Search results for: inherent feature

Authors: Raana Babadi Fathipour

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The production of dairy holds significant importance in the sustenance of billions of individuals worldwide, as they rely on milk and its derived products for daily consumption. In addition to being a source of essential nutrients crucial for human well-being, such as proteins, fats, vitamins, and minerals; dairy items are witnessing an increasing demand worldwide. Amongst all the factors contributing to the quality and safety assurance of dairy products, the strong focus lies on maintaining high standards in raw milk procurement. Raw milk serves as an externally nutritious medium for various microorganisms due to its inherent properties. This poses a considerable challenge for the dairy industry in ensuring that microbial contamination is minimized throughout every stage of the value chain. Despite implementing diverse process technologies—both conventional and innovative—the occurrence of microbial spoilage still results in substantial losses within this industry context. Moreover, milk and dairy products have been associated with numerous cases of foodborne illnesses across the globe. Various pathogens such as Salmonella serovars, Campylobacter spp., Shiga toxin-producing Escherichia coli, Listeria monocytogenes, and enterotoxin producing Staphylococcus aureus are commonly identified as the culprits behind these outbreaks in the dairy industry. The effective management of food safety within this sector necessitates a proactive and risk-based approach to reform. However, this strategy presents difficulties for developing nations where informal value chains dominate the dairy sector. Whether operating on a small or large scale or falling within formal or informal realms, it is imperative that the dairy industry adheres to principles of good hygiene practices and good manufacturing practices. Additionally, identifying and managing potential sources of contamination is crucial in mitigating challenges pertaining to quality and safety precautions.

Keywords: dairy value chain, microbial contamination, food safety, hygiene

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Authors: Sadia Basar, Naila Habib, Awais Adnan

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The paper presents the Technique for Image Segmentation Using K-Means Clustering Classification. The presented algorithms were specific, however, missed the neighboring information and required high-speed computerized machines to run the segmentation algorithms. Clustering is the process of partitioning a group of data points into a small number of clusters. The proposed method is content-aware and feature extraction method which is able to run on low-end computerized machines, simple algorithm, required low-quality streaming, efficient and used for security purpose. It has the capability to highlight the boundary and the object. At first, the user enters the data in the representation of the input. Then in the next step, the digital image is converted into groups clusters. Clusters are divided into many regions. The same categories with same features of clusters are assembled within a group and different clusters are placed in other groups. Finally, the clusters are combined with respect to similar features and then represented in the form of segments. The clustered image depicts the clear representation of the digital image in order to highlight the regions and boundaries of the image. At last, the final image is presented in the form of segments. All colors of the image are separated in clusters.

Keywords: clustering, image segmentation, K-means function, local and global minimum, region

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Authors: Jason West

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Low profitability in agriculture production along with increasing scrutiny over environmental effects is limiting food production at scale. In contrast, the mining sector offers access to resources including energy, water, transport and chemicals for food production at low marginal cost. Scalable agricultural production can benefit from the nexus of resources (water, energy, transport) offered by mining activity in remote locations. A decision support bioeconomic model for controlled environment vertical farms was used. Four submodels were used: crop structure, nutrient requirements, resource-crop integration, and economic. They escalate to a macro mathematical model. A demonstrable dynamic systems framework is needed to prove productive outcomes are feasible. We demonstrate a generalized bioeconomic macro model for controlled environment production systems in minesites using systems dynamics modeling methodology. Despite the complexity of bioeconomic modelling of resource-agricultural dynamic processes and interactions, the economic potential greater than general economic models would assume. Scalability of production as an input becomes a key success feature.

Keywords: crop production systems, mathematical model, mining, agriculture, dynamic systems

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Authors: I. A Tijani, Y. F Wu, C.W. Lim

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Concrete Damaged Plasticity Model (CDPM) is capable of modeling the stress-strain behavior of confined concrete. Nevertheless, the accuracy of the model largely depends on its parameters. To date, most research works mainly focus on the identification and modification of the parameters for fiber reinforced polymer (FRP) confined concrete prior to damage. And, it has been established that the FRP-strengthened concrete behaves differently to FRP-repaired concrete. This paper presents a modified plastic damage model within the context of the CDPM in ABAQUS for modelling of a uniformly FRP-confined repaired concrete under monotonic loading. The proposed model includes infliction damage, elastic stiffness, yield criterion and strain hardening rule. The distinct feature of damaged concrete is elastic stiffness reduction; this is included in the model. Meanwhile, the test results were obtained from a physical testing of repaired concrete. The dilation model is expressed as a function of the lateral stiffness of the FRP-jacket. The finite element predictions are shown to be in close agreement with the obtained test results of the repaired concrete. It was observed from the study that with necessary modifications, finite element method is capable of modeling FRP-repaired concrete structures.

Keywords: Concrete, FRP, Damage, Repairing, Plasticity, and Finite element method

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Authors: Geetanjali Singh, R. J. Choudhary, Anjana Dogra

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Here we report the effects of anti-site disorder, present in the sample, on the magnetic properties of Sm₂NiMnO₆ (SNMO) thin films. To our best knowledge, there are no studies available on the thin films of SNMO. Thin films were grown using pulsed laser deposition technique on SrTiO₃ (STO) substrate under oxygen pressure of 800 mTorr. X-ray diffraction (XRD) profiles show that the film grown is epitaxial. Field cooled (FC) and zero field cooled (ZFC) magnetization curve increase as we decrease the temperature till ~135K. A broad dip was observed in both the curves below this temperature which is more dominating in ZFC curve. An additional sharp cusplike shape was observed at low temperature (~20 K) which is due to the re-entrant spin-glass like properties present in the sample. Super-exchange interaction between Ni²⁺-O-Mn⁴⁺ is attributed to the FM ordering in these samples. The spin-glass feature is due to anti-site disorder within the homogeneous sample which was stated to be due to the mixed valence states Ni³⁺ and Mn³⁺ present in the sample. Anti-site disorder was found to play very crucial role in different magnetic phases of the sample.

Keywords: double perovskite, pulsed laser deposition, spin-glass, magnetization

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Authors: Petronella Jonck, Freda van der Walt

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Despite the benefits of innovation diffusion in the South African public service, implementation thereof seems to be problematic, particularly with regard to e-governance which would enhance the quality of service delivery, especially accessibility, choice, and mode of operation. This paper reports on differences between the public service and the private sector in terms of innovation diffusion. Innovation diffusion will be investigated to explore identified obstacles that are hindering successful implementation of e-governance. The research inquiry is underpinned by the diffusion of innovation theory, which is premised on the assumption that innovation has a distinct channel, time, and mode of adoption within the organisation. A comparative thematic document analysis was conducted to investigate organisational differences with regard to innovation diffusion. A similar approach has been followed in other countries, where the same conceptual framework has been used to guide document analysis in studies in both the private and the public sectors. As per the recommended conceptual framework, three organisational characteristics were emphasised, namely the external characteristics of the organisation, the organisational structure, and the inherent characteristics of the leadership. The results indicated that the main difference in the external characteristics lies in the focus and the clientele of the private sector. With regard to organisational structure, private organisations have veto power, which is not the case in the public service. Regarding leadership, similarities were observed in social and environmental responsibility and employees’ attitudes towards immediate supervision. Differences identified included risk taking, the adequacy of leadership development, organisational approaches to motivation and involvement in decision making, and leadership style. Due to the organisational differences observed, it is recommended that differentiated strategies be employed to ensure effective innovation diffusion, and ultimately e-governance. It is recommended that the results of this research be used to stimulate discussion on ways to improve collaboration between the mentioned sectors, to capitalise on the benefits of each sector.

Keywords: E-governance, ICT, innovation diffusion, comparative analysis

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Authors: Daniel M. Wolff, Patricia Gray, Rafael de la Parra Venegas

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An unsupervised machine listening system is constructed and applied to a dataset of 17,195 30-second marine hydrophone recordings. The system is then heuristically supplemented with anecdotal listening, contextual recording information, and supervised learning techniques to reduce the number of false positives. Features for classification are assembled by extracting the following data from each of the audio files: the spectral centroid, root-mean-squared values for each frequency band of a 10-octave filter bank, and mel-frequency cepstral coefficients in 5-second frames. In this way both time- and frequency-domain information are contained in the features to be passed to a clustering algorithm. Classification is performed using the k-means algorithm and then a k-nearest neighbors search. Different values of k are experimented with, in addition to different combinations of the available feature sets. Hypothesized class labels are 'primarily anthrophony' and 'primarily biophony', where the best class result conforming to the former label has 104 members after heuristic pruning. This demonstrates how a large audio dataset has been made more tractable with machine learning techniques, forming the foundation of a framework designed to acoustically monitor and gauge biological and anthropogenic activity in a marine environment.

Keywords: anthrophony, hydrophone, k-means, machine learning

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Authors: Faraj Ahmed E. Alhegagi, Anagia M. Khamkam Mohamed, Bassam F. Alhajaji

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Duplex stainless steels (DSS) are widely employed in industry for apparatus working with sea water in petroleum, refineries and in chemical plants. Fracture of DSS takes place by cleavage of the ferrite phase and the austenite phase ductile tear off. Pop-in is an important feature takes place during fracture of DSS. The procedure of Pop-ins assessment plays an important role in fracture toughness studies. In present work, Zeron100 DSS specimens were heat treated at different temperatures, cooled and pulled to failure to assess the pop-ins criterion in crack initiation prediction. The outcome results were compared to the British Standard (BS 7448) and the ASTEM standard (E1290) for Crack-Tip Opening Displacement (CTOD) fracture toughness measurement. Pop-in took place during specimens loading specially for those specimens heat treated at higher temperatures. The standard BS7448 was followed to check specimen validity for fractured toughness assessment by direct determination of KIC. In most cases, specimens were invalid for KIC measurement. The two procedures were equivalent only when single pop-ins were assessed. A considerable contrast in fracture toughness value between was observed where multiple pop-ins were assessed.

Keywords: fracture toughness, stainless steels, pop ins, crack assessment

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Authors: Waqas Ahmed Khan Afridi, Subhas Chandra Mukhopadhyay, Bandita Mainali

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The research paper presents a unique approach to evapotranspiration (ET) prediction using a Support Vector Machine (SVM) learning algorithm. The study leverages real-time sensor node data to develop an accurate and adaptable prediction model, addressing the inherent challenges of traditional ET estimation methods. The integration of the SVM algorithm with real-time sensor node data offers great potential to improve spatial and temporal resolution in ET predictions. In the model development, key input features are measured and computed using mathematical equations such as Penman-Monteith (FAO56) and soil water balance (SWB), which include soil-environmental parameters such as; solar radiation (Rs), air temperature (T), atmospheric pressure (P), relative humidity (RH), wind speed (u2), rain (R), deep percolation (DP), soil temperature (ST), and change in soil moisture (∆SM). The one-year field data are split into combinations of three proportions i.e. train, test, and validation sets. While kernel functions with tuning hyperparameters have been used to train and improve the accuracy of the prediction model with multiple iterations. This paper also outlines the existing methods and the machine learning techniques to determine Evapotranspiration, data collection and preprocessing, model construction, and evaluation metrics, highlighting the significance of SVM in advancing the field of ET prediction. The results demonstrate the robustness and high predictability of the developed model on the basis of performance evaluation metrics (R2, RMSE, MAE). The effectiveness of the proposed model in capturing complex relationships within soil and environmental parameters provide insights into its potential applications for water resource management and hydrological ecosystem.

Keywords: evapotranspiration, FAO56, KNIME, machine learning, RStudio, SVM, sensors

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Authors: Konstantinos Perifanos, Eirini Florou, Dionysis Goutsos

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This paper presents and benchmarks a number of end-to-end Deep Learning based models for metaphor detection in Greek. We combine Convolutional Neural Networks and Recurrent Neural Networks with representation learning to bear on the metaphor detection problem for the Greek language. The models presented achieve exceptional accuracy scores, significantly improving the previous state-of-the-art results, which had already achieved accuracy 0.82. Furthermore, no special preprocessing, feature engineering or linguistic knowledge is used in this work. The methods presented achieve accuracy of 0.92 and F-score 0.92 with Convolutional Neural Networks (CNNs) and bidirectional Long Short Term Memory networks (LSTMs). Comparable results of 0.91 accuracy and 0.91 F-score are also achieved with bidirectional Gated Recurrent Units (GRUs) and Convolutional Recurrent Neural Nets (CRNNs). The models are trained and evaluated only on the basis of training tuples, the related sentences and their labels. The outcome is a state-of-the-art collection of metaphor detection models, trained on limited labelled resources, which can be extended to other languages and similar tasks.

Keywords: metaphor detection, deep learning, representation learning, embeddings

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Authors: Panagodage Janaka Sampath Fernando

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Brain circulation has become a buzzword in the skilled migration literature. However, promoting brain circulation; returning of skilled migrants is challenging. Success stories in Asia, for instances, Taiwan, and China, are results of rigorous policy interventions of the respective governments. Nonetheless, the same policy mix has failed in other countries making it skeptical to attribute the success of brain circulation to the policy interventions per se. The paper seeks to answer whether the success of brain circulation within the Knowledge Services Industry (KSI) in Sri Lanka is a policy driven or a market driven process. Mixed method approach, which is a combination of case study and survey methods, was employed. Qualitative data derived from ten case studies of returned entrepreneurs whereas quantitative data generated from a self-administered survey of 205 returned skilled migrants (returned skilled employees and entrepreneurs) within KSI. The pull factors have driven the current flow of brain circulation within KSI but to a lesser extent, push factors also have influenced. The founding stone of the industry has been laid by a group of returned entrepreneurs, and the subsequent growth of the industry has attracted returning skilled employees. Sri Lankan government has not actively implemented the reverse brain drain model, however, has played a passive role by creating a peaceful and healthy environment for the industry. Therefore, in contrast to the other stories, brain circulation within KSI has emerged as a market driven process with minimal government interventions. Entrepreneurs play the main role in a market-driven process of brain circulation, and it is free from the inherent limitations of the reverse brain drain model such as discriminating non-migrants and generating a sudden flow of low-skilled migrants. Thus, to experience a successful brain circulation, developing countries should promote returned entrepreneurs by creating opportunities in knowledge-based industries.

Keywords: brain circulation, knowledge services industry, return migration, Sri Lanka

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Authors: Denila Jinny Arulraj, Thillai Rajan Annamalai

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This paper aims to study the relationship between funding sources and firm performance of Indian private limited SMEs using cross-sectional data obtained from a nation-wide census. A unique feature of the study is that it analyses firms that use only one form of external funding. Employing Propensity Score Matching, we find that obtaining any form of external finance has a negative influence on equivalents of profit margin and return on assets and a negative influence on asset turnover of small firms. But, the impact of institutional sources of funding on small enterprises is found to be lesser than that of non-institutional sources of funding. External/institutional sources of funding have a less negative impact on the profit margin for medium enterprises and have no significant influence on other measures of performance. The contribution of this research is the discovery of institutional sources wielding a lesser influence on performance measures considered. It is also found that institutional sources can benefit small enterprises more than medium enterprises.

Keywords: external finance, institutional finance, non-institutional finance, performance, India, SME

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Authors: Kashi Raj Pandey

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Transformative learning is a form of learning rooted in learners' life experiences and their inherent love for learning. It emphasizes the importance of incorporating students' everyday work through the use of learning diaries and reflective journals. It encourages learners to take a proactive role in their own improvement, fostering creativity and promoting informed discussions about the learning process. Reflecting on the personal experience with English language learning in a rural village in Nepal where rote memorization was the prevailing teaching method, this traditional approach hindered a deeper understanding of the language, prompting the author to recognize the need for more effective pedagogy. In this study, the author delved into the cultural contextualization of English language learning, taking into account learners' backgrounds. The study’s findings highlighted the importance of equity, inclusion, mutuality, and social justice in the classroom, emphasizing the significance of integrating students' lived experiences into the pedagogical approach. This, in turn, can encourage students to engage in profound and collaborative learning practices within the realm of English language education. Upon successfully implementing the research findings, including the eight key conditions of transformative learning, in multiple classrooms, the author collaborated with international educationists and government stakeholders in Nepal. The purpose was to disseminate the research findings, conduct teacher training workshops, and systematically enhance Nepali students’ English language learning. These methods have already demonstrated a significant improvement in student engagement within the same school where the author once learned English as a child. This study aims to explore teachers’ decision-making process regarding the transition from traditional teaching methods to interactive ones, which have gained national recognition within the ESL/EFL teaching community in Nepal. By sharing these experiences, it is expected that other teachers will also contemplate adopting transformative learning pedagogy in their own classrooms.

Keywords: reflection, student engagement, pedagogy, transformative learning

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Authors: Roshani Gusain, Deep Sinha, Karan Nayal, Anmol Kumar Mishra, Manav Singh

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In this research paper, we introduce "Mind Care Assistant - Companion App", which is a Flutter and Firebase-based mental health monitor. The app wants to improve and monitor the mental health of its users, it uses noninvasive ways to check for a change in their emotional state. By responding to questions, the app will provide individualized suggestions ᅳ tasks and mindfulness exercises ᅳ for users who are depressed or anxious. The app features a chat-bot that incorporates cognitive behavioural therapy (CBT) principles and combines natural language processing with machine learning to develop personalised responses. The feature of the app that makes it easy for us to choose between iOS and Android is cross-platform, which allows users from both mobile systems to experience almost no changes in their interfaces. With Firebase integration synchronized and real-time data storage, security is easily possible. The paper covers the architecture of the app, how it was developed and some important features. The primary research result presents the promise of a "Mind Care Assistant" in mental health care using new wait-for-health technology, proposing a full stack application to be able to manage depression/anxiety and overall Mental well-being very effectively.

Keywords: mental health, mobile application, flutter, firebase, Depression, Anxiety

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Authors: Zineb Ouahdi

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Spirocyclic compound derivatives, with their unique heterocyclic motifs, serve as a continual source of inspiration in the pursuit of developing potential therapeutic agents. These compounds are diverse in their chemical structures; some have fully saturated skeletons, while others are partially unsaturated. Nevertheless, these compounds share a characteristic feature with natural products - the presence of at least one heteroatom in one of their rings. The inclusion of a C = O dipolarophile in pyridazinones imparts an exciting aspect for 1,3-dipolar cycloaddition reactions, the focal point of our study. Our research has involved a detailed theoretical investigation of the reaction between ethyl (Z)-2-bromo-2-(2-(p-tolyl)hydrazono)acetate and 6-methyl-4,5-dihydropyridazine-3(2H)-one. This has been accomplished using the DFT/B3LYP/6-31g(d,p) method, intending to illuminate the chemical pathway of this reaction. The chemical reactivity theories we used for this purpose included FMO, TS, and local and global indices derived from conceptual DFT. The theoretical framework outlined in this study allowed us to propose a reaction mechanism for cycloaddition reactions. It also enabled the identification of the potential activities of the analyzed compounds (P1, P2, P3, P4, P5, and P6) against the major protease of the coronavirus disease (COVID-19). This was achieved using various computational tools, including AutoDock Tools, Autodock Vina, Autodock 4, and PYRX.

Keywords: MEDT, pyridazin, cycloaddition, FMO, DFT, docking

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Authors: Adrienne Kline, Jaydip Desai

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Electroencephalogram (EEG) is a noninvasive technique that registers signals originating from the firing of neurons in the brain. The Emotiv EEG Neuroheadset is a consumer product comprised of 14 EEG channels and was used to record the reactions of the neurons within the brain to two forms of stimuli in 10 participants. These stimuli consisted of auditory and visual formats that provided directions of ‘right’ or ‘left.’ Participants were instructed to raise their right or left arm in accordance with the instruction given. A scenario in OpenViBE was generated to both stimulate the participants while recording their data. In OpenViBE, the Graz Motor BCI Stimulator algorithm was configured to govern the duration and number of visual stimuli. Utilizing EEGLAB under the cross platform MATLAB®, the electrodes most stimulated during the study were defined. Data outputs from EEGLAB were analyzed using IBM SPSS Statistics® Version 20. This aided in determining the electrodes to use in the development of a brain-machine interface (BMI) using real-time EEG signals from the Emotiv EEG Neuroheadset. Signal processing and feature extraction were accomplished via the Simulink® signal processing toolbox. An Arduino™ Duemilanove microcontroller was used to link the Emotiv EEG Neuroheadset and the right and left Mecha TE™ Hands.

Keywords: brain-machine interface, EEGLAB, emotiv EEG neuroheadset, OpenViBE, simulink

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Authors: Chayanika Hazarika Bordoloi

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This paper is an attempt to work on a highly inflectional language called Assamese. This is also one of the national languages of India and very little has been achieved in terms of computational research. Building a language processing tool for a natural language is not very smooth as the standard and language representation change at various levels. This paper presents inflectional suffixes of Assamese verbs and how the statistical tools, along with linguistic features, can improve the tagging accuracy. Conditional random fields (CRF tool) was used to automatically tag and train the text data; however, accuracy was improved after linguistic featured were fed into the training data. Assamese is a highly inflectional language; hence, it is challenging to standardizing its morphology. Inflectional suffixes are used as a feature of the text data. In order to analyze the inflections of Assamese word forms, a list of suffixes is prepared. This list comprises suffixes, comprising of all possible suffixes that various categories can take is prepared. Assamese words can be classified into inflected classes (noun, pronoun, adjective and verb) and un-inflected classes (adverb and particle). The corpus used for this morphological analysis has huge tokens. The corpus is a mixed corpus and it has given satisfactory accuracy. The accuracy rate of the tagger has gradually improved with the modified training data.

Keywords: CRF, morphology, tagging, tagset

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Authors: Ifeanyichukwu Edeh, Tim Overton, Steve Bowra

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Currently biodiesel is produced from plant oils or animal’s fats by a liquid-phase catalysed transesterification process at low temperature. Although biodiesel is renewable and to a large extent sustainable, inherent properties such as poor cold flow, low oxidation stability, low cetane value restrict application to blends with fossil fuels. An alternative to biodiesel is renewable diesel produced by catalytic hydrotreating of oils and fats and is considered a drop in fuel because its properties are similar to petroleum diesel. In addition to developing alternative productions routes there is continued interest in reducing the cost of the feed stock, waste cooking oils and fats are increasingly used as the feedstocks due to low cost. However, use of oils and fat are highly adulterated resulting in high free fatty acid content which turn impacts on the efficiency of FAME production. Therefore, in light of the need to develop, alternative lipid feed stocks and related efficient catalysis the present study investigates the potential of producing renewable diesel from the lipids-extracted from activated sludge, a waste water treatment by-product, through catalytic hydrothermal decarboxylation. The microbial lipids were first extracted from the activated sludge using the Folch et al method before hydrothermal decarboxylation reactions were carried out using palladium (Pd/C) and platinum (Pt/C) on activated carbon as the catalysts in a batch reactor. The impact of three temperatures 290, 300, 330 °C and residence time between 30 min and 4hrs was assessed. At the end of the reaction, the products were recovered using organic solvents and characterized using gas chromatography (GC). The principle products of the reaction were pentadecane and heptadecane. The highest yields of pentadecane and heptadecane from lipid-extract were 23.23% and 15.21%, respectively. These yields were obtained at 290 °C and residence time 1h using Pt/C. To the best of our knowledge, the current work is the first investigation on the hydrothermal decarboxylation of lipid-extract from activated sludge.

Keywords: activated sludge, lipid, hydrothermal decarboxylation, renewable diesel

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Authors: Jim Shi, Jasmine Chang, Nesreen El-Rayes

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The world has been experiencing a steadily increasing trend in both the production and consumption of plastic. The global consumer revolution should not have been possible without plastic, thanks to its salient feature of inexpensiveness and durability. But, as a two-edged sword, its durable quality has returned to haunt and even jeopardized us. That exacerbating the plastic crisis has attracted various global initiatives and actions. Simultaneously, firms are eager to adopt new technology as they witness and perceive more potential and merit of Industry 4.0 technologies. For example, Blockchain technology (BCT) is drawing the attention of numerous stakeholders because of its wide range of outstanding features that promise to enhance supply chain operations. However, from a research perspective, most of the literature addresses the plastic crisis from either environmental or social perspectives. In contrast, analysis from the data science perspective and technology is relatively scarce. To this end, this study aims to fill this gap and cover the plastic crisis from a holistic view of environmental, social, technological, and business perspectives. In particular, we propose a mathematical model to examine the inclusion of BCT to enhance and improve the efficiency on the upstream and the downstream sides of the plastic value, where the whole value chain is coordinated systematically, and its interoperability can be optimized. Consequently, the Environmental, Social, and Governance (ESG) goal and Circular Economics (CE) sustainability can be maximized.

Keywords: blockchain technology, plastic, circular economy, sustainability

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Authors: Antonios Paraskevas, Michael Madas

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For a university to maintain its international competitiveness in education, it is essential to recruit qualitative academic staff as it constitutes its most valuable asset. This selection demonstrates a significant role in achieving strategic objectives, particularly by emphasizing a firm commitment to the exceptional student experience and innovative teaching and learning practices of high quality. In this vein, the appropriate selection of academic staff establishes a very important factor of competitiveness, efficiency and reputation of an academic institute. Within this framework, our work demonstrates a comprehensive methodological concept that emphasizes the multi-criteria nature of the problem and how decision-makers could utilize our approach in order to proceed to the appropriate judgment. The conceptual framework introduced in this paper is built upon a hybrid neutrosophic method based on the Neutrosophic Analytical Hierarchy Process (N-AHP), which uses the theory of neutrosophy sets and is considered suitable in terms of a significant degree of ambiguity and indeterminacy observed in the decision-making process. To this end, our framework extends the N-AHP by incorporating the Neutrosophic Delphi Method (N-DM). By applying the N-DM, we can take into consideration the importance of each decision-maker and their preferences per evaluation criterion. To the best of our knowledge, the proposed model is the first which applies the Neutrosophic Delphi Method in the selection of academic staff. As a case study, it was decided to use our method for a real problem of academic personnel selection, having as the main goal to enhance the algorithm proposed in previous scholars’ work, and thus taking care of the inherent ineffectiveness which becomes apparent in traditional multi-criteria decision-making methods when dealing with situations alike. As a further result, we prove that our method demonstrates greater applicability and reliability when compared to other decision models.

Keywords: multi-criteria decision making methods, analytical hierarchy process, delphi method, personnel recruitment, neutrosophic set theory

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Authors: Nazmul Shahadat, Anthony S. Maida

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In recent years, hypercomplex inspired neural networks improved deep CNN architectures due to their ability to share weights across input channels and thus improve cohesiveness of representations within the layers. The work described herein studies the effect of replacing existing layers in an Axial Attention ResNet with their quaternion variants that use cross-channel weight sharing to assess the effect on image classification. We expect the quaternion enhancements to produce improved feature maps with more interlinked representations. We experiment with the stem of the network, the bottleneck layer, and the fully connected backend by replacing them with quaternion versions. These modifications lead to novel architectures which yield improved accuracy performance on the ImageNet300k classification dataset. Our baseline networks for comparison were the original real-valued ResNet, the original quaternion-valued ResNet, and the Axial Attention ResNet. Since improvement was observed regardless of which part of the network was modified, there is a promise that this technique may be generally useful in improving classification accuracy for a large class of networks.

Keywords: axial attention, representational networks, weight sharing, cross-channel correlations, quaternion-enhanced axial attention, deep networks

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Authors: A. I. A. Rahman, Sh-Hussain Salleh, K. Ahmad, K. Anuar

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Analysis of vocal fold vibration is essential for understanding the mechanism of voice production and for improving clinical assessment of voice disorders. This paper presents a Dynamic Time Warping (DTW) based approach to analyze and objectively classify vocal fold vibration patterns. The proposed technique was designed and implemented on a Glottal Area Waveform (GAW) extracted from high-speed laryngeal images by delineating the glottal edges for each image frame. Feature extraction from the GAW was performed using Linear Predictive Coding (LPC). Several types of voice reference templates from simulations of clear, breathy, fry, pressed and hyperfunctional voice productions were used. The patterns of the reference templates were first verified using the analytical signal generated through Hilbert transformation of the GAW. Samples from normal speakers’ voice recordings were then used to evaluate and test the effectiveness of this approach. The classification of the voice patterns using the technique of LPC and DTW gave the accuracy of 81%.

Keywords: dynamic time warping, glottal area waveform, linear predictive coding, high-speed laryngeal images, Hilbert transform

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Authors: Y. Bai, M. Bodlak, V. Frolov, S. Huber, V. Jary, I. Konorov, D. Levit, J. Novy, D. Steffen, O. Subrt, M. Virius

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Modern experiments in high energy physics impose great demands on the reliability, the efficiency, and the data rate of Data Acquisition Systems (DAQ). This contribution focuses on the development and deployment of the new communication library DIALOG for the intelligent, FPGA-based Data Acquisition System (iFDAQ) of the COMPASS experiment at CERN. The iFDAQ utilizing a hardware event builder is designed to be able to readout data at the maximum rate of the experiment. The DIALOG library is a communication system both for distributed and mixed environments, it provides a network transparent inter-process communication layer. Using the high-performance and modern C++ framework Qt and its Qt Network API, the DIALOG library presents an alternative to the previously used DIM library. The DIALOG library was fully incorporated to all processes in the iFDAQ during the run 2016. From the software point of view, it might be considered as a significant improvement of iFDAQ in comparison with the previous run. To extend the possibilities of debugging, the online monitoring of communication among processes via DIALOG GUI is a desirable feature. In the paper, we present the DIALOG library from several insights and discuss it in a detailed way. Moreover, the efficiency measurement and comparison with the DIM library with respect to the iFDAQ requirements is provided.

Keywords: data acquisition system, DIALOG library, DIM library, FPGA, Qt framework, TCP/IP

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Authors: Ignatius Madu

Abstract:

The study aims at assessing the environmental and socioeconomic determinants of climate change resilience in rural Nigeria. This is necessary because researches and development efforts on building climate change resilience of rural areas in developing countries are usually made without the knowledge of the impacts of the inherent rural characteristics that determine resilient capacities of the households. This has, in many cases, led to costly mistakes, delayed responses, inaccurate outcomes, and other difficulties. Consequently, this assessment becomes crucial not only to policymakers and people living in risk-prone environments in rural areas but also to fill the research gap. To achieve the aim, secondary data were obtained from the Annual Abstract of Statistics 2017, LSMS-Integrated Surveys on Agriculture and General Household Survey Panel 2015/2016, and National Agriculture Sample Survey (NASS), 2010/2011.Resilience was calculated by weighting and adding the adaptive, absorptive and anticipatory measures of households variables aggregated at state levels and then regressed against rural environmental and socioeconomic characteristics influencing it. From the regression, the coefficients of the variables were used to compute the impacts of the variables using the Stochastic Regression of Impacts on Population, Affluence and Technology (STIRPAT) Model. The results showed that the northern States are generally low in resilient indices and are impacted less by the development indicators. The major determining factors are percentage of non-poor, environmental protection, road transport development, landholding, agricultural input, population density, dependency ratio (inverse), household asserts, education and maternal care. The paper concludes that any effort to a successful resilient building in rural areas of the country should first address these key factors that enhance rural development and wellbeing since it is better to take action before shocks take place.

Keywords: climate change resilience; spatial impacts; STIRPAT model; Nigeria

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Authors: Jörg Lenhardt, Wolfram Schiffmann, Jörg Keller

Abstract:

While the feature sizes of recent Complementary Metal Oxid Semiconductor (CMOS) devices decrease the influence of static power prevails their energy consumption. Thus, power savings that benefit from Dynamic Frequency and Voltage Scaling (DVFS) are diminishing and temporal shutdown of cores or other microchip components become more worthwhile. A consequence of powering off unused parts of a chip is that the relative difference between idle and fully loaded power consumption is increased. That means, future chips and whole server systems gain more power saving potential through power-aware load balancing, whereas in former times this power saving approach had only limited effect, and thus, was not widely adopted. While powering off complete servers was used to save energy, it will be superfluous in many cases when cores can be powered down. An important advantage that comes with that is a largely reduced time to respond to increased computational demand. We include the above developments in a server power model and quantify the advantage. Our conclusion is that strategies from datacenters when to power off server systems might be used in the future on core level, while load balancing mechanisms previously used at core level might be used in the future at server level.

Keywords: power efficiency, static power consumption, dynamic power consumption, CMOS

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Authors: D. P. Ray, L. Ammayappan, S. Debnath, R. K. Ghosh, D. Mondal, S. Dasgupta, S. Islam, S. Chakroborty, P. K. Ganguly, D. Nag

Abstract:

Nature being bountiful is generous enough to provide rich resources to mankind. These resources can be used as an alternative to synthetics, thereby reducing the chances of environmental pollution. Natural fibre based composites have emerged as a successful trend in recent automobile industry. Natural fibre based composites used in automobile industries not only reduces their fuel consumption but also do not pose any health hazards. In spite of the use of natural fibre based bio composite in automobile industries, its use is only being limited to interior products. However, its major drawbacks which contributed to limited scope in the field of industry are reduced durability and mechanical strength. Thereby, the use of natural fibre based bio composites as headliner in case of automobile industries is also not successfully deployed. Out of all the natural fibres available, jute can widely be used as automobile parts because of its easy availability, comparatively higher specific strength, lower density, low thermal conductivity and most importantly its non polluting and non abrasive nature. Various research outcomes in the field of jute based biocomposites for the use of automobile industries has not successfully being deployed due to certain inherent problem of the fibre. Jute being hydrophilic in nature is not readily adhered to the hydrophobic polyester resin. Therefore introduction of a chemical compatibilizer, in the preparation of jute based composites have been tested to enhance the mechanical and durable properties of the material to a greater extent. This present work therefore focuses on the synthesis of a suitable compatibilizer, acting as a chemical bridge between the polar jute fabric and the non polar resin matrix. This in turn results in imparting better interfacial bonding between the two, thereby inducing higher mechanical strength. These coupling treated fabrics are casted into composites and tested for their mechanical properties. The test reports show a remarkable change in all of its properties. The durability test was performed by soil burial test method.

Keywords: jute, automobile industry, biodegradability, chemical compatibilizer

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Authors: Serap Keskin Tunç, Cennet Neslihan Eroğlu, Sevinç Şahin, Selda Seçkin

Abstract:

It has been suggested that various viruses may play a role in the pathogenesis of cancerous oral lesions in the literature. The aim of this study was to investigate the presence of both possible precancerous viral markers (HPV, HHV8, HSV1, HSV2, and EBV), and p53 and Ki-67 in the dental follicles of asymptomatic impacted teeth. A hundred healthy volunteers, older than 18 years old, included in the study. Dental follicles of extracted impacted teeth were excised and fixated in 10% formaldehyde. Histopathological and immunohistochemical examinations using HPV (containing HPV 8 and HPV 11), p16 (containing HPV 16), HHV8, HSV1, HSV2, EBV, p53 and Ki-67 antibodies were carried out. Also, the immunohistochemical results were correlated with the clinicopathological feature by Chi-square test statistically No dysplasia or neoplasm was observed. 62% of the cases were positive for p16, 32% were positive for EBV, 26% were positive for HSV1, immunohistochemically. All cases were immunonegative for HPV, HSV2, and HHV8. There was statistically significant correlation between overexpression of p53 with both EBV and p16 positivity (p<0.05). Direct correlation between higher expression of Ki-67 between EBV immunopositivity was detected (p<0.05). Thus, these viruses may be suggested to show trophism to the dental follicles acting as a reservoir. In conclusion, all dental follicles of extracted impacted teeth should be examined histopathologically in order to detect and prevent possible viral oncogenesis.

Keywords: dental follicles, Ki67, p53, precancerous markers viral markers

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Authors: Muhammad Zubair Khan, Yugyung Lee

Abstract:

Deep learning has recently achieved enormous response in semantic image segmentation. The previously developed U-Net inspired architectures operate with continuous stride and pooling operations, leading to spatial data loss. Also, the methods lack establishing long-term pixels connection to preserve context knowledge and reduce spatial loss in prediction. This article developed encoder-decoder architecture with bi-directional LSTM embedded in long skip-connections and densely connected convolution blocks. The network non-linearly combines the feature maps across encoder-decoder paths for finding dependency and correlation between image pixels. Additionally, the densely connected convolutional blocks are kept in the final encoding layer to reuse features and prevent redundant data sharing. The method applied batch-normalization for reducing internal covariate shift in data distributions. The empirical evidence shows a promising response to our method compared with other semantic segmentation techniques.

Keywords: deep learning, semantic segmentation, image analysis, pixels connection, convolution neural network

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Authors: Zahra Salehi Shahrbabaki, Zahra Chavoshzadeh, Fahimeh Abdollahimajd, Samin Sharafian, Tolue Mahdavi, Mahnaz Jamee

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Background: Inborn errors of immunity (IEIs) are monogenic diseases of the immune the system with broad clinical manifestations. Despite the increasing genetic advancements, the diagnosis of IEIs still leans on clinical diagnosis. Dermatologic manifestations are observed in a large number of IEI patients and can lead to proper approach, prompt intervention and improved prognosis. Methods: This cross-sectional study was carried out between 2018 and 2020 on IEIs at a Children's tertiary care center in Tehran, Iran. Demographic details (including age, sex, and parental consanguinity), age at onset of symptoms and family history of IEI with were recorded. Results :212 patients were included. Cutaneous findings were reported in (95 ,44.8%) patients. and 61 of 95 (64.2%) reported skin lesions as the first clinical presentation. Skin infection (69, 72.6%) was the most frequent cutaneous manifestation, followed by an eczematous rash (24, 25 %). Conclusions: Skin manifestations are common feature in IEI patients and can be readily recognizable by healthcare providers. This study tried to provide information on prognostic consequences.

Keywords: primary immuno deficiency, inborn errror of metabolism, skin manifestation, skin infection

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Authors: Benjamin Newman

Abstract:

The picture of an adverse contest between two parties has often been used as an archetypal description of the Anglo-American adversarial criminal trial. However, in actuality, guilty pleas and plea-bargains have been dominating the procedure for over the last half-a-century. Characterised by two adverse parties, the court adjudicative system in the Anglo-American world adhere to the adversarial procedure, and while further features have been attributed and the values that are embedded within the procedure vary, it is a system that we have no adequate theory. Damaska had argued that the adversarial conflict-resolution mode of administration of justice stems from a liberal laissez-faire concept of a value neutral liberal state. Having said that, the court’s neutrality has been additionally rationalised in light of its liberal end as a safeguard from the state’s coercive force. Both conceptions of the court’s neutrality conflict in cases where the by-standing role disposes of its liberal duty in safeguarding the individual. Such is noticeable in plea bargains, where the defendant has the liberty to plead guilty, despite concerns over wrongful convictions and deprivation of liberty. It is an inner liberal tension within the notion of criminal adversarialism, between the laissez-faire mode which grants autonomy to the parties and the safeguarding liberal end of the trial. Langbein had asserted that the adversarial system is a criminal procedure for which we have no adequate theory, and it is by reference to political and moral theories that the research aims to articulate a normative account. The paper contemplates on the above liberal-tension, and by reference to Duff’s ‘calling-to-account’ theory, argues that autonomy is of inherent value to the criminal process, being considered a constitutive element in the process of being called to account. While the aspiration is that the defendant’s guilty plea should be genuine, the guilty-plea decision must be voluntary if it is to be considered a performative act of accountability. Thus, by valuing procedural autonomy as a necessary element within the criminal adjudicative process, it assimilates a liberal procedure, whilst maintaining the liberal end by holding the defendant to account.

Keywords: liberal theory, adversarial criminal procedure, criminal law theory, liberal perfectionism, political liberalism

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