale Onsekiz Mart University (Approval date: 1.7.2015, Decision No: 2015-01) and written official permissions from the administrations of the hospitals included in the study. The data analysis was carried out using means and standard deviations (SD) as descriptive statistics, one-way analysis of variance for inter-group comparisons and the independent samples t-test for paired group comparisons. A significance threshold of p < 0.05 was used to evaluate the findings. The data were collected using the Five Factor Personality Inventory. The study included 900 nurse managers, who obtained the highest mean score on the conscientiousness dimension (X=4.22 ±0.35). This dimension was followed by their mean scores on the agreeableness (X=4.06±0.40), intelligence (X=4.05±0.37), extroversion (X=3.50±0.43), and emotional instability (X=2.07±0.53) dimensions. Statistically significant differences were found between the independent variables of age, gender, marital status, education level, work institution, professional experience, institutional experience, managerial experience, administrative position, work unit and managerial education when compared using the five factor personality inventory (p < 0.05). In conclusion, the nurse managers described themselves having high conscientiousness. Statistically significant differences were found between the five factor personality inventory mean scores and their personal and professional characteristics.

Keywords: nurse manager, personality, personal characteristics, professional characteristics

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Authors: Md Monirul Islam, Shahaduz Zaman, Mosarraf H. Sarker

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Context: Medical waste management (MWM) is a critical sector in Bangladesh due to its impact on human health and the environment. There is a need to assess the current policies and identify the role of policy actors in the policy formulation and implementation process. Research Aim: The study aimed to evaluate the role of policy actors in the medical waste management policy-making process in Bangladesh, identify policy gaps, and provide actionable recommendations for improvement. Methodology: The study adopted a qualitative research method and conducted key informant interviews. The data collected were analyzed using the thematic coding approach through Atlas.ti software. Findings: The study found that policies are formulated at higher administrative levels and implemented in a top-down approach. Higher-level institutions predominantly contribute to policy development, while lower-level institutions focus on implementation. However, due to negligence, ignorance, and lack of coordination, medical waste management receives insufficient attention from the actors. The study recommends the need for immediate strategies, a comprehensive action plan, regular policy updates, and inter-ministerial meetings to enhance medical waste management practices and interventions. Theoretical Importance: The research contributes to evaluating the role of policy actors in medical waste management policymaking and implementation in Bangladesh. It identifies policy gaps and provides actionable recommendations for improvement. Data Collection: The study used key informant interviews as the data collection method. Thirty-six participants were interviewed, including influential policymakers and representatives of various administrative spheres. Analysis Procedures: The data collected was analyzed using the inductive thematic analysis approach. Question Addressed: The study aimed to assess the role of policy actors in medical waste management policymaking and implementation in Bangladesh. Conclusion: In conclusion, the study provides insights into the current medical waste management policy in Bangladesh, the role of policy actors in policy formulation and implementation, and the need for improved strategies and policy updates. The findings of this study can guide future policy-making efforts to enhance medical waste management practices and interventions in Bangladesh.

Keywords: key informant, medical waste management, policy maker, qualitative study

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