Search results for: the connectivity of innovative network

Authors: M. Aruna, Uma Gunasilan

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Hospitals play an ample role in securing the health of a country. Nevertheless, they also have an unhealthy side. Ecological issues strengthen ill-health throughout the domain which subsequently puts pressure on hospital supply chains. Medical waste indeed is hazardous for environment and subsequently for human. The hospital waste management is of immense prominence due to its infectious and hazardous nature that can source many effects on human health and the environment. Government regulations and public cognizance regarding hospital waste issues have imposed hospital units to admit these strategies. The innovative technologies and instruments have been developed to handle hospital wastes. Green supply chain management practices are common in the United States. In India, Green Supply Chain management (GSCM) has just started to be recognized and practiced. GSCM are green, integrated and ecologically optimized. In Green supply chain management environmental sustainability is found to be an important driver. Eleven barriers are identified in this work. Interpretive Structural Modeling (ISM) technique is used for ranking the obstructions.

Keywords: green supply chain management (GSCM), hospital waste management (HWM), interpretive structural modeling (ISM), medical waste (MW)

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Authors: Ademola Ibukunolu Atanda

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Research undertakings are meant to proffer solutions to issues and challenges in society. This justifies the need for research in ivory towers. Multinational and non-governmental organisations, as well as foundations, commit financial resources to support research endeavours. In recent times, the direction and dimension of research undertaking encourage collaborations, whereby experts from different disciplines or specializations would bring their expertise in addressing any identified problem, whether in humanities or sciences. However, the extent to which collaborative research undertakings are perceived and embraced by academic staff would determine the impact collaborative research would have on society. To this end, this study investigated academic staff’s perception and willingness to be involved in collaborative research for the purpose of proffering solutions to societal problems. The study adopted a descriptive research design. The population comprised academic staff in southern Nigeria. The sample was drawn through a convenient sampling technique. The data were collected using a questionnaire titled “Perception and Willingness to Participate in Collaborative Research Questionnaire (PWPCRQ)’ using Google Forms. Data collected were analyzed using descriptive statistics of simple percentages, mean and charts. The findings showed that Academic Staff’s readiness to participate in collaborative research is to a great extent (89%) and they participate in collaborative research very often (51%). The Academic Staff was involved more in collaboration research among their colleagues within their universities (1.98) than participation in inter-disciplines collaboration (1.47) with their colleagues outside Nigeria. Collaborative research was perceived to impact on development (2.5). Collaborative research offers the following benefits to members’ aggregation of views, the building of an extensive network of contacts, enhancement of sharing of skills, facilitation of tackling complex problems, increased visibility of research network and citations and promotion of funding opportunities. The study concluded that Academic staff in universities in the South-West of Nigeria participate in collaborative research but with their colleagues within Nigeria rather than outside the country. Based on the findings, it was recommended that the management of universities in South-West Nigeria should encourage collaborative research with some incentives.

Keywords: collaboration, research, development, participation

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Authors: Chao Wang

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Under rapid innovation of information technology, the media plays a very important role in the dissemination of information, and it has a totally different analogy generations face. However, the involvement of narrative images provides more possibilities of narrative text. "Images" through the process of aperture, a camera shutter and developable photosensitive processes are manufactured, recorded and stamped on paper, displayed on a computer screen-concretely saved. They exist in different forms of files, data, or evidence as the ultimate looks of events. By the interface of media and network platforms and special visual field of the viewer, class body space exists and extends out as thin as sebum layer, extremely soft and delicate with real full tension. The physical space of sebum layer of confuses the fact that physical objects exist, needs to be established under a perceived consensus. As at the scene, the existing concepts and boundaries of physical perceptions are blurred. Sebum layer physical simulation shapes the “Topical-Similarity" immersing, leading the contemporary social practice communities, groups, network users with a kind of illusion without the presence, i.e. a non-real illusion. From the investigation and discussion of literatures, digital movies editing manufacture and produce the variability characteristics of time (for example, slices, rupture, set, and reset) are analyzed. Interactive eBook has an unique interaction in "Waiting-Greeting" and "Expectation-Response" that makes the operation of image narrative structure more interpretations functionally. The works of digital editing and interactive technology are combined and further analyze concept and results. After digitization of Interventional Imaging and interactive technology, real events exist linked and the media handing cannot be cut relationship through movies, interactive art, practical case discussion and analysis. Audience needs more rational thinking about images carried by the authenticity of the text.

Keywords: sebum layer, topical and similarity, interactive technology, image narrative

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Authors: Zhao Gao, Eran Edirisinghe

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The production of an accurate sketch of a suspect based on a verbal description obtained from a witness is an essential task for most criminal investigations. The criminal investigation system employs specifically trained professional artists to manually draw a facial image of the suspect according to the descriptions of an eyewitness for subsequent identification. Within the advancement of Deep Learning, Recurrent Neural Networks (RNN) have shown great promise in Natural Language Processing (NLP) tasks. Additionally, Generative Adversarial Networks (GAN) have also proven to be very effective in image generation. In this study, a trained GAN conditioned on textual features such as keywords automatically encoded from a verbal description of a human face using an RNN is used to generate photo-realistic facial images for criminal investigations. The intention of the proposed system is to map corresponding features into text generated from verbal descriptions. With this, it becomes possible to generate many reasonably accurate alternatives to which the witness can use to hopefully identify a suspect from. This reduces subjectivity in decision making both by the eyewitness and the artist while giving an opportunity for the witness to evaluate and reconsider decisions. Furthermore, the proposed approach benefits law enforcement agencies by reducing the time taken to physically draw each potential sketch, thus increasing response times and mitigating potentially malicious human intervention. With publically available 'CelebFaces Attributes Dataset' (CelebA) and additionally providing verbal description as training data, the proposed architecture is able to effectively produce facial structures from given text. Word Embeddings are learnt by applying the RNN architecture in order to perform semantic parsing, the output of which is fed into the GAN for synthesizing photo-realistic images. Rather than the grid search method, a metaheuristic search based on genetic algorithms is applied to evolve the network with the intent of achieving optimal hyperparameters in a fraction the time of a typical brute force approach. With the exception of the ‘CelebA’ training database, further novel test cases are supplied to the network for evaluation. Witness reports detailing criminals from Interpol or other law enforcement agencies are sampled on the network. Using the descriptions provided, samples are generated and compared with the ground truth images of a criminal in order to calculate the similarities. Two factors are used for performance evaluation: The Structural Similarity Index (SSIM) and the Peak Signal-to-Noise Ratio (PSNR). A high percentile output from this performance matrix should attribute to demonstrating the accuracy, in hope of proving that the proposed approach can be an effective tool for law enforcement agencies. The proposed approach to criminal facial image generation has potential to increase the ratio of criminal cases that can be ultimately resolved using eyewitness information gathering.

Keywords: RNN, GAN, NLP, facial composition, criminal investigation

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Authors: Mehmet Karaata

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Given two distinct vertices (nodes) source s and target t of a graph G = (V, E), the two node-disjoint paths problem is to identify two node-disjoint paths between s ∈ V and t ∈ V . Two paths are node-disjoint if they have no common intermediate vertices. In this paper, we present an algorithm with O(m)-time complexity for finding two node-disjoint paths between s and t in arbitrary graphs where m is the number of edges. The proposed algorithm has a wide range of applications in ensuring reliability and security of sensor, mobile and fixed communication networks.

Keywords: disjoint paths, distributed systems, fault-tolerance, network routing, security

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Authors: Doaa El-Shereef

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The Canaanite civilization was one of the early great civilizations of the Near East, they influenced and been influenced from the civilizations of the ancient world especially the Egyptian and Mesopotamia civilizations. The development of the Canaanite trade started from the Chalcolithic Age to the Iron Age through the oldest trade route in the Middle East. This paper will focus on defining the Canaanites and from where did they come from and the meaning of the term Canaan and how the Ancient Manuscripts define the borders of the land of Canaan and this essay will describe the Canaanite trade route and their exported goods such as cedar wood, and pottery.

Keywords: archaeology, bronze age, Canaanite, colonies, Massilia, pottery, shipwreck, vineyards

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Authors: D. Lalitha Rani, K. Sankar Rao

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MSMEs, due to their unique characteristics, are found to have inherent capabilities to undertake technological and non-technological innovations successfully across industries and nations. While there is considerable empirical evidence to throw light on SME innovation contributions in the context of developed countries, there is hardly any evidence to reveal how innovative SMEs are in rapidly industrializing economies like India. Indian MSMEs are largely incremental innovators, prompted by their customers and involved in product and/or process innovations. But majority carried out innovations with internal efforts only whereas the minority which obtained external support, had better technical strength, indulged in more frequent and both product & process innovations. Such MSMEs achieved better innovation performance as well as better economic performance. Some of them internationalized themselves in the process. However such achievements are “an oasis” in the vast Indian SME sector. How to promote (i) innovations, (ii) quality of innovations and (iii) patenting culture among the SMEs is a challenge for Indian Policy Makers. However this paper examines what are the innovation practices which are being carried out in this sector and identified the barriers for innovations in this sector and concludes with proposing some policy recommendations for promoting innovations in MSME sector in India.

Keywords: MSMEs, incremental innovators, policies, non-technological innovations

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Authors: Sunil Chandra, Triparna Barman, Vikas Gusain, Himanshu Rawat

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Forest fires are a recurrent phenomenon in the Himalayan region owing to the presence of vulnerable forest types, topographical gradients, climatic weather conditions, and anthropogenic pressure. The present study focuses on the identification of forest fire-affected areas in a small part of the West Himalayan region using a differential normalized burnt ratio method and spectral unmixing methods. The study area has a rugged terrain with the presence of sub-tropical pine forest, montane temperate forest, and sub-alpine forest and scrub. The major reason for fires in this region is anthropogenic in nature, with the practice of human-induced fires for getting fresh leaves, scaring wild animals to protect agricultural crops, grazing practices within the reserved forest, and igniting fires for cooking and other reasons. The fires caused by the above reasons affect a large area on the ground, necessitating its precise estimation for further management and policy making. In the present study, two approaches have been used for carrying out a burnt area analysis. The first approach followed for burnt area analysis uses a differential burnt normalized ratio index (dNBR) approach that uses the burnt ratio values generated using Short Wave Infra Red (SWIR) band and Near Infra Red (NIR) bands of the Sentinel-2A image. The results of the dNBR have been compared with the outputs of the spectral mixing methods. It has been found that the dNBR is able to create good results in fire-affected areas having homogenous forest stratum and with slope degree <5 degrees. However, in a rugged terrain where the landscape is largely influenced by the topographical variations, vegetation types, tree density, the results may be largely influenced by the effects of topography, complexity in tree composition, fuel load composition, and soil moisture. Hence, such variations in the factors influencing burnt area assessment may not be effectively carried out using a dNBR approach which is commonly followed for burnt area assessment over a large area. Hence, another approach that has been attempted in the present study utilizes a spectral mixing method where the individual pixel is tested before assigning an information class to it. The method uses a neural network approach utilizing Sentinel 2A bands. The training and testing data are generated from the sentinel-2A data and the national field inventory, which is further used for generating outputs using ML tools. The analysis of the results indicates that the fire-affected regions and their severity can be better estimated in rugged terrain using spectral unmixing methods which have the capability to resolve the noise in the data and can classify the individual pixel to the precise burnt/unburnt class.

Keywords: dNBR, spectral unmixing, neural network, forest stratum

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Authors: Suraj Mehrotra

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The stock market is a volatile, bustling marketplace that is a cornerstone of economics. It defines whether companies are successful or in spiral. A thorough understanding of it is important - many companies have whole divisions dedicated to analysis of both their stock and of rivaling companies. Linking the world of finance and artificial intelligence (AI), especially the stock market, has been a relatively recent development. Predicting how stocks will do considering all external factors and previous data has always been a human task. With the help of AI, however, machine learning models can help us make more complete predictions in financial trends. Taking a look at the stock market specifically, predicting the open, closing, high, and low prices for the next day is very hard to do. Machine learning makes this task a lot easier. A model that builds upon itself that takes in external factors as weights can predict trends far into the future. When used effectively, new doors can be opened up in the business and finance world, and companies can make better and more complete decisions. This paper explores the various techniques used in the prediction of stock prices, from traditional statistical methods to deep learning and neural networks based approaches, among other methods. It provides a detailed analysis of the techniques and also explores the challenges in predictive analysis. For the accuracy of the testing set, taking a look at four different models - linear regression, neural network, decision tree, and naïve Bayes - on the different stocks, Apple, Google, Tesla, Amazon, United Healthcare, Exxon Mobil, J.P. Morgan & Chase, and Johnson & Johnson, the naïve Bayes model and linear regression models worked best. For the testing set, the naïve Bayes model had the highest accuracy along with the linear regression model, followed by the neural network model and then the decision tree model. The training set had similar results except for the fact that the decision tree model was perfect with complete accuracy in its predictions, which makes sense. This means that the decision tree model likely overfitted the training set when used for the testing set.

Keywords: machine learning, testing set, artificial intelligence, stock analysis

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Authors: S. Adjiri-Bailiche, S. M. Boudia, H. Daaou, S. Hadouche, A. Benzaoui

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In this work the study of changes in the wind speed depending on the altitude is calculated and described by the model of the neural networks, the use of measured data, the speed and direction of wind, temperature and the humidity at 10 m are used as input data and as data targets at 50m above sea level. Comparing predict wind speeds and extrapolated at 50 m above sea level is performed. The results show that the prediction by the method of artificial neural networks is very accurate.

Keywords: MATLAB, neural network, power low, vertical extrapolation, wind energy, wind speed

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Authors: Pascal Gliesche, Kathrin Seibert, Christian Kowalski, Dominik Domhoff, Max Pfingsthorn, Karin Wolf-Ostermann, Andreas Hein

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Robotic assistance in nursing care is an increasingly important area of research and development. Facing a shortage of labor and an increasing number of people in need of care, the German Nursing Care Innovation Center (Pflegeinnovationszentrum, PIZ) aims to address these challenges from the side of technology. Little is known about nurses experiences with existing robotic assistance systems. Especially nurses perspectives on starting points for the development of robotic solutions, that target recurring burdensome tasks in everyday nursing care, are of interest. This paper presents findings focusing on robotics resulting from an explanatory mixed-methods study on nurses experiences with and their expectations for innovative technologies in nursing care in stationary and ambulant care facilities and hospitals in Germany. Based on the findings, eight scenarios for robotic assistance are identified based on the real needs of practitioners. An initial system addressing a single use-case is described to show perspectives for the use of robots in nursing care.

Keywords: robotics and automation, engineering management, engineering in medicine and biology, medical services, public health-care

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Authors: Duncan Wallace, M-Tahar Kechadi

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In recent years, Machine Learning (ML) approaches have been successfully applied to an analysis of patient symptom data in the context of disease diagnosis, at least where such data is well codified. However, much of the data present in Electronic Health Records (EHR) are unlikely to prove suitable for classic ML approaches. Furthermore, as scores of data are widely spread across both hospitals and individuals, a decentralized, computationally scalable methodology is a priority. The focus of this paper is to develop a method to predict outliers in an out-of-hours healthcare provision center (OOHC). In particular, our research is based upon the early identification of patients who have underlying conditions which will cause them to repeatedly require medical attention. OOHC act as an ad-hoc delivery of triage and treatment, where interactions occur without recourse to a full medical history of the patient in question. Medical histories, relating to patients contacting an OOHC, may reside in several distinct EHR systems in multiple hospitals or surgeries, which are unavailable to the OOHC in question. As such, although a local solution is optimal for this problem, it follows that the data under investigation is incomplete, heterogeneous, and comprised mostly of noisy textual notes compiled during routine OOHC activities. Through the use of Deep Learning methodologies, the aim of this paper is to provide the means to identify patient cases, upon initial contact, which are likely to relate to such outliers. To this end, we compare the performance of Long Short-Term Memory, Gated Recurrent Units, and combinations of both with Convolutional Neural Networks. A further aim of this paper is to elucidate the discovery of such outliers by examining the exact terms which provide a strong indication of positive and negative case entries. While free-text is the principal data extracted from EHRs for classification, EHRs also contain normalized features. Although the specific demographical features treated within our corpus are relatively limited in scope, we examine whether it is beneficial to include such features among the inputs to our neural network, or whether these features are more successfully exploited in conjunction with a different form of a classifier. In this section, we compare the performance of randomly generated regression trees and support vector machines and determine the extent to which our classification program can be improved upon by using either of these machine learning approaches in conjunction with the output of our Recurrent Neural Network application. The output of our neural network is also used to help determine the most significant lexemes present within the corpus for determining high-risk patients. By combining the confidence of our classification program in relation to lexemes within true positive and true negative cases, with an inverse document frequency of the lexemes related to these cases, we can determine what features act as the primary indicators of frequent-attender and non-frequent-attender cases, providing a human interpretable appreciation of how our program classifies cases.

Keywords: artificial neural networks, data-mining, machine learning, medical informatics

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Authors: Sandeep Mehmi, Harsh Verma, A. L. Sangal

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Ever since the idea of using computing services as commodity that can be delivered like other utilities e.g. electric and telephone has been floated, the scientific fraternity has diverted their research towards a new area called utility computing. New paradigms like cluster computing and grid computing came into existence while edging closer to utility computing. With the advent of internet the demand of anytime, anywhere access of the resources that could be provisioned dynamically as a service, gave rise to the next generation computing paradigm known as cloud computing. Today, cloud computing has become one of the most aggressively growing computer paradigm, resulting in growing rate of applications in area of IT outsourcing. Besides catering the computational and storage demands, cloud computing has economically benefitted almost all the fields, education, research, entertainment, medical, banking, military operations, weather forecasting, business and finance to name a few. Smart grid is another discipline that direly needs to be benefitted from the cloud computing advantages. Smart grid system is a new technology that has revolutionized the power sector by automating the transmission and distribution system and integration of smart devices. Cloud based smart grid can fulfill the storage requirement of unstructured and uncorrelated data generated by smart sensors as well as computational needs for self-healing, load balancing and demand response features. But, security issues such as confidentiality, integrity, availability, accountability and privacy need to be resolved for the development of smart grid cloud. In recent years, a number of intrusion prevention techniques have been proposed in the cloud, but hackers/intruders still manage to bypass the security of the cloud. Therefore, precise intrusion detection systems need to be developed in order to secure the critical information infrastructure like smart grid cloud. Considering the success of artificial neural networks in building robust intrusion detection, this research proposes an artificial neural network based model for detecting attacks in smart grid cloud.

Keywords: artificial neural networks, cloud computing, intrusion detection systems, security issues, smart grid

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Authors: Thulane Paepae, Tumisang Seodigeng

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This paper demonstrates the use of a method of synthesizing process flowsheets using a graphical tool called the GH-plot and in particular, to look at how it can be used to compare the reactions of a combined simultaneous process with regard to their thermodynamics. The technique uses fundamental thermodynamic principles to allow the mass, energy and work balances locate the attainable region for chemical processes in a reactor. This provides guidance on what design decisions would be best suited to developing new processes that are more effective and make lower demands on raw material and energy usage.

Keywords: attainable regions, dimethyl ether, optimal reaction network, GH Space

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Authors: Lawrence Williams

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As the mechanism which converts domains to internet protocol (IP) addresses, Domain Name System (DNS) is an essential part of internet usage. It was not designed securely and can be subject to attacks. DNS attacks have become more frequent and sophisticated and the need for detecting and preventing them becomes more important for the modern network. DNS tunnelling attacks are one type of attack that are primarily used for distributed denial-of-service (DDoS) attacks and data exfiltration. Discussion of different techniques to detect and prevent DNS tunneling attacks is done. The methods, models, experiments, and data for each technique are discussed. A proposal about feasibility is made. Future research on these topics is proposed.

Keywords: DNS, tunneling, exfiltration, botnet

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Authors: Wanyi Zhu, Xia Ming, Huafeng Wang, Junda Chen, Lu Liu, Jiangwei Jiang, Guohua Liu

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Anomaly detection is a first and imperative step needed to respond to unexpected problems and to assure high performance and security in large data center management. This paper presents an online anomaly detection system through an innovative approach of ensemble machine learning and adaptive differentiation algorithms, and applies them to performance data collected from a continuous monitoring system for multi-tier web applications running in Alibaba data centers. We evaluate the effectiveness and efficiency of this algorithm with production traffic data and compare with the traditional anomaly detection approaches such as a static threshold and other deviation-based detection techniques. The experiment results show that our algorithm correctly identifies the unexpected performance variances of any running application, with an acceptable false positive rate. This proposed approach has already been deployed in real-time production environments to enhance the efficiency and stability in daily data center operations.

Keywords: Alibaba data centers, anomaly detection, big data computation, dynamic ensemble learning

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Authors: Ankit Shah, R. K. Shrivastava

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Flood and drought are two main features of hydrology which affect the human life. Floods are natural disasters which cause millions of rupees’ worth of damage each year in India and the whole world. Flood causes destruction in form of life and property. An accurate estimate of the flood damage potential is a key element to an effective, nationwide flood damage abatement program. Also, the increase in demand of water due to increase in population, industrial and agricultural growth, has let us know that though being a renewable resource it cannot be taken for granted. We have to optimize the use of water according to circumstances and conditions and need to harness it which can be done by construction of hydraulic structures. For their safe and proper functioning of hydraulic structures, we need to predict the flood magnitude and its impact. Hydraulic structures play a key role in harnessing and optimization of flood water which in turn results in safe and maximum use of water available. Mainly hydraulic structures are constructed on ungauged sites. There are two methods by which we can estimate flood viz. generation of Unit Hydrographs and Flood Frequency Analysis. In this study, Regional Flood Frequency Analysis has been employed. There are many methods for estimating the ‘Regional Flood Frequency Analysis’ viz. Index Flood Method. National Environmental and Research Council (NERC Methods), Multiple Regression Method, etc. However, none of the methods can be considered universal for every situation and location. The Narmada basin is located in Central India. It is drained by most of the tributaries, most of which are ungauged. Therefore it is very difficult to estimate flood on these tributaries and in the main river. As mentioned above Artificial Neural Network (ANN)s and Multiple Regression Method is used for determination of Regional flood Frequency. The annual peak flood data of 20 sites gauging sites of Narmada Basin is used in the present study to determine the Regional Flood relationships. Homogeneity of the considered sites is determined by using the Index Flood Method. Flood relationships obtained by both the methods are compared with each other, and it is found that ANN is more reliable than Multiple Regression Method for the present study area.

Keywords: artificial neural network, index flood method, multi layer perceptrons, multiple regression, Narmada basin, regional flood frequency

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Authors: Sehrish Iftikhar, Ahmad Ali Shahid, Kiran Nawaz, Waheed Anwar

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Discovery and development of new compounds require intense studies in chemistry, biochemistry. Numerous experiments under laboratory-, greenhouse- and field conditions can be performed to select suitable candidates and to understand their full potential. Novel fungicides are fundamental to combat plant diseases. Fusarium solani is important plant pathogen. New broad spectrum foliar fungicides against complex II were designed in this study. Complex II, namely succinate dehydrogenase (SDH), or succinate quinone oxidoreductase (SQR) is a multi-subunit enzyme at the crossroads of TCA and ETC at the inner mitochondrial membrane. The need for new and innovative fungicides is driven by resistance management, regulatory hurdles and increasing customer expectations amongst others. Fungicidal activity was assessed for the effect on mycelial growth and spore germination of the fungi using fungicide amended media assay. In mycelial growth assay compounds C10 and C6 were highly active against all the isolates. The compounds C1 and C10 were found most potent in spore germination test. It fully proved that the SDHIs designed in this paper displayed as good inhibitory effects against Fusarium solani.

Keywords: Wilt, Fusarium, SDH, antifungal

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Authors: Sokratis Koskinakis, Georgia Frakolaki, Magdalini Krokida

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Regular consumption of omega-3 fatty acid-rich lipids is said to provide a wide range of health benefits, including prevention of inflammation, cardiovascular disease, diabetes, arthritis, and ulcerative colitis. Because of their potential nutritional and health benefits, the omega-3 PUFAs are increasingly being supplemented in functional food products meant to improve human health and wellbeing. However, dietary fortification with PUFAs is difficult due to their low water solubility, tendency to oxidize quickly, and inconsistent bioavailability. These issues can be solved through application of modern encapsulation technologies, which typically entail integrating omega-3 oils into well-designed matrices made from food-grade components. Electrospinning, for example, is an effective encapsulation method for producing sub-micron or nano-scale polymer fibers. For this purpose, various combinations of hydroxypropyl-β-cyclodextrin and cellulose nanocrystals/ nanofibers were assessed for the encapsulation of omega-3 fatty acids through the innovative technology of electrospinning. The encapsulation yield was evaluated through GC-analysis, and the morphology of the final products was assessed through SEM analysis.

Keywords: electrospinning, encapsulation, omega-3 fatty acids, cellulose nanocrystals / nanofibers

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Authors: Srinivasa Reddy Mallampati, Byoung Ho Lee, Yoshiharu Mitoma, Simion Cristian

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In recent years, environmental nanotechnology has risen to the forefront and the new properties and enhanced reactivates offered by nanomaterial may offer a new, low-cost paradigm to solving complex environmental pollution problems. This study assessed the synthesis and application of multi-functioned nano-size metallic calcium (nMC) composite for detoxification of hazardous inorganic (heavy metals (HMs)/organic chlorinated/brominated compound (CBCs) contaminants in automobile shredder residue (ASR). ASR residues ball milled with nMC composite can achieve about 90-100% of HMs immobilization and CBCs decomposition. The results highlight the low quantity of HMs leached from ASR residues after treatment with nMC, which was found to be lower than the standard regulatory limit for hazardous waste landfills. The use of nMC composite in a mechanochemical process to treat hazardous ASR (dry conditions) is a simple and innovative approach to remediate hazardous inorganic/organic cross-contaminates in ASR.

Keywords: nano-sized metallic calcium, automobile shredder residue, organic/inorganic contaminants, immobilization, detoxification

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Authors: Espinosa-Garza G., Loera-Hernandez I., Antonyan N.

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To innovate, it is necessary to perform projects directed towards the search of improvement. The agricultural technique and the design of greenhouses have been studied by undergraduate engineering students from the Tecnológico de Monterrey using the campus areas. The purpose of this project was to incite students to create innovations and help rural populations of the state to solve one of the problems that they are dealing with nowadays. The main objective of the project was to search for an alternative technique that will allow the planting of the “chile piquín” plant, also known as Capsicum annuum, to grow quicker as it germinates. The “chile piquín” is one of the original crops of Mexico and forms the basis of the Mesoamerican cultures’ diet since the pre-hispanic era. To fulfill with today’s demand, it is required to implement new alternative methods to increase the “chile piquín’s” growth. The project lasted one semester with the participation of engineering students from multiple majors. The most important results from this academic experience were that, from the proposed goal, the students could analyze the needs of their town and were capable of introducing new and innovative ideas with the aim of resolving them. In the present article the pedagogic methodologies that allowed to carry out this project will be discussed.

Keywords: academic experience, chile piquín, engineering education, greenhouse design, innovation

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Authors: Yi-Min Lan, Hung-Wen Cheng, Hsuan-Ling Chang, Jou-Ping Yu

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The study applies machine learning models to construct momentum strategies and utilizes the information coefficient as an indicator for selecting stocks with strong and weak momentum characteristics. Through this approach, the study has built investment portfolios capable of generating superior returns and conducted a thorough analysis. Compared to existing research on momentum strategies, machine learning is incorporated to capture non-linear interactions. This approach enhances the conventional stock selection process, which is often impeded by difficulties associated with timeliness, accuracy, and efficiency due to market risk factors. The study finds that implementing bidirectional momentum strategies outperforms unidirectional ones, and momentum factors with longer observation periods exhibit stronger correlations with returns. Optimizing the number of stocks in the portfolio while staying within a certain threshold leads to the highest level of excess returns. The study presents a novel framework for momentum strategies that enhances and improves the operational aspects of asset management. By introducing innovative financial technology applications to traditional investment strategies, this paper can demonstrate significant effectiveness.

Keywords: information coefficient, machine learning, momentum, portfolio, return prediction

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Authors: Fouzia Shersad, Sabeena Salam

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Introduction: Quality assurance cycles like RADAR, PDCA, ADRI are cycles of planning, implementation, assessment and improvement. These cycles are required when institutions apply for reaccreditation to accreditation bodies and for adoption of holistic models of institutional quality. Method of Study: The adoption of these cycles at the higher education institutions under the Dubai Medical University is studied to explore the feasibility and the benefits in institutions outcomes. After adequate faculty training, these steps were incorporated in all new activities and embedded in every new initiative and approach undertaken at unit and institutional levels. Conclusions: Improvement in student satisfaction rates and performance levels has been achieved. Wherever weaknesses or deficits have been identified, improvement strategies are implemented in a timely manner. The feedback has become an incentive for faculty members to implement new ideas. Implementation of these cycles for core processes at micro and macro levels have ensured that a systematic mechanism for corrective actions existed. This has led to increasing adoption of innovative initiatives. Another outcome was the recognition through national level awards for the overall institutions which have been certified by external reviewers.

Keywords: higher education, quality, accreditation, institutional improvement

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Authors: Anneline Chetty

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Innovation is not always born in a sterile lab or is not always about applications and technology. Innovative solutions to community challenges can be borne out of the creativity of community members. This was proven by Professor Anil Gupta who for more than two decades scoured rural India for its hidden innovations motivated by the belief that the most powerful ideas for fighting poverty and hardship will not come from corporate research labs, but from ordinary people struggling to survive. The Ethekwini Municipality is a city in South Africa which adopted a similar approach, recognising the innovativeness of youth (students and school pupils) in its area. The intention was to make the youth a part of the solution to challenges faced by the Municipality. In this regard, five areas were selected and five groups of students were identified. Each group was sent into the community to identify challenges and engage with community leaders as well as members. Each group was tasked to come with solutions to these challenges which were to be presented at an Innovation Summit. The presented solutions were judged and the winning solution would be implemented by the Municipality. This paper, documents the experience of the students as well as the kinds of solutions that were presented. The purpose is to highlight the importance of using the ingenious minds and creativity of youth and channel their energy into becoming part of society’s solutions as opposed to being the problem

Keywords: innovation, indigenous, entrepreneurship, community

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Authors: Beatriz Cortés, Andrés Boza, David Pérez, Llanos Cuenca

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The Internet of Things (IoT) field is been applied in industries with different purposes. Sensing Enterprise (SE) is an attribute of an enterprise or a network that allows it to react to business stimuli originating on the internet. These fields have come into focus recently on the enterprises and there is some evidence of the use and implications in supply chain management while finding it as an interesting aspect to work on. This paper presents a revision and proposals of IoT applications in supply chain management.

Keywords: industrial, internet of things, production systems, sensing enterprises, sensor, supply chain management

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Authors: Santos Andressa M. N.

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The research brings reflections on the importance of Instagram for the clothing trade, aiming to analyze the use of this social network as a sales tool by small companies in the fashion/clothing sector in Boqueirão-PI. Thus, field research was carried out, with the application of questionnaires, to raise and analyze data related to the topic. Thus, it is believed that Instagram positively influences the dissemination, visibility, reach and profitability of companies in Boqueirão do Piauí. The survey had a low number of companies due to the lack of availability of the owners during the COVID-19 pandemic.

Keywords: Instagram, sales, fashion, marketing

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Authors: B. Daee, H. M. El Naggar

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This paper presents an experimental study on structural performance of an innovative noise barrier consisting of poly-block, light polyurethane foam (LPF) and polyurea. This wall system (flexi-wall) is intended to be employed as a vertical extension to existing sound barriers in an accelerated construction method. To aid in the wall design, several mechanical tests were conducted on LPF specimens and two full-scale walls were then fabricated employing the same LPF material. The full-scale walls were subjected to lateral loading in order to establish their lateral resistance. A cyclic fatigue test was also performed on a full-scale flexi-wall in order to evaluate the performance of the wall under a repetitive loading condition. The result of the experiments indicated the suitability of flexi-wall in accelerated construction and confirmed that the structural performance of the wall system under lateral loading is satisfactory for the sound barrier application. The experimental results were discussed and a preliminary design procedure for application of flexi-wall in sound barrier applications was also developed.

Keywords: noise barrier, polyurethane foam, accelerated construction, full-scale experiment

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Authors: Farhad Ghorbandordinejad, Shamsoddin Aref

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Wikis and blogs, defined as educational tools in line with the objectives of collaborative writing, are regarded as innovative ways of writing addressing the problems of conventional types of writing. Although writing in wikis and blogs step in different contexts, they are both aiming at betterment of collaborative writing procedures. It is believed that due to certain reasons bringing in wikis and blogs to learners' life can lead to better performance of writing. This study aimed at dipping into pedagogical aspects of wikis and blogs in the hope of eliminating prior traditional mistakes and bringing students together in a more constructive L2 context. To this end, three groups of intermediate students were experimented in three settings of wiki-group, blog-group and conventional (control) group. Despite conventional group learners, participants in both experimental groups experienced L2 writing in a new telecollaborative context. An achievement test was administered after the treatment to check learners’ degree of improvement in EFL writing. The results of this study provide a deep insight towards the effectiveness of writing in the contexts of wikis and blogs compared with conventional writing procedures. The overall conclusion drawn from the distinction of conventional writing, on one hand, and wikis and blogs, on the other hand, indicates that the latter channels of writing are more constructive for learners’ writing improvements.

Keywords: collaborative writing, wikis, blogs, writing achievement

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Authors: Wenfan Yan, Yumei Han

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Objectives: Educational inequality persists between China's ethnic minority regions and the mainland. The key to rectifying this disparity lies in enhancing the quality of educators. This paper delves into the Chinese government's innovative policy, "Group Educators Supporting Tibet" (GEST), designed to bridge the shortage of high-quality teachers in Tibet, a representative underprivileged ethnic minority area. GEST aims to foster collective action by networking provincial expert educators with Tibetan counterparts and collaborating between supporting provincial educational entities and Tibetan education entities. Theoretical Framework: The unequal distribution of social capital contributes significantly to the educational gap between ethnic minority areas and other regions in China. Within the framework of social network theory, motivated GEST educators take action to foster resources and relationships. This study captures grassroots perspectives to outline how social networking contributes to the policy objective of enhancing Tibetan teachers' quality and eradicating educational injustice. Methodology: A sequential mixed-methods approach was adopted to scrutinize policy impacts from the vantage point of social networking. Quantitative research involved surveys for GEST and Tibetan teachers, exploring demographics, perceptions of policy significance, motivations, actions, and networking habits. Qualitative research included focus group interviews with GEST educators, local teachers, and students from program schools. The findings were meticulously analyzed to provide comprehensive insights into stakeholders' experiences and the impacts of the GEST policy. Key Findings: The policy empowers individuals to impact Tibetan education significantly. Motivated GEST educators with prior educational support experiences contribute to its success. Supported by a collective -school, city, province, and government- the new social structure fosters higher efficiency. GEST's approach surpasses conventional methods. The individual, backed by educators, realizes the potential of transformative class design. Collective activities -pedagogy research, teaching, mentoring, training, and partnerships- equip Tibetan teachers, enhancing educational quality and equity. This collaborative effort establishes a robust foundation for the policy's success, emphasizing the collective impact on Tibetan education. Contributions: This study contributes to international policy studies focused on educational equity through collective teacher action. Using a mixed-methods approach and guided by social networking theory, it accentuates stakeholders' perspectives, elucidating the genuine impacts of the GEST policy. The study underscores the advancement of social networking, the reinforcement of local teacher quality, and the transformative potential of cultivating a more equitable and adept teaching workforce in Tibet. Limitations of the Study and Suggestions for Future Research Directions: While the study emphasizes the positive impacts of motivated GEST educators, there might be aspects or challenges not fully explored. A more comprehensive understanding of potential drawbacks or obstacles would provide a more balanced view. For future studies, investigating the long-term impact of the GEST policy on educational quality could provide insights into the sustainability of the improvements observed. Also, understanding the perspectives of Tibetan teachers who may not have directly benefited from GEST could reveal potential disparities in policy implementation.

Keywords: teacher development, social networking, teacher quality, mixed research method

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Authors: Dagnija Blumberga, Toms Prodanuks, Ivars Veidenbergs, Andra Blumberga

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Gas condensing units with inner tubes heat exchangers represent third generation technology and differ from second generation heat and mass transfer units, which are fulfilled by passive filling material layer. The first one improves heat and mass transfer by increasing cooled contact surface of gas and condensate drops and film formed in inner tubes heat exchanger. This paper presents a selection of significant factors which influence the heat and mass transfer. Experimental planning is based on the research and analysis of main three independent variables; velocity of water and gas as well as density of spraying. Empirical mathematical models show that the coefficient of heat transfer is used as dependent parameter which depends on two independent variables; water and gas velocity. Empirical model is proved by the use of experimental data of two independent gas condensing units in Lithuania and Russia. Experimental data are processed by the use of heat transfer criteria-Kirpichov number. Results allow drawing the graphical nomogram for the calculation of heat and mass transfer conditions in the innovative and energy efficient gas cooling unit.

Keywords: gas condensing unit, filling, inner heat exchanger, package, spraying, tunes

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